June 4, 2020

Mobile App Personalization: 10 Ways to Convert and Retain App Users at Scale

By Pradyut Hande, Netcore

We live in an inter(net)-connected world where smartphone penetration, ever-improving mobile network infrastructure, and enhanced access to cheaper data are driving the app economy.

With over 2.7 billion smartphone users and 1.35 billion tablet users globally, there are 2.8 million apps on the Google Play Store and over 2.2 million apps on the Apple App Store. In fact, there were over 205 billion app downloads in 2019 alone.

That’s staggering!

Sure there’s a massive opportunity to empower users, strengthen your online presence, generate top-line revenues, and counter competition - but, that’s easier said than done.

One fool-proof way to consistently power user engagement, conversions, and retention at scale is by integrating a strong personalization strategy into your mobile (and omnichannel) marketing machinery.

And, personalization - today - has gone above and beyond the obvious. Addressing your users by their first names over a push notification or sending them a discount coupon on their birthdays over an email campaign is great.

But, it’s not enough! And, especially so in the current COVID-19 environment where hyper-competition for users’ screen-share, mindshare, and wallet-share has only heightened.

Your users expect and demand an end-to-end, tailor-made customer experience, right from first-time app launch. This assumes even greater significance across industries such as e-commerce, OTT, and news and media.

At Smartech, we understand how important 1:1 personalization is across platforms, channels, and devices to your omnichannel marketing efforts. And, with that in mind, we’ve bolstered our mobile app personalization module.

Here’s how you can now deliver highly differentiated user experiences at scale on and through your mobile app:

1. Build a solid foundation of and on user data:

Effective personalization is dependent on you gathering the right user data across channels and platforms. Gathering the right demographic, geolocation, and device-type data-points is important. Start capturing these basic data-points at the registration or login stage of your user onboarding flow.

But, you also need to capture your users’ in-app behaviors, actions, inactions, responses and interactions to multi-channel marketing campaigns. This will help you create and constantly enrich a unified view of every user, in real-time.

For instance, if you’re an e-commerce app, you need to log actions and details such as products searched, product categories browsed, products added to cart or wishlist, products purchased, payment mode chosen, and most common paths towards conversion.

If you have a physical store, you also need to ensure your transactional data is funnelling back into your data backend so as to personalize subsequent shopping experiences across both your website and/or app.

Simultaneously, you need to track relevant metrics attached to these actions to gather granular insights - recency and frequency of app launches and purchases, average time spent per screen, ratio of products added to cart and finally purchased, actual conversion rates, etc.

Analyzing these diverse data-points will help you gain actionable insights and develop relevant user segments.

Our AI engine, Raman, can actually help you dive deeper. Now slice and dice behavioral data to arrive at a segment of one, with AI doing all the heavy-lifting for you! Our cutting-edge collaborative and content-filtering algorithms make it possible for us to ingest large amounts of user data-points and behavioral footprints.

Raman also constantly learns from both clickstream and customer eyeball data; i.e. from both live actions and inactions that can be attributed to a user.

That’s not it, though! We also help you map and leverage data-points such as device price and brand of device to further fortify device-related information.

Once you have your user data and analytics backbone in place, you can focus on adding muscle to your personalization strategy.

2. Personalize the app home screen on first-time launch:

Modern marketing has driven home the fact that one size doesn’t fit all. Depending upon your app category and the quality (and quantity) of demographic data-points that you’re able to gather during the first-time onboarding flow - you can immediately start delivering a personalized user experience on your app homepage.

For instance: If you are an OTT music streaming app; data-points like name, age, gender, preferred genres and languages, favorite artists, etc. can be used to curate a first-degree personalized list of content recommendations on your app home screen instantly.

You need to strike the right balance between deploying an onboarding flow that educates new users on functionality, key features, etc. and capturing relevant demographic data-points (without being explicitly intrusive) to start delivering 1:1 user experiences ASAP.

Notice how Hungama Music, one of India’s leading home-grown music streaming apps, does this during the user onboarding process to quickly begin personalizing content recommendations when the home screen launches for the first time.

You can subsequently highlight the most relevant recommendations on the app home screen on future app launches. This can increase your CTRs by 90-120% while uplifting content consumption by 5-7%!

3. Tailor-make the app navigation experience:

Apart from your users’ search, browsing, click behaviour and consumption history, you can personalize how your users navigate across your mobile app.

Other parameters such as gender, buyer personas, geolocation, time of day/timezone, seasons/weather, etc. can be harnessed to personalize the banner images, graphics, CTAs, trending products and offers, etc.

Also, leverage insights from your users’ most common paths towards conversion to further optimize their navigation journey. The more individualized the navigation journey, greater the probability of your users finding exactly what they want, faster!

Check out how AJIO, a leading e-commerce clothing retailer offers a gender-based app navigation experience for both men and women.

4. Personalize the search experience:

Any search made by a user on your app is a solid signal of intent. Intent to purchase a product. Intent to book a ticket. Intent to consume content. And, you need to value these search inputs.

Every search action undertaken by specific users tells you about their instant wants, needs, and preferences. And, your objective must be to direct your users to exactly what they want faster.

With the assistance of our AI engine, Raman, you can now instantly populate product or content recommendations based on the partial or full search terms inputted in the search tab. This would also take into account historical searches made.

These recommendations continue to get more accurate with each subsequent search action that a user takes, helping you to significantly reduce the path from product discovery to purchase.

For instance: As an e-commerce platform that specializes in the online retail of cosmetics, you can start giving the most relevant product recommendations for lipsticks when a user is searching for a particular brand of lipsticks.

Here’s how Amazon leverages search personalization to show me sub-product categories that are most relevant to me.

5. Showcase real-time AI-led predictive recommendations:

Falling user attention spans and quick access to alternate apps in the same category can fuel switching behavior. This is why you need to respect the time an individual user potentially invests when he/she launches your app. Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption.

Our AI engine, Raman, enables you to show the most relevant product or content recommendations across your Home Screen, Product Display Screen, and Product Listing Screen.

Much like our existing onsite dynamic personalization for websites, you can replicate the same for your mobile app. These live recommendations are optimized for mobile display and click, ensuring that you maximize the use of screen-space on every scroll.

Moreover, these recommendations get more refined with each shopping session that an individual user engages in.

Not only does our AI engine account for clickstream data, but it also accounts for customer eyeball data. This implies that negative signals for any product or content recommendations that are “seen-and-not clicked” and “not seen-and-not clicked” feed right back into the AI engine - all in real-time.

This helps you improve behavioral predictions by almost 20%.

6. Re-order product or content categories for greater context:

If you have an e-commerce, OTT, or news and media app; you are bound to have hundreds of products or content options within your product catalog or content library.

Our patented AI algorithms can re-order these categories in real-time for individual users so the most relevant product or content recommendations show up right at the top.

Remember the Golden Rule?

Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption!

In the below example, our AI engine can actually re-order listed product categories to show the most relevant categories from top to bottom, to individual users in real-time.

7. Create an in-app personalized storefront or playlist:

Go one step further and allow Raman to specially curate a mobile boutique - composed of only those products or content recommendations that an individual user is most likely to view, buy, or consume on your app!

Delight your users with a unique online shopping or content consumption experience while increasing CTRs by 120-150%, every time they launch your app.

Neural networks bolster Raman’s real-time, reinforced learning capacity; as it refreshes the list of recommendations every time a user chooses to launch the personalized storefront, watch-list, playlist, or read-list.

Netflix curates a personalized watch-list for each subscribed user that is refined and refreshed after each active session on their app or website.

Spotify actually goes a step further to create multiple weekly playlists based on preferred genres, favorite artists, and music language.

8. Deploy live contextual recommendations via in-app messages:

You can optimize your in-app personalization strategy by triggering relevant product or content recommendations and appropriate offers at individual users through in-app messages.

These can be triggered when a user lands on certain pre-defined screens or when a user scrolls a finite percentage on certain pre-defined screens.

Our AI engine, Raman, intelligently triggers these in-app messages only when they truly make sense, without disrupting the ongoing user experience.

For instance: As an e-commerce app that has a health and hygiene product category, if a user has added hand sanitizers to his/her cart, you can trigger a relevant recommendation for facemasks and hand-washes, as a dynamic product bundle.

Such a strategy can also help you increase your average order value through effective cross-sell and upsell opportunities.

9. Personalize your recommendations across other channels:

While personalizing the entire in-app mobile experience is important, what do you do when your user is not in an active session? You can’t leave that to chance.

The essence of personalization lies in delivering a rich and unique 1:1 user experience across multiple digital touchpoints. This implies that you need to activate other critical mobile marketing channels such as emails and app push notifications to continue delivering these personalized recommendations.

With Smartech, you can now trigger laser-focused product recommendations across these channels to maintain top-of-mind recall, bring inactive users back to your app, and potentially nudge them towards a conversion event.

Here are the kind of contextual recommendations that you can deliver to pursue conversions beyond just your mobile app, especially in the e-commerce space:

• “Suggested for You” Recommendations: These are the best possible recommendations tailored to individual users based on their general historical behaviour; i.e. product/product categories viewed, items added to cart, purchase, etc.
• “Cart Abandonment” Recommendations: These are generated based on the products added to an individual user’s digital shopping carts where the user may have dropped-off or left a purchase incomplete.
• Buying Pattern Recommendations: These are generated based on an individual user’s most recent product purchases.
  For instance: If you identify a user segment that has repeatedly purchased Washing Detergent and Fabric Softeners and these out of stock due to a demand surge - you can trigger personalized app push recommendations updating this segment when these products are back in stock.
• Viewing Pattern Recommendations: These are triggered based on an individual user’s most recent products or product categories viewed.
• Bestselling Recommendations: These are generated based on the highest selling products on your mobile app. These are products that are being purchased the most when compared to other products over a period of time.
  For instance: In the current scenario, health safety and hygiene products such as facemasks, disinfectants, and hand sanitizers have quickly become bestselling products - over the last 2 months - and will see a steady demand even when the lockdown period is relaxed.
• Trending Recommendations: These are generated based on the most trending products on your mobile app. Essentially, these are products whose consumption has shown a percentage increase over a period of time.
  For instance: Building on the above example, health safety and hygiene products like facemasks rapidly became a trending product within its category and very soon emerged as a bestselling product, as the COVID-19 turned into a pandemic.
• Recently Viewed Recommendations: These are generated based on the most common products that have been recently viewed by individual users on your mobile app.
• “New Arrival” Recommendations: These are generated based on the new products that have been added to your product catalog. Our AI engine, Raman, maps these products to the ones that are most relevant to individual users and displays the ideal recommendations, capable of nudging him/her towards an eventual purchase.

Depending upon what channels of customer engagement are working best for which customer segments and buyer personas, you can optimize your multi-channel mix, as well as the send-times for these recommendation campaigns.

Raman evaluates the real-time performance of these triggered recommendations for relevant user segments to offer you deep-dive insights on the preferred channels and send-times that are likely to produce the best engagement and conversion rates.

10. Re-target existing users with personalized ads:

Regardless of your best efforts at encouraging your users to make a purchase or spend more time consuming content on your app, there will be drop-offs and users that straddle dormancy.

That’s the harsh reality of mobile marketing!

But, our AI engine, Raman, can actually target individual users with the most relevant product or content recommendations through optimized re-targeting ads across Google, Facebook, and Instagram - at the right time.

For instance: As an e-commerce app specializing in fashion apparel, you can re-target existing users with the most relevant t-shirt, shirt, or trouser related recommendations that are most contextual to individual male users. Depending upon where a user is most likely to see this ad, Raman will display a mobile-optimized ad, nudging the user to click and re-launch your app.

Personalization at Scale = Stickier 1:1 Experiences = User Retention

Mobile app personalization is not a set-it-and-forget-it project. While our AI engine, Raman, will help you deliver relevant recommendation-led 1:1 user experiences; human intelligence also plays an important role. When AI and human intelligence comes together, you can continue to optimize experiences for a segment of one!

Also, remember that mobile app personalization is another weapon in your larger omnichannel personalization arsenal. Wowing your users just on one platform is not going to cut it! They expect a consistent and frictionless experience across your website, mobile app, and marketing channels (across devices).

To learn how you can get started on your mobile app personalization journey - with AI as your sidekick - like the giants in the industry do; get in touch with our growth experts today!

[P.S. Hang on. We’ve got something special in store for you! We understand that it may not be business as usual for you currently.

To help you tide over these challenging times in the COVID-19 era, we are offering you complimentary access to Smartech’s personalization suite till September, 2020!]

May 25, 2020

Use of Predictive Recommendation Models by CDPs

By Vishal Sukheja, FirstHive

To solve the problem of fragmented sources of data, marketers have traditionally used rule-based approaches, but today that is simply not enough. Rule-based approach only fits into those scenarios within the limited set of rules, falling way short of what is needed and keeping the marketer out of the other set of unknown possibilities.

These limitations brought us to introduce machine learning algorithms in our core CDP platform that can support the following use cases.

Cohort Auto-segmentation

This is one of the top preferred features among FirstHive users. Using data such as look-alike customers, response rate, demographics, and many other parameters, machine learning algorithms can predict customer segments with formulation cues. These are segments created using automation within the platform. This helps in building more mature segments that would hence be designed for optimization.

Advanced Customer Data Management

Predictive models are used to develop advanced identity resolution algorithms that come with the ability to support multiple logical data stores and apply different rules to them. They come with in-built connectors and capabilities for advanced data transformations. They also carry out complex data management and schema changes on an ongoing basis using a graphical interface.

Offline Aggregation to include Omnichannel Strategy

Only a few of the CDPs like FirstHive also cater to the function of offline data aggregation which most often occurs at PoS terminals, QR codes reading, connected smart devices, and other similar instances. This is critical for content recommendations that are in the offline universe of marketing channels. To estimate which of those offline channels are best for content delivery, predictive recommendations bring in true value.

Customer Support

Integrated systems such as email, ticket resolution, chat, and voice calling that build a customer support interface can be managed better for optimized resolution. Based on customer profile and persona tagging, each customer could be handled in a way that is most appreciated by her. Customer support associates will be equipped with information about how each customer is comfortable with a support channel and their response. If the customer is a first-time support user, then the associate will be informed about her preferences.

Algorithms provide proactive recommendations that the customer support executive can have handy while tackling customer queries.

Real-time Customer Data

Apart from use cases where algorithms are deployed to churn out recommendations and have them stacked in a dashboard, real-time data is also a predictive model capability. Machine learning algorithms are formulated to churn out recommendations using a combination of historic and real-time customer data.

Such data is most often used as a feedback mechanism for campaign activation across different channels.

More Use Cases

Some other use cases where predictive modelling can be actively deployed are outbound marketing campaign support, e-commerce recommendations and optimization, lead scoring and predictive scoring models. Within a CDP the most common models that are put to use are clustering models for customer segmentation, propensity models that determine probability and predictions, collaborative filtering for recommendations, and content-based models that are used at times when your systems lack historical data to build recommendations upon.

Should you have any questions about your specific use case, feel free to reach out to us at marketing@firsthive.com.

April 30, 2020

5 Ways Marketers Get Better Results with Customer Data Platforms

By Christina Stubler, Arm Treasure Data

If you are a marketer, you may be wondering what’s behind all the interest in customer data platforms? At the top of the list is the ability for your team to transform the way it works, as well as the way customers interact with your brand. CDPs break down martech silos, unify online and offline customer experiences and data, make marketing more efficient, and drive better results. A CDPs capabilities range from collecting and storing comprehensive customer data, to seamlessly integrating with existing marketing technology, and delivering actionable, data-driven insights.

These are just a few of the features that make CDPs a wise investment for any data-driven marketing team – which should be every marketing team. While addressing marketing and IT’s key concerns, CDPs play a key role in improving marketing’s efficiency, data quality, and security.

What Is the Definition of a CDP?

David Raab, founder and head of the CDP Institute, defines the term CDP like this: “The official CDP Institute definition is ‘packaged software that creates a persistent, unified customer database that is accessible to other systems.’” Raab says the following points are critical in understanding what a CDP is:

• A CDP is packaged software, rather than a custom-build project like a data warehouse or data lake, which makes it quicker, easier and cheaper to deploy.
• A CDP builds an actual database rather than assembling information on the fly by querying external systems, which yields better performance and allows time-series analysis.
• A CDP works with identified customers, not just anonymous cookies like a DMP.
• A CDP lets other systems read its data rather than just holding it internally.

One critical thing to realize is the distinction between a customer database in general and a CDP in particular: a CDP is packaged software to build a customer database; you could also get a customer database by building it yourself or buying a larger product that included it.

Here are five ways you and your team can get better results with an enterprise CDP:

1. Finely Tuned Segmentation

The more data you have – and the more correctly unified it is – the better you’ll understand your customers, including their purchase behaviors, desires, and motivations. This, in turn, helps you create smart audience segments for more relevant customer personalization.

With clear, unified views of your customers, you’ll know exactly how to market to them and lead them further down the path to purchase – and a CDP can provide you with that view.

2. Real-time Engagement

What’s even better than detailed data about an individual’s history, preferences, and behavior online? Getting that information in real-time.

To deliver more targeted offers and better outcomes, a holistic understanding of customer engagement is crucial. It involves the real-time aggregation and analysis of data across marketing, sales, and customer service. A CDP can deliver this information and even use it to orchestrate campaigns, promotions, and tailored user experiences. As a result, you can more consistently provide personalized omnichannel brand experiences for your customers, no matter what channel they’re on.

A CDP also helps keep your customer data accurate, reliable, and secure by constantly cleaning, translating, and updating data over time. With round-the-clock access to this up-to-date data, you’ll quickly improve the quality of your customer interactions.

3. Omnichannel Customer Experience

With an enterprise CDP, you can unify all customer data securely and provide a consistent, positive experience that helps guide customers through their journey. A good CDP can process information from multiple applications – Marketo, Facebook, Salesforce – and combine them to get a full understanding of what your customers want, buy, read, visit, watch, and more.

Advanced CDPs will automatically analyze new data based on the unique rules you establish within the system. You can analyze and organize data for audience segmentation, customer personalization, campaign optimization, push and pull notifications, syndication, and more.

From there, you can use this newly categorized information to answer your most pressing marketing questions and begin improving results across channels by improving or changing your marketing strategies.

4. Cross-Sell and Upsell

By using your CDP to quickly consolidate information from all channels, you’ll know when your customers make a purchase and it’s time to shift to a cross-sell or upsell strategy. Once a customer decides to buy, you can improve retention with personalized, data-driven recommendations. This can extend your relationship far past the first purchase.

5. Improve Products and Services & Identify New Revenue Streams

Customers expect personalized content and better customer experiences – but not at any cost. Marketers need to get creative and rethink the formats of content used to reach customers across devices. This includes what promotions marketers send – and when – as well as what types of alerts and notifications are pushed when customers step into a physical store.

Customers want special attention and improved products and services that meet their specific and unique needs. A CDP helps you gain a more complete understanding of your customers’ preferences, so you can prioritize your product and service offerings to more closely align with customers’ wants. Or, you can create an entirely new stream of revenue by identifying and capitalizing on trends borne out of your customer data.

Delivering Customer Data and Better Results

Marketers can’t ignore customers’ increased demand for frequent, personalized marketing messages. It’s time to invest in a customer data platform to transform your marketing department and deliver exceptional customer experiences.

Learn how Arm Treasure Data enterprise CDP can help you take advantage of the big opportunities in omnichannel marketing. Request a demo to get started.

April 23, 2020

The Omniscience Option: Next-best-action Recommendations that Work

By Dilyan Kovachev, Arm Treasure Data

What marketer wouldn’t want to be omniscient, especially when it comes to understanding customers? Unfortunately, it’s not yet a martech option, but next-best-action prediction is as close to omniscience as marketers are likely to get in this lifetime. Its purpose is to use data-driven insights and analytics to predict the next action to take, whether the application is a customer service call or a marketing or social media campaign. Often called “next-best-action decisioning,” it’s been a marketing Holy Grail for a while.

But the difference lately is that predictive martech and analytics – fueled by advances in data lake technology, Customer Data Platforms (CDPs), and Big Data analytics – have gotten superbly good at it. So good, in fact, that “the omniscience option” is no longer a science fiction fantasy.

Next-best-action Helps You Market to Individual Customers

Next-best-action is uniquely suited to service, support, and marketing because it typically uses data from all of these functions. It focuses on specific customer preferences instead of positioning a product for larger nebulous groups of buyers. The idea is this: if you can accurately forecast what a buyer wants – exactly when they’re looking for it – and you can figure out how to reach them when they’re ready to engage, you can provide a great experience for them that improves sales and cements customer relationships. Plus, you also deliver all that at the lowest cost. It’s a win-win!

While easily explained in a blog, this vision is a bit harder to achieve in the field. It requires a lot of data about customer behavior, a real-time engagement feedback loop, and the power of prediction. These three capabilities alone are hard to solve, so you can imagine the challenge of putting them together into one seamless process, especially if you have to do all the technical work in your own organization.

While some marketers have prematurely written off next-best-action decisioning as an improbable application for customer analytics, other marketers are investing in the expertise and technologies they need to build next-best-action systems.

The Key Ingredients in a Next-best-action Recommendation System

The basics of next-best-action recommendations require marketers to understand what, where, and when buyer engagement happens. If this engagement is happening through digital channels – which is likely, given that 89 percent of buyers start their process of discovering products and services with a search engine – behavior becomes easily quantifiable. You get to understand what products visitors to your website are viewing and how many. You can determine their interest through numbers of clicks on a page or on an advertisement. As well, you can analyze patterns people display across channels (such as logins to mobile apps and number of previous in-store purchases), that signal they’re ready to purchase something.

With rich data sets that are unified around individual customers, the what, where, and when of engagement is ready for action. We’ll break them down for you in the next sections and dive into the technological capabilities to make “best actions” happen.

Campaign Activity Defines the What in Next-best-action Systems

For marketers, the “what” in next-best-action systems is defined by campaign activity. This is how marketers engage with buyers. So, when a marketer sends an email, delivers a message through an ad, or sends a promotion in the mail, they’re looking for a response. Maybe that response is a coupon redemption or click-to-download content.

Sometimes, a buyer’s response is to do nothing. This is important to know for next-best-action decisioning, so you need to ensure that your methods of aggregation can handle “null” values without a lot of translation and workarounds. No matter the reaction, what that buyer did (or didn’t do) to respond is going to determine what the next offer should be. Are you collecting what buyers do in response to your campaigns? And, where is all of that response data?

Most likely, that data lives in the systems you used for the individual campaign tactics – your email system, your social site, or your agency’s database. Some of it might be in your loyalty program database or point-of-sale systems. And, I’m going to bet that it’s not all in one place, unless you’re using a CDP, in which case you might already have a set of accurate, unified customer profiles.

The Where and When Requires Predictive Analytics

Once behaviors are analyzed and patterns are discovered, marketers can make some decisions about the next touchpoint. Do they want to retarget people who visited the site two or three times but never purchased anything? Do they want to send a follow-up email to people that stopped by their booth for samples? Maybe they want to do both, but when do they initiate the next touchpoint? Generally, marketers reach people by using a couple of different strategies:

1. Where those people were when they responded (such as online or in the store)
2. Where that marketer expects them to engage next based on a customer journey analysis

Both have their merits, but what’s really important is where and when a person is most likely to want to engage with your content, message, or offer. This is the context of “where” and “when” in the next-best-action recommendation system, and if the prediction is accurate, it can save you from unnecessary campaign activity and free up time for more meaningful projects.

Again, the details of your campaign data can be put to work here to help predict which channels matter the most to specific people and when they can be reached. Where do they respond and which channels do they ignore? Are they more likely to engage at night while they’re online? Through machine learning, daily or even hourly data can be analyzed to produce a channel propensity score. A high score represents a strong correlation to a particular channel (such as social site or email) and time. These scores can then be added to an individual buyer’s profile.

To use those scores, a marketer will need a way to discover individuals according to their behaviors (what) and their propensity to engage in a specific channel (where) and at what time (when). Just as they have for years with demographic and household data, marketers can use segmentation tools to discover people with similar behaviors and preferences. The process of audience segmentation is the same, save for one major adjustment; it requires the flow of large, fast moving data sets to place individuals into specific segments automatically.

Without the ability to dynamically segment people, using behavior as a basis for analysis would be very hard for marketers. Can you imagine the time you would need to discover when shoppers abandon their carts and sort them one by one into groups for your next email campaign?

Dynamic segmentation tools allow marketers to zero in on precise groups of people as they exhibit the behaviors to be followed up on, for example, all people who browse a site without purchasing. As well, marketers can use those tools to drill down further into where and when those same people are most likely to respond to another message, offer, or piece of content.

Putting the Next Best Action Pieces Together

Making next-best-action decisions requires the “what, when, and where of buyer engagement.” Marketers want to make smart choices to engage buyers, and they want to make those decisions quickly and effectively. There are important capabilities and knowledge needed to support the process of using behavioral data for next-best-action decisions. Here’s what you need to know to make the recommendation system work.

First, event-level data needs to be captured continuously and timestamped for understanding the sequences of those events. Visitor behavior on your website needs to be streamed in and organized alongside email and ad response data. This unification process isn’t trivial and requires data management expertise, but the outcome is an individual profile of every visitor, customer, and potential buyer. It reveals their behavior and when they exhibited good and bad responses (such as downloading content or abandoning a cart).

Second, machine learning algorithms can pick up on signals marketers wouldn’t be able to discover on their own. How likely a buyer is to engage in a given channel at a particular time can be more accurately determined by moving past frequency analysis to propensity modeling, which captures more data points based on how correlated they are to the behavior you want to predict. With greater accuracy, marketers make more confident decisions on where and when to engage a buyer.

And last, dynamic segmentation tools can automatically discover behaviors as soon as they’re exhibited and help marketers initiate communications, offers, and other touchpoints quickly. Propensity scores add key information about when and where to best reach people for the greatest impact. With scores and event-level details at their fingertips, marketers can get as granular as they’d like with their segmentation and target people with the best offers, in the right places, and at the optimal time. With greater precision, they can be more effective, fast.

For more on what makes next-best-action recommendations work, watch Become a Data Pioneer with Arm Treasure Data.

April 20, 2020

5 ways your digital analytics strategy is hindering your customer experience

By Tiffany Carpenter, SAS

These days, an ever-increasing number of customer interactions are taking place over digital channels and every single digital interaction offers an incredible source of customer intelligence for organizations to tap into.

With every visit, customers leave a valuable trail of digital breadcrumbs. These breadcrumbs give organizations the ability to follow each individual customer journey and each customer’s experience along the way. With every browse, click, like and share your customer creates their own digital footprint. And with their consent, brands can harness this rich source of data to anticipate and deliver on the needs of each individual customer, optimize each customer’s journey, and unlock new competitive value for the organization.

Of course, this data must be treated as personal data and companies should provide comprehensive cookie notices to educate users on how they plan to use their personal data, on an opt-in basis.

But despite many customers still opting-in to share this data, organizations are struggling to tap into this readily available digital intelligence in a meaningful and effective way.

The reason? These five recurring challenges create barriers to unlocking the true value of digital data:

• Tagging is still the predominant method for digital analytics tools to capture data. Not only is there cost and time involved in creating, testing and deploying these tags, but they need to be constantly updated. Updates are required every time there’s a new area of interest or there are changes to the website. This invariably leads to delays in campaigns, and lost data and opportunities.
• Many digital analytics solutions focus on visits, page views, clicks and campaign triggers. The data collected is rarely at an individual customer level. This makes it challenging to join digital data up with offline data from CRM or single customer view systems, where data needs to be held at individual customer level.
• Too many organizations are focused only on behavioral data – what a customer clicked on and what they saw, rather than experiential data. Experiential data could include what a customer didn’t see, what price they were quoted or what products were not in stock. Collecting behavioral data without experiential data often leads to an incomplete or misleading picture of cause and effect.
• The number of digital channels, technologies and techniques for measuring customer experience within those channels has exploded but the data and insight is held in siloes make it difficult to obtain a joined-up view of the customer experience.
• By the time data is extracted and analyzed for insights, the customer has already completed their interaction. Organizations are still reporting on the past and unable to use data in real time to impact customer experience ‘in the moment.’

Organizations leading the field in digital intelligence are opting for a single view of individual customer-level behavioral and experiential data across digital channels that can be easily joined up to offline data to gain much deeper insight into the customer journey.

The ability to analyze data at this level of detail is helping these organizations go beyond the “what” and “how” of traditional digital analytics and answer the more valuable “who” and “why” questions. Who are my most and least valuable customers? Why do they behave as they do on my digital properties? What simple changes could I make to alter some of this behavior?

By capturing granular, time-stamped customer-level data from every digital interaction about everywhere your customer went, everything they did and did not do and everything they see and did not see, organizations can optimize their customer experience and create competitive advantage.

April 16, 2020

5 Types of Ad Tech Marketers Need to Know

By Henry Li, Arm Treasure Data

Digital advertising has reached new heights of complexity. Today’s omnichannel ad campaigns reach across many different platforms at once, from publisher websites and mobile apps to search engines and social media. Yet paradoxically, campaigns have become more and more personalized, using highly tailored, targeted ads to reach specific audiences.

All of this makes for an intricate process with many different participants, from advertisers and publishers to third-party vendors.

As you move further into ad tech, you’ll find yourself navigating a complicated and ever-changing ecosystem. Here are some of the most important types of technology you’re likely to encounter, including important differences in function and usage.

1. Demand-Side Platforms

As the name suggests, demand-side platforms (DSPs) serve people who want to purchase ads from publishers. If you’re in marketing, that means you.

A DSP is an automated system that vastly simplifies the buying process for advertisers, who can purchase targeted ad impressions from many different sources through a single interface. DSPs usually use real-time bidding – that is, automated auctions that take place in just milliseconds.

To make targeted ad buys, DSPs may use audience data from other sources such as publishers, advertisers, and external data providers. A DSP can use these datasets to guide targeted ad buys. The system can also track campaign performance and use this data to improve ad targeting and media buys.

So where do DSPs find ad impressions to buy? DSPs may buy ads directly from publishers, supply-side platforms, and ad exchanges.

In recent years, DSPs have come under pressure to provide more transparent pricing, better ad quality, and more reliable data about campaign performance. Ad fraud continues to be a major challenge – as it is for the entire industry – and leading DSPs have tried to differentiate themselves by providing buyers with better protection against phony publishers and malicious ads.

2. Supply-Side Platforms

While DSPs serve advertisers looking to buy ad inventory, supply-side platforms (SSPs) serve publishers looking to sell ad inventory to generate ad revenue from their websites or apps.

Publishers use SSPs to sell ad impressions through various external platforms. These may include DSPs, ad exchanges and ad networks. An SSP integrates with all of these technologies, so publishers can manage and fulfill all of their inventory sales through a single interface.

Ad impressions are only a means to an end, though. What publishers really offer is access to audiences. And that requires data.

Through an SSP, a publisher can share a wide range of data with different buyers, including advertisers, ad networks, and DSPs. For example, publishers can share information about the type of content being displayed, or concerning the demographics, location, and purchase behavior of website visitors. Such information helps advertisers segment their audiences and hone their targeting.

Like DSPs, SSPs have faced complaints about low-ad quality, high pricing, and fraudulent ads. These trends have pushed large providers to develop better auditing technologies, more transparent fees, and better industry standards – although these efforts are very much works in progress.

3. Ad Exchanges

An ad exchange is an online system that provides a marketplace for ad buyers and publishers. Such platforms operate a lot like online stock trading platforms. On an ad exchange, publishers auction off the inventory they haven’t already sold to ad networks. Buyers then compete to acquire these unsold ad impressions, often through real-time bidding.

All transactions are highly automated, since it’s extremely difficult for humans to perform the calculations required to trade so many targeted ad impressions in real time. While making their trades, buyers and sellers also exchange data that allows advertisers to segment and target their audience.

Some ad exchanges provide open marketplaces for buyers and sellers. But this model makes it hard for publishers to control what kind of ads their audiences see. That’s a drawback for companies that want to protect their brands.

As a result, publishers are seeking alternatives to open ad exchanges. In some cases, they are using private exchanges that limit entry to select buyers, so they can prescreen who places ads on their properties. Other publishers are turning to a “programmatic direct” model. This method uses automated systems to enable more efficient publisher-to-advertiser sales, without any auctions.

4. Data Management Platforms (DMPs)

Data management platforms (DMPs) enable their users to store, manage, and analyze data about ad campaigns and audiences. Unlike a DSP or SSP, a DMP does not help you buy or sell ad inventory. Instead, a DMP feeds useful data to these other platforms, enabling both marketers and publishers to make more effective decisions.

For marketers, a DMP and a DSP complement each other to make more effective ad purchases. The DMP provides information that helps the DSP manage and direct ad buys. The DSP, in return, sends back valuable campaign performance data. Some DSPs include DMP functionality, creating hybrids that fuse aspects of both systems.

Using a DMP, marketers can create temporary user profiles and target audiences based on demographics, behavior, or other characteristics. As advertising platforms, DMPs are restricted in their ability to use personally identifiable information. As a result, they mainly use anonymous, short-lived data acquired from third-party vendors to build their profiles.

Recent privacy laws have limited the usefulness of platforms that rely on third-party data, forcing DSPs to change how they operate. In particular, the European Union’s General Data Protection Regulation (GDPR) has made it harder to acquire and use certain kinds of data for targeting. Meanwhile, the most advanced DMPs continue to add features and develop new capabilities – for example, the ability to use richer sources of first- and second-party data.

5. Customer Data Platforms

Like a DMP, a customer data platform (CDP) ingests data from other systems and builds profiles that can be used to target audiences. The difference is that a DMP is primarily designed to improve ad targeting for new prospects, while a CDP provides insights into your existing customers’ journeys and informs every aspect of your marketing.

A CDP (such as Arm Treasure Data) consolidates all your customer data and presents a single, actionable view of every individual customer. That enables you to develop more targeted, effective, personalized experiences for audiences on all channels.

Unlike a DMP alone, a CDP helps you build complete profiles of known individuals based on personally identifiable information from any source. An enterprise-level CDP can collect and store unlimited amounts of data from every system that interacts with customers, both online and offline. In addition, it continually maintains and enriches its data, giving you a full history of every customer. The best ones are integrated with more than 100 off-the-shelf integrations with data coming from sources as diverse as CRM and ERP.

With its detailed customer insights, a CDP can enhance the performance of personalized ad campaigns. For example, a CDP can be used to identify the company’s most valuable customers and create a lookalike audience based on their behaviors and attributes. It can then push this information to a DMP to use in making targeted ad buys from SSPs, ad exchanges, and ad networks.

In short, a CDP doesn’t replace other ad tech platforms. Instead, a CDP can activate and orchestrate such systems, improving the reach of your ads and integrating your campaigns with the rest of your marketing. Some companies, such as Subaru, Wish.com, and Kirin, are already using CDPs this way.

As the preceding discussion makes clear, ad tech is growing increasingly diverse, and it is automating much of the drudgery of campaigns, often at a pace faster than humans can hope to operate. Some martech, such as CDPs, is evolving to help produce better control and higher-quality marketing insights. Find out more about your options for increasing efficiency, targeting and insights.

April 9, 2020

Customer Data and CDP Martech: What Does a Unified View of the Customer Really Mean?

By Tom Treanor, Arm Treasure Data

Every marketer wants to know their customers – from online and offline and across multiple social channels and devices. But what does a “unified view” of your customers really look like?

We know consumers demand personalization, and their expectations continue to rise. Meaningful customer personalization requires data. And that data must be complete and up to date to be any good.

So how do you know when you are seeing the whole picture and not pieces of each customer? A unified customer view will uncover opportunities that used to be missed while also building deeper brand loyalty and increasing purchases and revenue.

Here’s what we believe a true unified view looks like – and how you can get there with your customer data platform.

Customer Data and the Unified View

There are already parts of the customer journey that marketers can see with great clarity: We can track social media traffic to a blog or product page, we can retarget ads to those who have visited the website, and more. Creating a complete, unified view is about filling in the gaps; it’s bringing together data from across the organization, sanitizing, consolidating, and making it useful.

Finding Customer Clones

What do these five people have in common?

• The person who read your blog
• The person who browsed your product pages
• The person who visited your brick-and-mortar location
• The person who chatted with your chatbot
• The person who made a purchase online

In many organizations, these five people appear in different databases in different departments. Marketers might only concern themselves with the first two. The sales department may only be aware of the middle two. Customer service might only know about the last guy.

When you start consolidating data, including offline and siloed data, you can see the truth: these five people are all the same person. Each point of contact was a stop in the customer journey.

But that’s still not the whole view. Let’s add in three more people:

• The person who purchases
• The person who subscribes to your blog
• The person who calls customer service with a question

It’s easy to imagine how much more persuasive, relevant, and robust your contact with this customer could be. Your next email drip could include helpful tips for the product they called customer service about. It could suggest accessories for their purchase and fresh content that’s relevant to their interests.

Making Marketing Smarter

Using this data wisely can give your marketing some memory. Think of it this way: You know that feeling when you run into acquaintances and they blank on your name? And you have to say, “It’s Grace… we went to the same middle school… I drove you home after Amber’s birthday party… we have worked in the same building for three years…” And eventually they pretend to remember?

That experience doesn’t make you feel special or valued. But that’s often how marketing treats consumers. We send an email that says, “Hello, VALUED CUSTOMER, would you like some SHOES? Here’s an article on BUYING SHOES.” And they’re thinking, “It’s me, Grace… I bought a pair of shoes last week.”

How much better would it be if your next email said, “Hi, Grace, I hope those new stilettos are working out for you! Here’s an article on how to rainproof the velvet, and a link to a handbag that matches perfectly.”

Instead of an awkward blank stare and a sales pitch, now you’re being helpful, and maybe even initiating a conversation or deepening the relationship.

Upgrading Relationships

Consolidating marketing, customer service, and sales data makes it easier to solve problems, inspire loyalty, and drive referrals and repeat business. A complete view of customer data levels up relationships at every stage of the journey:

• A casual browser nurtured with hyper-relevant content becomes a repeat visitor.
• A repeat visitor nurtured with smart retargeting and contextual offline reinforcement becomes a customer.
• A customer who receives personalized follow-up becomes a repeat customer.
• A repeat customer with continued smart nurturing becomes a raving fan.

A Single ‘Golden Profile’ for Every Customer

It starts with seeing the customer as a single, complete, multifaceted person, not a series of unrelated brand contacts. That means not just pulling data from multiple sources, but also combining, sanitizing, and consolidating that data, intelligently using it to fill in your blind spots, so that you arrive at a single “golden profile” of each and every customer. Each profile can then be updated with each purchase, phone call, loyalty program awards, mobile app data, and more.

Outstanding data management is the foundation of a truly unified customer view, and enterprise Customer Data Platforms (CDPs) are designed to help businesses unify, analyze, and activate all of their data.

Want to see what unified customer data looks like in action? Learn how retailer Muji used customer data and an online app to increase in-store revenue by 46 percent.

March 26, 2020

Understanding Customer Data Platforms

By Rohit Srivastav, Netcore

“Customer is indeed the king, but running a kingdom is no child’s play
Knight, minister, jester, advisor, are you there for your king in every way?”

A marketer’s job isn’t easy. With each passing day, your customers’ expectations are rising, the amount of customer data available to you is increasing, and the challenge to acquire, engage, and retain customers is becoming more and more difficult.

Well, just like every challenge comes with a solution, so does this one. And the core solution lies in knowing your customers well. The more you understand them, the better positioned you are to win their trust and loyalty.

There is no dearth of customer data today. It depends on brands how efficiently they collect the information and leverage it. And with a proliferation of data collection methods, sky is the limit when it comes to uncovering customer needs and decoding what exactly they want.

As per a recent article by McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result.

However, leveraging customer data for marketing can sometimes prove to be a double-edged sword – marketers across industries often find themselves struggling to make sense of data when there’s too much of it.

While customer relationship management (CRM) platforms definitely made it easier to manage data, extracting meaningful and readily-usable insights from customer journeys is still a challenge. Now, Customer Data Platforms (CDPs) have emerged that offer promise to fill the gap, not as a replacement for other tools, but to augment their functioning.

What is a Customer Data Platform?

A CDP is a database software that organizes customer data across numerous touch points and interactions to create a unified view of the customer, accessible to other systems and software. A CDP builds a 360-degree picture of customers on an individual level by collecting real-time data from a multitude of sources – CRM, DMP, social media, web forms, email, website, et al.

This single customer view can then be accessed by third-party tools such as marketing automation tools to execute strategies and measure performance. A CDP is primarily meant for marketers like you, who can use the tool with little technical support.

The CDP Edge

Customer Data Management tools have been around for a long time now. When the CRM software was launched in the 90s, organizations found they could manage interactions with current and potential customers with ease, apart from being able to perform data analysis to drive retention. However, its major limitation was that it managed data for registered clients only, using predefined first-party data. The next frontier was the launch of Data Management Platforms (DMPs) in the 2000s, aimed at planning and executing media campaigns. DMPs could segment anonymous data and work with second and third-party data.

The industry soon realized the need for a more sophisticated tool for delivering an improved customer experience through omni-channel strategies. CRM and DMP platforms created data silos that presented a challenge to marketers. CDPs solved this problem by creating a unified view of the customer on a single, comprehensive platform.

What Kind of Customer Data Does a CDP Collect?

The sheer volume of digital data overwhelms traditional database software. A CDP is built to manage customer data from various channels and touch points. Following are the primary kinds of customer data that CDPs collect and organize:

• Identity Data: Identity data lies at the heart of an individual customer profile in a CDP, allowing an organization to prevent costly duplication. This typically includes information like name, demographics, contact, location, social media handles, company-specific user IDs, etc.
   
• Descriptive Data: This data presents a more comprehensive picture of a customer. This will include information on their lifestyle, career, hobbies, family, etc.
   
• Quantitative Data: This data allows companies to understand how a customer has engaged with it through actions and transactions. This includes information on transactions, email communication and interaction, website visits, product views, and customer service interactions.
   
• Qualitative Data: This offers a clearer context for customer profiles, focusing on motivations, opinions, attitudes expressed.

As is evident, CDPs collect a wealth of customer data, most of which depends on a company’s business and industry.

Difference Between CDP & Other Data Management Platforms

While there is some overlap between a CDP, CRM, and DMP, there are clear differences.

Both CDP and CRM collect customer data for sales and marketing activities. However, CRMs focus on intentional customer data and interactions; for instance, a customer’s telephonic interaction with a salesperson. A CRM collects general customer data, not huge data sets from multiple touch points. On the other hand, a CDP focuses on the lifecycle of a customer’s actions. While a CDP collects offline data, a CRM cannot retrieve offline data unless manually entered.

A DMP sorts and analyzes customer and ad data from multiple sources, with the aim to aid you in learning about customer demographics and buying triggers. DMPs focus on anonymous data (devices, cookies, IP addresses), looking at general behaviours rather than customer-specific ones. While CDPs are built for marketing, DMPs are meant for advertising. While DMP data typically emerges from third-parties, CDP data is collected via a company’s internal user base. As a CDP collects more data, it gets more powerful. On the other hand, DMPs store data only for a short period since ad targeting changes quickly and data soon becomes outdated.

How to Use a CDP – Use Cases

Deploying a CDP can help you achieve both high-level goals as well as lower-level ones. Here are some of its most important use cases:

• Online to Offline: Merging online and offline activities to create a unified customer profile for easy customer identification.
• Customer Segmentation: Segmenting customers based on behaviour to deliver a personalized and omni-channel experience.
• Predictive Scoring: Enhancing customer profiles with predictive data
• Behavioural Retargeting: Running acquisition and retention campaigns through integration with online ad channels.
• Product Recommendations: Building recommendation models and delivering a personalized shopping experience to drive engagement, up-sell, and cross-sell.
• Omni-channel Automation: Personalized messages across channels to enrich customer lifecycle and enhance acquisition and retention.

Using CDP to Improve Customer Lifetime Value

Fostering customer loyalty rests on delivering a quality, consistent, and personalized experience. CDPs make this possible at scale, allowing for nurturing loyalty by solving the problem of siloed data. When data is siloed, creating a consistent and omnichannel customer experience is impossible. By unifying customer data, CDPs make it accessible to everyone in an organization, at all times. By gathering first-party data i.e. information directly from customers, CDPs enable the most effective marketing decisions informed by accurate data. In essence, CDPs equip marketers like you with a powerful tool to manage customer relationships accurately and effectively.

Customers today have high expectations of businesses – personalized services, consistent experiences across channels, and tailored recommendations. To be able to deliver the experience a customer is looking for, and keep them coming back to you, investing in an excellent CDP that offer deep insights, is now a necessity, not a good-to-have thing. Are you CDP-ready already?

March 23, 2020

Change Management Strategies for a Successful CDP Initiative

By Ryan Greene, ActionIQ

According to research from McKinsey, almost 50% of digital transformation initiatives fail to achieve their expected value, with only 10% exceeding expectations. If you’re planning to leverage a CDP to help transform your company into a customer-centric organization, then your initiative is no exception to this somewhat daunting statistic.

People and Process are Critical to CDP Success

Fortunately, there’s an almost surefire way to beat these odds. At ActionIQ, we developed and deliver one of the industry’s leading CDPs. But curiously enough, the solution to this challenge doesn’t lie in technology. Rather, it’s dependent on people and process.

Based on our experience in helping deliver CDP-centric transformations at some of the largest and most respected brands in the world (like Michael Kors, The New York Times, Pandora, Saks Fifth Ave, Verizon and more), we’ve learned that the most successful CDP initiatives always lay out a strong change management plan at the outset.

“In fact,” says James Meyers of ActionIQ, who previously served as an analyst and CDP advisor at a leading international research firm, “the vast majority of inquiries I received over the years were from executives who were far more concerned about change than they were about technology.”

(Read more about James’s experiences advising senior executives about customer centricity and CDPs in his blog post Why Your CDP Vendor Must Bring Change Management Expertise.)

The Importance of a Change Management Plan

Why is a change management plan so important? Put simply: changing people’s behavior is difficult. You can run a flawless, buttoned up project to swap out old martech and bring in new systems, but if users don’t adopt the new systems, your project is a failure. And, getting people to adopt new CDP technology is about much more than simply training them to use it.

Here’s why. Your CDP puts your customer at the center of everything you do. It takes today’s multi-step, multi-department processes (like determining which audiences merit prioritization for various campaigns) and enables marketers to operate with100% self-service. It breaks data silos. But for companies and teams that are still organizationally siloed and focused only on traditional metrics – say, aligned around a channel and measured purely by its P&L – customer-centricity is a foreign concept. They don’t have the right org structures, goals, incentives and skill sets in place to realign around the customer. So they fail to maximize the benefit of using the CDP.

“Reflecting on my 20+ years in the field of customer analytics, I’ve come to the realization that one of my biggest challenges has always been getting my business counterparts in the organization to look at a customer holistically,” says Tamara Gruzbarg, head of ActionIQ’s change management practice. “Very few are ready to accept the fact that the customer couldn’t care less who owns the P&L, or whether she is considered a ‘store customer’ or an ‘online customer.’ She wants an experience that makes sense for her. And if it doesn’t, she will leave.”

(Get more organizational change insights from Tamara in her blog post CDP Change Management: Change Comes from Within.)

Setting Up Your CDP Transformation for Success

Without a well-constructed change management plan – backed by the full support of your company’s top executives – the inertia of the status quo will win. Users will stick to the old systems and processes, and your customer-centric vision will remain unrealized.

So what goes into a well-constructed change management plan? It starts with the realization that people and processes require equal weight to technology when designing your transformation.

“In the early days of ActionIQ, our most forward-thinking clients began to ask us for help with change management,” says ActionIQ’s Ryan Greene. “Back then, we assisted in more of an organic ‘roll up your sleeves and get it done’ kind of way. But over the years we’ve developed repeatable best practices that are now a standard part of ensuring the success of every client implementation.”

(Read more about the best practices that make up ActionIQ’s change management approach in Ryan Greene’s blog post Planning for CDP Change Management: A Proven Approach.)

Here are three foundational change management elements that will get your CDP initiative started off on the right foot:

1. Executive sponsorship – Identify the goals and use cases most important to your company, and then get key executives on board as sponsors.
    
2. Cross-functional alignment – Gather input on challenges and requirements from all stakeholders from all relevant departments, and co-opt them as partners in achieving your goals.
    
3. Governance and accountability – Set up the task force that will drive your transformation; establish accountability; document the jobs to be done as well as the governance and reporting frameworks.

Lean on Experts for CDP Change Management Guidance

Of course, this is a very high-level framework. Mapping these practices to your specific organization takes thoughtful planning, diligent execution and a lot of hard work. Just like you lean on partners to provide and implement technology, you can also lean on partners to help with the people and process aspects of your CDP initiative.

When looking for a partner, you should only trust people who have deep experience as marketing, analytics and data practitioners, who have a track record of implementing organizational change, and who can help you mitigate risks before you encounter them.

Digging Deeper

If you’re interested in more detailed change management frameworks, including:

• How to align your project to goals and desired outcomes
• Identifying where you are in the customer centric org maturity model
• Defining tasks and roles for before, during and after your implementation

then be sure to download our CDP Change Management Best Practices ebook now.

Have any change management war stories, or just want to get in touch? I’d love to hear from you. Contact me at rg@actioniq.com.

March 19, 2020

Customer Data Platforms vs. Data Management Platforms: A Definitive Guide

By Rohit Srivastav, Netcore

Modern brands share a relationship with their customers vastly different from the ones that traditional organizations did. If one were to outline each interaction (both physical and digital) between a brand and its customers, we’d find an extensive map with multiple touch points. With these numerous contact points comes the dilemma of delivering a seamless transition between different channels and a consistent, unified messaging throughout. Though brands have been relying on Data Management Platforms (DMPs) for achieving this goal, the emergence of Customer Data Platforms (CDPs) has presented a compelling alternative to marketers. By the looks of it, the two may seem similar but they hold major differences. However, one cannot necessarily replace the other.

This guide will help you understand each of these platforms and the value they deliver.

What are Customer Data Platforms?

The CDP market is growing rapidly. According to a study conducted by the CDP Institute, the CDP market size grew by more than 50% during the year 2018 alone, to a net worth of around $740 million. And the growth is not merely limited to numbers. A study conducted by Forbes Insights involving 400 marketing leaders found out that 44% of them believed in the power of CDPs in driving customer loyalty. So, what exactly are Customer Data Platforms?

Say, you have been given a 500-piece puzzle which you easily solve. To your surprise, you’re told that it is simply part of a bigger puzzle and you’re given 200 pieces more as a result. You carefully finish that as well though it is relatively time-consuming. But, before you could take a deep sigh of relief, you’re asked to expand the puzzle further with 500 more pieces!

Well, let’s see how a Customer Data Platform (CDP) fits into this puzzle analogy. Consider each piece of the puzzle to be a data point about an individual customer and your “understanding” of this person keeps expanding in varying degrees (with every new puzzle piece). Now, the final puzzle will represent the unified profile for that customer and the board/area on which you’re building the puzzle will depict the CDP database.

In technical terms, a Customer Data Platform or CDP is a marketing system or software that creates a unified and steady customer database accessible to other systems. The purpose of a CDP is to gather all customer data and to stitch them in order to create unified customer profiles that can be used for marketing campaigns and customer service initiatives.

What are Data Management Platforms?

The Data Management Platform (DMP) market size is expected to have a net worth of whopping $3 billion by the year 2023 with a Compound Annual Growth Rate (CAGR) of 15% between the years 2017-2023. Let’s take a slightly different approach to understand the platform with such impressive prospects. Consider the example of a dealer or merchant whose employees wear blindfolds and earplugs full-time. As a result, they are unable to see or hear their customers. All they do throughout the day is punctually stock the shelves with products and take care of the register. There’s no contact whatsoever with the customers. In fact, they don’t even know who they are! Hence, naturally they are clueless regarding their customers’ preferences and likes/dislikes. And after a “good” day’s work, when they sit down to tally the register, all they can figure out is the number of goods sold and at what cost. They have no clue of who bought what and why (was it for self-use or maybe as a gift?). And the worst part is not oblivion but indifference – the dealer doesn’t know and he couldn’t care less. This is exactly what a marketer or business professional that does not use a Data Management Platform (DMP) looks like.

A DMP is more or less like a data warehouse – it is a system or software that gathers, analyzes, stores, and delivers useful customer information to marketers, web publishers, and businesses. The data managed by DMPs is used to generate audience segments which are in turn used to target specific users in online marketing campaigns.

Aren’t they the same?

Well, they do sound the same and a superficial understanding can even give one the impression that they indeed are the same but nothing could be further from the truth. CDPs and DMPs differ on several grounds:

• Personally identifiable Information (PII)
  Perhaps, the greatest difference between CDPs and DMPs can be established in terms of their use of customer identities or Personally Identifiable Information (PII). In marketing terms, a PII is a combination of data points used to identify a specific customer. Since CDPs function in a manner that the more data you collect about a specific customer, the more relevant experience you can provide to them; it relies on PII to operate. Whereas, DMPs process data that is anonymous and this makes it more difficult to determine whether or not the data was sourced ethically.
   
• Data Sources
  Another drastic difference between the two platforms can be found in terms of the data used by each. CDPs primarily use first-party data (data collected by your company) and a little of second-party data (first-party data collected and sold to you by a non-competitive partner company). DMPs, on the other hand primarily make use of third-party data (first-party data collected by a data collection company and made available for purchase) and a little of second-party data as well.
   
• Use Cases
  CDPs and DMPs also differ in terms of their use cases. Where, on one hand, Customer Data Platforms are used in gathering customer data in its organic form, powering different marketing systems, cross-device data coordination, and leveraging Artificial Intelligence (AI) for channel marketing. The use cases for DMPs include optimizing acquisition, prospecting and modelling, audience suppression, cross-channel segmentation, and remarketing.
   
• Data Retention
  Another important difference between the two platforms lies in how long each stores customer data. CDPs store customer data for long time periods. Smart CDPs even allow you to set the time limit for which the data must be retained. This is especially useful in cases where, say, a furniture dealer wants to give a discount of 20%to his most valuable customers (CDP database for the previous year can help determine the high-value customers). DMPs are completely the opposite. They function best when data is retained for shorter periods (like 90 days). This is especially useful in cases where an advertiser is seeking to target travel aficionados (there’s no point in targeting people who were interested in travelling five years ago but aren’t right now).
   
• Scope of Reach
  Since CDPs mainly have access to the data of your existing customer base, they cannot help you to create lookalike audience segments to target potential customers in external databases. DMPs, on the other hand, can help you in extending your target group by creating lookalike audience segments.
   

Do they have anything in Common?

Well, yes! Now that we’ve looked into the differences between CDPs and DMPs, let’s take a look at the several similarities:

• Both CDPs and DMPs function by leveraging existing customer data.
• Creation of a Single Customer View (SCV) or a 360-degree view of the customer is the aim of both CDPs and DMPs. By creating a single view of the customer, the platforms aim at helping businesses understand their customers better.
• The data gathered via both platforms is used for Audience Activation. Meaning, the data is used for powerfully engaging with high-value audience segments and for delivering personalized and relevant user experiences.
• Both platforms render the following functionalities – reporting, analysis, and optimization.

CDP or DMP: Which one should you choose?

Since CDPs help in engaging and improving relations with existing customers, marketing departments of individual companies will find them the most profitable to manage customer data and to generate personalized messages via different marketing channels.

DMPs are your best bet if you need to manage and process large sets of audience data and wish to extend your target group to external databases. DMPs prove to be the most profitable to web publishers, marketing agencies, media houses, etc.

Furthermore, as mentioned earlier, CDPs and DMPs do not necessarily replace one another but are complementary in nature. Meaning, data collected by CDPs can be enriched for better segmentation using DMPs and CDP data can help in creating better lookalike audience segments when used within DMPs. Hence, depending upon your marketing needs, choose either one or both of these valuable platforms.

March 16, 2020

Data Privacy Has a Day – And Companies Better Pay Attention

By Lisa Loftis, SAS

Data Privacy Day, occurring every January, is an international effort to raise awareness and promote data privacy and protection best practices. It originated in Europe in 2007 and was adopted by the US several years later. While searching for quotes on data privacy to honor the day, I came upon an eye-opener from 2009 by former Google CEO Eric Schmidt:

“If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.”

To be fair, the quote was in response to a conversation about how tech companies share information with authorities, but the context was that the amount of information said companies really know about consumers would “shock” and “confuse” them. We really have come a long way on data privacy - or maybe not.

The largest fine levied under the GDPR so far, $57 million, came shortly before last year’s Data Privacy Day, and was given to Google for not properly disclosing to users how data is collected across its services — including Google Search, Google Maps and YouTube. The regulators claimed that Google did not meet the requirement of obtaining clear consent and that consumers are largely unaware of the data collected and shared by Google. Note that Google disputes the claims.

Unfortunately, I think I know what the regulators mean. Early last year, a Google screen popped up on my phone asking me to rate places and businesses including a law firm, a retail store and a national park. These were all places I had visited recently. It turns out that every location I had physically been to in the last several months with my cell phone in tow - which is almost everywhere I went - had been tracked, stored and visible to me and who knows who else. I certainly never knowingly gave explicit permission for them to track my physical location. Even worse, rescinding this permission was an arduous and non-intuitive process that involved navigation across six different screens.

This is the antithesis of clear and unambiguous consent. I don’t mean to pick on Google here – because in our data-driven world this type of tracking is the rule rather than the exception. We must change our thinking on this. Both consumers and legislators are demanding it.

Consumer Expectations are Significant

While my informal poll of non-tech working consumers indicates that most are not aware of International Data Privacy Day, they do have definite expectations around data privacy.

A recent survey of global consumers, CX2030, illustrates how focused consumers are on the issue. CX2030 did not focus on privacy specifically, but instead was designed to predict what customer experience would look like into the future. Privacy, specifically trust, came up as one of the pillars that companies will be increasingly compelled to deal with. Unfortunately, the overriding sentiment from consumers was one of concern:

• 76 percent of consumers are concerned with the amount of data brands gather when they search for or purchase a product.
• 71 percent feel companies should not be able to share their data.
• 78 percent want to see what data has been collected and want control over changing, updating or deleting this information.
• 73 percent are concerned with how brands are using their personal data to the point where they feel it is out of control.
• 61 percent feel they have no control over the level of privacy they need for themselves, their family, or their children.
• 50 percent believe brands are hiding “bad things” they’ve done with user data and privacy.

When consumers use phrases like “out of control” and “hiding bad things”, companies had better sit up and take notice.

Legislators are Paying Attention As Well

Consumers are not the only ones paying attention.

In another European privacy enforcement action last year, German antitrust regulators ordered Facebook to seek users’ explicit consent to combine non-Facebook data from Instagram, WhatsApp and various 3^rd party websites into a comprehensive social media profile. Facebook must submit compliance proposals or face significant fines of up to $5 billion. Facebook plans to appeal, however, the top antitrust regulator for the EU has indicated that it is watching this case.

Facebook is also facing numerous lawsuits over data misuse and ad targeting including one brought by Washington, D.C., Attorney General Karl Racine, accusing the social media giant of wide-ranging privacy violations. They are also under investigation by the FTC to determine if they violated a 2011 FTC consent decree requiring them to give consumers clear and prominent notice of how information is collected and used and to obtain consumers’ express consent before sharing information beyond established privacy settings.

Both Google and Facebook have been sued multiple times for violating the Children’s Online Privacy Protection Act which imposes requirements on companies on collecting data on children under 13 years of age. Moreover, the City of Los Angeles has sued the IBM subsidiary, The Weather Channel, for “covertly mining the private data of users and selling the information to third parties, including advertisers.”

A battle is also brewing in the US over state and federal privacy laws. Several states have passed laws aimed at data privacy and ethical use. The most prominent and restrictive of these is the California Consumer Privacy Act of 2018 - taking effect now and billed to be the toughest data privacy law in the country (incorporating many GDPR-like restrictions). Silicon Valley has lobbied hard against this and other state bills, pushing for less restrictive measures and asking that a uniform federal law supersede all state legislation. To this end, both the US Chamber of Commerce and the Internet Association, which represents companies like Amazon, Facebook, Google, and Twitter, have released their own recommendations for a federal bill. The Data Care Act introduced by a group of US senators, a competing congressional bill, The Information Transparency and Personal Data Control Act, and the White House Administration’s recommendations round out the plethora of proposals.

Regardless of where we end up in terms of data privacy regulations – several things are clear. The privacy mandate is expanding. Consumers expectations are increasing. And there will be regulation here in the US as well as in Europe. If you don’t keep up, there will be consequences.

March 12, 2020

Augmented Analytics To Transform Big Data Into Smart Data

By Rohit Srivastav, Netcore

It was in 2005 that Roger Mougalas coined the term Big Data. Ever since, it has captured the imagination of industries across the spectrum, across the globe. After more than decade, the world is now staring at the next frontier in data – Augmented Analytics.

It was back in 2017 Gartner predicted Augmented Analytics to be the future of data, and in 2019, it already is the number 1 trend in data analytics. As per a research published by Allied Market Research this year, the global market for Augmented Analytics will reach USD 29.86 billion by 2025.

Data On Its Own Holds No Value

Data-driven insights are no longer just good-to-have, they are crucial for staying ahead of the curve. Most future-ready organizations today have embraced data analytics to deepen their understanding of customers and drive bottom-line growth. The problem is, their ability to leverage the power of data is severely limited, leading to a failure of data analytics projects. According to Gartner estimates, an incredible 60% of big data projects fail!

Data, by itself, holds no value for a business.

Say, a company’s data reveals that sales figures are dropping by 5% every month. But what does that really mean? This decline could be attributed to a failure of advertising methods, industry trends, or something entirely different. There is no way to figure the cause out unless you take a deep dive into the issue to uncover the real reason behind the decline in sales. For instance, you may come to the realization that your paid ads are less effective and need a different approach. Now you have an actionable insight that tells you exactly what to do.

The lesson – you need actionable insights, not simply informative data.

A data analytics project involves a number of processes – data aggregation, extraction, cleansing, pattern analysis, insights generation, to name a few. While the process itself isn’t so complicated, the tricky part is generating the right insights. Because data scientists are in short supply, other than being an expensive resource, companies need an advanced yet affordable tool for analysis at scale.

By harnessing the power of AI and ML, Augmented Analytics offers freedom from the tedious process of processing, aggregating, and visualizing data. Naturally, it is the next big disruptor in the world of business intelligence.

What is Augmented Analytics?

Augmented Analytics (AA) presents a novel solution to businesses to make sense of swathes of chaotic data. Augmented Analytics combines Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate the process of insight extraction from data.

The AA-driven tools organize, manage, filter, and analyze datasets to produce actionable insights, speeding the process of turning data into digestible information. Owing to the reduced manual involvement and dependence, businesses can rapidly analyze data at scale, and easily obtain patterns and trends.

Augmented Analytics reduces an organization’s dependence on data scientists and other manual processes by automating this crucial process with little to no supervision from a technical expert. Essentially, it cuts down on the human intervention part, weeding out less relevant insights automatically. Thus, the risk of missing important insights or making errors is vastly reduced, resulting in a streamlined and reliable data analytics process.

Augmented Analytics is set to create a new standard for business growth as organizations consume and generate massive streams of data from multiple sources but face challenges in making the data readily usable. Let’s see how.

How Does Augmented Analytics Work?

A business’ engine needs data to fuel growth. By automating a crucial part of the insight generation process, Augmented Analytics fuels this engine at an accelerated rate. When repetitive data cleaning and organization tasks are automated, data scientists will have more time on their hands for strategic analysis and decision-making. Additionally, this shrinks the scope for human error.

Smart data, fueled by Augmented Analytics, brings together the whole picture. When an organization’s data is siloed i.e. distributed across several different platforms, it presents a hurdle in smart decision making. To solve problems and identify areas for improvement, the decision makers must be able to view how the engine works on a whole, not how different parts work separately. By integrating data points into a unified system, decision makers and CMOs can track the entire picture on one platform.

In a paper published this year, Gartner outlines the different facets of augmented analytics:

• Data preparation and discovery
  Currently, most existing augmented analytics technologies lie in this stage. Here, the algorithm’s primary job is to automate data preparation tasks such as cleaning, labeling, collection, etc.
  
  At this stage, business stakeholders can use machine learning to automatically detect, visualize, and narrate relevant findings without having to build complicated models or algorithms.
   
• Signal detection
  At this stage, the analytics algorithm is able to detect true signals in data with a good measure of reliability. But, it cannot connect the discoveries with business actions or situations.
  
  So it still needs assistance from data scientists to transform such discoveries into concrete business insights. The upside is that the time they must spend on each insight is reduced drastically.
   
• Insight generation
  This is where the augmented analytics engine can directly interface with executives with almost no input from a data scientist. The algorithm will leverage its knowledge of past business cases to connect trends in the data with the larger business context.
  
  Then, it can go further and offer concrete action steps based on its insights. In fact, the engine can also track the implementation of such actions and offer additional insights to the business for optimizing its operational effectiveness

Benefits of Augmented Analytics

Augmented analytics takes the benefits of business intelligence to the next level, with unprecedented efficiency and accuracy:

• Improved accuracy
  When data scientists manually work on datasets and prepare them for analysis, there is room for error owing to the human element. Statistically speaking, the larger the volume of data, the greater the possibility of an error. By leveraging machine learning, the room for such mistakes shrinks greatly.
   
• Increased speed
  With standard business intelligence tools, the time required to manually prepare data and the wait time for related parties to respond to requests delay the completion of projects. Augmented analytics speeds this by immediately beginning request processing and leveraging AI to cull appropriate data – at the speed of a machine, not a human.
   
• Reduced bias
  When data scientists work with analytics tools, there is scope for blind spots and biases to creep in, leading to missed insights. Machines, on the other hand, take a more thorough and infallible approach, with no inherent bias. By quickly analyzing exhaustive data combinations, augmented analytics can identify the right insights.
   
• Greater resources
  Instead of reducing the need for human data scientists, augmented analytics can increase their value by freeing them from manual labor, allowing them to focus on more high-value tasks i.e. creating richer, deeper insights.

The automated insights generated by augmented analytics can thus be leveraged to assess business performance, identify growth pockets, and understand how a brand compares to the marketplace, thus contributing to a solid business strategy.

Ultimately, this results in cutting-edge insights driven by algorithms that would otherwise demand a huge investment of time and energy. This means data is democratized so that data scientists aren’t the only people in an organization that can make sense of the results.

Democratizing Data Analytics for all Stakeholders

Augmented analytics is fast becoming a popular data analytics tool, one that doesn’t need the involvement of data scientists, effectively collapsing the wall between asking questions and getting the right answers. One of the biggest advantages of embracing augmented analytics is the democratization of data.

Data scientists and analysts enjoy freedom from repetitive and low-value tasks like running routine reports. Instead, they can focus on solving complex queries and data science projects, offering critical business insights to the relevant stakeholders.

For small companies that don’t have the resources to build a team of expensive data scientists, augmented analytics will infuse accessibility and affordability into data-driven insights.

A bigger advantage lies for marketers like you. Augmented analytics is set to change how you make sense of customer data on a daily basis. Unlike earlier you don’t have to rely on an analytics team for in-depth research and reporting, a dependency that made your work time consuming and inefficient. With augmented analytics tools, you can regain control and track the entire customer journey, right from acquisition metrics to retention insights.

With augmented analytics, everyone in the organisation will hold the power to make informed and data-driven decisions, without having to depend on data scientists to furnish the required information. Naturally, this opens the doors for businesses to accelerate their growth at an exponential rate.

How can Marketers Benefit from Augmented Analytics?

Augmented analytics reduces the gap that existed between data scientists and other business users. The benefits of advanced data analytics are now available to every employee in every department, including marketing.

• Lower costs
  Because of a dependence on data analytics professionals, marketers faced a shortage and strain on resources. There is a high demand for the rare expertise of data scientists. However, hiring them is a lengthy and expensive process.
  
  By opening the doors of insights to everyone, augmented analytics empowers marketers to take charge of data and thus allocate resources in an efficient way.
  
  Moreover, Augmented Analytics also solves the problem of a waste of resources. Because traditional business intelligence tools demand grunt work (cleaning, unifying, entering, sorting data) out of data scientists before they actually get to the real job, they waste both time and talent.
  
  By automating the processes involved in data preparation and handling, Augmented Analytics tools allow marketers to reduce workload and extract the maximum value out of the data scientists they recruit.
   
• Cutting-edge insight
  For effective decision-making, marketers need granular insight at speed. Given that customers today expect personalized communications across channels, marketers need a real-time view of their individual needs and behaviors to offer what they’re looking for. Augmented Analytics tools allow marketers to do this without wasting any time.
  
  They quickly organize data and run analysis using a combination of ML and NLP. Interacting with huge quantities of data, they uncover patterns, trends, and anomalies – information that is gold to marketers.
  
  For instance, augmented analytics may identify the channels a specific audience segment engages with the most, and the types of ads that receive maximum response. With this insight, marketers know exactly where and how to reach customers to elicit a desired action. They can also minimize wastage by allocating resources to the channels with the highest probability of capturing audience interest.
  
  Additionally, augmented analytics can help with customer retention too. For instance, the data uncovered by Augmented Analytics tools can tell marketers not just where customers are spending their time but also how to appeal to them better. When marketers tailor campaigns to suit customer preferences and deliver relevant information, retention will increase over time.
   
• Effective performance measurement
  Augmented Analytics tools have the capability to measure the outcomes they drive. Marketers can, thus, evaluate progress and measure performance through an analysis of channels and audiences.
  
  This has powerful implications – marketers always stay in the know of what’s working and what’s not so they can adjust campaigns and reallocate resources.
   
• Actionable insights
  Once patterns have been identified, the results must be communicated to executives. The traditional ways of sharing data through reports and presentations is additional work for executives who are hard-pressed for have time to interpret data.
  
  Augmented Analytics tools can read a chart or report and translate the findings in a simple manner.
  
  Marketers can leverage the visualization abilities of augmented analytics to understand and share findings in a simple format with the C-suite. With actionable insights, the organizations can participate in data-driven decision making.

Businesses of all sizes have something to gain

Traditional data analytics platforms come with a major disadvantage. Before one can even get down to uncovering patterns, a great amount of manual labor is involved. While a business may onboard a data scientist to uncover insights, it may soon find that they spend most of their time cleaning and harmonizing data, not extracting insights from it.

Augmented analytics is on its way to transform the way businesses analyze data. Marketers, in particular, have much to gain. They can finally regain control of massive sets of data and meet customer expectations with personalized communication and experiences.

Ultimately, this can have a significant impact on the business’ bottom-line. By automating large-scale analysis and allowing marketers to generate insights, Augmented Analytics is paving the way for a more productive business landscape.

It is important that modern businesses understand the benefits of augmented analytics – speed, democratization, and insights. Armed with these, businesses are better equipped to anticipate what customers want, improve business processes, and lay the groundwork for success.

The Road Ahead For Augmented Analytics

In the present landscape, businesses are producing such a large volume of data that it has become impossible for data scientists to explore it on their own. Manual data exploration always runs the risk of missing key insights.

With augmented analytics, organizations have a tool to explore all possible hypotheses from the collected data and automate a great deal of data science tasks. When data scientists and augmented analytics work together, data insights will become democratized i.e. become available to a wide pool of business users.

There is no doubt that augmented analytics is here to set a new standard for business growth. The quicker you leverage this technology, the faster you will reap its benefits and be able to exploit growth opportunities.

March 9, 2020

How to Pick the Right Customer Data Platform (Learn from CDP Experts)

By Jordan Torpy, Exponea

You’ve decided a Customer Data Platform is the right tool for your company. It’s a good choice: the market has matured and there are many vendors to choose from.

But that also means…there are many vendors to choose from. And of course you want to choose the right CDP for your company. It’s a tough decision, and the wrong choice can yield poor results and create an impression that CDPs are all hype.

But the right choice will create new possibilities for your company, a better experience for your customers, and improved cross-departmental collaboration.

To help us navigate this topic we talked to some of the top CDP experts and picked their brains to help you with the processes of choosing and implementing a CDP.

They’ll discuss two important things:

1. how to purchase the right CDP for your company
2. strategies to make sure that CDP is a success

Let’s introduce our expert panel. It’s a group with decades of combined experience in making CDP projects successful.

Hugh Kimber – Global Sales VP

Hugh leads a global sales team and has over 18 years of experience in the digital marketing industry. He understands how a company can make new technologies successful.

Daniel Viglas – Solutions Manager

Daniel helps clients get the most out of their chosen CDP. He identifies where a CDP brings value and creates solutions that bring fast results.

Bruno Gorgulho – Head of Solutions

As the Head of Solutions at a major CDP company, Bruno leads a team that helps bridge the gap between project initialization and implementation. His experience allows him to speak with confidence about what makes a project work.

Dale Farrey – Senior Sales Manager

Dale uses his years of experience in SaaS sales to ensure that companies have a successful CDP implementation, and he’s sharing that knowledge with us here.

Step One: the Purchase

The first step in the process may seem overwhelming: choosing the right vendor. How should you proceed? What should you look for?

Below, the experts share what your first steps should be after deciding to go with a CDP.

Question #1: How would you proceed if you were the one (CEO, CMO, etc.) in charge of getting a new CDP?

Dale Farrey, Senior Sales Manager

First I’d ask my team to provide insights as to why they believe they need a CDP. I’d then turn those insights into a brief with 5-10 key deliverables for the CDP, which would form the basis of the review criteria I’d use during the selection process.

Bruno Gorgulho, Head of Solutions

I would first validate the key value proposition of a CDP and make sure it aligns with the business problem I’m solving. If I truly require a system that can centralize customer data for a B2C business, provide analytics-driven insights, and then send that intelligence to other systems to trigger campaigns, then I need a CDP.

Then I would map existing internal capabilities - which systems I have and why. Then I could see which systems I could eliminate entirely, which could be replaced, and which are critical for my business. I would have to build a system analysis independent of my existing teams, since new technology is likely to disrupt the way my teams are organized.

Once the business needs, teams, and systems have been mapped I could start talking to vendors who could also help shape the requirements.

Takeaway:

• Be crystal clear about which issues you want to address with a CDP
• Understand that new technology can mean a paradigm shift
• Map internal capabilities (people and systems) – what should you eliminate, keep, and replace
• Understand your needs before looking at vendors

Now that you know exactly how you’ll use a CDP, it’s time to find the right one. With a larger and larger pool of vendors, how do you narrow down your choices? We asked the experts to weigh in.

Question #2: How would you evaluate different CDP vendors? What are some of the indicators that tell you a vendor is right for you?

Bruno Gorgulho, Head of Solutions

I’d want an answer for the following:

• Is the platform real-time, for both reading data and executing campaigns?
• Can it handle data from multiple sources? Be sure you know those sources before you start your vendor search, so you can get informed answers from vendors.
• Can it merge customer profiles in a dynamic way?
• Does it integrate with the systems I use in a simple and reliable way, and is the data it stores easily extractable?
• Does it have capabilities like customer segmentation, AI, and native campaign execution, or will I need another provider for those?

Hugh Kimber, Global VP Sales

I’d want to know about their customer base — do they work with companies like mine? What kind of experience do they have? I’d want to see evidence of businesses growing thanks to their technology (and not just 10% increase in conversions, but how have they helped a business over a period of 12+ months).

What’s the cost of change: how easy is the implementation? Will it make my company more efficient?

Daniel Viglas, Solutions Manager

I need to know if the company can create a single customer view. Then I need to look at my short- and long-term goals, and compare them to what the vendor offers. These are the areas you might consider when thinking about CDP relevancy:

• Security, Stability & Scalability
• Insights and intelligence (including AI)
• Data-based campaign orchestration

Takeaway:

• Make sure the vendors you’re looking at meet all of your requirements
• Narrow your list of vendors down to those that have proven track records and are familiar with your industry

Customer data platforms are still new, and how we define a CDP continues to shift. This leads to some misunderstandings about CDPs and just what they can do for you.

What are some of these misunderstandings? Our panel outlines several below to help you with your selection process.

Question #3: What are the most common misunderstandings about CDPs out there?

Dale Farrey, Senior Sales Manager

Within the early stages of the sales process, the CDP is commonly brushed off as having the same functionality as a marketing automation platform or marketing cloud, but it’s actually much more. The customer is typically fully educated by the end of the second meeting.

Bruno Gorgulho, Head of Solutions

A common mistake is when people expect the CDP to provide some of its own data (which is usually the scope of a DMP or similar platform – a CDP works with a company’s data). Also, some companies expect the CDP to be able to resolve customer identity, but their existing processes to contact the customer are completely unstructured - remember that the CDP will only read the real-life events between your company and your customer.

Takeaway:

• A CDP can provide value for your company, but don’t expect a DMP

Question #4: What would your advice be to anyone buying a new CDP?

Daniel Viglas, Solutions Manager

Think about what you want to achieve with a CDP, short-term and long-term. Maybe in the short-term I want to improve my emailing with behavioral data, and that may give me an answer about which CDP can satisfy that. Maybe my long-term play is something else, and that narrows down my options even more.

Hugh Kimber, Global VP Sales

Make sure the CDP you are choosing is future-proofed. A single customer view is an immediate requirement, but you need a CDP that can act on the data or integrate with existing suppliers you wish to keep.

Takeaway:

• Remember to weigh your long-term goals. This will help you find the right CDP.

Question #5: What else can help companies choose the right CDP?

Daniel Viglas, Solutions Manager

I would ask myself how a CDP could benefit my company from these perspectives:

• use cases (what can I do practically?)
• time (will it improve efficiency?)
• security & clarity (will I avoid risk?)
• strategy (will it activate future goals?)

Dale Farrey, Senior Sales Manager

Take into account the history of the provider. Some companies positioning themselves as a CDP are built on a flat file database because 95% of their customers use them for email only...they were built that way historically. Consider a =more modern CDP approach built in the era of the CDP SCV (Single Customer View).

Hugh Kimber, Global VP Sales

Clearer differences between CDPs; some push “AI everything!”, some push execution. Look for a deeper explanation of different CDPs and what they are good for. Who is a ‘jack of all trades, master of none’ and who is a specialist and why?

Takeaway:

• Most CDPs on the market were originally something else. Understand their history and you’ll know whether they can help you achieve your goals.

Step two: After the Purchase

The post-purchase phase is crucial to making a CDP work successfully for your company. While every company is different, most successful CDP implementations follow a similar pattern. We spoke with two more experts to find out what that pattern is.

Expert 1: Adam Lebeda – Global Head of Partnerships

Adam currently works to help find valuable opportunities for businesses looking for advanced marketing platforms. Previously, Adam was the Senior Manager of Digital Services at T-Mobile Czech Republic, where he successfully implemented a modern CDP.

According to Adam, there are three key points that lead to a successful implementation of a customer data platform:

• Start small
• Create a habit
• Use a tool people love to use

Start small

It’s unrealistic to think you can buy a platform and immediately change all the processes of your business, especially with a larger company or stakeholders involved. An effective strategy is to start small with quick time-to-value use cases. This proves the value of the tool to the people who work with it and leads to buy-in throughout the company.

Create a habit

When the people who work with it become internal advocates for the platform, their enthusiasm catches on and helps to make the new CDP a habit. Then it’s much easier to onboard the rest of the organization.

Use a tool people love to use

For all of this to fall into place, the CDP must be a tool marketers really enjoy using. A good CDP gives marketers the ability to execute their own ideas, without needing to overly rely on the assistance of more technical employees. This is what led to a successful implementation at T-Mobile, for example.

Expert 2: Peter Solnicka – Client Success Manager Lead

Peter leads a team of CDP Client Success Managers. His experience implementing CDPs at multiple companies has given him great insights into what leads to the success of a project. He tells us what he believes are the most crucial aspects of the implementation process:

• First steps
• Mindset and attitude
• A good vendor

First steps

Know who the key stakeholders of the project will be, both internally and on the side of the vendor. Implementing a CDP requires buy-in from multiple roles: IT people, data analysts, CRM owners, digital and campaign teams, project managers, and more.

All these internal stakeholders need to have a clear counterpart on the vendor’s side. For the project to succeed, alignment across all of these teams is a must.

Mindset and attitude

Implementing a CDP will require your team members to embrace changes. Processes will look different and routines will be upended; don’t expect things to stay static. This will be a time to discover new capabilities that weren’t possible with your previous solution. This might require a mindset and attitude shift in your organization.

A good vendor

A good vendor will not blindly agree to everything you request. They should be willing to challenge you in a beneficial way, out of respect and a desire for your growth. They will be more a partner, focused on reaching mutually set goals and delivering value.

Finally, be sure that your vendor is future proof. Study their product roadmap and ask, will they accommodate my needs in the future? You don’t want to get locked in to a contract with an outdated solution.

If you can establish good starting logistics, have the right mindset, and work with a good vendor, your odds of success are significantly higher.

In Summary

The processes of choosing and implementing a CDP can both be daunting, but there are things you can do to make them easier and give yourself a higher chance of success. Interviews with the above experts pointed to several similar steps:

• Be clear about what you expect from a CDP, before you look at vendors
• Think about the short-term and long-term to find a future-proof solution
• Understand your options and look into vendor history (do they have the modern CDP capabilities you need?)
• Success can hinge on the right mindset and attitude

Taking on a CDP project is big. But with the right preparation, success is much more likely. Set yourself up for a positive experience from the very beginning, and take advantage of all the capabilities a modern CDP has to offer.

March 2, 2020

How will enterprises drive effective engagement during these times of cookie extinction?

By Ricardo Rengifo, FirstHive

Webkit browser engine which powers Apple Safari and all IOS browser has released its latest update to Intelligent Tracking Prevention (ITR) feature – ITP 2.3. It follows in the footsteps of previous releases beginning from 2017 which aims to provide the users with a higher degree of privacy by restricting how Ad tech companies track users online and the duration of time data can be held for a user, and is in line with the general direction of a cookie-less future in the industry.

A quick look at changes brought by ITP so far:

1. Cookie and data storage

• Third-party cookie has been completely blocked
• First-party cookie set to expire in 24 hours if used for tracking
• Local storage set to expire in 7 days

2. Cross-site request referral header to be stripped off from links
3. Mandatory first-party relationship with the user for any data from the linked website

Now, let’s look at how these changes are disrupting today’s marketing ecosystem. If your organization is leveraging any of the below systems or technology, you will have to take a relook at your current marketing operations.

1. DMP’s: Perhaps the biggest casualty of these changes will be the Data Management Platform (DMP’s). DMP’s are heavily dependent on third-party cookies and with ITP the business model is facing an existential crisis today.

2. Web analytical platforms: Platforms such as Google Analytics and Adobe Analytics use cookies to capture the information on web traffic and customer interactions. They will have to take a relook to deliver the same capabilities within the ITP framework.

3. Campaign attribution: With a significant lesser attribution window, marketers will misattribute credits to campaigns. This results in marketers not identifying what worked and what didn’t and crafting ineffective campaigns

4. Testing Period: A/B testing window has to be within 7 days else it may result in showing the different variation to the same test group users if he comes after 7 days rendering the results inaccurate./li>

One open question that is asked in the marketing universe is where to slot CDP in midst of these changing privacy landscape, is it a beneficiary of the changes or is it one of the systems that is bound to be negatively affected by it.

In this context let’s look at how a CDP works; put simply CDP builds unified customers views based on their interactions across multitudes of source systems and interfaces deployed by the organization and makes it available for analysis and action. CDP breaks down data silos within the organization so that every system connected to it starts seeing the customer, their preferences and behavior consistently; helping to craft consistent experience across each touch point.

Organizations are now re-evaluating their entire marketing stack to identify the systems impacted most by these changes and evaluating ways to ensure they are still able to market effectively to their customer base. Organizations are coming to be increasingly dependent on CDPs and its capabilities to archive the same.

1. CDP is driven by first-party data: For opted in customers; a marketer can still target them either through third parties (DSP’s, Facebook, Google, etc) directly or through segment/ audience push capability of a CDP; the real impact will be felt in attribution. While third party attribution window has been cut short to 1 day leading the same customer to be counted twice or more; a CDP will be able to attribute it to the same single customer since it identifies the customer and merges his multiple visit and activities to the same profile.

2. Unified View of customer: The biggest advantage of CDP is that it creates a persistent unified customer profile based on all first-party data available both offline and online. This provides marketer clear advantage in using CDPs to create their target segment rather than using third parties such as (DSPs, Facebook. Google, etc) in isolation as they depend exclusively on online data which is now severely curtailed by ITP.

3. Personalization: The ability of CDP to personalize marketing communication based on customer context will not be severely impacted by ITP changes as CDPs are able to consume real-time data from all kinds of channels such as Email, SMS, Notification, etc and is not just dependent on cookies. For example, a Customer has clicked on email link with Product A, he will be identified when he visits a website and can be retargeted with product A. While the post-login personalization capabilities remain intact; CDPs will face few limitations on pre-login personalization. Additionally with GDPR and CCPA being considered norm going forward; a CDP is increasingly becoming a critical system that owns the entire customer data. Without a CDP, the organization will struggle to comply with provisions of the regulations as the data will be siloed across multiple systems with different identities for the same customer. Organizations cannot dependent on third parties to manage consent with a limited view they have into a customer with the latest changes to ITP.

The upshot is that even though CDP systems started off with the objective of solving the marketer’s problem with even increasing touchpoints, it has evolved into a system of intelligence for the whole organization. In this light, the recent changes to ITP will act as a constant force of evolution on CDPs but will not take away from it what it really does well and this is why the CDP segment continues to grow and is now over a US$ 10 billion market.

We are always interest in comments from the industry, so please let us know what you think here, or drop us a note at marketing@firsthive.com

February 25, 2020

Why you should take a leaf out of HSBC’s book and remember your email system is ‘not an island’

By Anthony Botibol, BlueVenn

There’s a lot of hype these days about omnichannel marketing and the importance of crafting a consistent customer experience across every platform. The fact is, though, that all channels are NOT equal when it comes to customer engagement. According to industry analysts Gartner, an Adobe 2018 Consumer Email Survey found that 50% of consumers named email marketing as their preferred brand communications platform, compared with a maximum of 20% who said they preferred any other channel.

So, to maximize sales it is important to get your email targeting right – especially since Gartner’s own 2018 State of Personalization report found that, while 86% of consumers surveyed were fans of personalized communications, 48% said that they would unsubscribe from irrelevant or annoying emails. Meanwhile, the same survey found that brands saw a 20% increase in commercial benefits when customers perceived their communications to be helpful.

In other words, just addressing your marketing email to ‘Mary Smith’ won’t cut it anymore. These days, Mary will expect you to send her personalized and relevant content, carefully tailored to match her interests and speed up her path along your brand journey.

The challenges of personalization

However, that’s not as easy as Mary might think. Why? Because Email Marketing Systems are, by nature, siloed. Everything they know about the customer comes from the one platform, and remains within that one platform. This means that marketers are able to determine which emails a customer has opened, what time they tend to read their communications and which links they have interacted with, but won’t be able to share that information with their other platforms to help enrich the engagement with that customer across the journey.

Nor will they be able to tell that the customer has just bought the advertised product, which might suggest that further email communications should be either suppressed, to stop the customer receiving ads or offers for the item they have just bought, or changed to promote different products.

As well, a lot of information might be held about the customer on other platforms, such as their last purchase, what they’ve looked at on the website, products they’ve liked or commented on in your social media, and what they’ve swiped their loyalty card for in store. However, if you’re to use that information to help you select an audience for an email campaign or personalize your email creatives, you’ll need to leave the email platform, collect together the necessary insights, and then manually insert them into your emails on re-entering the platform. If interesting results come in from your email campaign, you’ll have to perform the same process in reverse to share them with other platforms.

What you need is an email marketing channel powered by a Customer Data Platform (CDP) that integrates your email marketing with your other channels, and is therefore able to utilize all the customer data, transactions and interactions from across the organization - not just the data residing in the email platform itself.

The benefits of CDP-powered email marketing

A shared databank

Being able to see everything your company knows about the customer – their demographics, location, preferences and past transactions/interaction history – in a CDP will make it much easier to understand what your customer actually wants from your brand and how you can give it to them. Having every byte of data about your customers available for the segmentation and personalization of an email ensures that your communications will stand a better chance of reaching the right person with the right information, whilst also bypassing the arduous or expensive process of data wrangling to move data between systems.

Actioning your insights

With CDP-powered Email Marketing you’ll be able to better segment your customers into suitable audiences to receive bespoke campaigns. Emails designed to engage women aged 30-50, who are residents of Portland, Oregon and have a propensity to respond to discount offers by email, for example, will be sent only to people within that niche bracket, resulting in incredible open and conversion rates. Better still, that same email can be highly personalized en masse, using dynamic content driven by all the data variables from across your business, to ensure that each recipient gets content relevant to them or will be sent their promotion via an alternative channel if they have repeatedly ignored your past emails.

Boosting sales with real-time interactions

It’s not just about how you sell the right product, it’s about when you sell it too. If a customer is on your website looking at perfumes, then providing them with a website pop-up or nudge to use an exclusive discount code is proven to be a great real-time personalization tactic. However, if they ignore the pop-up, or perhaps don’t see if for whatever reason, now might be the time to send them a discount code by email. After all, the margin on such products is huge and a bottle can last for several months, perhaps even a year, so it pays to strike before they have a chance to browse for competing products and retailers. And, if someone has just bought a new bike in your store, they might genuinely appreciate an email containing advice on how to maintain the bike, along with recommendations of associated products they can purchase to help them do so.

Similarly, if an online customer abandons the items in their cart, knowing that immediately will give you a chance to trigger an email to jog their memory, or send them a small survey to determine what their concerns might be (such as price, slow checkout or a lack of product information), so that you can attempt to allay these or identify issues with your website.

Continually improving your omnichannel approach

With CDP-powered email marketing, data can be utilized from any system and shared with any system too. That means you’re not only able to glean information from your CMS, SMS or social media channels to enrich your emails, but have the freedom to feed back the insights gained through your email marketing campaigns. As a result, it won’t just be your emails that will hit the spot with your target audience more consistently, all your marketing efforts will become more refined.

Together, your marketing efforts will thrive

Just as HSBC argues that Britain’s international character makes it stronger, so achieving a two-way flow of information between all of your Martech systems and platforms will enrich every campaign you run.

A CDP-powered email marketing channel will overcome all of the challenges of personalization by ensuring that your email campaigns are powered by data insights gleaned from a 360 degree Single Customer View, so that customers will only receive emails that are relevant to them. It will enable an offline purchase to trigger an online action, it will mean a complaint or call center interaction can suppress the request for a product review, and it will ensure that the 80% of your audience who don’t interact with your email are automatically selected be marketed to via another channel next time, to maximize the impact of a multi-channel campaign.

To find out more about how better data insights will help you to get more opens, clicks and conversions than ever before, read about what our new email module has to offer now.

Discover how a CDP could be your route to real-time, cross-channel marketing with our live demonstration.

Join us at 10am EST / 3pm GMT on February 27, 2020 when we will cover:

• Using predictive analytics to make real-time decisions that positively affect the customer journey
• Improving the targeting of campaigns using customer segmentation and RFV analysis
• Using real-time personalization to better engage customers
• Integrating online and offline channels into the BlueVenn Customer Data Platform to create a true Single Customer View

February 17, 2020

Third Party Data Is Not Dead Because of Customer Data Platforms. Here’s Why

By Jenna Devinney, Webbula

Over the past few years, the industry buzz has been about the rise of Customer Data Platforms (CDPs) and how they’ve extended the value of first-party data, while third-party data took another bad rap.

In short, we aim to prove that while first-party data certainly has its benefits, relying on it alone will only get you so far.

Think about how often your data goes bad? Every month about 3% of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, or pass away. Whatever the reason, this fact is why marketers are always in need of updated and accurate customer data.

Let’s go through the facts on why we need third-party data more than ever.

1) Fraud lives in first-party data

You may think that your own data is the cleanest, safest data you can have, but the truth is your first-party data will show its true colors.

Ask yourself how often an individual misrepresents their identity or gives you false information when filling out forms, or surveys when applying for that discount, free sample, or any other special offers. You receive that data from the consumer, but that doesn’t necessarily mean it’s correct.

Even when it comes to a simple email address, there are many ways for fraudulent data to end up in your email marketing lists such as:

Disposable Domains: These are temporary emails that WILL deliver to the inbox. An example can be a shared public email account with no passwords for different people to use for a multitude of reasons. These emails are used for marketing purposes and are a waste of resources if you send to them.

Bots: Programs designed to locate signup forms on the web and submit fake email addresses and other information. They are purely malicious and mess up your metrics, and they provide no real usable information. As such they are not only pointless to market to but can also be dangerous.

Moles: Are fake emails that report campaign stats to real-time blacklists.

Seeded Trackers: Are addresses used in marketing campaigns to track delivery rates. They are there for a simple reason of someone tracking your activity, so if you send to them, they won’t help your open or click-through rates, because they aren’t real.

2) You Need More Data

Automotive dealerships are receiving leads every day, but they aren’t exactly sure which leads are qualified or not when measuring with their own metrics. The good news is they do have a good understanding of the target audience, but when a lead comes in there are two potential outcomes, is the consumer browsing or are they a potential buyer?

Another industry that has obstacles with first-party data is the Consumer-Packaged Goods (CPG) Industry. If you’re familiar with the industry most CPGs don’t have the rights to the consumer data, the retailers do.

We live in a world where data is what is keeping personalized shopping experiences moving, and CPGs will need to find ways to stay on top of this issue and not get left behind. Ultimately, they need more consumer insights to move forward.

If these industries want to know their customers, they need to create a wider view of their customers and not just rely on their own data.

How do they do this?

Third-Party Data Wins Thanks to CDPs

CDPs’ appeal to marketers is their ability to collect and connect first-party data from multiple sources across the organization.

What they often lack is keeping your data up-to-date, and this is where certain CDPs and organizations considering a CDP purchase partner with third-party data providers to help clean and add additional insights to their customers’ data such as purchase history, demographics, interests and much more.

There isn’t anything bad about third-party data providers, the data simply is just someone else’s area of the customer picture. You’re always going to have those low-quality data providers who tarnish the industry reputation that you must watch out for.

Everyone wants to work with trusted, compliant data quality providers. We understand the fear, but you’re never going to run successful campaigns if you’re not working together to capture the full picture of your customers and prospects.

Third-party isn’t dying, it’s only getting better. We forecast there will be more CDPs and CDP customers in the future willing to work with third-party data providers to help enhance data.

See www.webbula.com to learn more.

February 13, 2020

How Unified Comms Can Improve Business Productivity

By Stefan Docherty, Call & Contact Centre Expo

Unified Communications refers to the integration of various real-time communication channels such as voice, video, text and data into one solution, which aims to boost communication and collaboration within a business. Unified Comms come in many forms, including the integration of a company’s customer communication channels (i.e. email, social media, web and chat) or simply the pairing of various internal channels such video conferencing and instant messaging (i.e. Skype for business).

Improving productivity is a challenge all businesses face, and Unified Communications proposes various ways it can address this challenge. A study found that businesses adopting UC experienced on average a 52% improvement in workplace productivity as well as a subsequent 25% increase in profit. Sounds promising, but how does UC help achieve this?

Improved Speed

Firstly, Unified Comms can significantly add speed, and therefore productivity to business processes. For example, in contact centres, UC can help by improving customer response times. With all of an organisation’s communication channels merged onto one platform, (i.e. social, email, web, chat, telephone, SMS) it becomes much easier to pick up these queries/conversations as well as respond to them. For sales teams, unified comms can be vital as it means each team is alerted of queries much quicker and so have a better chance of converting each ticket coming in. With the help of UC, staff across various departments now address more queries during their working hours, thus boosting productivity.

Internal Collaboration

Secondly, UC can have a positive impact on workplace collaboration and communication. Presence apps let you know in real-time if your colleagues are available for meetings, calls/call transfers, or collaboration on projects. Having this information can be very practical, as often you cannot physically see your colleagues and therefore determine whether they are available to speak or not. Presence apps can eliminate unnecessary call transfers and can also ensure that when a colleague needs to assemble a team or communicate or reach various people, they know when and how they can do so, which improves internal communication and productivity.

In addition, video, visual and messaging capabilities integrated together (onto one platform) enable employees to remotely facilitate virtual meetings with each other, eliminating the need to move between offices/ locations and ensure everyone is present at the same time. This helps speed up internal communication and can significantly improve productivity.

Mobility

Lastly, implementing UC can enable employees to work remotely, accessing their data and work files even when they are not in the office. An article written by the Telegraph found that many employees who commute to work also like to get work done whilst making their journey in. More than a fifth (22%) said working on their commute allowed them to get more work done, and more than a quarter (29%) said that they were able to do so because of the access they had to technology. This goes to show how UC can create tangible benefits for productivity, by giving workers access to their work station even when they aren’t at the office.

Similarly, a nine-month study by HBR found that productivity in call centres increased when employees were given the ability to work remotely from home. The study found that those who worked from home completed 13.5% more calls than their counterparts in the office, and the business itself saved around $1900 per employee on furniture and space over the nine-month period. Working from home couldn’t happen without enablement from UC technology, and so its implementation can facilitate home working which can boost productivity.

To sum, Unified Comms can boost business productivity by improving speed in the contact centres, which can lead to quicker response times and more conversions. In addition, UC can boost internal communication and collaboration, making teams more efficient and effective which can boost productivity. Lastly, UC enables remote working which means employees can work even when they are not in the office (ie commuting or at home) which has been shown to increase productivity.

Stefan Docherty is Marketing Executive at Call & Contact Centre Expo which runs in London, March 18-19, 2020). Information is at www.callandcontactcentreexpo.co.uk/

February 10, 2020

Sometimes, Bad Customer Data Is Worse than None

By Tom Treanor, Arm Treasure Data

To remain competitive in today’s data-driven world, sales success is highly dependent on a superior omnichannel customer experience. That means your customers have a consistently positive and seamless experience across your brand’s many touch points as they journey from awareness through evaluation to finally making their purchase decisions.

Unfortunately, many companies still struggle with integrating and analyzing data from the disparate technology platforms and apps that make up the omnichannel experience. In fact, a new Arm Treasure Data report, “2019 State of the Customer Journey,” found that nearly half (47 percent) of respondents struggle to gain insights from their marketing data due to silos.

Making sense of the data

The marketing technology landscape has exploded in recent years, with more than 7,000 vendors offering martech solutions. These companies provide tools for independently managing everything from mobile advertising to interactive content to influencer marketing activities (plus dozens of other sales and marketing functions). Due to the rapid growth of tools and data, it is no surprise that businesses are struggling to access and properly analyze the data.

The report also found that 54 percent of companies say they don’t have a full picture of their data and thus their customer journey. These blindspots put achieving an exceptional customer experience at risk. It’s like trying to finish a puzzle with only half of the pieces. Without a complete view of each customer journey, the odds of sending the wrong marketing message at the wrong time increase considerably.

Other key findings from the report:

● Customer journeys are complicated. Most (61 percent) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32 percent) reporting six or more touchpoints.

● Many don’t know what works. Nearly half (48 percent) say they are not using a formal attribution strategy, making it difficult to determine which of their efforts produced a sale.

● It’s a marathon, not a sprint. Long buying cycles make it critical to keep track of customer journeys. About 40 percent report the timeline from first engagement to purchase is four months or longer. Nearly a quarter (23 percent) of respondents don’t have any idea how long their customer journey takes from first interaction to purchase.

● Unreliable data sources lead to marketer confusion. The lack of a clear data picture means companies have potentially misplaced confidence in the effectiveness of their own marketing channels. Respondents cite salespeople and the company website as two of their three most influential marketing channels, while simultaneously acknowledging those aren’t the primary channels they turn to when making their own purchase decisions.

Why is it so hard to get the customer journey right?

Siloed data, combined with gaps in data analysis skills and lack of resources in marketing, technology, and data science, makes it tough for many organizations to develop accurate pictures of their customer journeys. Companies with many different types of marketing technology often suffer the most from the silo effect because data in one system is difficult to use with software and data in other systems. For example, an email marketing solution might not be able to easily share data with an advertising platform, decreasing the usability of the data.

To help break down silos it’s important to integrate the data you already collect. Useful marketing technology, such as customer data platforms (CDPs), combine data from many sources, online and off, to create a full picture of the different customer journeys your buyers and prospects take. With more unified data you can find out what compels customers to buy and why.

Silos Might Be a Big Point of Failure in Marketing Programs

Some of the questions CDPs and unified profiles can help answer are everyday problems. Ask yourself: Is something off about the responses you get to your marketing campaigns? Do you sometimes get odd or conflicting results?

You’re definitely not alone if you do. Our recent research on the state of the customer journey found companies are still struggling with data integration. More than half of those surveyed (54%) say their biggest barrier to leveraging data is fragmented or siloed data, which makes it difficult to get an accurate, integrated view of the customer journey.

And it’s no wonder — customer journeys are often long and complex. Most of our survey respondents (61%) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32%) reporting six or more touchpoints. And these touchpoints frequently happen over a course of several months.

With complicated buyer’s journeys becoming the new normal, you’d expect to see an increase in the use of multi-touch attribution strategies to ensure companies understood the path to purchase for their customers. (A multi-touch strategy divides up credit for sales or conversions among lots of touchpoints, rather than just using the last customer touchpoint as “the cause” of the conversion.) Yet nearly half (48%) say they are not using a formal attribution strategy at all, let alone one that can track multiple omnichannel interactions with the customer. This makes it increasingly difficult to determine which sales and marketing efforts produced a sale.

Relying on the Unreliable

Unfortunately, in the absence of a reliable source of integrated customer data, people tend to rely on unreliable sources — even though they know better. That’s why we see marketers reporting on easy-to-access vanity metrics or only tracking the final touch before a sale, and guessing at what came before it. That easy-to-come-by customer data can lead to some costly but ultimately avoidable customer data marketing missteps:

● Email platforms make it easy to see which emails garnered the highest open and click-through rates. But you often have to dig deeper to understand what is being clicked on. That means you may not realize that your high CTR email, the one that shows up in the top-line reporting, is driving people to unsubscribe or to view the email as a web page due to poor formatting or image sizes.

● Social media platforms make it easy to identify the customers who engage the most with content you post and your most engaged followers. But without a layer of sentiment analysis — and looking at what the engagement actually entails — you can end up boosting content that your ideal customer has actually been annoyed with, or showcase content that’s trending, but as an example of what not to do.

● Website analytics can show you how a customer that was ready to make a purchase found you. But, if you aren’t using advanced tracking, and integrating other channel data, you may decide to double down on your website or search engine advertising and not realize those customers were hearing about the product in a podcast or through influencer word-of-mouth and searching for your brand name specifically as a result of that initial engagement.

When we use easy customer data to make decisions, it makes us feel better than just going with our gut. But it may actually cause us to just make bad decisions with more confidence.

Gartner Estimate Says Poor Data Costs $15 Million

Gartner research found organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Marketers at Shutterfly ran into this when they made some big assumptions presumably based upon browsing data. They sent emails congratulating new parents on the addition to their family — only, many of the recipients definitely hadn’t just had children. While some of the people on the receiving end were amused by it, and took to social media to post at the brand’s expense, that email also likely ended up in the mailbox of customers who weren’t able to conceive, had miscarried, or had lost a child.

Timing Is also Everything

Also, not all bad personalization is off the mark solely due to its message. Sometimes timing is an issue. Like the pair of shoes that follows you all over the web...starting the day after you purchased them from that retailer online. This sort of customer journey mismatch is caused by a lack of real-time data integration.

Between the time that customer viewed the shoes on the web, had them sitting in their shopping cart, and ultimately bought them, that customer’s data had to make its way through your internal processes and systems to eventually fuel a retargeting campaign.

If your data had been up-to-the-minute, you could instead be pitching that same customer on buying the shoes in another color, or on purchasing a completely different pair that your customer data shows is popular with purchasers of the initial pair of shoes.

Subaru, for example, used a customer data platform to unify all of its marketing and sales efforts. Not only was the company able to easily distinguish those who were ready to buy from those who weren’t, but the buying process was streamlined and accelerated, and when the sale was finally closed, Subaru didn’t have to waste resources on someone who had already bought a car. Rather, the company could begin automated marketing efforts steering customers back into dealerships for service, upgrades, and after-market products.

“Blasting Emails to Everyone” Doesn’t Work, But CDPs Do

Good, complete customer data, on the other hand, helps you tease out subtle shifts in customer attitudes and behavior, something Shiseido learned when it began using its own customer data platform (CDP) to unify its loyalty, browsing, and ad campaign data.

"Our new customer data platform built on Treasure Data is fundamentally changing how we communicate with our customers,” says Kenji Yoshimoto, Chief Analyst for Direct Marketing, Shiseido. “Blasting emails to everyone who tried samples or bought a particular product won’t lead to customer delight. Detecting a mood swing in each customer and changing the tone of push notifications does.”

And of course, when you rely on bad data to make significant business decisions, you not only miss out on that opportunity to delight the customer, you may even permanently turn them off to your brand. You’re not only missing today’s opportunity; you’re risking an unsubscribe or losing to a competing offer that forecloses on tomorrow’s opportunities.

Consumers want to buy from brands who provide them with an omnichannel “know-you” experience. But to deliver on that expectation, brands must invest in both data integration and tracking customer activities throughout the purchase process to ensure accurate attribution to use for future marketing decision-making.

February 6, 2020

Experience 2030: The Future of Customer Experience is... NOW!

By Wilson Raj, SAS

Modern technology has upended the way brands and consumers engage. New products, services, consumers, and competitors have arrived and keep evolving. Consumer behavior, likes and dislikes continue to change. What will the customer experience look like in 2030? And how will brands evolve to meet the expectations of future consumers? These are some of the questions addressed in “Experience 2030: The Future of Customer Experience” by Futurum Research and sponsored by SAS, the leader in analytics – free to download now.

The research found that technology will be the major driver behind the reimagined customer experience (CX), and that brands must rethink their customer ecosystems to keep pace with empowered consumers and evolving consumer technologies.

According to the research, consumers expect to embrace new technologies by 2030:

• 80% say they expect to accept delivery of a product by drone or autonomous vehicle.
• 81% say they expect to engage with chatbots.
• 78% expect to use an augmented, virtual, or mixed reality (AR/VR/MR) app to see how a product will look – such as how a piece of clothing might look on a shopper or how a piece of furniture might look in a home.
• 56% expect to be “visiting” remote locations or experiencing vacation and entertainment events through mixed reality devices by 2025.
• Eight out of 10 expect to use a Smart Assistant (such as Google Home, Amazon Alexa, etc.) to make an online purchase or control a Smart Home.
• 78% say they expect they’ll be controlling other devices with their wearables.

Additionally, by 2030, 67% of engagement between a brand and consumer using digital devices (online, mobile, etc.) will be completed by smart machines rather than humans, according to the research. And by 2030, 69% of decisions made during customer engagement will be completed by smart machines. This agility and extreme automation will drive the customer experience.

For more findings and insights, click above to download the global report, which highlights the findings from a global survey of more than 4,000 panelists, spanning three dozen countries across a range of consumer, industry and government sectors.

February 3, 2020

CDPs Could Revolutionize the Way We Drive — And Improve Dozens of Other Everyday Products Too

By Lisa Stapleton, Arm Treasure Data

Self-driving cars could prove to be a wonderful technological advancement, but what is there to help drivers in the meantime, before the technology becomes generally available and accepted everywhere? The answer might just lie in the same AI, analytics, and data technology that customer data platforms (CDPs) use to help marketers understand their customers. An interesting example is a new Advanced Driver Assistance System (ADAS) that electronics giant Pioneer recently introduced.

The Pioneer ADAS system uses real-time data streams from a variety of sources — weather data to determine driving conditions from moment to moment, car instrument data on the driver’s abilities and habits, road data, IoT data, and more. With the help of AI and machine learning, the Pioneer system comes up with a constantly updated display that shows how likely the driver is to have an accident at any given moment. Drivers can influence the “score” by slowing down, driving less aggressively, and by generally being more cautious.

How Pioneer’s ADAS Uses Predictive Modeling to Help Drivers with Real-world Hazards

But how would that look in our everyday lives? Picture this scenario.

You’re driving to work, the same route you take every day. But this time, a storm passes over: you’re suddenly faced with heavy rain and reduced visibility. All of a sudden, the “accident score” meter on your car’s dashboard moves into the red. You ease off the gas, move out of the passing lane and your score drops down to amber — you can’t get it into the green due to the adverse weather conditions — and it stays yellow as you finally pull safely into the parking lot — and breathe a sigh of relief.

Such real-time monitoring requires leveraging many different technologies, including data management of sensors powering the Internet of Things (IoT), AI, and machine learning. Arm Treasure Data enterprise Customer Data Platform (CDP) integrates all of these technologies to provide predictive customer scoring, which Pioneer is leveraging as part of its Intelligent Pilot offering for the Asia-Pacific region.

What’s at the Heart of Real-time Predictive Modeling?

Advances in predictive modeling and predictive scoring — in retail customer personalization, fraud detection, finance, and insurance — are stimulating heightened interest in using AI in accident mitigation. Accidents are also an important emotional issue, both for their impact on victims and for the many citizens that feel more needs to be done to reduce the problem. And some of the causes of accidents will only get worse over time, as cities become increasingly traffic-bound and rural roads don’t always receive the timely upgrades and maintenance, making the driving environment even more challenging.

Why Do We Crash? Customer Data Helps Calculate Individual Scores

Professor Kazuya Takeda, a well-known expert on analyzing accident data and part of the team that developed Pioneer’s Intelligent Pilot, an AI-driven ADAS, has found that factors highly correlated with accidents include a variety of behavioral data specific to each driver — such as reaction time, age, overall approach to driving, and other individual influences.

Environmental factors such as weather changes can dramatically up the odds of a collision. Rain, snow, or obscurants such as smoke or fog can block views, and water or ice increases stopping times. In addition, limitations in the streets and driving surfaces themselves can create a risk; some roads are better designed and maintained than others. The amount of traffic can also dramatically change the risk of having an accident. And even the most skilled drivers can be a little “off” in their reactions if they’re distracted, angry, or just having a bad day.

Capturing all of these variables to make decisions that positively affect the outcome of a journey — whether it’s a physical journey or what marketers call a customer journey — is no easy feat. Yet Pioneer, using Arm technology, is looking at how it can be achieved — and in real time.

Pioneer is exploring the intersection of converging data through Arm Treasure Data’s enterprise Customer Data Platform (CDP) with Pelion Data Management. This platform crunches data from multiple sources to provide easy predictive customer scoring.

Predictive Analytics Power Risk Scoring

Using this technology, Pioneer has developed “YOUR SCORING,” an in-car display feature that shows drivers a real-time, constantly changing estimate of their accident risk. The score is based on external map and road-condition data as well as the behavior of the driver. By combining and analyzing these data sources to create an overall picture of a journey in real time, YOUR SCORING is able to help drivers reduce their risks.

The AI and data management capabilities help Pioneer to quickly gather and analyze numerous data feeds (such as street layout, traffic signals, telematics, and third-party data) and apply machine learning (ML) techniques to deliver a single score through the system. At any given moment, the system could be processing reaction-time data from the driver, factoring in previous driving history and combining this information with terrain and current weather data from the nearest IoT sensors.

Personalized Data-driven Displays Could Be Next

Arriving at an accurate accident-risk score is one thing. But delivering this real-time, potentially life-saving information in a well received, actionable way is another important technical feat as well. Data plays a major role in this respect as well, and displays could be the next frontier for personalization.

Research has shown that people vary quite a bit in how they react to different types of displays and instructions — just as they react differently to various types of sales initiatives and targeted marketing efforts. A simple thing like the design of the risk display can affect some people’s willingness to take instructions.

These displays and user interfaces can now be personalized to people’s preferences and refined by the AI’s monitoring of a driver’s compliance as the display is varied. The next generation of driving technology might be individually tailored in many dimensions, to meet such individual customer behavior and preferences, just as retailers use the same technology for personalized customer journeys and customer experiences. Full-featured CDPs with the ability to integrate many different types of data, are obviously a critical technology for enabling such on-the-fly data unification and real-time analytical decision-making.

So, if you’re hearing a lot from the backseat drivers in your life, stay patient and calm. Someday, they might relinquish their roles to a smarter — and potentially much less annoying — AI. And unlike your current backseat chorus, the AI won’t ask “Are we there yet?” — because it will already know.

January 30, 2020

If You Want Customer-Centricity, Dismantle Your Data Silos

By Lisa Loftis, SAS

It is not surprising that CDPs have rocketed to the top of many marketers’ wish lists. Marketers have a very real need to corral customer data currently residing in disconnected silos both inside and outside the organization. In an HBR survey on using real-time analytics to improve customer experience the top challenges marketers faced were legacy systems, data silos and multichannel complexities. In a Forbes Insights study on the rise of CDPs, only 1 in 5 executives surveyed considered their companies to be leaders in customer data management and only 13% believe they fully utilize customer data. These difficulties persist despite the fact that we have been trying to uniquely identify customers and consolidate first-party customer information since the late 1980s.

Marketers are not the only ones facing these challenges. Data silos and misaligned technology also make every list of issues impeding companies as they try to move toward customer-centricity across all their business lines. The sheer number of technologies in today’s martech stack, many of which create or store their own copies of customer information, is astounding. Add to that the customer-oriented data warehouses and data lakes facilitating analytics and master data management applications facilitating operational activities that we still see, and the magnitude of the problem is clear.

Even with the adoption of CDP, if we are not careful, the technology alignment problem will simply continue to grow.

Enabling customer-centricity

While there is no silver bullet for this problem, there are steps that companies can take to ensure their technology enables customer-centricity rather than disabling it:

Develop a customer technology strategy. A strategy for customer tech is critical. Customer experience (CX) leaders should work with their IT partners to answer questions such as the following:

• What capabilities exist?
• Are they being fully leveraged?
• Where are the integration gaps?
• How many areas have their own customer data?
• How will these align or integrate with the CDP?

Once the existing data silos are understood (this includes marketing and CX technology, sales and service automation applications, web and mobile applications, and analytics), the assessment should expand to include gaps in technology, disintegrated sources of data, incomplete information, and independent applications that are not or cannot be integrated. The CX team can then use customer journey maps as prioritization tools to fill the gaps and realign the technology.

Get tough on acquisition. The vast set of shiny new tools available to the CX team can be very tempting, but if the goal is to dismantle data silos, tough decisions will have to be made. The technology strategy can help. Every potential purchase should come with a clear integration plan and budget. If the money or resources for integration are not available, the purchase should be reconsidered. Vendor solutions, including CDPs, should be closely examined for their ability to integrate with other applications. Closed applications or black box solutions should be considered only as a last resort.

Understand that there is no virtual view. Of customers, that is. The shortcut that no CX leader can afford to take is skipping the single customer view. Identity management is the top requested capability for CDPs for a good reason. It is difficult to achieve, and digital channels complicate the situation significantly. The temptation to implement multiple customer profiles is quite strong today because many large technology applications come with their own customer database. Attempting to match across these applications on the fly is difficult at best and fraught with peril at worst.

Picking a single customer master application like a CDP and doing the block and tackle integration work as applications with customer databases are added is a critical step to tearing down customer data silos. Multiple disintegrated customer databases are never the right answer, and looking to them to solve a CX problem will add to customer-centricity challenges rather than helping to resolve them.

Becoming the de facto integrator between siloed business groups will be increasingly critical for the CX leader of the future. Start planning today for what your organization will need tomorrow.

January 27, 2020

MobilityWare Uses CDP and Predictive Scoring to Learn What Makes Some Games So Irresistible, Play After Play?

By Lisa Stapleton, Arm Treasure Data

What makes a game stay appealing, play after play after play? That’s what mobile games purveyor MobilityWare wants to know — and it’s using a Customer Data Platform (CDP) and predictive analytics to help figure that out.

If you’ve ever played games on your phone, chances are you’ve played a MobilityWare game — either its flagship Solitaire or any of the many other games the company offers, such as BlackJack, Spider, or Jigsaw Puzzle. Founded in the 1990s, the company’s continued success and platform transitions are due in part to its deep understanding of its players and their customer journeys. And lately, MobilityWare’s creative use of customer insights is getting an assist from Arm Treasure Data enterprise CDP.

How to Improve Customer Lifetime Value? Keep Players Playing

One of the toughest things for a gaming company to do is keep its best customers playing, but that’s pivotal in increasing customer lifetime value (CLV). And the answer is usually highly dependent on the game, the demographics of the players, and even individual customer journeys and player histories. Teasing out player incentives or other customer retention strategies requires careful customer data analysis, the kind that CDPs can quickly and automatically perform.

The other problem games sellers face is how to monetize their games, and a major concern with any contemplated change is not cannibalizing revenues from current play. MobilityWare makes money from in-app purchases and ads, plus rewarded video.

Predictive Scoring & Modeling Help Dissect the Churn Problem

The challenge that the MobilityWare team faced was this: How do you figure out when a customer is likely to quit playing? And which player incentives could delay or stop customer churn?

Chris Densmore, director of analytics for MobilityWare, set out to get answers. He focused primarily on “dedicated players,” who had installed the game more than two weeks ago, but who had not played in the previous two weeks.

“Dedicated players can be incentivized, they’re more valuable, and we have a larger behavioral dataset because they’re more likely to have played a lot in the past,” Densmore says.

“Also, we can do more to influence the outcome, which is important,” he adds.

Densmore used MobilityWare data and analytic features from the company’s Arm Treasure Data CDP. Then he chose a logistic regression model, because it can still produce valid results even if some variables are correlated — as they usually are in real, live users — and because logistical regression makes it possible to interpret contributing factors as well. Another bonus: The coefficients that come out of regression can be easily placed in SQL scripts to produce predictions.

Customer Data Insight #1: Coins Don’t Work Like Boosters

Densmore’s work got some surprising results. Rewarding people with a coin that can be redeemed within the game wasn’t the clear winner. It did reduce churn, but had a negative impact on monetization. People tended to hoard them for unspecified “later use,” rather than play more and use them in the game. And who wants to take steps that hurt revenue if they don’t have to?

Customer Data Insight #2: Gamers Want Boosters, Not Coins, and They Pay More If You Help Them

The real surprise was that giving someone a “booster,” or a small assist or tool they can use to win, was almost as effective as coins in combating churn, PLUS it increased ARPU (average revenue per user) by more than 450 percent for those with a probability of churning between 60 and 80 percent.

A little bit of help — but not so much that it devalued the accomplishment of winners — was the key to reducing churn and renewing player interest in the game. And without the CDP data, it would have been tough to get the customer insights that pointed the way to the right customer experience. With it, hypothesis testing and predictive modeling pointed the way to higher profits.

Just think how many other product “churn” problems could be informed by similar analysis. Many other areas where fashion or trends are short-lived come to mind. For example, Shiseido uses a CDP to analyze loyalty data and combine it with other data feeds to predict when people might be open to new beauty products — or even a major makeover. Telecommunications companies, which live or die by the difference between new customer acquisition rate and churn, could also use CDPs plus predictive analytics to gain insight about what makes someone switch to a new plan or provider. The insights that CDPs can provide are only just beginning to be harnessed for better marketing and even improved product design. Predictive analytics are clearly more than a fad, and with the help of CDPs, they are fast becoming an easier-to-use tool in every marketer’s workshop.

January 23, 2020

Customer anonymity can’t be guaranteed, but differential privacy can help change the game

By Susan Raab, CDP Institute

The relationship between companies and consumers is increasingly complex, with concern about privacy growing among consumers and pressure increasing on marketers at companies to use customer data more to engage with and find new customers. The key question is how to achieve a workable balance between the two.

With consumer experience projected to become more important than price or product in ensuring customer loyalty, the more trust you engender the more likely you are to grow your customer base. Customers want to understand how their data is being used and to know how to participate in the process of protecting their privacy.

The first step on the part of the marketer is to recognize that data is at risk whenever it is shared. You’ve heard about studies that have shown that even anonymizing data doesn’t always work. Back in 2007, Wired magazine reported that, “Netflix published 10 million movie rankings by 500,000 customers, as part of a challenge for people to come up with better recommendation systems.” The data was anonymized removing personal details, but researchers at the University of Texas were able to de-anonymize that data using only a small set of public data from the Internet Movie Database (IMDb). Last month, the New York Times’ Privacy Project showed how cell phone company data of GPS pings from the cell phones of 12 million Americans could identify almost anyone when matched with public address data.

This has been a topic in the privacy field for decades and has been done with all kinds of data, including medical, financial and genetic. It’s scary and seems overwhelming, yet companies are still being asked to work to meet legal requirements laid out in legislation, including GDPR and the new California Consumer Privacy Act (CCPA) and to show they are putting appropriate privacy protections are in place.

While the nature of data security is that it can inevitably be breeched, regulators want to know that a company is being proactive in their compliance and consumers value companies that are transparent with their process.

This doesn’t mean you can’t use data for anything, but it does mean that you have to be careful to anonymize it effectively. Experts say there is no guarantee, but one powerful approach is a statistical technique called differential privacy, which is used by Apple, Google and other big tech companies via an algorithm that “adds random data into an original data set” (Digiday, April 2019). In September 2019, Google announced that they were allowing access to their differential privacy library to help developers at other companies achieve this.

Robin Röhm, chief executive officer of apheris AI GmbH, a start-up that develops artificial intelligence algorithms for biomedical data says, “what we’re addressing is really an operational question about whether a company has defined a top-down logic to ensure semantic-enabled data privacy protection. This means everyone in the organization needs to adhere to the process. Then you have privacy by design, in which the process is incorporated into the company’s architecture with algorithmic rules customized to its needs.”

We’re in early stages of understanding how best to ensure individual privacy both tactically and legally, but those who are focused on this area agree that it takes collective knowledge and ongoing diligence to evolve strategies that work in the present and over the long-term.

January 20, 2020

eTail 2020 Report: Retailers Are Relying on Personalization and CDPs to Go for the Win

By Lisa Stapleton, Arm Treasure Data

Is retail finally getting serious about customer data? Are businesses growing impatient with getting only vague ideas about who their customers are, and how to maximize the chances they’ll buy, or buy again? And are they serious about using data throughout their organization as a strategic weapon in the fight for profitability?

A new retail report from eTail, WBR Insights, and Arm Treasure Data reveals an industry that’s betting big on leveraging data-driven technologies to drive more tailored and personalized customer experiences (CX). In doing so, retailers aim to better engage with their customers throughout the purchasing lifecycle. The responses suggest that for some retail marketers, using customer data to create customized customer journeys and experiences is not only a big part of their strategy, it’s the whole game. And to do all that, they’re increasingly turning to customer data platforms.

Customer Data Is Transforming Retail Operations

The report, “Retail CX and Data Management Strategies in 2020,” is based on a roughly even mix of department heads and senior executives at VP level or higher, all at retail companies with more than $1 billion in revenue.

Most of the surveyed executives believe better customer data, smartly applied, is critical to crafting customer experiences that lead to greater sales — and they plan to go all-in on data-driven customer experience (CX) in 2020. As one department-store VP emphasizes, his company’s investments are largely oriented toward personalized omnichannel CX, and “are mostly made to provide customers a more localized, personalized, and smarter shopping experience.”

When asked about their most important strategy for retail success, 29 percent of respondents said that empowering CX teams to build better relationships with customers was their highest priority. Over a fourth of respondents (26 percent) are prioritizing creating a single view of their customers across all their touchpoints, including physical and digital. Another 26 percent of respondents are prioritizing improving their analytics to unlock the full potential of their customer data.

Majority of Retailers to Implement a Customer Data Platform (CDP) in 2020

But making the right decisions about how to appeal to each individual customer is tough, especially when retail databases can include thousands or even millions of customer touchpoints. Data silos across businesses add to that challenge, creating an environment where retailers may unknowingly have multiple interactions with the same customer (for example, via an email campaign, social media reply, customer service engagement, etc.).

Perhaps that’s why more than three out of four retailers surveyed either already have a Customer Data Platform (CDP) or plan to invest in one in 2020 to help unify all of their customer data and build a single, holistic view of each customer. This is critical in enabling retailers to provide more personalized experiences, offers, and service to their loyal customers regardless of whether they shop on mobile, in-store, or online. But this trend is new, evidenced by the fact that so many respondents — nearly half — are apparently just starting to invest in CDPs, compared to only 31 percent of respondents that already have one.

Early Adopters Realize That All CDPs Are Not Created Equal

Advanced retailers are beginning to understand the benefits of implementing a CDP for delivering better customer insights, and they are prioritizing tailored solutions that address specific business needs. According to the study, more than half (51 percent) of retailers mentioned they look for a CDP solution that will accommodate the specific needs of their organization, compared to 31 percent who want an off-the-shelf solution.

CDPs and Customer Data Are a Critical Retail Link to Customers, Profitability

Retailers realize that creative use of customer data — particularly to drive customer experience — is their best chance for retail growth, and they see CDPs as increasingly central to their operations. The head of a large department store sums up the critical connection: “A Customer Data Platform is the most important link between digital tools and the customers themselves.”

Once retailers have unified their customer data, they can use it for better experiences such as clienteling, delivering more targeted and engaging experiences (for example, augmented reality) and providing choice of checkout methods (whether that’s human interaction or automated quick-checkout through use of mobile phones.)

With all of the potential of a single, unified customer profile, it’s no surprise that 64 percent of respondents with centralized systems were satisfied with the quality of their data, compared to only 52 percent of those that had decentralized systems. This speaks to the importance of choosing a flexible CDP, for easy integration with other existing martech solutions already in use.

Expect More Customer Data Collection in 2020 — Especially from Mobile Devices and Sensors

The trend toward collecting ever more data for tailored user experiences is accelerating, as retailers try to supplement the online shopping, loyalty, and demographic data they already have with more mobile phone and sensor data from IoT devices.

As one C-level executive at a specialty retailer puts it, “Our stores and examining devices will carry more sensors to help us provide the best possible product suggestions to our customers.”

Retail Moves Toward Data-Driven CX

It’s an approach that’s changing both customer experiences and all of the back-end operations needed to increase profitability. “Customer data forms the base of transforming retail operations,” wrote a C-level executive in specialty retail.

A different respondent wrote, “effective use of customer data does help in increasing store profitability.” And perhaps the best survey explanation of the trend toward data-driven CX is a C-suite responder’s comment that, “the goal is to connect customer data across our operational platform so that our people in operations can make quicker decisions.”

Another C-suiter from a specialty retailer has a much broader strategic goal: “Aggregating data from multiple channels and creating a single fabric of customer data is what we’re focusing on with our Customer Data Platform.”

That’s a sentiment that’s becoming increasingly common for companies undertaking major personalization initiatives. For example, a recent Forbes study on personalization found that retailers who are ahead of the curve in implementing successful personalization initiatives — people Forbes calls “leaders” — value both CDPs and personalization as important parts of their strategies. In addition, 76 percent of leaders believe a customer data platform is critical to their personalization initiatives.

Could a CDP Be a Part of Your Success This Year?

So as you plan your strategy for making your numbers in 2020 and beyond, consider the findings of this complimentary eTail report. The findings are clear: retailers are going big on customer data, and CDPs are central to the customer-experience strategies of the future. Are you ready too?

January 2, 2020

Building a Modern Marketing Organization

By Joshua Neckes, Simon Data

How an integrated marketing team can drive transformation

The idea of a “modern marketer” is always a moving target. New channels and new technologies fuel an ever-changing set of customer expectations. What’s clear is that in a world where no consumer is untouched by data-driven giants like Google and Amazon, the modern marketer needs a modern marketing team – one organized around producing holistic, data-driven, customer-centric experiences – to compete. But whether you’re a digital-native challenger or an iconic legacy brand, building the type of team that can deliver these types of experiences presents a challenge. At Simon Data, we focus on supporting great customer experiences everywhere. Along the way we’ve learned what it takes to build a team that can deliver.

Let go of legacy structures

At Simon, we’ve worked with numerous emerging and legacy brands. While it’s clear that they come at the challenge of building a modern marketing team from different places, they nevertheless encounter similar challenges. For established brands with legacies that extend to the pre-digital era or even simply to the pre-web 2.0 era, the marketing function likely didn’t spring into existence in a fully integrated form. Instead, as new channels emerged, marketing teams added new roles and functions to accommodate them, creating silos. For example, a team responsible for email communication may have no communication with the team creating advertising content. Even if those digital functions sit under the same umbrella, it’s possible that they have no meaningful contact with the group responsible for creating in-store experiences at a physical location and no way to share insights gathered from their respective efforts.

However, while much has been made of progressive brands’ talent for adapting to new channels, many of today’s digital-native challengers still struggle to integrate new channels and functions as they grow. In some cases this might mean learning to buy more traditional forms of media, or linking a digital experience to a newly launched physical retail space the siloing of new functions and new data channels is still common. In many cases the pace of progress means it’s hard enough to plug in a new capability let alone find a way to fully integrate it into a team.

Overcoming this history can be difficult since it requires taking on institutional inertia to rethink the marketing team org chart. But changes to team structure can often spur new ideas, facilitate better communication, and enable the kind of cross-functional data sharing required to execute a multi-channel campaign. Technology solutions can also play a role, using a customer data platform to unite and make actionable all of your customer data from new and legacy channels.

Build a team around experiences, not channels

Once we break down the silos between marketing functions, we’re still left with an open question: namely, how should the resulting team be structured to best take advantage of its newfound openness and cross-functional accessibility. The answer, for many of the most progressive brands that we’ve partnered with, is building their new marketing team around customer experiences and using stages of the customer lifecycle, rather than broad messaging channels, as their key organizing structure.

Instead of channel-based teams, these progressive brands create cross-functional pods set up around moments in the customer journey. For instance, a marketing organization might feature a pod solely focused on the experience of new customers. This group, encompassing advertising teams, performance marketers, brand strategists, creatives, data scientists, engineers, and in-store experience experts, would be responsible for all aspects of the new customer experience ensuring that all aspects of the onboarding process are consistent and oriented toward the way consumers are interacting with the business at that stage. Data collected from these similar interactions can be used to optimize for retention, upselling or moving customers into the next phase of their relationship.

Orient yourself toward behavior, not demographics

In the past it was useful to view customers demographically, as men or women, as 18–34, as urban or rural. Those descriptors spoke to customer behavior in a meaningful way, and marketing teams were built to think of customers as demographic segments. But modern consumers don’t fit so neatly into boxes. The rise of personalized experiences, from Netflix queues to Amazon recommendations, has changed the way consumers experience commerce and reset their expectations. Regardless of age, gender or location, customers are increasingly expecting experiences to be tailored to their individual needs and preferences.

Many businesses are finding it more advantageous to orient themselves toward a psychographic view of their customers. Looking to the way individuals interact with content rather than trying to form wide customer buckets based on external characteristics. It’s often more advantageous to think of customers as browsers with an interest in skincare products than as women aged 18–34. However, understanding all of these interactions requires a holistic view of your customers and a team that’s able to understand and apply data across all your channels and touchpoints.

Prioritize communication

Successful modern brands are more communicative on a one-to-one basis than ever before. They ask you, “What’s important to you? What information or experience is most valuable to you?” Asking these questions early and often, especially of new customers, takes some of the guesswork out of marketing communications. This questioning could take the form of explicit customer surveys and requests for feedback, as well as through testing and experimentation to determine which types of information and offers customers respond to. Regardless of the collection method, an agile modern marketing team can communicate with customers across channels, collect the right information, and use it to inform how and what each customer is served.

This type of responsive, real time communication with the brand is now possible at scale. You don’t need to call customers individually or survey them in the store. Everyone has access to websites and apps in their pocket that enable use to keep human customers in the loop and collect real-time feedback. And if you can’t get your customers the information they need, a real human contact can be a single touch away. The brands that are able to offer and clearly articulate this blend of automation and human interaction will be the most successful and creating fully integrated experiences.

Transformation is continuous

Finally, it’s important to remember that transformation is an active process, not a single end goal. Building a centralized plan isn’t enough to ensure that you continue to see the benefits of transformation. Modern marketing teams should be designed to test, experiment, pilot new programs, and iterate on an ongoing basis. For a modern marketing team, the ideal state isn’t one of endlessly future-proofing by adding new channels tools, channels, and skills, but rather on of consistent reinvention which delivers consistent results.

December 26, 2019

Three Big Barriers to Scaling Personalization

By Jason Davis, Simon Data

Whether they were hosting a dinner party or sending a wedding gift, the etiquette columnist Emily Post often advised her audience to add “a personal touch.” Emily Post authored most of her famous columns at the turn of the last century, so it might surprise her that the people most in need of her advice today aren’t society hostesses and housewives, but rather modern consumer-savvy businesses. Personalization is no longer just an aspirational goal for modern marketers. With each passing day, customized experiences are becoming more commonplace, whether they’re in our streaming queues and search results or on the retail floor. These customized experiences have the cumulative effect of raising the bar on consumer expectations. Brands that can’t clear that bar risk not only missing an opportunity but also looking a bit rude by comparison.

In truth, most brands know that personalization across all their channels is critical to success. What’s more, brands have access to more consumer data now than at any point in history. Most digitally progressive brands interact with customers across multiple digital and physical touchpoints, all of which yield signals and customers data points that can be used to fuel a personalization strategy. However, despite this abundance of data and a definite will to succeed, personalization efforts have run into some fairly persistent stumbling blocks. Here are the three biggest barriers preventing brands from fully hitting the mark in personalization:

Organizational Silos

One of the biggest challenges for brands looking to implement fully realized one-to-one personalization is the inability to access data. Recent years have seen many forward-thinking businesses drastically amp up their data collection efforts. As a result, much of the data required to execute a scaled personalization strategy is already available, but the siloed nature of many marketing teams makes it difficult to access, let alone build a unified view of the customer.

Today’s marketing teams are often assembled in a piecemeal fashion, over a period of years, to accommodate new channels and objectives. As a result, their data is collected and stored in a commensurately piecemeal fashion, distributed across different systems, tied to specific channels, and often managed by a variety of different teams. This siloing of data makes challenging to deliver consistent messaging across channels which can, in turn, lead to inconsistencies in communication both within teams and between brands and their customers. Since continuity between marketing channels, customer support, and on-site experience is increasingly key to customer acquisition and retention, it’s critical for savvy brands to unsilo their data and bridge gaps between teams. This is one of the primary challenges that customer data platforms have emerged to solve. By uniting data from disparate sources, a CDP can build a unified customer view of the customer enabling a more integrated approach to marketing.

Scale

While breaking down data silos is a significant step in the right direction, marketers do face other challenges on the road to personalization. Getting data into marketing systems is still tremendously difficult, even today. Many marketing systems were not conceived to handle the scale of data needed to implement a one-to-one personalization strategy and therefore can’t accommodate the scale of enterprise data or the complex forms in which it is represented. These technical limitations significantly hamper the way brands are able to leverage the data they already own.

Many of these challenges are endemic to businesses managing digital transformation. As data-driven growth strategies become paramount to the survival of businesses, it’s important to remember that there’s only so much one can achieve with a point solution based on cookie-cutter one-size-fits-all strategies. The most effective road to greatness is one that builds towards long-term strategies while also showing immediate ROI.

Technology and Innovation

The final hurdle standing between us and true one-to-one personalization is a little harder to clear. Personalization technology has evolved rapidly in recent years, producing breakthroughs that have made a more unified customer view possible for many brands. But going all the way is going to require another technological leap. Specifically, we’ll need the kind of next-generation AI technology that we’ve just begun to scratch the surface of. From today’s vantage we can see the beginning of the path that will ultimately lead us to the personalization promised land. Just seven years ago deep learning experienced a huge emergence, and today those same algorithms can drive cars and defeat human chess masters. We may be just one major innovation away from a similar AI explosion.

The marketing technology of the future will need to be scalable and flexible enough to account for rapid growth and to anticipate new phases of digital transformation. We expect these next generation solutions will focus more on actionable insights built on a wider range of data inputs including real-time behavioral, historical, product, and deep access to content.

What’s next?

Despite the stumbling blocks, true scalable one-to-one personalization is close at hand. Brands increasingly understand what their organizational and technological challenges are, and as a result they’re moving to solve them. By restructuring marketing teams to reflect a more integrated approach to customer interactions, the old channel-based silos are starting to come down. Similarly, a new generation of marketing technology has emerged to help scale in-house data operations and manage the ever-expanding array of data sources and inputs needed to create a complete unified customer view. Meanwhile, a string of promising advances in AI combined with the huge investment of resources the technology has attracted, promise to soon crack the type of next generation technology that will tie all these threads together.

December 23, 2019

How to keep your sock drawer (and your customer data store!) tidy this holiday season

By Anthony Botibol, BlueVenn

Matching, de-duplicating, disparate storage locations… is this your customer data? Or your socks?

Have you ever been afraid to open your sock drawer because of the chaos you know will greet you? Tights tangled around socks, a potpourri of colours, types and brands, not to mention the dreaded assortment of single socks spilling over the edges, with no partner in sight.

An unkempt sock drawer is, in many ways, very similar to a messy customer database. Duplicates, mismatches, holes and the general wear and tear that happens each year… if you don’t regularly clear and maintain it, that drawer becomes a jumbled mess! And how many of us routinely put off the dreaded task of re-organizing our sock drawer, just as we do updating our database?

So, we thought the best possible holiday gift from BlueVenn would be some helpful advice on how to maintain a Single Sock View and keep your sock drawer tidy, functional and easy to navigate in the festive season.

Let the holiday sock incursion commence!

Just as the Yuletide spending spree provides retailers with a flood of new customer data, so the holidays bring a massive influx of new socks for everyone.

These traditional stocking fillers and token gifts are given to folk throughout the land, which means that it will soon be time to brace yourself for the dreaded annual reorganization of the cluttered sock drawer, an essential task if you’re ever to find a matching pair in the New Year.

Data Sock silos

Many of us retain so many pairs of socks that they overflow into multiple drawers – ‘sock silos’, if you will. Some in the allocated drawer, several pairs in the one below, others on the line, a soggy specimen left in the rim of the washing machine, not to mention the ones abandoned in yesterday’s shoes or kicked off in the night and buried under the covers. With socks, as with data, this is perhaps the most dangerous hazard to be avoided when trying to maintain order and cleanliness, making it far easier to end up with a muddle of useless single socks (with many of the intended partners left unwittingly languishing just feet away).

So, if you’re to have any hope of pulling out a matched and clean pair of socks in the morning when the celebrations are done, it’s time to knuckle down for a serious session of planning, sorting and pairing – something you’ll need to do with your customer data too, if you’re ever going to find the right piece of data at the right time to optimize your marketing campaign.

After extracting all the socks from their different drawers, you’ll need to search out fugitives hidden around the house (the hosiery equivalent of running a data planning project to uncover your data sources), then match them up. But not before you’ve removed the old socks that don’t fit any more, accommodated your spangly new festive pairs and thrown out the ones that have been vetoed as surplus to requirements or unfit for purpose by your fastidious partner (or in the case of your data, the ICO or California Attorney General).

BUT, without setting yourself a series of rules (your very own house sock retention policy) going forward, you’re just going to end up in the same shambolic mess again this time next year.

So, what rules do you need to follow to maintain an organized sock drawer (the single sock view)?

a. Ensure you match socks correctly before they enter the drawer. Remember, it can be difficult to pair up similar socks without defined matching rules (okay, they’re all black, but what about the different sizes, shades, materials and varieties of cuff). These will help sort your data too!

b. Merge matching socks using a joining rule that ensures they stay with their mates. There are several ways to merge your socks (e.g. balling, folding, gathering) and customer data (e.g. through the email address, postal code or Social Security number), but ultimately all achieve the same result.

c. Cleanse by removing unrequired or decayed socks (or data). It’s essential that you make a decision on how long to retain them. When did you last wear them? Is there a matching pair? How big is the hole? Similarly, when did your customer last buy something? Or open a communication? Or visit your website?

By following this simple schema you will ensure you remove the potential for multiple silos. Not only that, you will benefit from a new-found trust that your socks (customer profiles) will match, so that you will not need to spend an inordinate amount of time in the future sorting out the mess.

False positives

Like socks, customer data can be matched incorrectly, with ‘false positives’ and duplicates rearing their ugly heads. Plus, at certain times – the winter holidays, for example - we can have a high volume and velocity of socks (and data) from many new sources, and need to know how to fit it into our overcrowded storage facility. Otherwise, the deluge could result in additional silos that never really get used, containing solitary socks or unmatched, unusable fragments of data.

When it comes to your socks, ideally you’ll want just one drawer - the ‘single source of sock truth (SSOST)’ if you will - where you can rest assured the socks have been efficiently matched and merged.

Sound like hard work to achieve? Then perhaps you need to bring in a specialist to get your drawer in order and ensure you only have to manage the Single Sock View and that every pair inside is well kept, useable and accurately paired (notice your mother is trying to avoid eye contact at this point). Someone to build the rules and schema to ensure long-term maintenance, or perhaps make a checklist of core sock-sorter capabilities (we have a Real CDP Certification, so perhaps there should be a Responsible Sock Coordinator Classification).

The Single Sock Customer View

At BlueVenn we may not be masters of the Single Sock View, but when it comes to the Single Customer View, we’re experts. BlueVenn provides Customer Data Platforms that ensure your records are cleansed, matched, de-duplicated and kept free of holes (through patching with third party data enhancement), not just during the holidays but 365 days a year.

December 19, 2019

Getting ahead of privacy legislation is crucial for data-driven companies

By Susan Raab, CDP Institute

The alphabet soup that began with GDPR is about to get thicker with CCPA in California (Jan 1 2020), LGPD in Brazil (early 2020) and forthcoming legislation in India and across APAC.

“Analyzing and implementing the California Consumer Protection Act (CCPA) in 2020 will be a major challenge for companies across virtually every industry sector, according to Kirk J. Nahra, writing in Bloomberg Law. “We can anticipate an impact that mirrors the European Union’s General Data Protection Regulation (GDPR) process in the spring of 2018, with less clarity, a shorter time frame to work with, and an even more confusing set of obligations.” In the U.S., Nahra envisions additional privacy regulation likely to follow from other states’ Attorneys General and possibly at the national level.

But despite knowing that CCPA was coming for some time, a majority of U.S. companies view themselves as unprepared. According to a report from privacy tech company, Ethyca, “88% of companies feel they have not reached an adequate state of compliance ahead of the implementation of CCPA.”

This is a microcosm of what’s happening globally as more laws come into effect. Companies are struggling to comply in their own regions and are even less prepared to evolve a global game plan. Yet data-driven companies must address this rapidly, both for reasons of compliance and to please consumers who increasingly want to know what’s being done with their information.

John Mitchison, director of Policy and Compliance at the Data & Marketing Association UK says “prior to GDPR, serving ads to people used to be impersonal,” but now the relationship with consumers is far different and they view sharing their data as part of an exchange. He says “when asked in a survey how happy they were with the amount of personal information they gave to organisations, sixty-one percent initially claimed to be happy with it and sixty-nine percent said sharing data is part of the modern society. But at the same time eighty-six percent said they would like more control over the data they give, and eighty-eight percent wanted more transparency about how their data is collected and used.” When asked who benefited the most from the data exchange, seventy-eight percent said the business, with only eight percent feeling consumers got more.

Companies have many decisions to make. First is to understand what laws are in their home country and what they need to do to comply. Secondly, they need to understand how outside laws may impact them, and thirdly, they need to decide how they will handle the data they collect and have.

Key areas of concern are:

· Access – who in marketing and in other areas will have access to specific customer data,

· Storage - how long will the company keep Personally Identifiable Information (PII), and

· Transparency – how will the company ensure customers are kept informed of how their PII data is used.

Christoph Bauer, of ePrivacy GmbH in Hamburg, says his consultancy sees a wide range of response from companies. “Some pride themselves on being very consumer friendly and want policy and their relationship with customers to reflect that. In marketing that becomes a selling point. Other companies want to know what they must do to comply but still are most concerned about maximizing use of the data they have.”

The World Federation of Advertisers identifies eight key areas of GDPR of relevance to marketing: extra territorial scope, basis for processing, children’s data, data breach notification, right to erasure, sanctions, data protection officer, and impact assessments.

According to Bauer, when companies were asked how they are doing with compliance, most estimated that they were at about the 30% level with some saying they were at 80% or more, but “the tendency has been to wait until more decisions are made” on ePrivacy and on GDPR enforcement. Bauer and Mitchison agree that companies need advice from their own legal team on all of their legal options and finally on compliance - and, going forward, will need advice on other countries’ legislation from local market experts there.

The International Association of Privacy Professionals (IAPP) is a key source for understanding what is happening globally and runs conferences, a list-serve and peer-to-peer networking events. They also publish extensive content for their members, who come from across the corporate landscape, including from marketing, product design, engineering, sales and, of course, legal.

According to Caitlin Fennessy, IAPP’s Research Director, “direct marketing folks are among the top people who should be looking at this today. Marketers should be proactive in making sure there is two-way, conversation between themselves and privacy experts at their company to ensure: 1) that they have a full picture of the data marketing is working with; 2) that they have a clear understanding of what is and is not personal data; 3) that it is determined what special protections should be put in place with regard to personal data; and 4) that there is a clear picture within the corporation of who needs to be informed with regard to how this information is handled and that a regular schedule is set up to check in.”

“You need to be clear who is touching that data inside and outside the company,” says Fennessy. She stresses that this is not a one-time process and that it requires diligence and persistence to ensure that you’re staying on the right side of privacy law.

With CCPA, there’s a lot of focus on the advertising industry and how data is being used by your company’s vendors. If customer data you collect is shared with a vendor or other company and there’s a chance data may be used for commercial purposes, the customer needs to be able to opt out with a “do not sell” button or something similar. “This gets complicated in the ad space,” Fennessy says, “where you drop a cookie that can be used by others.” She emphasizes the importance of enabling consumers to have access to their data, giving them the chance to opt out, and making certain all is clearly outlined in your privacy policies.

Here’s an IAPP infographic on CCPA compliance:

^{© IAPP}

We’re in early stages of understanding how this will work, but those who are focused on this area believe that it is an issue for companies and marketers to address proactively particularly with CCPA and other new legislation. Fennessy also explained that CCPA 2.0, which is much closer to GDPR, is anticipated in the future as well.

Mitchison sums up saying, “I believe the data marketing industry is at a crossroads. We can be an industry that puts short-term profit above long-term loyalty, an industry that uses data, technology and creative to trick customers into a quick sale. Or we can be an industry that chooses to create truly engaging customer experiences…an industry that builds trust.”

December 16, 2019

Marketing Challenges of an Enterprise and Role of CDP

By Vishal Sukheja, FirstHive

“The engagement rates showcase positive response, but my paid campaigns targeted at those audiences do not reflect high conversions. I am unable to focus on those important few who are truly loyal to the brand and leave out the rest. Some channels work well for older customer groups, While others work best for new and recent ones. I am unable to grasp the data in entirety in a single view. Designing exclusive campaigns for the high-converting groups is a challenge.”

Do you also go through a similar marketing experience? Such scenarios are not just a matter of frustration but loss of opportunity where marketers are six times more likely to increase profits (45 percent vs. 7 percent), and are five times more likely to achieve a competitive advantage in customer retention (74 percent vs. 13 percent) [1].

Many marketers who strive to create a great customer experience stumble upon similar problems related to customer data, integration and security. But, the early adopters of Intelligent Customer Data Platforms have been able to solve these problems.

A Customer Data Platform (CDP) allows a marketer to work in tandem with other data hubs within an enterprise and comprehend insights in unison with the rest of the enterprise. In short, only a CDP can help in creating personalized and yet consistent customer experience across all touchpoints that interact with the brand.

It gets more obvious that a CDP is a most sought after tool into a marketer’s toolbox to maintain better customer relationships. While a CDP’s importance is apparent, we have set out to answer how exactly does a CDP solve different problems of a marketer, both at the levels of strategy and implementation.

A marketer’s problems are directed towards Data, Systems, and the Customers.

Marketing Challenges of an Enterprise and Role of CDP

Data

Data Structure

How data is stored, archived, and retrieved within an organization is the beginning of a marketer’s obstacle lane.

Marketer’s Problem

Fractured data structures is not a recent challenge, it is an age-old maze that marketers struggle to stitch together. Siloed data across the organization fails a marketer to execute her top operational priority: analyze data to automate insight generation for its own team and for the sales personnel. Even though there is a quick response to this problem with the emergence of advanced predictive analytics, the real problem stays with the traditional methods applied to access data. 80% of marketer’s operational work involves acquiring and preparing data [2].

The CDP’s Role

The emergence of CDP has relieved marketers from accessing siloed data. A CDP ingests first-hand data coming from all data sources, which includes internal, external, and third-party sources. Even if any of the existing software vendors demand a data lock-in, such data could be ingested into your system of CDP. This simply means a good CDP can ingest both streaming and batch data.

A CDP uses this data to organize it for further use such as cohort management, cohort segmentation, campaign management, analysis and insight creation, and so on. The privacy of data is secured within a CDP that is designed to comply with data regulations.

Data Control and Accessibility

Though data exists within the organization, it is seldom at easy reach of a marketer. A marketer needs to travel through systems, processes, and people to access relevant data, and that means a loss in time.

Marketer’s Problem

Data has to change hands and a marketer fails to receive data in-time. Data accessibility and a marketer’s control do not, unfortunately, scale with the growth of the organization. A marketer faces political challenges in the data retrieval process. These cost money which lower ROIs.

The CDP’s Role

A marketer does not have to wait for exclusive data access with the use of CDP. A CDP provides complete control to retrieve relevant data in time. Further, data-crunching and processing are also automated to the extent of insight creation. The processed data can be put to use anytime to kickstart responsive campaigns, draw quick insights, and action other processes that add up to the bottom line. There is no dependency on technological or data specialists to execute these tasks.

Systems

Real-Time Response

With far-fetching data reach, real-time response to customer and real-time data availability is a challenge to every marketer. 43% marketers agree that they are not lacking data; they are missing the ability to transform data into real-time action [3].

Marketer’s Problem

With traditional forms of data access, marketers use and upload data using a batch cadence. There is no single system to anchor the data streaming activity for the entire organization. The customer stays away from the marketer and the brand for the same reasons. The time decay results in poor response and customer experience. Apart from this, adulterated, duplicated data cascades to create a poor quality of data bank within your organization.

The CDP’s Role

A CDP ingests data in real-time. There is no lag time that curbs the marketer from using data to respond to a customer. The response could be via a chatbot, an amendment to an ad campaign, revision to an on-ground offer, or an SMS. Automated triggers, auto-cohort segmentation powered by Artificial Intelligence and Machine Learning helps a marketer to make prompt revisions to the existing plan.

Marketing System Integration

Data silos also limit different marketing systems from talking to each other as well as with other systems within the organization. Data heterogeneity complicates further data management.

Marketer’s Problem

Identifying a customer or a high-value cohort is as good as owning a gold mine. With changing customer behavior across different marketing channels, predicting a path is very complex and expensive. To avoid complexity and high-cost, marketers settle with a generic persona or identity that helps them execute campaigns seamlessly. However, the lack of identity kills their ability to personalize customer experience, hence hurting the marketing ROI.

The CDP’s Role

A CDP integrates data from across different systems and creates a unique customer identity that is unified throughout several touchpoints. Every customer identity is enriched with each new action and interaction with the brand. The algorithm used to update each identity considers other factors that influence a customer’s journey and decision to purchase. These are demographic, geographic, campaign-specific, channel-exclusive, device-specific, behavioral, product-specific, and other similar factors.

Customer

Consistent Customer Experience

The number of marketing channels continues to expand, increasing the importance of highly personalized and relevant messages and offers [4]. The same number also throws the challenge to a marketer of mapping the touchpoints to maximize a personalized experience for each customer.

Marketer’s Problem

A customer expects different stimuli to purchase in each marketing channel. Each customer’s interaction is different on each channel. This leaves the marketer with so many permutations and combinations to design a campaign that is consistent across multiple marketing channels. Though a marketer achieves in doing so, the dynamic trends that have constant influence of a buyer’s journey distort the navigation that is determined by the marketer. The sheer number and increasing complexity constraints a marketer to bring out a consistent customer experience for every customer.

The CDP’s Role

The unique and unified customer identification that is generated by a CDP helps a marketer to tweak the experience for every individual customer. A layer of Artificial Intelligence stitched into the system allows a marketer to automate changes using stimuli as triggers and rules that generate the response to each interaction. It indicates the necessary changes in communication and budget that would be required to achieve a better customer experience for a high-value multi-channel campaign or a single-channel promotion. The single view of a customer that is inherent to and is provided by a CDP helps in retaining a consistent customer experience at any scale.

Diluted Customer Relationships

Either B2B or B2C, when ‘scale’ steps in, maintaining a strong and personal relationship with each customer is almost impossible for the brand. With personalization, names could be used, but marketers want to do more to ensure that relationships with customers are not diluted.

Marketer’s Problem

Marketers need to often fill the vacuum between the data management strategy and customer experience strategy. This means finding answers to scenarios such as “What exactly is the Customer looking for?”, “Predicting customer behavior”, “Identifying triggers that bind a customer community”, and many other abstract matters.

This needs a human eye and intelligence. The emotional connect that mere data lacks has to be compensated in the larger canvas of customer experience by the marketer intends to create. If these systems that drive the two critical strategies do not talk to each other, the marketer needs to manually instill intelligence, which adds to the complexity of building better customer relationships.

The CDP’s Role

A CDP behaves like a central system of intelligence that empowers other systems to interact in a unified manner. It eliminates the cumbersome activity of lengthy configurations, interpretations, and insight generation. In fact, a CDP plays the roles of a seamless Insights Engine that brings Intelligence to the network of systems within an organization.

While this answers about the role of CDP in an organization, you can read further about its Strategic Value to a Marketing Plan.

December 9, 2019

Real-Time Data Aggregates for Today’s Dynamic Customer Journeys

By Todd Hinton, RedPoint Global

A customer who drives away from a home improvement retailer with a new refrigerator has little awareness of the domino effect the transaction generates, happy enough to have a working freezer. Unbeknownst to the customer, however, the purchase introduces a flurry of changes to the customer’s data aggregates, which have a material impact on the continuing customer journey. Aggregates that require updates will include categories such as date of last in-store transaction, last retail purchase amount, last purchase store number, last purchase date, last purchase amount, and year-to-date purchase amount, to name a few. Other potential changes could include data points such as a new physical address or email, or a customer’s status as head of household.

Within each of these broad categories there may be dozens of calculation rules that update a customer’s status, such as customer lifetime value (CLV), and others that determine the retailer’s next-best action based on the aggregations. Does the retailer send a thank you note? An offer for storage organizers and ice trays? Does the updated CLV warrant an invitation to a gold member club? Or does the purchase trigger free delivery and installation?

The permutations that kick into high gear with a single transaction underscore the importance of using automated, rules-based decisions that are updated and pushed out in real time to keep pace with a customer journey. Consider a scenario where the customer with the new refrigerator arrives home and sees an email offer for a discount on a water connection kit, with free hook-up. The offer is personalized, relevant, and in the context and cadence of the customer journey. Now consider the alternative where aggregates are updated using lists and batch data. At best, the same offer is made three days after the purchase – after the customer has already bought and installed the water connection kit.

Always Keep Pace with the Connected Customer

What differentiates the RedPoint Customer Data Platform from a list-based approach is that aggregates are not only updated in real time, they are also pushed out to subscribers in milliseconds. The master data management component eliminates lag time between data ingestion, processing, and calculation updates – ensuring a frictionless customer journey. This means that every engagement channel and external system that subscribes to the real-time updates is in synchronization with a customer at any stage of the customer journey.

To be considered an enterprise-grade CDP, a solution must ingest data from all sources, retain full detail of the data, store it, convert it into a unified customer profile, and make the profiles available to all external systems. Because these requirements do not specifically include real-time aggregation calculations, there are CDPs in the market today that lay claim to providing a single view of the customer through the aforementioned capabilities. In truth, however, the lack of rules-based aggregations means that they’re not equipped to manage an omnichannel customer journey.

Typically, a list-based CDP will instead ingest data, calculate some aggregates for a nightly batch upload to an FTP site, and a subscriber or external system will pick it up via web services or API integrations.

A Personalized Experience Requires a Rules-Based Approach

A traditional cadence sufficed when customer journeys were linear, static and rarely involved multiple channels. Marketers could safely rely on lists to generate outbound campaigns where there was little expectation from the customer for a personalized experience.

That’s not true today. According to the Harris Poll survey commissioned by RedPoint, 63 percent of consumers say that personalization is now part of the standard service they expect, with 37 percent of consumers going so far as to say they will flat out stop doing business with a company that fails to offer a personalized experience.

Consumers also expect a brand to know who they are – preferences, behaviors, permissions – across every channel. Satisfying the always-on, connected consumer requires two-way, real-time communication across a host of new and emerging channels. To achieve this, brands must have the capability to apply rules dynamically at any stage of the customer journey, producing a next-best action in the context and cadence of an individual journey.

The modern customer journey renders a list-based approach and list-based decision-making obsolete. To learn more about the benefits of real-time aggregates and what sets the RedPoint CDP apart from others in the market, I encourage you to read a recent BOSS Magazine article that goes into extensive detail on the shortcomings of what it called “out-of-the-box” CDPs with a “one size fits all approach” that mostly fail to match brands’ aspirations to target customers with highly personalized engagements.

In the article, my colleague George Corugedo explains how real-time aggregates provide a fast path between data and revenue, and why many RedPoint clients have declared that the RedPoint platform is the primary revenue generation platform for their organizations.

December 2, 2019

Customer Journeys are Dynamic, Your Engagement Technology Should Be as Well

By John Nash, RedPoint Global

Published in 2007, Tom Davenport’s groundbreaking work Competing on Analytics: The New Science of Winning explored the then relatively new science of harnessing customer data for competitive advantage. CIO Insight lauded it as one of the top 15 most groundbreaking management books.

The first chapter focuses almost exclusively on Netflix as an example of a data-driven company that parlayed business intelligence into a (then) innovative new business model – mailing DVDs to customers and recommending titles for their queue. As we now know, the book’s publication pre-dates several more innovations from Netflix, which underscores the rapid pace of digital transformation and the danger of inaction (see Blockbuster).

Rules-Based, Automated Machine Learning Models

The current entertainment streaming wars further illustrate how much has changed since the incipient times of 2007 with the use of analytics to compete on customer experience. With price and product largely commoditized, companies in retail, finance, healthcare, travel, and industries across the board recognize the urgency to use data and analytics to create a personalized omnichannel customer experience.

Creating a personalized customer experience for the always-on, connected customer requires two-way communication across every channel. The traditional approach to analytics that created a static list of customers for an outbound marketing campaign based on a fixed data model is incapable of keeping pace with an omnichannel customer journey.

To support a consistent, personalized customer experience across channels, decisions need to be rules based, not list based. While this entails a real-time element, which requires that a brand take the optimal action at the precise moment a customer appears in a channel, it also entails dynamic flexibility. Unlike static lists that that cannot respond to new or changed data, rules-based decisions can now account for the totality of customer behaviors up to the customer appearing in a given channel.

All of this is made possible through real-time data, or a golden record, for each customer coupled with automated machine learning. Lights-out, evolutionary programming makes dynamic flexibility possible. With a continual ingestion of customer data from every source and of every type, automated machine learning models (code-free) are tuned to optimize a dynamic, personalized customer journey without human intervention across channels.

Compete on Customer Experience

Dynamically managing a customer journey is required to provide customers with the type of online and offline experiences they increasingly demand from the brands they frequent. Results of a 2019 Harris Poll survey commissioned by RedPoint Global clearly show that competing on customer experience is an imperative; brands that ignore this reality run the risk of alienating core customers.

According to the survey, 63 percent of consumers agree that personalization is part of the standard service they expect. For instance, they expect a brand to know they are the same customer across all touchpoints (in-store, email, mobile, social, call center, etc.). Further, consumers were unsparing in their critique of brands that fail to deliver personalization, with 37 percent claiming that they will stop doing business with a brand that does not offer a personalized experience.

It’s easy to see why static lists, which are familiar to any marketer who has run an outbound, drip campaign based on segments, are unable to keep pace with a dynamic customer journey. A typical non-sequential, non-linear customer journey includes many touchpoints, and an ability to deliver a personalized experience throughout the journey, across any channel, requires knowing everything there is to know about the journey. Consider a customer who logs in to a retailer’s website. Which pages the customer views and page durations vary with each visit and for each customer. A list derived from an analysis of past behaviors will not account for subsequent behavior that may fundamentally alter the next-best offer, recommendation, or action, and thus will not by synchronized to a customer journey.

Personalization failures, which an Accenture study estimates costs US firms $756 billion annually, clearly do not go unnoticed – or unpunished – by the savvy consumer. A reliance on static, list-based analytics could easily result in a customer receiving an offer for a recently purchased product, or a recommendation that is irrelevant to the journey in that precise moment in time.

Keep Up with a Dynamic Customer Journey

Rules-based approaches eliminate these types of customer frustrations by being in pitch perfect sequence with a customer journey regardless of which direction it takes. One RedPoint customer, a specialty retailer, uses automated machine learning within the RedPoint Customer Data Platform to deliver an innovative, personalized buy-online, pay in-store (BOPIS) experience. Within minutes of purchasing a product on-line, the retailer is able to deliver a relevant, personalized experience to the customer even before the customer arranges for the in-store pick-up. Personalization touches include emails with pick-up instructions (directions, store hours, etc.), relevant offers for product accessories, and website content tailored to the customer’s preferences.

Another RedPoint client, a web services company, uses the platform’s millisecond response time to know everything there is to know about a customer in real time. When a customer calls into the call center, for example, an agent will be aware of every action that customer has taken – up to and including the call. If the customer is currently having an administrative issue on a hosted website, the call center agent will have a record of it and have the information needed to resolve the issue.

These outcomes stem from the ingestion of all customer data – every source and type – in real time, and rules-based, hands-free automated machine learning models that offer the same dynamism present in every customer journey today.

In 2007, the year it launched its streaming service, Netflix customers still happily waited a few days to receive DVDs in the iconic red envelopes. Today, of course, customers expect to be able to stream content of their choosing at any time on any device. Relying on list-based analytics is the equivalent of mailing red envelopes and hoping your customers are okay with an experience that may have been delightful a decade ago, but is archaic by the standards of modern technology. Customers demand a relevant, personalized, and seamless experience across channels. Meeting this expectation results in more satisfied, loyal customers which directly translates into revenue.

November 28, 2019

Customer Identity: Immediate conversion of sales targets & the revenue multiplier effect

By Ricardo Rengifo, FirstHive

While attending a recent technology conference, I knew almost everyone except for a few new attendees. My colleague, a new joinee, accompanied me to the event. I thought it was a good idea to bring her along so that we could network more! However, I noticed that each time she interacted with a person, it took longer for her to break the ice than for me, and I was able to get more leads and get new connections through known acquaintances.

To me, this difference was not because she lacked experience. She was very good at her job. But, the real gap was a lack of a connected network. She could neither recognize anyone nor could the attendees associate with her.

I had a stronger identity, which made socializing easier for me than it was for her.

This clearly showed me having a strong identity within my network contributed to my ability to have a more effective event. To me, the value that a ‘customer data platform’ brings to a martech stack is something very similar.

It has a direct impact on the sales conversions and bottom line.

A single customer interacting with your brand across multiple touch points and channels needs to be identified. She may choose to react in a different way each time. Identifying her helps you understand her intents irrespective of the channel.

You could have just started marketing with two known channels or two dozen of them. No matter what the number is, your customer explores and expects different things from each channel. It is interesting to spot how the same customer responds in a different manner on each of the channels that you exist.

Overall, how does it feel when you know that 57% of your customers stop buying from your company because a competitor provided a better experience? This statistic is backed by research. Connecting it back to my experience at the conference, I can conclude that it is all a matter of how well you relate, connect and identify your customer as much as you expect them to know your brand.

How is Customer Identity created?

While there are multiple micro-steps involved in creating a unique customer identity, they can be categorized into 6 steps.

Data Ingestion

Every brand relies on more than two channels to begin with. Each channel provides a variety of opportunities to communicate about your brand. Omnichannel is defined as the use of all channels available for service, distribution, and transaction within a unified experience. This includes online and offline channels. It could spread across these categories of channels — internet marketing, social media channels, physical marketing, paid advertising, public relations, event marketing, telemarketing, and so on.

The initial customer data is collected in the form of email IDs, name, phone number, social profiles, and so on. A unique customer ID is created for each customer. The ID stays as a reference point to add further attributes and secondary data to the ID. This reference point is used in further steps to make it foolproof for a marketer to enhance the customer experience.

Data Unification

Unique customer IDs enable a consistent brand image and unified customer experience across all channels of interaction, information, and transactions. For customer data unification, FirstHive uses AI-assisted techniques to complete each unique customer profile stitched to the respective customer ID.

As different sources provide information about a customer, it is tied to the unique customer ID in different database systems as well as the platform. It provides accurate information when the platform starts using it for real-time activity. It supports processes such as campaign management, cohort segmentation, personalization, and so on.

Data Enrichment

As much as furniture or silverware cannot be left alone in the shelf, it needs to be maintained, customer data also needs to be enriched. Data is organized during the steps of ingestion and unification. It needs to be cleaned to avoid duplication, attributed to the right data sources to help identify patterns, build data format consistencies, and to create consumable customer data.

During this step, IDs are enriched with other real-time data as well as organized based on automated rules and pre-configured processes. Further, related behavioral attributes, transactional updates, and product interactions are updated to that unique ID.

Persistent ID

A persistent ID is assigned to a unique ID. It is a long-lasting reference point that can be used to validate relative attributes. For instance, if you are a bank targeting two people, you may want to confirm if they are related in some way or not to ensure you share information that increases mutual and collective influence. Most often, telemarketers try to sell a product to the husband and wife at the same time or call the spouse of a person who has experienced a demise.

The persistent ID provides intelligent cues to avoid such dissonance in the customer experience. This is possible because FirstHive also ensures identity resolution. Historical data pertaining to a particular ID is not completely eliminated even after deletion.

Deterministic mapping

Using identifiers with deterministic mapping, marketers can connect the relevance of a campaign or offer to a target segment or an individual ID. This helps in gaining insights across cross-device usage, time preferences, product-device usage correlation, and others.

Probabilistic mapping

This is what differentiates our CDP from that of our competitors. With multi-source interaction and multiple accounts of a customer, the goal of a marketer tends to be to identify if the same customer is returning and interacting to achieve different objectives from different touch points. Probabilistic mapping takes into consideration other customer data across the universe that would eliminate any possibility of anonymity.

When sources provide less deterministic value to a customer’s data, statistical methods are applied under probabilistic mapping to identify the closest match. The customer ID includes these aspects, too.

What is Customer Identity used for?

Customer identity is no longer just a marketer’s magic bullet, it also serves the purpose of reputation managers, human resource personnel, anti-fraud managers and other counterparts within an organization. Here are some immediate use cases of customer identity.

Targeted communication

It is more efficient to eliminate guesswork and provide information packaged in the most preferred tone and voice when a marketer knows the customer better. Customer identity enables 1:1 communication building highly relevant context. A leading insurance company scrubbed its customer data using FirstHive. Over time, they did a splendid job of attributing data using the platform. They used this to target customers for upgrades and renewals.

Personalized experience

A leading salon and cosmetics consumer brand used customer IDs to track third-party influencers and of their own customers. Each influencer attracted different customer personas. The brand used customer IDs available on FirstHive to communicate offers for all their associated salons across the nation. They were able to provide coupons and offers that would deliver higher conversions.

Customer loyalty

It is not restricted to a single sector. Use of customer identity is very popular to roll out customer loyalty programs. My favorite one is how a hospitality and realty brand used customer IDs to improve customer retention using customer loyalty programs. Each customer assigned with an ID was tracked to provide highly customized cross-channel communication that led to personalized user experience. This was not just in the pre-acquisition, stage but also while delivering the service. Even before the customer made requests to the concierge, these brands had enough information to provide proactive recommendations to every customer. It included the use of taxi services, spa and gym, clubhouse, and so on.

Some interesting implementations were developed around concepts such as rebates, influence, and privileged access.

Identifying Fraud

Did you notice that when you created a profile for your company with generic email ID on Facebook, in about a day your account gets blocked because there is duplication? Fraud identification is more complex than this when it comes to banks and credit unions. Transaction duplication and fraudulent access to personal information can be prevented with the addition of customer ID.

Accurate customer identification is directly correlated to better customer experience. Forbes has mentioned that consistent brand presentation across all platforms increases revenue by up to 23 percent. However, great customer experiences are always tied to how well the brand knows its customers and how well each customer is able to associate with your brand.

We believe every great customer experience begins with a small step of identifying your customers and then building a relationship. We would be delighted to hear out your efforts to create better customer experiences. Drop a note to marketing@firsthive.com or share your story in the comments section below.

November 25, 2019

Top 10 Benefits of Rules-Based Orchestration

By George Corugedo, RedPoint Global

There is not a single multinational company thriving today that would deploy an integrated supply chain system that failed to account for volatility, disruption, and consumer demands for immediacy in the delivery of customized products and services. Putting the right product at the right time with the right price in front of a customer is simply too important to rely on static, list-based decisions and systems that fail to account for the dynamics of a global economy.

The same is true for keeping pace with always-on, connected consumers as they move through an omnichannel customer journey. Consumers demand a personalized customer experience, and evidence shows that delivering such an experience provides a direct line to revenue. Yet, unlike the universal acceptance of a dynamic and highly functional supply chain, many organizations are slow to embrace empowering marketers with the same power and flexibility under the spirit of “it’s just marketing” or “marketers just don’t think that way”. The hard truth, though, is that if “marketers don’t think that way”, they won’t be long for their jobs, as marketing is the direct line between data and revenue and expectations have risen both inside and outside the organizations for marketing to be the tip of the spear for the enterprise as a revenue-generating engine.

A New Mindset That Leaves Audience Lists Behind

Marketing, perhaps more than any other line of business, has complexities that require dynamic systems of engagement. An influx of communications channels. Online and offline engagement touchpoints and personas. Continuous updates to preferences and permissions. The bottom line is that no line of business deals with things like data lists anymore, and none should be bound by legacy technology designed for the age of outbound, batch, or drip communications through audience lists. Everything in marketing is now dynamic and enterprises cannot afford to have toys standing in for hardcore data-driven tools needed to achieve the ultimate marketing objective: omnichannel (all outbound and inbound channels) optimized messaging.

With this context in mind, here are 10 reasons why a customer engagement platform that derives audiences, channels, actions, and preferences dynamically at the time an action is taken is a requirement for keeping pace with a dynamic, omnichannel customer journey.

1. Rules Are Dynamic: In a rules-driven environment, marketers are not locked in by pre-defined, static lists of data, customers, or prospects that paint a picture of a moment in time at odds with the entirety of a customer journey. The dynamism and flexibility of a rules-based system underscores every reason why a rules-based approach is superior to a reliance on lists.

2. Rules Are Data-Model Agnostic: Being dynamic, rules can organize and re-orient themselves into whatever a changing business requirement or KPI dictates. Lists, being inflexible, are static in a table. Models based on lists will therefore become outdated the moment a business objective or KPI changes.

3. Rules Are Multi-Channel Driven: A marketing campaign based on a static list – such as sending a credit card application to a list of prospects – is likely not just impersonal, but it runs a risk of being irrelevant if a prospect signs up for the credit card after the list is created. Because a rules-based system is dynamic, eligibility for one channel versus another channel – or multiple channels – is decided at the time the communication is initiated based on the totality of a consumer’s actions.

4. Rules Are Journey-Based: A list-based system is locked in time and cannot keep pace with a customer in a non-linear, non-sequential journey. A rules-based system dictates a next-best action relevant to a customer’s journey at the precise moment and channel of engagement. It does not rely on a list that recommends an action for a customer based on an engagement that may have lost its relevance.

5. Rules Are Re-Usable: Lists are a one-and-done proposition. As soon as a customer takes an action – signs up for the credit card, buys the product, etc. – the list is obsolete. Rules are dynamic, which means they can be updated in real time based on a customer’s behaviors and re-used as a “living” document. A rule about what constitutes a gold customer means that the “list” of gold customers is fluid, and is re-usable within an automated machine learning model.

6. Rules Are Flexible: Once created and activated, a list cannot adapt to an unforeseen change in conditions. When an item on a grocery list is not in stock and the shopper makes a substitution on the fly, the list is relegated to the trash bin. Likewise, a static list of customers loses relevance when any change occurs that is not directly accounted for in the list.

7. Rules Are Not Batch-Driven: Running batch-based business processes means that decisions are – by definition – based on lists of data. Businesses – and connected consumers – move faster, which require that decisions rest on dynamic, analytically driven machine learning models.

8. Rules Account for Behavior Post-Campaign: An “if this, then that” dynamic rule builds on its own success, infusing value into campaign results by moving the chains, if you will, providing marketers with an – automated – logical next step to drive desired behavior. Results from a campaign derived from a list can be matched back to the list, but a new list must be created to advance any findings.

9. Rules Do Not Require Coding for Dynamic Personalization: A list-based email campaign set up for personalization requires scripts, as an example. The time-consuming, resource-intensive effort devalues the benefit of personalization because by the time a customer receives a personalized email, they may be steps ahead in a customer journey. Scripts simply cannot be written to keep pace or to account for every step of a dynamic customer journey. By not requiring coding, rules are unencumbered by manual intervention for dynamic personalization.

10. Rules Are Secure: A previous blog in this space focused on the importance of applying customer permissions dynamically in the customer lifecycle. The fact of the matter is that regulations frequently change; new provisions, bylaws, and legislation crop up continuously. A list of customer preferences and permissions becomes outdated with each new provision, introducing compliance risk and creating a self-defeating cycle where a new list becomes irrelevant almost the moment it’s published.

Applying rules dynamically at the time of each customer interaction seamlessly integrates all segment and preference data into the process at each stage of the journey, making it dynamic and eliminating the potential of frustrating a customer by ignoring or mishandling their preferences, or by ignoring where the customer is in their journey.

The RedPoint Customer Data Platform is rules-based. It creates rules for how, where, and when customer data is ingested. It creates rules for how it is managed, and for how it provides advanced identity resolution. It creates rules for how code-free automated machine learning models recommend a next-best action that is always perfectly in the context and cadence of a customer journey. These rules are the most powerful part of the solution, providing the foundation to enable segment-of-one marketing at scale. Any system that relies on lists is simply incapable of matching this power.

A rules-based platform embraces the enormous complexity of providing a personalized customer experience for every customer at every stage of a dynamic customer journey. The reward for tackling this complexity is to have a mission-critical solution that is accountable for generating revenue.

November 18, 2019

Key Questions to Ask When Evaluating an Enterprise CDP

By Steve Zisk, RedPoint Global

The Customer Data Platform Institute created the RealCDP designation to clear up marketplace confusion about the true purpose and capabilities of a CDP. While over 100 vendors offer what they consider CDPs, the institute bestows the RealCDP label on just 44 of them, RedPoint Global included.

According to the institute, there are five qualifications for a CDP to earn the RealCDP distinction:

• Ingests data from all sources
• Retains full detail of all ingested data
• Stores the ingested data as long as the user wants
• Converts the data into unified customer profiles
• Makes the profiles available to all external systems/li>

That’s a lot to unpack for companies that are evaluating CDPs as the single source of truth for customer data and as the foundation of a future-proof martech stack. To further clear up confusion, we will address five questions every company should ask during the evaluation phase.

Dynamic, Rules-Based Application of Customer Data

The five RealCDP qualifications do not explicitly state that a CDP must perform all of those functions dynamically, but that is perhaps the most important consideration for any company evaluating enterprise-grade CDPs. A solution can ingest, retain, store, and convert data and make it available to external systems, but done statically it will not keep pace with a dynamic customer journey.

A rules-based solution that offers an in-the-moment, real-time, flexible decision at the moment of interaction makes all the difference between a real-time, dynamic journey and a list-driven journey that will, by definition, always be at least one step behind the customer. Attaching static information to a persistently updated golden record devalues the ultimate purpose of a CDP, which is to guide a customer journey with segment-of-one personalization in the context and cadence of a unique, omnichannel journey.

Data Enrichment for Deeper, Richer Insights

An enterprise-grade CDP should offer a broad set of data enrichment capabilities. Data enrichment is the merging of third-party data from an external authoritative source with existing first-party customer data.

Data ingestion is a core requirement of a CDP, but data enrichment is needed to make raw data useful. It adds additional layers of detail to first-party data, generating insights that help marketers deliver a more hyper-personalized customer experience.

Data enrichment is a key element of integrated identity resolution. And while it is not expressly covered by RealCDP, it is nevertheless an important topic and an important distinction between an enterprise-grade CDP and an also-ran. Brands poised to deliver omnichannel personalization recognize that well-functioning data enrichment is a key CDP capability.

Advanced Identity Resolution for an Anonymous-to-Known Journey

Identity resolution is another question to ask when evaluating a CDP, because creating relevant, personalized interactions requires not just enriched records but accurate and timely understanding of the customer journey across all touchpoints. Marketers should expect the CDP to dynamically map partial sets of details from identified and anonymous records to resolve a customer identity across an unknown-to-known customer journey.

Identity resolution is foundational for building an accurate customer profile. It should automatically identify customers across databases and engagement systems. Companies evaluating CDPs must be wary of solutions claiming to offer advanced identity resolution, but providing little more than matching and stitching together known identities. Advanced identity resolution requires understanding, tracking, and relating the complete range of identity elements – offline and online, anonymous and identified, shared and individual – to build identities in the context of households, organizations, and changing preferences and goals.

Identity Resolution Done Right: Building a Golden Record

To provide the performance, agility and control marketers need for engagement at the cadence of the customer, a CDP must natively include all the tools required to convert raw data into a golden record. This includes handling normalization, validation, and transformation of raw data – names, addresses, numbers, business details – from both structured and unstructured sources, along with all kinds of “housekeeping” items, like handling nicknames, abbreviations, incomplete or missing fields, and data entry errors.

The RedPoint Customer Data Platform standardizes the data and uses probabilistic and deterministic matching – with more than 375 built-in functions – to decipher individuals, households, cookies, IP addresses, and IoT smart devices, among others, and handles both digital and offline identities.

This process turns raw data into an identity graph that marketers use to create a personalized customer experience in any channel. Relying on data scientists to convert data into a unified customer profile is an unnecessary, time-consuming step when trying to keep pace with a customer in an omnichannel journey.

Not Just Another Solution for the Martech Stack

An evaluation should begin with an understanding that a CDP is not just another martech solution, but is an enterprise tool. As such, a major consideration is whether a CDP offers pre-built connectivity to all enterprise sources of customer data, or whether its performance relies on limited martech and channel connections.>

An open garden architecture that supports connection to all enterprise systems is different than “open garden light” which limits connections to the martech stack. While the latter may integrate limited e-commerce and CRM data, or offer the “escape hatch” of file imports, it still requires the IT department to do the heavy lifting to bring all relevant back-office data into the CDP. A real open-garden CDP should provide high-quality, high-performance native connections to data sources above and beyond the martech world – first-party, second-party, and third-party data, batch and streaming, big data, databases, message queues – to ensure that keeping pace with an omnichannel customer journey does not require an IT project to make a new connection happen.

And for Good Measure …

There are two additional questions to ask when evaluating a CDP that are related more to underlying performance rather than off-the-shelf capabilities. First, it is important to confirm the CDP you are evaluating meets an IT department’s security, compliance, privacy and governance requirements. In a modern, customer-centric world where data is king for creating a personalized customer experience, the data that goes into a CDP is high-value, high-risk data centered on the relationship between a brand and the customer. It is vital that this data is handled appropriately (in transit and at rest) as a high-value asset.

Second, a CDP must offer performance that matches the cadence of the customer. Like the dynamic application of rules at the moment of each interaction with a customer, this requirement underpins the requirement for real-time throughout the data lifecycle, real-time at the data level, at the analytics level, and at the orchestration level. In fact, in describing the five qualities needed to be a RealCDP, the CDP Institute takes pain to say that there are topics not covered under the RealCDP umbrella – and real-time data processing is first and foremost on the list.

If your CDP checklist includes all five core capabilities of a RealCDP, kicking the tires of real-time ensures you’ll walk away with a solution that’s ready for the high-speed twists and turns of a dynamic, omnichannel customer journey.

November 11, 2019

In a Heartbeat: The True Value of a CDP’s Real-Time Capabilities

By John Nash, RedPoint Global

According to The Relevancy Group CDP Buyer’s Guide 2019, real-time targeting is a key CDP use case, used by 54 percent of marketers with a CDP to execute highly relevant campaigns, with messaging that reaches an intended target at the appropriate time to drive intended behaviors. According to the Guide, “Speed of data is critical to (these) marketers and the data agility that a CDP affords is a key toward reaching the right target, at the right time, with the right message.”

For retailers and brand marketers, “speed of data” translates to successful personalization through practical use cases such as delivering a perfectly timed offer the instant a customer appears in a channel, or delivering tailored content to a first-time visitor to a website tuned to their preferences and behaviors.

The benefits of real-time extend far beyond the retail sector, however, and nowhere is the benefit more important than care management, where life or death decisions may depend on offering real-time support to a patient. Using real-time patient data to manage the health of an at-risk population arrives at the very essence of CDP’s purpose.

The real-time processing engine that supports real-time engagement is ultimately what drives a CDP’s value, which makes it more than just a pillar of a successful martech stack. For retailers and brand marketers, that value is measured in driving revenue. For healthcare payers and providers, value is instead measured in better health outcomes, improved patient engagement and satisfaction, and lower total costs.

Value-Based Care and Telemedicine

Value-based care is a growing performance-based compensation model that rewards physicians, hospitals, and other healthcare providers on the quality of care they deliver rather than services rendered. Under the value-based care model, providers have a financial incentive for the long-term health of a patient.

As the leading cause of hospitalization for people older than 65, congestive heart failure provides a model value-based care use case. A treatment plan typically consists of regular monitoring, blood tests, medication, and lifestyle management. This makes telemedicine (defined as the distribution of health services and information electronically and with telecommunication technologies) ideal to help minimize hospital re-admittance rates and prevent disease progression – the two main treatment goals.

These were key metrics for an innovative telemedicine research program piloted by a hospital, with RedPoint Global as one of its technology partners. The hospital was managing care for 300 congestive heart failure patients, and wished to gather real-time data from connected devices to monitor their health and recommend care.

Real-time data such as vital signs could then determine a patient’s care pathway, unique for each patient based on real-time conditions. A pathway could consist of a call from a nurse, a motivational message, a visit from an ambulance, or scheduling a new treatment plan – all dependent on real-time signals from a connected device.

Optimizing Care Pathways with a CDP

Congestive heart failure patients typically have a team of providers invested in their long-term care, including primary care physicians, cardiologists, nurses, dieticians, pharmacists, exercise specialists, and social workers. For the research program to work, it had to centralize patient data into a single platform – a central requirement for real-time care management.

The RedPoint Customer Data Platform integrates data from any source or type into a central platform, providing care managers with a unified customer profile and a single point of control over data, decisions, and interactions to expertly manage a patient’s care pathway in real time based on up-to-date data.

Organizations benefit from a CDP’s real-time capabilities because it consistently provides a next-best action or recommendation in any channel in the context and cadence of a unique customer journey. In care management, a next-best action is similarly tied to a customer journey – in this case a patient journey or care pathway – based on everything that is knowable about a patient.

The pilot study provided invaluable patient data, but a real-time next-best action is not based solely on a real-time connected device, but rather a patient’s entire medical history. A unified customer profile, also known as a golden record, is persistently updated to include real-time data from every source. Combined with in-line analytics and automated machine learning, the golden record provides caregivers with a next-best action that is relevant to the patient’s journey at a precise moment in time. The analytics are key to scaling this to personalized engagement for thousands or millions of consumers.

A connected device could, for example, monitor and transmit a patient’s heartrate, respiratory rates, and physical activity. The device data would then be integrated in real time with a patient’s entire medical history and records – interactions with dieticians, exercise specialists, and other providers, test results – as well as second-party and third-party data, and structured, unstructured, and semi-structured data to become part of the golden record. An automated machine learning model programmed to determine risk factors, a re-admittance score, or another metric will intelligently orchestrate a next-best action optimized for the patient’s care pathway.

An Ally in the Healthcare Space

In addition to the heart research program, RedPoint is partnering with other healthcare providers to help advance value-based care strategies in an ongoing basis. Last year, RedPoint partnered with Lucerna Health to advance the transformation to value-based care through personalized engagement.

Patient behavior is the number one determinant of healthcare outcomes for several conditions, and the partnership was formed to help shape behavior through those personalized engagements. While the CDP was conceived as a marketing solution, and a real-time engine make it perfectly suited to personalizing a custom experience in the context of a unique customer journey, there is no more noble purpose than saving lives. With one heartbeat nearly every second, the impact of a CDP on real-time data and engagement clearly shows the linkage to tremendous value opportunities.

November 6, 2019

How to Get Your Data Ready for Your Next CDP

By Jenna Devinney, Webbula

There is no doubt that a big pain that marketers face is having to string together data from multiple sources to access prospect, customer, and product information. In fact, according to Salesforce, “the number of data sources used by enterprise marketing departments is growing each year, and it’s growing fast. Research shows a 25% annual growth rate for significant data sources, but others are much smaller. If you look at this 25% growth rate and think ahead 10 years, you’re going to go from 15 to 40 data sources.” Marketers must have a way to unify this data in one place, and this is where a CDP can help.

What is a CDP?

A Customer Data Platform (CDP) is a software that centralizes your customer data from multiple sources into one place. Like the statement above, if you’re struggling with a variety of different data sources, a CDP is where you’ll want to gather your data at one source, but before you do this, there is one important step you should take: Clean your data.

Clean Your Data with Email Hygiene

Before you gather your data with a CDP, you’ll want to ensure that you maintain updated and accurate data. Whether you’re new to your role and inheriting a bunch of data sources that need to be organized, or you’re ready to discover new segments, build models, and deliver real-time personalization, you’ll want to make sure your data is in tip-top shape. To ensure your data is accurate, you’ll need email hygiene.

What is Email Hygiene?

Email hygiene is the process of identifying “problem” email accounts from email lists. Email hygiene can go above simple verification and validation by identifying harmful email addresses hiding in your data. Why does that matter exactly? An email list can be a ticking time-bomb, ready to explode at any minute. There are hidden threats within your email lists that can damage your ability to use your data properly, impact your sender reputation, and directly affect revenues. (Read What’s hiding in Your Email) Email hygiene can defuse the threats in your data and ensure that your next CDP will be happy.

Once your data is clean from harmful threats, you now want to gain more valuable customer insights from your previous lists with a Data Append Solution.

What is Data Appends?

Every month, about 3% of customer data becomes obsolete due to changing conditions as customers move, get married, or change names. Whatever the reasoning, this fact is why marketers are always in need of updated and accurate customer contact information. They can either obtain this information by searching for sufficient data or purchase new data. These options can lead to time lost, money wasted, and no guarantee that the information you have purchased is real. Data Appends is the best solution for marketers who need to fill in the missing or outdated information in their data lists.

Conclusion

Webbula can provide email hygiene AND Data Appends before you upload your data to a CDP. With our solutions, you will be able to clean your data with our industry-leading email hygiene and enhance your customer lists with missing information like emails, addresses, phone numbers, and demographic segments.

Now that your data is ready to go, you’ll have multiple ways to best use your CDP, not only for data unification, but for identifying resolutions, real-time engagement across all channels, people-based marketing, and most importantly to make better data-driven business decisions. Just think about the opportunity that you have now that your data is cleaned. You now can better segment the data for marketing and analysis purposes, and also personalize your messaging more efficiently now that your data has been enriched with expanded information.

Before you migrate your data over to a CDP, be sure to use Webbula’s email hygiene and Data Appends services to get your data in tip-top shape.

November 1, 2019

Want to Offer Exceptional Customer Experiences (CX)? Ask Your Data

By Suresh Menon, Informatica

One of the things I enjoy most about my role at Informatica is meeting with people in all types of organizations and from all sorts of industries around the world. And while their business models and geographic locations may be wildly different, one thing is universally understood: to keep current customers and gain new ones, you must be able to provide an exceptional customer experience.

More than ever, customer experience plays a critical role in both initial and repeat purchase decisions, as well as customer advocacy and public perception of your brand or your company’s products or services. Can you think of the last time you bought something or went somewhere new without consulting Yelp or asking a trusted friend or family member? Most of us can’t.

Which means that, as far as your current customers or your future customers are concerned, it all comes down to how well you remove the friction from your processes and interactions—and that comes down to not only how well you understand them, but also how well-informed your teams are. So you need to not only understand how and why your customers choose your product or service, but also if your customer service, sales, finance or operations areas—and even chatbots—have the information they need to deliver an exceptional experience when engaging with customers.

But what makes an exceptional customer experience? Believe it or not, your customers are telling you exactly what they want—whether it’s the type of pillow they prefer whenever they stay with your hotel, the mortgages they’ve researched over the past couple of months, or the best way to reach them if you need to alert them to a last-minute change at the doctor’s office.

It’s all in your data.

How Well Do You Know Your Customer?

You most likely feel that you already have quite a bit of information about your customer. And with every click or call, with each order or return, you can gain a little more knowledge and an opportunity to build a trusted relationship with them. Did they discuss a service issue with a chatbot? Register a complaint by email? Were they talking about your company on social media? Each interaction is a chance to discover just that much more about your customers. The trick is, of course, looking across these channels and stitching it together in a way that allows you to deliver intelligent and engaging customer experiences in real time.

For many companies, putting all of that data together—from the many different systems, channels and functions that exist throughout the organization, and in various stages of completeness and correctness—can be quite a daunting effort. And, to complicate things further, your customers are constantly changing how they interact with you. Every hour, more than 200 businesses will change addresses and 400+ business telephone numbers will change or be disconnected. In the next sixty minutes, nearly 6000 people will change jobs, and half as many will change their address. Before the hour is up, more than 500 people will get married and more than 250 will get divorced. It’s been calculated that nearly one third of your data degrades every year. This is a problem, because as data becomes less reliable, the people who use it lose trust in it, or will spend more time than they should to correct it. So, step one is creating a way to maintain your data and ensuring it’s up to date and remains trusted. Once that’s done, you have a foundation for a powerful 360-degree view.

A Spectacularly Powerful 360º View

Creating a data-driven customer experience means you are able to use relevant and trusted data to create an intelligent 360-degree view of your customer that resonates across your organization and is delivered to the teams that need it, where they need it, in their systems, channels, and functions. To create an even more powerful view of your customer, you can apply a hybrid of matching algorithms that mimic an expert human user to add real-time information to existing customer profiles—such as social, email, click stream, chat, analytics, or survey data.

This additional transaction, interaction and behavioral data offer up an even richer, more complete understanding of your customer, and give you better insights to create the next best experience—whether it’s being aware that your customer has a new baby and wants to make sure their insurance is up to date, knowing not to send an offer for a tourist attraction to someone traveling on business, or understanding that your customer doesn’t want to talk about refinancing a house that they plan to sell in three months.

Applying Intelligence for Relationship Discovery and Deeper Engagement

The good news is that these insights are more accessible than ever. Customer intelligence systems (also called customer intelligence platforms) are purpose-built to connect your data so you can connect with your customers. Because they contextually match and synthesize your interaction, transaction, and behavioral data with your customer profile, customer intelligence systems can transform each experience, and use AI-driven customer insights to help you deliver that next best customer experience—not just once, but at scale across the organization. And, with an added layer of data governance, synthesis, and identity resolution, you finally gain the context from your raw data so you can take action on what’s important to your customers and isolate the trends and preferences that enable relationship discovery and deeper engagement.

If you’d like to know more about how these digital strategies are delivering differentiated and consistent customer experiences, join us for our second annual Customer Experience VIP Summit.

October 21, 2019

Kids, Data and Privacy: If Your Company’s Not Worried, It Probably Should Be

By Susan Raab, CDP Institute

Got data? Ask any company and you’ll get an affirmative. But most don’t realize they probably have kids’ data too, and that should be a concern. Knowing what data’s being collected, stored and utilized is critical and it’s not just about having children as customers. “Any company that has family profiles on employee health data, sponsors a “Take Your Child to Work Day,” or has an archive of photos that includes children, has kids’ data,” says privacy expert Kristina Podnar.

Today’s kids have an online identity that begins in infancy via parents, friends and family. By the time they are teens, 95 percent have a smart phone and spend close to half their time online. This raises important questions: What privacy rights should children have? Who will enforce those rights? What should a data protection framework look like? And, what do companies need to know to comply with current laws and mitigate future risk?

This has implications company-wide, stretching from the privacy policy to human resource data to the customer tracking and behavioral information that’s collected for marketing, advertising and sales. Podnar, an international privacy consultant who works with companies from global 1000 brands to small and mid-sized companies, says, “most companies think if they don’t produce a product or service that is for children this doesn’t apply to them,” yet she says it’s been applicable to 100% of her own clients. Podnar finds “organizations don’t realize what data they have and don’t segregate the data, so they can’t differentiate the kids’ data in the pool.”

Jarrod Craik, Vice President of Technology at BlueVenn, concurs saying, “This is a tricky area and a potential minefield for companies. Many don’t quite appreciate that holding children’s data is risky and may become a problem if the data gets into the wrong hands or is used for purposes that were not intended.” There are key questions of whether to keep it, for how long, and whether the benefit is worth the risks involved.

It is also complicated because laws protecting children’s privacy differ. Countries have different age designations regarding who is a child. The Children’s Online Privacy Protection Act (COPPA) in the U.S. “requires companies to obtain explicit, verifiable permission from parents before collecting, using or disclosing personal information from children under 13 or targeting them with ads,” according to the New York Times.

GDPR on the other hand, as explained by The Privacy Hub, “doesn’t set a single universally-applicable age. Instead, it gives the Member States the power to choose their preferred nation-wide age of data consent between 13 and 16. Most EU countries (including the UK) set this age at 13. But in Spain, the age is 14 and in The Netherlands, it’s 16.” In other places, South Korea requires parental consent when processing children’s data, Brazil has a law going into effect in 2020, and Australia and India are looking at adding children’s privacy protection regulation.

Customer Data Platforms can help significantly by ensuring that company data is unified, consistent and up-to-date. CDPs make it easier to identify what kind of data the organization is holding and whether data is being accessed. In consulting with their clients, Craik says that at BlueVenn, “we’ve always taken the view that holding children’s data is risky, and companies should only hold what has been agreed to and use it just for the stated purpose. It’s good to have a time limit after which you either get rid of the data if you’re not using it, or renew the agreement for a new installment of time. I generally advise taking as little information about children as possible, for example a birthdate to know age, but not gender or other information you don’t need.”

Podnar recommends companies get ahead of the problem now, saying, “if you define guardrails ahead, you won’t have to wonder what data can be used or whether you’ll get in legal trouble”. She says future forward companies are doing this and that “smart marketers are proactively sharing what they’re doing with their customers” as a way to show transparency in their process and instill trust. She believes this will be an increasingly important issue and one that is “ideal for companies to look at while they’re retooling, and before it becomes more heavily regulated,” which Podnar feels certain will come over the next 5 or more years.

October 18, 2019

Filling in the Missing Pieces: Data Appends

By Steve Wagner, Webbula

Every month about 3% of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, pass away, or leave the grid in search of a simple life in the woods. Whatever the reason, this fact is why marketers are always in need of updated and accurate customer contact information. They can either obtain this information by searching for sufficient data or purchasing new data. These options can lead to time lost, money wasted and absolutely no guarantee that the information you have purchased is real. Data Appends is the best solution for marketers who need to fill in the missing or outdated information in their data lists.

But What is Reverse Data Appending?

While Reverse Data Appending is not an “official” industry term, it typically refers to customer data that only includes email addresses. Reverse Data Appending can add matching names and postal mailing addresses to your existing data, among other data sets. This not only gives you more options for contacting your customers, but also allows for even further segmentation. Now you’ll have geographic information and can market to them via direct mail. According to the experts, “businesses can witness an average increase of 30% in their ROI while using email appending in their marketing campaign”.

Updating your Email Data

It is important to update your data lists frequently; sending to a list that is even 4 to 6 months old opens you up to considerable risks, including spam traps, disposable domains, and poor conversions. If you fail to keep up with your data lists, you will lose the existing customers and will also tarnish your opportunity to attract new customers. Data Appends and Reverse Data Appends give you more information about your customers and contacts, allowing you more flexibility when it comes to marketing and communication.

Best Practices

Appending your data will help freshen up your lists with updated and accurate information like name, phone number, email address, and mailing address, as well as information such as interests, hobbies, financials, mortgage, political, and other specific data. Data Appends and Reverse Data Appends can help you communicate with your audience more smoothly. It also can…

• Add missing information ranging from names, phone numbers, and email addresses to interests and hobbies.
• Give you a better understanding of your customers and prospects with more complete information at your fingertips.
• Boost your response rate by eliminating emails to invalid or outdated addresses.
• Keep your data clean, which can help increase deliverability and avoid landing in spam folders.
• Give you options for segmented and personalized marketing and communications based on enhanced data.

Why Webbula insightData Appends?

When it comes to appending your existing data, there is simply no other option that compares to Webbula insightData. Our data is constantly updated and certified with our industry-leading cloudHygiene filters that ensure accuracy and mitigate potential fraud, creating a truth-set that is unparalleled. Because of the strength of our dataVault, we are able to cross-reference against a greater variety of fields that often go back in time over a decade. At Webbula our passion is truth in data and we strive for the utmost quality, always choosing it above quantity.

How does insightData Appending work?

Conclusion

Grow and enhance your customer list through Webbula’s insightData append solution so you can understand your customers, their interests, and cross-channel behaviors while adding missing information like emails, addresses, phone numbers, and demographic segments. Deliver relevant ads at the right time to the most relevant audiences with industry-leading, premium data quality.

Visit Webbula’s insightData page to learn more. You can request a free trial to see how our data can help your business grow. Webbula insightData is just one of the reasons Webbula is THE Data Quality Expert.

Sources

https://www.emailappend.services/best-6-email-appending-tricks-for-2017/

https://www.emaildatagroup.net/reverse-appending.asp

https://www.emailappend.services/best-6-email-appending-tricks-for-2017/

https://www.emailappend.services/reverse-appending/

October 16, 2019

Next Best Action Needs Next Best Data

By David Corrigan, Informatica

The investment in analytics has accelerated. In fact, for most organizations the investment in analytics has outpaced the investment in data management. Organizations have been in an ‘arms race’ to fuel their digital transformation with Next Best Action, Next Best Experience, and Next Best Offer analytic engines. Most have received mixed results. Insights have been described by some users as “slightly better than common sense” as well as “misleading on occasion”. While helpful, most organizations have concluded that those solutions need to mature and that something is missing.

But so what?

Here’s an important question that you should answer – what data drives those analytic solutions? One banking organization invested in a modern Next Best Offer analytic solution that was, analytically speaking, very sophisticated. And they fed it data from their data warehouse, which was fed from account systems with a minimum of data quality and matching applied. The results were mixed. While some offers were relevant, many others were off target and based on historical information. A wealth management company rolled out next best action to their advisors, identifying actions such as sending a “guide to savings for millennials” to their customers who fit into that segment. Again, the results were mixed, and the organization concluded that they had taken a step forward, but it would ultimately be a long journey towards truly personalized Customer Experience. There is clearly more work to be done. Significant improvements will be gained by simply feeding better data to the existing analytic solutions.

Here’s a second important question to ask – if you believe that data is the problem, do you think the answer is a consolidated customer data foundation that is integrated with those analytic solutions? It isn’t. Complete customer data is a necessary foundation, but it contains an overwhelming amount of data that would flood the analytics tools. It will often contain contradictory and time-sensitive data points that may lead the analytic solutions astray. What is required is a Customer Insight System that leverages Artificial Intelligence to serve up Next Best Data (NBD) for each unique customer. From the 360 (a complete identity and relationship graph of customer and related data), Next Best Data utilizes analytics and Artificial Intelligence to determine the context of the usage; it then presents only relevant data for the moment and for the specific interaction that is occurring. Rather than analyzing the products a customer owns to determine that a rewards credit card should be offered, NBD takes in all Customer 360 data and understands relationship patterns, campaign responses from household members, allowing the Next Best Offer engine to correctly determine that a credit card offer will fail, but it is the right time to offer a brokerage account. Contextual data is the rocket fuel for Next Best Action, Next Best Offer, and your data-driven digital transformation.

Many organizations that have invested significantly in analytics are now investing in Next Best Data and utilizing new technology to bring all data together and determine what is relevant, or contextual for each interaction, each offer, each action. These new technologies drive deeper customer analytics, omni-channel personalization at the individual level, and marketing transformations. Market analysts such as 451 Research are referring to this new class of software product as customer intelligence systems or customer intelligence platforms.

To learn more about how intelligent, trusted data fuels engaging and relevant customer experiences, join us in New York on December 3, 2019 for our second annual Customer Experience VIP Summit.

October 11, 2019

Data Appends: Forward or in Reverse

By Steve Wagner, Webbula

If you happen to run a local retail store, or are the marketing director for a national fortune 500 retail giant, communicating with your customers is priority number one. And collecting your customer’s contact information is the first step to doing that effectively. But collecting customer data is easier said than done. No matter what processes you use, the results can be rife with input errors, misinformation, or worse. You may end up with a valid email address, but not a valid name or phone number. The bad data ends up collecting dust and never getting used, leaving a portion of your customer base in limbo.

What if there was a solution that could help put that bad data back to work for you? Instead of tossing aside unknown data after you go through the email verification and hygiene process, retailers may also want to start considering having another look by appending incomplete “dusty” data. But what does that mean exactly? Is there really a way to take a small piece of customer information and add more to it? Yes, there is; keep reading to learn more.

What is Data Appending?

Data Appending is simply filling in the missing pieces of your data, completing the puzzle you’ve already started. As long as you have some sort of information such as an email, phone number, or name, we can often get you the rest, revealing the complete customer picture and essentially saving data that might otherwise have been lost or set aside.

What is Forward Append?

A forward append means that you already have a name or address on file of an individual and you are seeking more information such as a phone number, email, demographics, property data, or vehicle information.

Forward appending is considered the most common type of append because companies usually have a name, but need to fill in the missing information.

What is Reverse Append?

Reverse appends is when you obtain one field of data and use it to return all other data that can be tied to it. Let’s say, for example, a business wants to send out a marketing campaign but wants to personalize it. They have a list of valid emails, but no full names. What can they do? They can reverse append their data using the email addresses, and receive the names, and any other information they may need to make the email campaign more personalized such as demographics or interests.

Reverse appending is open for almost any dataset, from names, to email addresses or phone numbers. Reverse appending match rates are not as good as forward appending. Forward appending is known to be more accurate and the process takes less time than the reverse appends.

What is Fractional Appending?

Fractional Appending is a mix between both forward and reverse appends. For example, if you have a name and an email, you can get a postal address, which can be extremely useful for reaching out to your customers with a direct mail campaign.

Webbula for Data Appending

If you’re a marketer working in retail, you’ve most likely found yourself scratching your head after an email data verification and hygiene test wondering why so much data turned out to be incomplete, wondering why you wasted so much marketing effort collecting customer data only to be left holding an incomplete picture. The good news is, the next time this happens, don’t panic. Webbula insightData can append your troublesome data and enhance your list, making you better prepared for your next email campaign.

So clean your email lists with Webbula cloudHygiene, and append the rest of your data with Webbula insightData. InsightData will enhance and append your customer list with missing information like emails, addresses, phone numbers, and demographic segments. With insightData you will truly understand who your customers are, their interests, and their cross-channel behaviors to better enable sending targeted and effective email campaigns.

Don’t be afraid, append your data with Webbula insightData. To learn more about insightData visit Webbula.com

September 18, 2019

TFM 2019: How Bacardi uses data to top up the bottom line

By Brenda Fiala, Techerati

The Technology for Marketing conference is among the world’s largest. Speakers will include CDP Institute Founder David Raab. You can click here to register.

Also speaking with be Brenda Fiala, Bacardi’s Global VP of Strategy, Insights and Analytics. Techerati interviewed her ahead of her TFM & ECE Keynote panel appearance.

When Bacardi was founded 157 years ago, rum wasn’t considered a refined drink. To transform the sugary-spirit into something sellable in upmarket Cuban taverns, Bacardi founder Facundo Bacardí Masso had to “tame” the drink by filtering it through charcoal and ageing it in white oaked barrels.

Over one and a half centuries later, Bacardi’s products are merrily swigged by millions across the world. Alongside its namesake rum, Bacardí, the company’s core brands now include a mouth-watering selection of the world’s most well-known tipples, including Grey Goose, Patron, and Bombay Sapphire — brands which have all flourished thanks to data and its impact on decision-making.

Actionable Insights

Today, we are undeniably in the dual age of big and small data. In fact, it’s hard for multinationals like Bacardi to know what to do with all data at their disposal. To transform its growing supply of data into actionable insight, Bacardi had to return to its origins. It had to tame data and refine it to enable better decision making across the company.

Data is fundamentally ore that needs to be refined before it can be made into a product, which in Bacardi’s case includes informed omnichannel marketing, business and brand strategies. At TFM 2019, Brenda Fiala, global VP of strategy, insights and analytics at Bacardi, will detail how her 33-strong insights and analytics team turns data into a competitive advantage.

As Fiala explained, turning a company into a data-driven engine not only requires leveraging technical tools but enacting structural and cultural changes so that data becomes the beating heart of almost every core process.

When Fiala arrived at Bacardi Ltd, one of the first changes she implemented was increasing the volume of meaningful data points so they came in weekly, rather than quarterly. The goal was to gain 100 percent visibility across advertising and promotional activity spend so brands and markets could see the effectiveness of their marketing. Her team’s guiding principles are simple but effective:

“It’s not just about looking at financial data. But ensuring one, we keep the consumer at the heart; two, we keep our brand health strong. And if we can do those two things, then the finances should follow,” Fiala said.

“Now that we have our data house in growing order, we’re able to move to predictive analytics. For the past two years, we’ve been making recommendations and validating our advertising spend.”

Fiala has extended her team’s influence across the organisation and united commercial, company and consumer data.

“Usually, there are silos in companies that look after those different types of data. And we’re breaking those silos to connect the data together, which allow us to run the business in a more agile manner,” Fiala said.

Often when data is connected with other data that was previously siloed, counterintuitive results glare back at you. Fiala says part of her company-wide data education involves making clear the heuristics that can be used to figure out if results are actually telling you something you need to know, or if a gremlin in the assumptions line is throwing up false flags.

“Number one, ‘Is the data wrong?’ So you take them through the data model. Number two, ‘Is the analytics wrong? Is the math wrong?’ So you discuss what the math can and cannot do. Number three, “What other factors are not in the model of the data that could be influenced?’ And then number four is ‘Is something wrong with our strategy? Or is this something that just needs to be optimised?” Fiala said.

“The data never tells you what to do. The analysis helps inform our conversations as business leaders, to help us make business decisions — and ideally faster.”

By using data to establish the properties of the numerous channels on which Bacardi Ltd can promote their brands, Fiala’s team have helped the company move from a “peanut butter” (or evenly spread) strategy to one that customises and optimises spending on every channel for maximum impact. By identifying channel-specific thresholds and their correlations to brand strength and potential sales, Bacardi brands can target each channel with confidence.

“Whether it’s the work that the team did on Sound of Rum last year with Major Lazer, to our work with Swizz Beatz and Anitta, we now have insight through our data to maximise the integrated channels by which they’re working.”

Another variable Bacardi Ltd has identified as making a significant difference to the bottom line is the degree to which people are talking about their products, which the team measures in conjunction with its PR companies, social listening techniques and other consumer research.

From these insights, Bacardi is able to identify ways of speaking to consumers directly, the knock-on effect of which being more consumers talk about their brands. Observing how consumers were increasingly making reference to Grey Goose-based cocktails, Bacardi created a cocktail recipe section on GreyGoose.com that also informed visitors where the cocktails could be purchased in several of its major markets.

Secrets to Success

No company would readily give away their secrets to data success, and Fiala is no exception. But she did reveal one quality that she believes helps to make data doyens out of those who possess it: creativity — something she first observed at Caltech during her chemistry degree.

“I could see the scientists who really interrogated the data from different angles and who connected data sets. The most important ingredient of all was creativity, even in analytics. If you’re going to make a difference and get people to notice you, get people to adopt your behaviour and buy your product in the moments that matter, then you need a great dash of creativity.”

Fiala says Bacardi’s global brand platform maximises business objectives by using creativity to understand consumers and their emotions better each year, with data as the platform. The key to success is then optimising PR and media teams so they can address consumers directly.

“The key to creativity is being able to see consumers as they are, through their data and behaviour. And to understand consumers as they want to be understood, through their emotional drivers and their aspirations. Putting those together into actionable insight is what you’re looking for.”

Fiala finishes by addressing the one law of big data – that big data is getting bigger. It’s not so much the volume of data that’s a challenge for Bacardi, she says, but the speed in which they can analyse it and align it in real-time.

One way Bacardi has sped-up this process, an idea born from the company’s cross-company “next-generation programme,” is unlocking understanding through data visualisation. People working at the company are now trained so that they have an intuitive understanding of what kind of information is understandable when it’s displayed. Fiala herself suggested they take inspiration from Edwin Tufty, the forefather of data visualisation.

“When I pointed them to him, they looked at me with blank eyes. But Edwin Tufty has been doing this for years with his wonderful books on the quantitative display of information. So I think there’s something that holds a lot of the keys to making this usable for people to make decisions.”

Join Brenda At TFM 2019, 25 - 26 September 2019, Olympia

The Science Behind The Art of Branded Content (Panel)
26 Sep 2019, 15:00 – 15:45, Keynote Theatre

September 13, 2019

AI-powered, personalised omnichannel experience is the future of customer experience

By Jasmine Kees, Incite Group

Customer service executives are alive to tech opportunities rating AI, personalisation and omnichannel capabilities as key to transformation in the future.

Across Incite Group’s new 2020 State of Customer Service Report, which is free to download now, there is a clear theme of customer service executives leaning into the possibilities of new technology. Principal among these are Artificial Intelligence (AI) and Machine Learning (ML) technologies that can help to automate simple tasks and drive a more personalised experience.

More than 900 respondents to the report’s survey gave their opinions about emerging tech. They said that they believe personalisation technologies will have the most potential in 2020 to drive improvements in customer service, with 46.8% choosing it as a key technology. This was followed by AI, which 44.3% feel is a major technological opportunity.

Direct communication tools were considered to offer major opportunities as well, with 43.1% seeing real-time messaging tools as offering big possibilities and 40.3% also seeing potential in chatbots and virtual assistants.

Furthermore, when asked what they thought the most talked-about topic would be in 2020, customer service execs chose the automation of simple tasks by AI, followed by a data driven customer experience and the transition of new digital channels and messaging services to the core of customer service.

While all these technologies offer opportunities in themselves, their true power will lie in combining them for maximum effectiveness. Chatbots, voice applications and virtual assistants will all be exponentially more powerful with personalisation driven by underlying analytics and AI. That same underlying structure can also help empower agents to be more effective at reaching solutions more quickly and accurately no matter what medium they are using through dashboards that can locate customer history or diagnose ongoing issues immediately and present them to the agent.

The ultimate goal of this is to drive a personalised, seamless omnichannel experience for the consumer. In the longer term, respondents to the report’s survey rate the development of a seamless omnichannel experience as the most transformative application of innovation in the next decade. Some 34% chose this as their trend to watch, followed by tools that can empower customer self-service (29.7%), improvements to automated responses (20.1%) and faster response times (16.2%).

This means that customer service departments will need to make major investments into data gathering and standardisation and the development and deployment of AI.

For more insight, click here to download the full report, which features key trends across more than 30 detailed charts and graphs taken from one of the biggest industry surveys out there across more than 40 pages of analysis. This report features hundreds of insights into how customer service is changing and innovating, including takes on artificial intelligence, automation, data management, measurement, personalisation, omnichannel and optimising organisation structures. Download the report now to get the data as well as views from top customer-service minds representing huge brands, including:

· Mastercard

· Microsoft

· Zurich

· Uber

· Plusnet

September 9, 2019

Clear Signs of B2B CDP Market Maturing, with Weaker Players Radius and Mintigo Exiting

By Doug Bewsher, Leadspace

There has been a lot of evidence in the last few months that the B2B Customer Data Platform (CDP) category is maturing. There was the recent Forrester report, the dramatic increase in RFPs and customer adoption of CDPs, and even Salesforce’s recent announcements about the pivotal nature that CDPs will play in their future business strategy.

We have also seen both Radius and Mintigo go away, subsumed into other relatively unrelated businesses. This reflects a trend where, as a category matures less successful players exit — in this case, companies either with limited functionality like targeting SMBs, or a narrow focus on a specific feature such as automated predictive modeling — while more complete platform strategies dominate. We saw similar patterns in categories like Marketing Automation and DMPs over the last decade. This is supported by Gartner’s Hype Cycle mode, where there is a focus on performance and results as a category approaches the “Slope of Enlightenment,” and providers survive by focusing on customer satisfaction.

Salesforce has also played a part in this maturation. They chose to sunset Data.com — which was a single-threaded data provider with D&B & Jigsaw data — to adopt a vendor agnostic, more comprehensive CDP strategy as their go forward plans. However, their focus is on B2C business, so there remains a significant gap in B2B for their customers.

Having been recognized as a leader in this space, we at Leadspace are encouraged by the evidence that the B2B Customer Data Platform (CDP) category is maturing. We’ve long believed that predictive AI and analytics is more a feature of a well-constructed B2B CDP. We’ve preached the value of a robust data foundation, powerful AI, and real-time, cross-channel activations combining in a holistic customer data solution that powers real B2B go-to-market success. And we’re seeing incredible customer success with SAP, HPE, Extreme Networks, and many more of the top (award-winning) B2B marketing and sales organizations.

It seems clear that CDP is becoming a critical component in the B2B tech stack now and into the future. Of course, with any critical and transformational technology, there is complexity and change to deal with. That’s why we’re here, and are committed to helping you take back control of your data, using the power of AI to fuel better engagement and relationships with your customers.

August 16, 2019

Groupon and CarMax Discuss Mobile and Digital Product Strategy at the Open Mobile & Digital Experience Summit

By Hayley Dunn, Incite Group

In 2019, an exemplary customer experience strategy will be the difference between your brand and your closest competitor. In fact, a recent Gartner study revealed 81% of executives believe they’ll compete on CX alone, but only 22% have claimed to have developed the right product, digital and marketing strategy that exceeds customer expectations.

Ahead of this year’s Open Mobile & Digital Experience Summit, we spoke with leading Digital, Product and Marketing leaders to uncover their goals, as well as how they are putting customer expectations at the center of their business.

Digital, Product and Marketing must all work together to deliver personalized digital experiences, timely and relevant marketing and on-the-go mobile access. Hear what these pioneers had to say…

For 2019 Incite Group has made a bold statement: ‘Customer First. Digital Second.’ What are your thoughts on product’s role to leverage the voice of the customer to build impactful product experiences?

Anne Yauger, AVP, Digital Products, CarMax: “One of CarMax’s core values is ‘We Put People First,’ and we believe the only way to create a differentiated and impactful customer experience is to see this experience from the perspective of the customer. The voice of the customer is central to everything we create.

While technology is a prominent enabler of customer experience in today’s digital world, innovative solutions don’t always involve technology. In many cases, we need to look beyond digital for the best solution to meet customer needs. Customers who are shopping for a large, complicated or emotionally-driven purchase (a car is all these things!) are often looking for a real human connection in addition to an enabling digital experience.

Ultimately, I believe that Product’s role is to discover, validate and build the iconic experiences that truly solve customer problems -- whether they are digital, physical or a combination of the two.”

CarMax will delve deeper into their product and CX strategy at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8^th) uniting digital, product and marketing leaders to discuss the latest tools and strategies you can implement across digital experience, product, mobile, growth marketing and more. Find out more here: https://events.incite-group.com/oms/

For 2019, we have identified the importance of building impactful experiences that add value to consumers’ lives beyond a transaction. What are your thoughts on this?

Bryan Ennis, VP of Product, CarMax: “The car buying transaction is an inherently complex and emotional experience – this is the second largest purchase most of us ever make, after a home. Knowing this about our customers’ journey, we focus on enabling our customers digitally alongside building a relationship with the customer that transcends the transaction.

For example, the center point of CarMax’s new omnichannel experience (which allows the customer to do as much or as little of the car buying process from home as they’d like) is our customer check out process we call the customer hub. The customer hub allows customers to take control of their shopping experience and progress digitally. Customers can see all the steps they’ve taken, whether that’s saving their favorite vehicles, getting prequalified for financing, or receiving an offer on their existing vehicle. Most importantly, the hub allows customers to easily seek out help from a CarMax consultant when and how the customer prefers (through text, e-mail or a phone call). CarMax consultants also have visibility to the hub, which enables us to know exactly where the customer is on their shopping journey and provide customized support. As we continue to build and evolve this product, our goal is to enable customers digitally, while continuing to build the relationship that’s critical to the experience transcending the transaction.”

Learn more from CarMax and 400+ other big brands at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8^th) which will shape your digital, product and marketing strategy for 2020. Find out more here: https://events.incite-group.com/oms/

As consumers engage across multiple channels, mobile remains a critical customer touchpoint. How is Groupon differing their mobile experience for 2019?

Sarah Butterfass, Chief Product Officer, Groupon: “We’re aggressively transforming our mobile experience so that it’s evolving with the customer. While we strive to make our app and mobile web experience great, we’re taking an extra step. Instead of focusing solely on how we can create a seamless experience on Groupon, we’re thinking about how our seamless experiences can help make someone else’s experience better and more valuable. In other words, how do we enable more great experiences, regardless of where they start? Meeting the customer where they’re at is an important part of how we’re continuing to be a mobile-first company with over 200 million downloads of our mobile apps.”

Groupon will join Mastercard, Hilton, Uber, Amazon and Walmart at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8^th) which will shape your digital, product and marketing strategy for 2020. Find out more here: https://events.incite-group.com/oms/

August 12, 2019

What CDPs Must Do in APAC -- the World’s Fastest Growing Tech Region

By Susan Raab, CDP Institute

The Asia-Pacific region is the world’s fast-growing economy. Organizations in many areas are investing in technology and retooling in response to rapid growth in consumer digital usage. Plus, there is increased concern about data use and privacy. Customer data platforms can be the right tool at the right time in APAC, according to executives already in the market from Lexer, Meiro, Segment, Tealium, and Venntifact, but it will take a concerted effort in education, marketing and region-specific customer support to make that happen.

CDP adoption in APAC is varied and behind the U.S., the UK, and EMEA. But according to the CDP Institute’s latest Industry Update, the industry is growing faster in APAC than any other region. There’s confusion and often a lack of knowledge there about what CDPs are and how they compare with other technology, particularly DMPs. This is due in no small part to other types of technology claiming to incorporate CDP capabilities, regardless of the degree to which they can deliver on that claim. APAC is also an exceedingly complex market with different economies, business hierarchies, and capabilities spread over great distances, so staffing and servicing it takes a lot of planning and investment.

Joseph Suriya, senior director of marketing for APAC at Tealium, calls it “an incredibly diverse and non-homogeneous market with regions that have many cultural and operational differences,” so, “there is no one-size-fits-all solution.” Pavel Bulowski, co-founder of Meiro, a CDP software company based in Singapore, concurs, saying, “if a company thinks it can go into business in Asia by setting up a Singapore office to ‘conquer Asia’, they’ll find they do not have a well worked out strategy.”

However, for those who know the APAC market, or are in a position to enter and operate effectively, the potential is vast. Prakash Durgani, global head of sales engineering at Segment, says, “the digital economy in SE Asia alone will triple to $240b by 2025. India’s outlook is similar, and the combination of these suggests that overall consumer technology growth across APAC could outperform Europe both in number of end-users as well as aggregate wallet share.”

In terms of awareness of CDPs in APAC, vendors and consultants have said it has been scant, but that’s changing now as well. Damon Etherington of Venntifact, a CDP consultancy based in Sydney, says with “large marketing cloud vendors such as Salesforce and Adobe now actively promoting their CDP products for a 2020 launch, the hype and excitement is taking hold in the market. Demand from brands to understand their existing marketing technology ecosystem…and to understand the gaps and requirements for their technology stack is becoming common.” Suriya at Tealium notes that, “Asia has a tendency to be very peer driven, so if their competitors are exploring something, more often than not, this will encourage other companies to look into the same thing.”

In terms of current drivers, surveys have indicated that APAC customers are concerned about how their data is being handled, so technology leaders there view ensuring data privacy and consumer trust to be very important.

Bulowski at Meiro says, since APAC is “a tremendous growth market,” customer retention is not necessarily the main focus now, but going forward when there is “any kind of economic slowdown, there will be an increased emphasis on maximizing current customers, so those will become very important to retain as a customer base. And, this will drive further interest in CDPs.”

Dave Whittle, CEO of Lexer, a CDP with offices in New York, Los Angeles, Sydney, and Melbourne, says that the current CDP market in Australia vs the U.S. is impacted by two drivers: the smaller population (about 1/13th of the size) and the 1-2 year lag in adoption of new technologies. “That said, we are seeing innovative Australian companies recognize the opportunity to implement a CDP to provide an immediate advantage against their slower moving local competitors. Clients are beginning to reorient their systems, processes and teams away from channel centric to the customer centric approach that Lexer enables.”

While it is hard to categorize the larger APAC market, Etherington of Venntifact explains that APAC is comprised of “a number of regions varying greatly in digital maturity. Broadly speaking, Australia and Japan are relatively advanced, South East Asia is still early in its digital sophistication, Hong Kong, Singapore and South Korea have pockets of maturity, while India and China are their own class altogether - huge, complex and rapidly expanding.”

Consensus from these executives on operating in APAC is that a company should either focus on one region or set up regional offices in multiple key locations. They stress that this is a market that takes a lot of time to cultivate and gain the trust of businesses there. But there is a lot of opportunity. Etherington observes that, “Australia has only a handful of active CDP vendors educating and actively selling in region. Those tend to be the larger, pure play CDP vendors such as Tealium and Segment,” so he thinks there’s a clear opportunity in the mid market where there are no active CDP players. For servicing, he says, “specialist consultancies such as Venntifact are emerging to help brands navigate the challenges, and develop effective use case driven roadmaps for CDP adoption.”

August 1, 2019

Are digital marketers losing sight of the value in connecting online and offline identity data?

By Dave Dague, Infutor

It will come as no surprise to this group that the options for and use of Customer Data Platforms (CDP) are on the rise. Marketers need a central place to hold, access and manage their vast customer data as well as personalize and better target their ad programs to reach the right consumers with the most relevant messaging.

As part of this dynamic, consumer identity markers (names, physical addresses, phone numbers, emails, hashed emails, mobile ad IDs) and identity resolution have become a key enabler for CDP users to fully realize the value of these platforms. How else can you ensure that the consumer data in your CDP is comprehensive, linked and up to date? At any given time, up to 30% of consumer records in brand databases are incomplete, duplicative or incorrect. So it’s critical that marketers have access to dynamically updated identity data. While many marketers assume this type of identity resolution is baked into CDPs, that isn’t always the case. And of the CDPs that do offer integrated identity resolution, there are often some gaps – specifically as it relates to critical offline data.

Enterprise-grade identity resolution offers the ability to resolve all identity markers to a single person, regardless of which enterprise system they reside within. This ensures that there is a single, linked view of the consumer. Typically utilizing an identity graph, or known truth set, to resolve all identity markers to a single view of a consumer, foundational identity resolution enables critical marketing functions such as personalization, segmentation, analytics, outbound activation, compelling inbound engagement in real-time -- you name it.

While the advent of CDPs has generally risen out of digital marketing, marketers today who solely focus on a consumer’s digital identity and digital footprint may be missing out on key offline indicators that provide a more comprehensive view of that person, and a critical way to use that information to link online identities. Offline indicators can include name, physical address, emails, phone numbers (mobile and landline) as well as historical auto/home ownership attributes. As consumers live their lives, these markers are constantly changing (marriages, moves, phone changes, email address changes, etc.).

To capture both online and offline consumer data, identity markers that power CDPs must be linked together with a persistent identifier through identity resolution. These identity markers provide an essential way for brands to integrate and update first and third-party consumer records in multiple, siloed datasets, e.g., CRM, billing, delivery, customer care, etc. This empowers marketers to maintain a single, 360-degree view of customer contact, transaction and attribute intelligence for de-duplicated and personalized outbound and inbound transactions. Identity resolution also helps improve outbound marketing scale by enabling multiple identity markers to be tied to a consumer, improving onboarding match rates and driving outbound omnichannel scale. Plus, integrated data enables cross-device and platform tracking and helps optimize campaigns, engagements and channel attribution.

Digital intelligence like browser cookies, tags and mobile ad IDs are by design, anonymized, but many can be tied back to offline data using privacy-friendly hashed emails. Physical addresses and phone numbers are another key component to match to digital identifiers to implement omnichannel marketing. And, some marketers overlook the fact that on average, U.S. consumers maintain three email addresses, and use them differently across platforms (e.g. one address for social accounts, another for retail platforms, another for personal use). Being able to access and maintain a record of multiple, linked email addresses helps ensure that the right consumers can be reached on as many platforms and channels as possible.

Foundationally, marketers need to leverage identity linking and ensure identity resolution is implemented across their entire internal dataset. This is essential to maintaining linked, de-duplicated and enhanced records for use within their CDPs. Whether identity resolution is already part of their CDP’s functionality or accessed through real-time APIs, marketers who leverage on-demand identity resolution, can do so on a record-by-record basis (even from fractional identity marker data), enabling them to minimize data decay to maintain their ability to reliably reach the right consumers at scale.

Since identity markers are constantly changing, those changes must be consistently captured in an organization’s CDP to ensure marketers are delivering relevant and compelling messaging and offers as their target consumers’ lives evolve and change.

July 29, 2019

The Future of Customer Data Platforms Needs to be Vertical

By Subra Krishnan, Lemnisk

Last year, especially towards the end, saw a lot of debates and discussions on Customer Data Platforms, their utility and future, and whether they truly represent the “holy grail” of centralizing disparate data sources and simplifying complex orchestration processes.

In a matter of weeks between October and November, three fairly divergent views presented themselves:

– Forrester’s Joe Stanhope came up with a critical review of B2C customer data platforms. The central premise of the report was that CDPs have “over promised and under delivered”.

– Michael Katz, mParticle’s CEO, wrote a defense to Stanhope’s article and in the process tightened the definition of CDPs. His article defines and builds on the notion of “foundational CDPs” that focus exclusively on building and maintaining customer-centric data pipes. It says that CDPs are only just scratching the surface of their potential.

– Last year’s DMS West had a panel on CDPs and how “everyone is a CDP”. It was easily one of the most engaging panel discussions of the event and one that added more fuel to the fire. The inevitable consensus was: Let the CDP space evolve.

Startups will obviously glorify and hype up the category. And it behooves analysts to temper expectations and bring buyers back to reality.

Here’s what we know for sure.

Barring rare exceptions, practically every company out there is struggling with exploding data. And barring rarer exceptions still, no one has been able to solve this problem at scale. Companies are either ill-equipped or are failing in massively high percentages in their attempts.

And so by extension, this is a vacuum that innovation will fill. CDPs are the right start and are here to stay. So what’s the real deal?

There are two obvious insights that everyone seems to miss but can give clarity on where CDPs are headed.

1. Poorly structured and disconnected data is not just a “Customer Experience” or “Activation” problem

In fact, it’s not even an exclusive digital marketing problem. Sure, the nature of early data sets getting captured is marketing-oriented. This is understandable. Marrying high volume, high velocity behavioral data from mobile and web assets with other high variety data sources brings incredible targeting benefits.

And so, cross-device analytics and experience/orchestration activation has naturally emerged as leading CDP use cases.

But digital marketing is not the only use case for well-structured data.

Let’s take the example of banks. Structuring bank data sources can have so many other benefits:

– Product teams start getting high quality real-time data as they design new products or create up-sell/cross-sell offerings

– Data governance across the organization becomes centralized and simplified

– Analytics teams can start developing nuanced, long-term understanding of users and start offering products to a user that they may otherwise drop or ignore

2. None of the players - large marketing clouds, identity resolution vendors or Google - would want to solve these vertical problems. They may not even be foundational CDPs.

This second insight derives by extension from the first. Large marketing clouds will keep solving horizontal digital marketing problems across various industries - and package them vertically for a particular market.

Vertical growth requires a deep-dive into an operating market. This means that an organization gets a chance to scale up its martech product within their line of business; as they are well-versed with the market dynamics and customer expectations./p>

Google probably couldn’t care less about whether core banking data can be processed and streamlined into the CDP. This means that all of these players will HAVE to be treated as “data sources” and not the central Customer Data Platform.

CDP companies need to start picking industries not called “E-commerce” and solve their digital marketing problems. They need to move to use cases beyond ones that involve DMPs (Data Management Platforms), DSPs (Demand Side Platforms) and other exotic three letter digital marketing acronyms.

And that’s where the CDP promise will get delivered.

Where the data rubber will firmly meet the value road.

Where founders need to stick to a hard vision.

Where easily large TAMs (Total Addressable Markets) will shrink, widely cast sales nets need to be replaced with fishing rods, account engagements will have to deepen. That’s very difficult.

Does this sound boring? Like a lot of hard work?

Great, welcome to 2019 and the future of the CDP!

July 25, 2019

Successful Marketing Campaigns Begin with Customer Data and Machine Learning

By Abhi Yadav, Zylotech

If you’re a data scientist, you’re familiar with the many processes data must go through before it can be used for machine learning models and analytics. If you’re a marketer, you may not be aware of the lengthy journey customer data takes before it reaches you. This post highlights some of the processes customer data goes through before it is made available to marketers for customer analytics and marketing campaigns.

Customer data is collected from multiple sources

You can’t have a marketing campaign without customer data, and there is a wealth of customer data available to marketers. Common customer data sources include CRM platforms, social media sites, company websites, and mobile applications. Collection is only the first step. Every system that stores customer data formats and stores that data in different ways - data could be structured, unstructured, or semi-structured and contain different data types such as numeric, text, or categorical. The data must be properly processed before it can be used for marketing campaigns or any customer data-driven business process for that matter.

The data is processed

Customer data must be cleaned, normalized, integrated, and deduped before it can be fed to machine learning models or used for analytics. And customer data often requires enrichment, where missing or out of date customer data fields are appended with new or updated information. For example, a customer record that includes only a name and email would be appended to include company name, job title, and phone number. Data scientists are usually the ones responsible for preparing data for analysis, and it takes about 80% of their time - unless they have tools to automate the process. For example, our customer data platform (CDP) saves data scientists time by cleaning and standardizing data automatically. Our platform also enriches customer data and can be integrated with existing martech solutions using our Enrich API. Once customer data is processed, it must undergo segmentation before it can be used effectively for marketing campaigns.

Customer data is fed to models for segmentation

Creating successful marketing campaigns is a difficult prospect without having a granular understanding of your customers. So, once customer data has been processed, it is segmented typically using one or more clustering models. Clustering allows customers to be segmented based on demographic attributes as well as behavioral patterns. The data can be segmented in hundreds of ways providing organizations a detailed understanding of customers. Segmentation is often used by organizations for lead scoring and propensity prediction. While organizations can build machine learning models for lead scoring and predictions in-house, they don’t have to. Our CDP includes a Lead Scoring API and a Predict API so that organizations can quickly integrate lead scoring and prediction capabilities into existing martech platforms.

The foundation for successful marketing campaigns

Customer data and machine learning form the foundation for successful marketing campaigns because they enable martech platforms to feature capabilities such as advanced customer analytics, contextual personalization, and timely customer engagement. However, it is impossible for marketing campaigns to be successful if the customer data powering them has not been processed and segmented properly.

July 22, 2019

The Asian Financial Services Industry Needs a Customer Data Platform

By Rahul Thomas Mathew, Lemnisk

Customers in the financial services industry want personalized experiences. They, in fact, expect and demand them from their service providers. They prefer to stay loyal to a company as long as they receive this special treatment. As a result, personalization has become the number one priority for marketers in the industry today. They are waking up to the realization that delivering personalized experiences highly depend on understanding customer data.

Very few companies have the means to understand this data and use it to enrich the customer experience. The technology that has been recently making waves in every industry is known as the Customer Data Platform (CDP). A CDP is a packaged SaaS (software-as-a-service) product that is designed to build a unified customer database for an organization. Implementing a CDP can help achieve consistent customer engagement, increased loyalty, and higher sales.

David Raab, CDP evangelist and Founder of the CDP Institute, was invited as a chief guest at the Customer Data Summit 2018 event organized by Lemnisk.

David is a widely recognized thought leader in marketing technology and analytics. He was one of the first people to recognize that digital marketing systems were not just proliferating but also the data that these systems were throwing up were getting grouped into silos, making it really hard for marketers to understand customers holistically.

David also realized that there was a tremendous opportunity if he could bring these disparate systems together. Around this insight, David coined the term CDP and founded his institute in 2016. The CDP Institute’s work has been seminal in helping marketers understand the need for a CDP and the ways that they can derive value from it.

Watch the Video

David’s thought-leadership session imparted the following key insights:

• The most challenging barrier to Marketing Automation success is data integration between the various marketing systems of an organization. Financial marketers in Asia face the same challenges as their peers elsewhere, which include unifying customer data, providing superior customer experience, working within compliance constraints, and finding the budget to pay for solutions.

• The CDP industry has seen a good growth rate of around 73% over the last 12 months. Two-thirds of the growth is attributed to new vendors and the remaining to existing vendors. The adoption rate has been high for B2C marketers as their businesses depend highly on user engagement and digital conversions. Companies that opt for a CDP prefer to have a complete packaged solution that includes the core CDP functionality along with analytics and engagement.

• A CDP works well when all marketing systems are interconnected. One interesting observation is that one-third of CDP users lack an integrated technology stack. Companies that claim to have a CDP do not have this system integration and, therefore, do not fall under the CDP-classified vendors.

• Things such as churn prediction and predictive modeling are a set of classic algorithms that thrive on good data. Artificial Intelligence (AI) is totally data-driven and works well with data that is highly detailed. A CDP can play a major role in developing custom algorithms and advanced intelligent systems such as AI. One of the things that it can do is create a standardized variable or model score and make that shareable to all systems that it connects to.

• Of its various capabilities, a CDP also enables cross-device personalization by associating each device with the customer’s master ID when they log in. The master ID is used to build a unified customer profile with all device data. The right message for each master ID is selected and shared with all devices. The unified and complete customer profiles help financial marketers in selecting the right message and deliver a consistent experience across all devices.

It is still early days for a CDP in Asia. Many organizations are still at the stage of learning for themselves why options such as DMP (Data Management Platforms), Enterprise Data Warehouses, and marketing clouds won’t solve the problem that a CDP addresses. The core technologies used in Asian financial institutions can support any level of marketing sophistication that their users are ready to deploy. The early CDP adopters are touted to have an advantage over others in the industry.

July 18, 2019

The Revolutionary Role of AI in B2B Marketing

By Greg Peverill-Conti, Zylotech

Over the years, I’ve had the opportunity to work with a number of companies that lie at the intersection of audiences, data, and technology. As time has passed, many of the challenges associated with using data to reach potential customers have been solved. Amassing audience information? Check. Recognizing a prospect across platforms and devices? Check. Integrating information and insights into a range of sales and marketing technologies? Check, again. Many of the fundamental problems of data-driven marketing have been recognized and addressed.

Now, we’re dealing with harder problems. Which isn’t to say the earlier challenges were easy, but rather that the new problems weren’t initially foreseen. For example, now that marketers have access to so much data, how can they evaluate its quality? How is data quality maintained over time? How can it inform smart planning, support decision-making, and fuel marketing programs that are engaging but not creepy?

Much of the thinking around using audience data comes from the B2C world, whose brands and marketers were among the first to see the potential for reaching and engaging customers online and through social channels. That is rapidly changing and more B2B brands are not only thinking about their customer data, but are also rethinking the ways they put that data to work.

Companies like VanillaSoft are making it easier for inside sales teams to use social channels to reach business buyers - and set up a cadence and process for efficient interactions. Even just a few years ago, the idea of reaching a B2B purchaser via SMS or Facebook or Twitter would have raised eyebrows. Now, they have become part of the daily routine and are recognized as valid channels for engagement.

This type of engagement depends on having accurate contact data, the type supplied by companies like DiscoverOrg. They do a great job of constantly vetting and refreshing their information but contact is just one piece of the puzzle; what about the rest of a marketing team’s data? That data also needs to be evaluated and refreshed on a constant basis, while also bearing in mind customer privacy, their preferences for engagement, and what approaches are most likely to be effective.

The challenge is that the current volume and velocity of data simply can’t be managed manually. To be successful, marketers need to rely on AI and machine learning to do the heavy lifting. While in its early stages, this approach is already paying dividends to forward looking marketing organizations.

These systems can do so much more than simply ensure data quality. Technologies, like those from Zylotech - a self-learning customer data platform that enriches audience data, predicts purchase behavior, and helps increase sales - are able to ingest, normalize, append, and segment customer data in new and interesting ways. These platforms are becoming incredibly sophisticated, transforming audience data into customer intelligence and putting that intelligence to work for sound business results.

That jump is a game-changer. For the first time, B2B marketers have an intelligent virtual ally in their corner. This doesn’t mean that marketers can take a hands-off, automated approach - not by a long shot. Marketers need to apply their own intelligence and creativity to develop campaigns and programs that harness machine learning and AI while preserving customer trust. It’s a fine line and one that is constantly shifting. Thankfully, there is a feedback loop that allows marketers to see what’s working or what might be causing prospects to flee.

AI can learn those business engagement boundaries, but human intelligence needs to trace their outlines for the AI to learn and operate effectively. That’s a higher level function for both marketers and AI. It may sound daunting, but it will soon come to seem obvious. Machine learning and AI - coupled with our own creativity and intelligence - will have a huge upside and are poised to rewrite the rules of marketing.

July 15, 2019

How CDPs Can Bridge the Gap between Financial Marketers and Customers

By Rahul Mathew, Lemnisk

As marketers, we constantly wear the hat of our customers to understand the motivation, buying behaviors, and rationale for making purchase decisions. Marketing decisions are often made with fractured customer data, which means expecting positive outcomes from marketing campaigns is like trying your hand at the slot machine. Financial marketers today are sitting on huge piles of customer data that is siloed in various martech databases and tools. They are unable to leverage this data for deploying targeted campaigns to customers.

How Financial Marketers Leverage Customer Data

Consider this scenario: Mark visits his bank’s website anonymously and visits the credit card section on the website. He views the Gold credit card and drops off the website. Three days later, Mark returns to login to his bank account and immediately sees a homepage personalized banner showing the benefits of getting a Gold credit card. Mark is a Senior Executive with a multinational firm and has his salary account here.

After logging into his account, he is given an offer to get a Gold credit card along with many more offers and benefits which he previously did not see. The bank sends Mark an email with a custom offer on a Gold credit card. Mark opens the email and signs up for a credit card. What began as a simple task of viewing a credit card anonymously, ended up being an upsell to a customer for the bank. The bank was able to bridge the gap between themselves and Mark by leveraging a Customer Data Platform (CDP).

Interest in data-activated Marketing is driving the need for CDPs

Data-activated marketing based on a customer’s needs, intent, and behavior in real-time is becoming a vital part of digital growth for a financial organization. This can boost total sales easily by 15-20% and improve the ROI on the marketing spend across various marketing channels. A growing number of financial marketers are turning to CDPs to resolve their scattered and siloed customer data problems.

Here are 3 ways by which a customer data platform can bridge the gap between financial marketers and customers:

• Understand each customer intimately

A customer data platform helps in un-wrangling customer data that is spread across various data sources. It aggregates and unifies this data for each individual customer. By getting a unified customer view, financial marketers can deeply understand customer preferences, their online behavior, transactional details, buying intent, etc. This ensures that financial marketers are in a much better position to market and engage with customers/prospects.

• Deliver personalized messages to millions of customers in real-time

Coupling a CDP with Artificial Intelligence can do wonders for a data-driven financial organization. Customer data platform enables marketers to understand each customer. However, a CDP paired with Artificial intelligence ensures that each customer is delivered a unique 1:1 personalized experience in real-time.

• Target customers with personalized offers through cross-device

Personalization is limited if it is restricted to a single device. Research has shown that almost 35% of customer conversions happen on a device other than the one where the first impression occurred. A CDP helps capture user details cross-device and unifies them to provide a single customer view. Financial marketers increase the marketing campaign effectiveness with personalized offers through cross-device.

Conclusion

In this digital age, financial institutions are struggling to increase digital engagement and growth. The gap between them and the end customer is growing wide day after day. Financial marketers must, therefore, bridge the gap by exploring, identifying, and leveraging new technologies such as CDPs and AI. This will help them to stay in the game and attract new business growth and increase digital customer engagement.

July 11, 2019

Rightsize Your Martech Stack with These 4 Essentials

By Chuck Leddy, Zylotech

As of this writing, there are over 7,000 martech solutions available, compared with only 150 in 2011. Having a big martech stack isn’t that big of a deal anymore. If you’ve got the budget, you can purchase loads of martech to do just about anything. But going on a martech shopping spree will cost you more than money: it may disrupt your focus. Try telling your customers, “we’re too busy buying and implementing martech to focus on you,” and see how that goes. Martech exists to help your B2B marketing team drive improved engagement with customers.

You need a martech stack that provides you with a personalization engine which you can use to drive more relevant content to your customers, something a customer data platform (CDP) helps give you (more on CDPs later).

Rightsizing your stack

The “right” size of your martech stack depends upon your team’s objectives and existing resources. You don’t have to be a minimalist like Marie Kondo to understand that “less can be more.” Start by clearly defining your goals. You don’t want more martech: you want the “right” martech to do the work your marketing team needs done to drive more personalized customer engagement. Understand that the “R” in ROI stands for the return you expect.

Rightsizing your stack is a careful process that requires: (1) analyzing your goals; (2) auditing existing capabilities/resources (including martech); (3) identifying gaps in your capabilities; (4) evaluating what’s available (including martech) to close those gaps; and (5) implementing (and integrating) solutions to enhance your capabilities -- which may mean buying the “right” martech to get your personalization engine revving. Defaulting to “just buy more martech” wastes money and potentially gets your team lost in a martech hairball, where goals and martech are so misaligned that you spend more time untangling the mess than driving ROI.

With all that said, there are basic martech tools that every marketing organization will want to have to drive personalization and relevance.

4 Martech stack essentials

1. MAP. Back when they became popular in the early 2000s, marketing automation platforms were viewed as “all-inclusive” solutions which marketers could just set up and watch perform every marketing task. Well, MAPs haven’t quite eliminated the need for human marketers. According to MAP vendor Marketo (competitors include Eloqua and HubSpot), MAPs allow B2B marketing teams “to streamline, automate, and measure marketing tasks and workflows, so they can increase operational efficiency and grow revenue faster.”
2. CMS. A content management system is an application that allows marketers to create and manage digital content in order to engage their customers. A CMS (WordPress and Drupal are popular vendors) is typically divided into two parts: a content management application (CMA) and a content delivery application (CDA). The CMA is a graphical user interface that allows marketers to easily manage the creation, modification and removal of content from a website. The CDA provides the back-end services that support content management and delivery. If you produce and distribute content, you need a CMS.
   
   
   
   
   
   
   
   
   
   
3. CRM. A customer relationship management system manages your relationships with customers and potential customers so you can drive ROI. A CRM helps everyone in your organization streamline and coordinate customer interactions, and improve KPIs such as customer retention, cross-selling, and profitability per customer. As CRM vendor Salesforce (competitors include Zoho and Insightly) explains, a CRM “gives you a clear overview of your customer” through “a simple, customizable dashboard that can tell you a customer’s previous history, the status of their orders, any outstanding customer service issues, and more.”
4. CDP. As the old data science truism goes, “garbage in, garbage out.” A customer data platform takes raw, unfiltered data and enhances its quality and usability in order to provide you an always-on engine to drive personalization and ongoing marketing relevance (and ROI). Unfiltered data goes into the CDP and comes out as a unified and complete view of your customers, as business intelligence, and as actionable insights that enable you to (among other things) personalize engagement with customers, build precise customer profiles and segments, optimize campaign performance, and boost marketing ROI. With a CDP, you can drive marketing relevance in real-time, at every step of the customer journey.

A CDP enables multiple approaches to B2B marketing, from customer segmentation to ABM, and beyond. CDPs with AI/machine learning (AutoML) enable you to identify customer behavioral patterns and apply predictive analytics to boost marketing ROI. You might “get predictive” to tackle customer churn and drive retention by anticipating when and why some customers leave your funnel, taking proactive marketing action to prevent leakage.

Through adopting an ABM strategy, you could identify, profile and segment a select group of your key accounts for your ABM team to target with messaging that better meets their pain points and expectations, thus driving more ROI per key account. You could also identify up-selling and cross-selling opportunities, or craft special offers for specific customer segments whether inside or outside your ABM programs, to drive relevance and profitability. The use cases for a CDP are massive.

Once you have these 4 martech essentials, how you build the rest of your stack depends on your goals, your capability gaps, and your budgets. With a CDP and these other tools, you can build an always-on personalization engine to continually engage your B2B customers and grow your revenues. If you want to learn more about the what, why, and how of CDPs, then visit www.zylotech.com today.

July 8, 2019

7 Use Cases of a Customer Data Platform for Financial Services Marketers

By Sharika Ahmed, Lemnisk

One of the biggest challenges faced by Financial Services marketers is to set up a systematic utilization of their vast database across different systems. A Customer Data Platform (CDP) is the answer to the woes of all financial marketers who want to make the best use of their customer data and leverage them for marketing and lead conversion purposes. As per CDP Institute, “A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.”

Here are 7 use cases where CDPs can be used to enhance customer engagement and increase lead conversions by Financial Services marketers:

1. Collection of data in its original form

Frequent modifications on the original source data make it difficult for marketers to pre-determine the use of data for their marketing strategies. So it is important to store customer data in its original form to ensure that the data can be useful in the future. A Customer Data Platform designed for financial services collects data in its original form from all sources. The data ingested could be structured, semi-structured, and unstructured data and it is stored without any modification. This data can be transformed, unified, and re-formatted by marketers to use in marketing programs and for analysis. CDPs include functions that simplify the addition of new data sources and transformations.

2. Creating customer one view

A CDP unifies the data captured from different sources into a single view for each customer. It brings together data from sources such as first-party behavioral data, second-party partner data, third-party audience persona data, CRM data, next best offer (NBO) data, and any other customer data touch points. The data is structured into a single customer profile with the help of a unique identifier (CRM ID, Email ID, Contact number, etc.). These unified profiles will provide a 360-degree view of each customer and help marketers in segmented targeting for customer engagements.

3. Powering different marketing systems

The collective data in the unified customer database of a CDP is easily available to other marketing systems, typically through an API or database query. It can be used to power multiple marketing stacks to improve customer engagement across channels. A CDP can be used as the central integration point for all other systems.

4. Cross-device data coordination

User interactions with different devices are captured by the CDP and a unified customer view is formulated to create a unique journey for each user. The unified database is used to identify the user’s preferred experience on each device by tracking their online behavioral patterns. Personalized messages and offers are sent to the preferred devices of each user based on their individual journeys.

5. Real-time personalization

A CDP uses real-time triggers to power personalized customer engagement. Users are targeted with messages based on their online activities and behavioral data. For example, a user visits the website of an insurance brand and visits the life insurance page but then drops off from the website without taking any action. So the next time when the user visits the website, he is shown offers based on his previous activity on the website, i.e. offers on life insurance products.

6. Call center integration

CDP can be used by marketers to tie their online and offline lead generation activities together. Data flow can be both ways to ensure that user engagement is optimized from both ends. Marketers can share real-time website revisits of a lead with a call center agent to contact the user immediately. On the other hand, call center data of a lead can be ingested into the CDP and this data can be used to send out emails or browser-push messages to the user as per their last conversation with the agent.

7. Leverage AI for channel marketing

A CDP can leverage Artificial Intelligence algorithms and can pull historical information about the user’s buying intent and calculate the next best offer on products based on a user’s interest. Financial marketers can create marketing campaigns based on such data and target users with the calculated price offer and products. AI can enable marketers to send push notifications, emails or SMS to users who have never made any purchases from the website. Personalization is based on look-alike modeling using the historical data of the visitor’s online activities.

July 4, 2019

PARCO Fights the “Retailpocalypse” with Omnichannel Personalization

By Micki Collart, Arm Treasure Data

“Data-driven company” is a title that many businesses covet. But PARCO Co. Ltd., which owns a chain of retail malls in Japan, has actually earned that title, in part through its innovative use of a CDP to leverage data from far-ranging sources. It’s on the front lines of successful efforts to combine CRM and in-store sales data with some of the sexiest, cutting-edge sources of data. Geolocation information, Internet of Things (IoT) data, loyalty program information, cell phone app data, and even weather sensors on mall rooftops feed real-time data into PARCO’s strategy for omnichannel personalization, which relies heavily on Arm Treasure Data’s CDP.

These data-driven marketing and sales innovations are getting results that deserve to be the envy of many retail organizations facing the brick-and-mortar “retailpocalypse” that has already taken out or taken down some of the most familiar names in retail.

Omnichannel Personalization Competes with Big Online Retailers

With the expansion of ecommerce caused by the widespread use of smartphones, the market has seen the emergence of big players such as Amazon and Rakuten, exposing retail businesses with brick-and-mortar stores to what is often called the “e-commerce threat.” But Naotaka Hayashi, Executive Officer for the Group ICT Strategy Office of PARCO, has a philosophy about competing with these giants.

“I do not think that e-commerce and physical stores conflict with each other. We have just embarked on a journey to find a way to utilize digital data and our physical stores, to increase customer engagement,” Hayashi says.

How does PARCO execute on its long-term strategy for strong brick-and-mortar stores in the digital age?

Increasing Customer Lifetime Value with Technology-backed Customer Service

PARCO’s core business is the operation of shopping centers, meaning that those who actually sell products are the staff members working at about 3,000 tenant shops in PARCOs nationwide. In other words, PARCO’s marketing mission is to support the customer service and sales of such tenant staff, acting as a bridge between customers and tenants.

That’s a much closer relationship than most American mall owners have with their tenants, and it means that PARCO’s strategy is far more tightly coupled with its tenants’ strategies.

“The tenant sales volume is the sum of customers’ lifetime values, while it also means the sum of the lifetime value of customer service by tenant staff. It is very important to increase their numbers,” says Hayashi.

The role of technology in PARCO is “expanding customer service through technology,” as Hayashi puts it. Instead of considering the digital channel as the complete opposite of physical stores, the company uses the digital channel as a tool to enhance the customer service in person.

PARCO’s omnichannel retail strategy was born in 2013 as an effort to expand customer service in its physical shops. First, the company optimized shop websites for smartphones and left all the communications on the website at the hands of tenant staff, rather than PARCO’s. Along with these shop blogs, PARCO released its official smartphone app, “POCKET PARCO” several years ago, with the aim of further fortifying digital touchpoints.

Mobile Apps: A World of Omnichannel Personalization Possibilities

The POCKET PARCO mobile app comes with a feature that distributes information about events, promotions, and coupons from PARCO and tenant shops. It also provides shopping support features during actual store visits. In return, it gathers a treasure trove of data that fuels new insights.

The POCKET PARCO app has provided a wealth of data and insights for omnichannel retailing, especially from geolocation data.

“Before, shopping centers such as ours did not have a way of knowing the entrance a customer takes to enter the store, which shops the customer stops at, and whether or not any purchases were made there. But the use of the app is helping us make sense of customer activity and personalize our interactions for each customer,” said Hayashi.

Omnichannel Personalization Strategy Reaps Rewards

According to Hayashi, data marketing using POCKET PARCO is roughly divided into two big efforts: the visualization of reward-motivated customer behaviors and understanding customers’ satisfaction level after shopping.

The company’s reward program uses “coins.” Customers are rewarded a coin when they clip (as a favorite) blog articles of tenants, check into shops, make payments using registered credit cards or prepaid cards, and so on. These coins serve their purpose as motivation: once a customer collects a certain number of coins, the customer can get a gift certificate in exchange. Through POCKET PARCO, customers’ shopping information and behavior in the store can be visualized as statistical information.

What Affects Customer Behavior Most?

Hayashi emphasizes the importance of a marketing tactic that uses a POCKET PARCO function to clip a shop’s blog. According to Hayashi, data analysis revealed a correlation between clipping a blog article at a shop and an increase in the number of shoppers or purchases in that shop. PARCO uses this insight, along with individual customer data profiles from its customer data platform (CDP), to optimize the offers for each customer.

By using such an “equation,” PARCO optimized recommendations in blog articles according to the customers’ preferences and behavioral data. Specifically, they use artificial intelligence (AI) and machine learning (ML) to analyze which shops’ articles customers frequently clip and where the customers go after clipping each blog. The results are used to decide which articles to show customers.

“Our one-year analysis revealed that a customer who clipped a shop article had a 35 percent higher tendency to visit a shop and make a purchase than a customer who did not clip. Such a big difference in subsequent actions depends solely on whether an article is attractive enough to induce a tap or not, so it is very important to increase recommendation accuracy by AI and other tools,” said Hayashi.

Geolocation Data, Smartphones, and Omnichannel Personalization

The company has also introduced a new POCKET PARCO function called “PARCO WALKING COIN” that uses the step counter of a smartphone and measures the number of steps a user takes in a PARCO store. The function essentially gamifies the literal customer journey through a mall; when customers achieve the target in-store step count, they get a reward coin for redemption toward purchases. To get the coin, customers often visit stores they’ve never gone to, making extra purchases and increasing customer lifetime value.

“The effect of coin promotions on purchases is great,” says Hayashi.

PARCO also sends push notifications to those who have not visited the store for a while after their first purchase. As a result of A/B testing, the company realized that customers who received such push notifications had an 8 percent higher repeat purchase rate than those who didn’t get the promotion. “Data-based customer segmentation and personalization of app notification are generating great effects,” says Hayashi.

Hayashi believes in the entwined, dual-power relationship between physical stores and digital channels in PARCO, a belief that has been reinforced with CDP insights into the data collected from so many shoppers.

It’s a vision similar to that of many experts such as Forrester, who say that successful retailers will combine interesting experiences that can only be done in person, with data from many different digital channels — such as social media, loyalty programs, online purchases, even weather and geolocation data — to give each customer what he or she craves, encouraging them to buy more overall. It’s a data-driven vision that PARCO is embracing wholeheartedly in its operations to “seize the day” and create its own destiny.

“We cannot change the past, but we can change the future,” says Hayashi. “We will steadily work to collect and analyze past data and use what we learn to create our future.”

July 1, 2019

3 Things to Miss When Implementing a CDP

By T.J. Hopes, QuickPivot

It’s no longer a secret that a customer data platform helps marketers achieve a unified view of their customers and improves their day-to-day efficiency. Or that they help marketers cut down on the time they spend cleaning and aggregating their customer data from various systems. Or that their segmentation capabilities help marketers create detailed customer profiles, and in turn help them create personalized marketing communications that resonate with their customers.

You get the picture.

What many people may not realize, however, is that there are unintended consequences to using customer data platforms. Sure, they do all the useful things mentioned above (and more), but here’s a look at three things marketers will definitely miss doing once they start using a customer data platform.

#1: Frequent chats with IT

One of the nice things about customer data platforms is that they make running database searches easy and storing customer data a breeze. Their simplicity is a large part of their appeal, in fact. So, marketers will be disappointed to learn that they will no longer have to rely on their favorite member of the IT staff to run database queries and update data for them. The data stored in the platform is easy to access and update, and running customized reports and performing list pulls doesn’t require any complicated SQL commands.

A close relationship between IT and marketing is important, though, so make sure to say hello to your friends over there every now and then. In fairness, though, they’re probably tired of hearing from you and are likely very excited about your newly embraced self-reliance.

#2: Killing time waiting for your query to finish

Traditional customer databases typically contain massive amounts of information in them, and as a result don’t process customized queries at the lightspeed pace most marketers work at. This can be a nightmare for those looking to quickly shift gears on existing campaigns or get a new one started. Luckily, customer data platforms can process queries nearly instantly, delivering easily digestible results. They also allow marketers to easily create custom queries, allowing marketers to experiment with and explore their data.

Unfortunately, this means marketers won’t be able to take their usual long lunch while waiting for their queries to finish. This in turn will lead to an increase in productivity, better overall campaign planning, and better campaign results. A real nightmare scenario.

#3: Remembering all your login credentials

Most marketing teams take advantage of a variety of systems to stay connected with their customers. A typical marketing technology stack often features different systems for automation, customer relationship management, email management, social media engagement, and more. Customer data platforms are integrated with these systems and pull in customer data from all of them to help create a more detailed view of their customers and contacts.

So there goes the fun game of trying to remember which Pa55w0rd has the ‘!’ at the end and which childhood pet was the favorite the day the account was setup. Marketers will have to turn to daily crossword or sudoku puzzles in order to keep their minds fresh, or look to Tom Brady’s brain training regimen to ensure their place as the GOAT.

If you’re looking to improve your marketing operations, be sure to check out QuickPivot’s customer data platform.

June 28, 2019

Beyond Multi-Channel: What’s an Omnichannel Customer Experience and Why Does It Matter?

By Tom Treanor, Arm Treasure Data

Businesses that create a true omnichannel experience can exceed customer expectations and move ahead of the competition. At the heart of it, omnichannel is about using customer (and potential customer) data wisely to create a seamless customer experience, regardless of how the customer chooses to contact the brand.

There are a few good reasons that marketers should be leading the charge to omnichannel. First, the marketing department is responsible for many of these channels, including email and social media. Second, a great deal of the data that makes omnichannel work really lives with the marketing department.

To help guide the organization to a more profitable customer experience, marketers need to understand true omnichannel: what makes it work, how it differs from multichannel, and how to upgrade from one to the other. Read on to get started.

What Does Multi-Channel Really Mean?

Multichannel customer service means that customers can communicate with your business in a variety of different ways. They can send an email, call you on the phone, text you or tweet at you and expect a prompt response.

This type of availability is a good place to start. It’s certainly an improvement over insisting customers use fax machines or landlines to get ahold of your business. But, just being available on multiple channels doesn’t guarantee a consistent customer experience across them.

In practice, customers often feel like they’re starting over whenever they switch channels. The person answering email doesn’t know about the phone call with customer service. The phone bank has no access to the customer’s chat history. It all adds up to a frustrating experience that is only multiplied with each new channel.

What Is an Omnichannel Customer Experience?

Omnichannel marketing is a step up from multichannel. It goes beyond being available on multiple channels; instead, the goal is that channel shouldn’t matter to the customer experience. Regardless of how a customer chooses to contact your business, they can pick up the conversation where they left off. Think of it like a home entertainment system that lets you move from room to room, watching your shows on whatever connected devices are there, flipping from device to device.

Customers prefer this type of seamless customer experience, but they rarely get it. Think about your experience as a customer: Aren’t you pleasantly surprised when you don’t have to repeat the same information ad nauseum? Most businesses struggle to retain information on the same channel — for example, across multiple phone calls — to say nothing of maintaining consistency across all channels.

Imagine if the next time you called a customer service line, they greeted you by name, knew all about your order history, and had a record of every previous interaction. That kind of personalization, that level of customer care, is what inspires raving fans. And that’s what true omnichannel really means.

Upgrading from Multichannel to Omnichannel Customer Experience

At the heart of it, omnichannel means a shift from an incident-based system to a customer-based one. This may seem like a daunting task. But with the right change in mindset and some minor investments in resources and technology, it can be done. Here are the crucial considerations for developing omnichannel in your customer experience:

• Data Management

First, you have to know who your customer is, what data they’re generating, and where it’s stored. It’s important to have a complete picture of your data landscape to start breaking down silos and making customer data seamlessly available across customer service. Companies already using a CDP have a real advantage here, since they’re already collecting and integrating data into unified customer profiles that can be used for targeting, segmentation, personalized offers, and more.

• Single View of the Customer

Once you have mapped your data landscape, work on bringing data together into a single dashboard. The right data management software can help you create a holistic view of the customer for every individual customer journey, uniting channels into one seamless conversation.

• Organizational Buy-In

Once you have customer information consolidated and readily available, it’s important to make sure the team has a will to use it. From leadership on down, it should be clear that the organization prizes an omnichannel customer experience, and that everyone should use the available tools to achieve it.

• Focus on Customers, not Channels

In the end, how the customer chooses to interact with the brand will almost be irrelevant. Anywhere the interaction takes place, the data will be added to the conversation. Your team can view each customer action in full context, not as a series of isolated issues.

Start the Omnichannel Conversation

Is your customer experience omnichannel, or just multichannel? It’s a crucial question. By the year 2020 customer experience will overtake price and product as the key differentiator for businesses. It’s high time to map your data landscape, break down silos, and create a consistent customer experience on every channel.

June 26, 2019

Seven Signs You Should Build a Customer Data Platform (CDP)

By Venu Gooty, HGS Digital

Companies and marketers often face challenges while understanding their customers, giving rise to ineffective marketing experiences. Many of them end up marketing products to customers who already have those products or have no interest in them. This is often due to fragmented data sources and the inability to utilize them in a smart manner. So, how does one create an integrated view of their visitors and customers?

This is where a Customer Data Platform steps in. A CDP gives marketers a unified view of their customers, with real-time interactions and data stitched in from multiple databases, compiling readable customer profiles to study, analyze and market to.

The best thing about a Customer Data Platform (CDP) database is its ability to plug it into other systems, allowing marketers to access and use it for further use. So, how do you find out if your brand can benefit from a Customer Data Platform? Here are a few signs you need to look out for.

1. You have an incomplete or limited view of your customers

Every smart business generates and collates data through multiple channels, such as websites, call centers, email and social channels, to name a few. All this raw data amounts to vast volumes of records spread across different systems. This data is a veritable mine of information on consumer behavior for marketers, if assembled and presented correctly. A Customer Data Platform automates this difficult process, eliminates inaccuracies and duplicates, and seamlessly merges, cleans, matches and combines data to form a comprehensive single source in real time that can be consumed by marketers to maximize sales opportunities.

This is how a Customer Data Platform provides a single, accurate and 360° view of your customers. It stitches different customer data sources on web, social, POS systems, mobile wallet apps, customer interactions, web ordering systems, SKU tracking, and in-app ordering together with ease to create a single customer profile, which is updated in real time. The data is accessible and actionable to any system, allowing you to drive innovation with the latest datasets.

2. Your online advertising is not providing the expected ROI

If your online advertising isn’t providing the right results, there’s a high probability that you’re reaching out to the wrong audience or advertising to them at the wrong time. By implementing the CDP and integrating it with your advertising platforms, you can significantly increase the effectiveness of online advertising through very pointed retargeting in real time.

3. You find the task of sifting through available customer data daunting

Marketers often face the challenge of cleaning and sifting through large amounts of data, which is a tedious and formidable process. A CDP allows the unification of data from different sources and automates this task, thereby reducing the need for data cleaning and prep. Now, you can segment, assess, manage and generate reports with ease.

4. You are not building campaigns and communications based on behavior

Are you unable to trigger campaigns to individual customers based on historical, locational and behavioral data? Are you facing difficulties in tracking customer behavior across all channels and providing intelligent recommendations based on customer-journey triggers? If you answered yes to both, then Customer DP is the solution for you. It allows you to set up targeted campaigns focusing on underperforming areas and enticing the right customers, instead of blind marketing.

5. You are not tracking consumer behavior and usage online

Several marketers admit that they do not have any analytics in place online or on their provided apps and services. If you are among those, you need to start tracking your consumer’s online behavior and usage now to successfully predict their behavior! With access to multiple internet accessible devices, being online has become an integral part of our lives and marketers need to keep pace with where their consumers are. A CDP efficiently enables observing and listening to your customers and their needs online in real time. Customers are very vocal and expressive on the web and this information can be accurately interpreted to swiftly serve them relevant content that is more likely to pique their interest.

6. Real-time personalization is missing from your brand’s website

Wouldn’t you want to know if a potential customer arrived on your website? Wouldn’t you want to alter the customer experience accordingly? To offer this level of one-to-one personalization, you need real-time information about your visitors. Customization is the key to successful interaction, and this is exactly what you can work towards when you choose to adopt a Customer Data Platform.

7. You have abandoned your omni-channel marketing strategy

Data that is integrated from a number of sources needs to be streamlined into channels that can be easily consumed by marketers. A CDP integrates data across channels and funnels it into a central location, allowing you a single point of data control and visibility to make better decisions about offering relevant interactions to your customers.

Moving towards a bright future with CDP

To conclude, a Customer Data Platform orchestrates relevant engagements and streamlines all elements across web, email, mobile and social for a seamless and profitable experience for all involved. By building the right CDP, you’re not only able to personalize the experience, but can also reduce the cost of advertising.

The Customer Data Platform has the potential to transform the marketing industry and customer engagement.

Want to learn how other organizations are building their CDP? Reach out to me at cdp@hgsdigital.com.

June 24, 2019

How The Motley Fool found more high-value customers at a lower cost

By Korina Velasco, Lytics

If The Motley Fool’s goal is to make the world smarter, happier, and richer, to do that, they need to scale — not just by creating more content or offering more newsletters, but also by personalizing for their readers based on their individual context. Here’s how they pulled it off.

If you’re in the financial space, you probably already know the name The Motley Fool.

The brand is on a mission to make the world smarter, happier, and richer, and over the years, they’ve been doing just that — and gaining a lot of reader trust and brand recognition along the way.

But as with any successful company, sticking with the same old strategies year after year isn’t a recipe for continuing success. Which is why as customers began demanding more and more personalization and companies like Amazon started seeing huge returns on their investment in customer data, The Motley Fool took note and started down their own path to data-driven personalization success.

We wrote about the brand before, telling you how they reduced cost per acquisition by 20%. And recently, we were thrilled to co-present a webinar with their Channel Marketing Manager, Sylvia Sierra.

She shared a few more insights about how The Motley Fool is finding high-value customers at low cost. Today, we’d like to pass some of those insights along to you. We recommend watching the whole webinar (you’ll find it here), but here are some of the highlights:

The problem of scaling relevance

If The Motley Fool’s goal is to make the world smarter, happier, and richer, to do that, they need to scale — not just by creating more content or offering more newsletters, but also by personalizing for their readers at scale.

The core question they came to Lytics asking was this: As customers and prospects multiply, how do we make sure we’re serving them all in the right channel at the right time?

The answer started with identifying what was holding them back from doing that. According to Sylvia, the three core challenges were siloed data (which created scarcity and inefficiency), a disconnected digital journey, and inefficient access to the insights they needed across the business.

As Sylvia explains in the webinar: “[The storage of client data] had scarcity and inefficiency built into it. We had a number of different [data silos] that were accessible not by the marketers but by the business information unit…Our technical staff was tasked with accessing the data. That was not an efficient way to do things. The data was not democratized…

“I think we [marketers] are all going through these same issues. Our technical staff are in demand all the time and trying to wait for business intelligence teams to pull out insights on a campaign-by-campaign basis makes scaling very difficult.”

And so The Motley Fool knew they needed insights and answers from their data that would allow them to scale relevancy. They needed to connect digital journeys to individual clients to serve them better. They needed to better understand the interactions of their highest value clients and identify more prospects like them. They needed to uncover real user intentions.

Before Lytics, that data wasn’t easy to come by. But once they had Lytics set up, things started to change.

Centralizing data to understand customers

The process started, as most data processes do, by centralizing customer data and making it directly accessible to the marketing team. No more asking business intelligence to uncover insights and deliver reports. No more waiting days or weeks for results.

Now, the data was at their fingertips and right away they could start understanding who their highest value clients were and what those clients had in common.

Converting prospects to high lifetime-value memberships

According to Sylvia, three huge, immediate benefits came out of this process. The first? The brand was able to decrease the cost of acquisition by 20% for their highest value members.

They did it by partnering with business intelligence to figure out who they believed were more likely to convert. They looked at frequency of interactions with content, what led up to conversions, and what touch points seemed vital along the way.

Then, using those insights, they created three audiences — platinum, gold, and silver — and used the data on those audiences (including what actions they took before purchasing) to identify who was likely to become a high-lifetime-value customer and focus their time, budget, and energy on those highest-value customers.

“The theory panned out,” Sylvia says. “We developed the three audiences and tested on Facebook, Google, and Yahoo! Finance. We observed and enjoyed a 20% lower cost of acquisition for some of our highest value members.”

Finding more of their best customers through lookalike audiences

Using tools like Facebook and Google, The Motley Fool also took that data on high-lifetime-value customers and created lookalike audiences to identify more potential customers who looked like their best.

The more they understood customer behaviors, interactions, and content affinities, the better their targeting got.

Serving members content they love

The third key benefit of The Motley Fool’s new customer data strategy and toolset was all about giving existing customers the best experience possible.

“We do this,” Sylvia explains, “through the personalization side of Lytics. We [used to] serve ads based on…the campaign of the day…[But] what if [instead] we served people based on what we know they’re actually reading?”

“If people are interested in investment vs. retirement, how do we serve the content differently [to those different audiences]? [For those who] react to messages about Amazon…how about sending them an exit pop-up [about ‘the next Amazon’]?

Serving more relevant content to people…creates more stickiness. More returns to the content. And a higher engagement with the brand…Good content drives purchases. So surfacing more of that content has been very positive for us.”

Results like these for your business

These results aren’t unusual for Lytics clients. A 20% decrease in acquisition cost. An uptick in purchases driven by surfacing the best content. Better customer data and insights from that data often result in big wins for marketing.

To learn more about The Motley Fool’s journey, check out the full webinar here or read their case study here.

And if results like these sound like something your business needs? We’d love to chat. Get in touch with our experts today.

June 21, 2019

How Customer Data Platforms Can Increase Advertising ROI

By Venu Gooty, HGS Digital

For advertising to work, it needs to be accurately targeted. Good ads speak to customers about their personal needs and interests, which is why they generate sales. But accurate targeting is notoriously difficult and it’s common to waste half a marketing budget on directing ads to people who simply aren’t suited to the product or service on offer. Many marketers are now trying to overcome the challenge of targeting by using Data Marketing Platforms (DMPs) to create lookalike audiences with limited understanding of their current customers. While DMPs are effective at providing aggregate-level data, they often fall short when it comes to improving targeting – and they don’t necessarily help marketers understand their customers across a lifecycle, encompassing their first visit, research, purchase and post-purchase.

Programmatic advertising is another emerging industry which marketers have been experimenting with varying degrees of success. Programmatic advertising helps buy ads in real-time and target to specific individuals. While programmatic advertising can seem efficient, it’s actually more expensive. If you simply rely on DMP data on whom to target, you still are making advertising decisions on how a 3rd-party company (DMP) categorizes and defines the user persona. This lack of transparency can make it difficult for brands to understand exactly where their budgets are going and whom they’re targeting.

Programmatic advertising and DMPs are both valuable tools for marketers. But a system that brings together all relevant data for targeting a known profile can lead to best possible results.

Solution to the Challenge of How to Achieve Accurate Targeting

There is a great opportunity for marketers to use big data to develop a better understanding of customers and improve targeting. The trouble is, most marketers lack the knowledge to do this effectively. Customer Data Platforms (CDPs) help marketers understand their customers by bringing together all relevant data already available within the organization to create a 360-degree customer profile. And unlike DMPs, which collect anonymous data, CDPs collect data that can be tied to an individual, which makes them extremely useful for marketers who want to target customers with personalized content. Perhaps unsurprisingly, an increasing number of brands recognize the benefits of CDPs. According to a report by the Customer Data Platform Institute, CDP vendors reported more than $300 million revenue in 2016, and the industry is expected to reach more than $1 billion by 2019.

Here’s a closer look at how CDPs can be used to increase advertising ROI:

• Step 1: Bring Together Data from Various Systems

CDPs collect, collate and centralize all incoming data across several sources and channels, helping marketers ensure they are targeting the right audience. The ability to view such a broad set of data for a profile makes it easier to segment the audience based on whom you’re targeting.

• Step 2a: Target the Right Visitors

The data about the audience from CDP is sent to a DMP to gather additional data points where possible and create a larger data set for the audience. This enables better targeting of the audiences. CDP data is then used for retargeting ads in conjunction with programmatic advertising platforms and demand-side platforms (DSPs).

• Step 2b: Target Look-Alike Audiences Using DMP

The targeted data set from CDP data is fed into a DMP to create a look-alike audience set. This audience is then targeted with advertising through integration with a DSP.

• Step 2c: Feed CDP Data into Other Social Advertising Platforms

CDP data is used for highly accurate retargeting on Facebook, Twitter, Instagram, YouTube, LinkedIn, and other social networks. CDP data is used to generate look-alike models on these sites.

Taking Customer Targeting to the Next Level

CDPs fulfil the marketing industry’s need for data-driven analytics while providing multi-channel integration that makes it much easier to provide customers with a seamless experience. CDPs consolidate all existing data, update it with the help of artificial intelligence and apply real interactions so marketers can chart micro-decisions that can make the difference between gaining and losing a customer. CDPs also solve several issues that have plagued advertising for years, including outdated or fragmented data, incomplete or incorrect information and a lack of customer behavior tracking. Marketers can now personalize their products and services with sophisticated algorithms supported by AI and machine learning – all thanks to CDPs. What’s more, information can be continuously fed into a CDP, ensuring that customer profiles are constantly updated.

Additional Best Practices to Further Enhance Advertising ROI

Here are a few more best practices that can help improve advertising ROI:

• Keep experimenting with advertising segments – perform A/B testing
• Measure results for each experiment and act on them
• Look-alike audiences can be expensive, which is why marketers should experiment with a smaller data set first before trying out larger data sets
• Create a feedback loop to take the activity data from various ads and input it back into CDP – this enables continuous fine-tuning of ads
• Create multi-stage, in-depth, multi-channel campaigns and leverage CDP, DMP, programmatic advertising, data sciences to achieve the best possible ROI

For help or more information on how to achieve better advertising ROI, reach out to me at cdp@hgsdigital.com.

June 19, 2019

Better late than never: Salesforce acquires Tableau… 3 years after Manthan-Tableau integration

By Varij Saurabh, Manthan

Manthan is always looking for ways to make data consumption easy and natural for those who need it the most and are pressed for time. From AI-driven intuitive interfaces to self-serve customizable dashboards, we are constantly pushing the bar to provide the B2C segment insights into their data using our analytics software.

In line with this vision, Manthan partnered with Tableau over 3 years ago to bring together Manthan’s deep expertise in pre-built, industry specific analytics with Tableau’s rich visual analytics.

This week, Salesforce came to the same conclusion, when it announced its intent to buy Tableau. Salesforce’s decision to acquire Tableau highlights their shift from CRM into the analytics field.

Is Salesforce just patching up a flop?

“The acquisition will test Salesforce’s focus”, said Zane Chrane, an analyst at Sanford C. Bernstein & Co. According to Chrane, Business Intelligence is “not Salesforce’s core competency and there is much Tableau does that doesn’t pertain to the CRM world, making the fit slightly imperfect.”

According to this Bloomberg article, the acquisition is also an “implicit admission” that Salesforce’s analytics product, Wave, “was a flop.”

Understanding the drive towards self-serve analytics

Manthan customers can directly access data using the Manthan Customer Data Platform through Tableau and publish dashboards. The solution merges the benefits of strong data governance and pre-packaged data sciences (served by Manthan), and business dashboards (served by Tableau), all in one holistic solution.

Manthan had a CDP in 2012, even before the term was formalized in 2013. Earlier this year, Salesforce recognized its need, and they are now building a CDP.

With over 6 years of serving both business and data science users in the retail industry, and now successfully venturing into the QSR segment, Manthan has long understood how critical customer data management is, and the need for democratizing data and analytics.

Before there was a Customer Data Platform, there was…

…Manthan Customer360, a unique customer data and AI platform to serve as the foundation for personalized marketing. It made the life of the marketer, analyst, data scientist, and IT team easier by helping them organize customer data effectively. Out of the box analytical capabilities directly helped in creating better and targeted campaigns much faster.

Manthan has been a pioneer in the CDP space and has been offering the vertical specific CDP with advanced analytics capabilities to retail businesses globally long before the term was even coined.

Salesforce is behind us on both counts. The way I see it, they’re playing catch up with Manthan.

Let’s not forget the AI-driven power-up

Last year, we added AI capabilities to our Customer Data Platform for B2C enterprises; this enabled the system to constantly upgrade unified customer profiles based on every action, generate astute customer insights and action them with personalized product and offer recommendations.

With Manthan Maya, these insights are made available through an easy conversational interface to business users. This is AI on your desk, and in your pocket on your mobile phone – you ask a question in English and get the answer, without having to sift through lengthy reports. This means the ability to proactively act on opportunities and respond to market.

Predicting what’s needed for a future-proof offering is something that comes easy to Manthan.

According to TechCrunch, post the Tableau acquisition, Salesforce is now looking into the possibilities of expanding their AI-based initiatives.

Any bets on who Salesforce will acquire next to bring Einstein to life?

June 17, 2019

Leveraging Customer Analytics to Reduce Churn Rates and Grow Marketing ROI

By Chuck Leddy, Zylotech

Customer retention, often measured by “churn” rate (the percentage of existing customers who leave in a specified period of time), is the most important success factor/KPI for any business. When customers stay, your business can build long-term profitability through repeat purchases, as well as cross-selling and up-selling opportunities. When you retain customers and optimize their lifetime value, you also create brand ambassadors who give you priceless word-of-mouth marketing and referrals. “Churn,” on the other hand, is a revenue killer.

As a recent Forbes article explains, “it can cost five times more to attract a new customer, than it does to retain an existing one. Increasing customer retention rates by 5% increases profits by 25% to 95%.” Not to belabor the point, but your business simply must retain existing customers in order to grow. This post will explain how customer intelligence via analytics can help you reduce churn.

Customer analytics for the customer journey

Customer analytics can drive retention and engagement throughout the entire customer journey, from generating top-of-funnel leads to the mid-funnel nurturing of leads to bottom-of-the-funnel conversion and closing, turning leads into revenue. Knowing more about your customers, which customer analytics enables, is the best way to engage your customers with relevant messaging that keeps them moving through the funnel to conversion (and greater lifetime value).

For example, you can improve lead generation with bots backed by machine learning who perform quick engagements with your customers, answering basic questions and routing them to relevant products, services, and salespeople. Contrast this real-time, bot engagement with a website form that a prospect might fill out, only to wait weeks for a busy salesperson to answer. Which of those two scenarios drives more engagement, faster?

Customer analytics can help “solve” customer churn

The best way to leverage customer analytics is to map out your entire customer journey and identify places where analytics can help (places where your funnel is “leaky”). In its simplest form, the process would work as follows:

1. Identify a particular problem that customer analytics can address.
2. Ensure that your data is ready to be used, that it’s actionable and can be deployed on the particular problem.
3. Deploy machine learning to address the problem and learn from the data you’re collecting, so your approach becomes “smarter” over time in addressing the problem.

Let’s identify customer “churn” as a big problem-to-be-solved (see step 1), which it certainly is. In step 2, you would need to collect and then leverage relevant data to better understand where and why customers are leaking out of your funnel. Then you’d build a model based upon this data, deploying machine learning, in order to help you predict the moments and the reasons your customers leave. So instead of passively watching customers leak from your funnel, you would know why they leave and be able to proactively engage them to prevent churn.

Machine learning basically uses math, statistics and probability to find connections among variables in your data, helping you optimize important outcomes such as retention. These machine learning models get even smarter at making predictions by constantly integrating new data. The result? You get data-driven insights that lead to marketing actions that retain your customers.

In another example of driving retention and ROI, you might apply customer analytics to better understand your customer’s past purchasing or browsing history, and then build a predictive model that could anticipate the next product a customer (or customer segment) might be interested in buying. This predictive model, using machine learning, would then help you identify the right customers (and the right times) for up-selling or cross-selling opportunities.

To do all this and more, you need the capacity to build models based upon quality data and deploy machine learning so these models get smarter over time, driving relevance and stronger customer engagement. You need a data and analytics platform that allows you to make your data actionable (the old saying remains true, “garbage in, garbage out”). By leveraging an automated customer data platform with machine learning analytical capabilities, you can leverage your data to reduce churn and boost ROI.

June 14, 2019

How Hyper-Personalization Helps Build Loyal Customer Relationships

By Venu Gooty, HGS Digital

Businesses and brands have always strived to segment and target their customers to be able to adapt their communication to the exact client needs. With the advent of the digital era, the customer segmentation is now undergoing an enormous transformation.

Today, it’s the age of hyper-personalization, a paradigm shift in the marketing industry that’s made the biggest businesses sit up and take notice. Hyper-personalization demands pushing the boundaries of personalization to an individual level and adapting functionalities and interactions in real time to offer each and every customer a truly unique experience that delights them and keeps them coming back for more.

For effective hyper-personalization, customer data is a prerequisite and rightly so, since it is one of the industry’s most valuable assets. So, what do customer satisfaction, customer loyalty, and hyper-personalization have in common, you may ask. Well, everything.

How does data help with hyper-personalization?

True one-to-one personalization requires a lot of data about every aspect of customer behavior. Mature brands have been pushing the envelope with hyper-personalization thanks to the help of advanced data management and data-driven marketing. And, this is exactly where a good Customer Data Platform (CDP) steps in to take over the reins. It is the Customer Data Platform that processes large amounts of information flows, analyzes this data and converts it into actionable insights that can be utilized by marketers to tactically anticipate a customer’s needs. Personalizing your business’s communication and campaigns can mean making or breaking a brand. Today’s consumers are tech-savvy and demand experiences that are relevant to their requirements, making hyper-personalization one of the best marketing investments you can make.

Gartner predicts that over 50% of companies will redirect investments towards customer experience innovations in 2018.

What’s more, today’s customers are comfortable, to a certain extent, with their favorite brands knowing their individual information, such as product and personal preferences so they can enjoy a tailored experience crafted specifically for them. Customers also value their time, and pre-set buyer journeys allow them to get to their destination faster. This means organizations can use data to hyper-personalize experiences, albeit with customer consent where needed.

How does hyper-personalization help achieve customer satisfaction and drive revenue?

Research supports hyper-personalization, with studies stating that reaching out to a customer personally drives engagement, brand loyalty and therefore, revenue. For instance, studies by VentureBeat displayed results that a simple change such as using a customer’s name in emails pushed the email open rate up 29.3 percent on average. The same study also stated that websites using personalization drove up their page views as well as conversion rate by a large percentage.

Once you segment your customers using behavioral data, you can understand each segment and tweak its respective buyer journey. This is easily achieved by a CDP that integrates data from multiple channels, online and offline. Marketers can study these segments, and the results can then be utilized to create a number of micro-segments, which can then be used to personalize journeys further, much to the customer’s delight.

By focusing on defining your customers, their preferences, issues and challenges, you can access intricate details about the pain points in their purchasing journeys and find out what exactly it is that affects their buying decisions.

Now that you have the capability to listen as a business, you automatically have the means to respond based on the intentions of a customer. For example, if a customer has abandoned their cart after adding products to it, you can deliver a relevant and timely communication with a form of incentivization, like a discount, which can then act as a trigger to the targeted customer. The best part about all this? It’s all automated, thanks to an effectively plugged in CDP.

What is big data’s role in hyper-personalization?

A large number of businesses manage to personalize their communication and product offerings; however, there are many companies who continue to struggle to scale and seamlessly stitch data across multiple channels. This is where technology has a vital role to play, and big data is what helps marketers tie it all together.

To put it simply, hyper-personalization can only be achieved through advanced analytics as this is precisely what allows businesses to react and reach out to customers individually and in real time. It is big data that holds the key to understanding the customer, their personality, attitudes, geographic locations and other underlying factors that can offer marketers the ability to drive personalization in an omni-channel and digitally connected environment.

Big data means nothing if not used correctly. The benefits need to be translated to the consumer and consequently, to the brand. According to McKinsey, personalization can reduce acquisition costs by as much as 50%, lift revenues by 5-15%, and increase the efficiency of marketing spend by 10-30%.

What are the challenges to hyper-personalization?

Well-thought-out customer personalization requires businesses to redefine their strategies to optimize results. A business serious about taking advantage of personalization needs to understand the state of their industry and build the required technological capabilities that support it.

One of the biggest challenges marketers face when it comes to personalizing customer journeys is gleaning relevant information fast enough. CDP steps in here with the help of Artificial Intelligence and Machine Learning to tackle this challenge head on.

Another challenge often faced by businesses is the security and compliance required to manage large amounts of data entrusted by customers to the company. Today, consumers are open to trading their personal information for free or improved services which leads to a large amount of Personally Identifiable Information (PII) data that your business is directly responsible for securing. Adding to this is the IoT (internet of things) and its hyper-connected nature, which can turn out to be a real security nightmare. With a CDP, businesses can control what data is accessed by whom, leading to better governance of data, leading to increased data security.

So, there you have it; to conclude, hyper-personalization doesn’t have to feel like a daunting and overwhelming undertaking that requires big bucks to implement. Successful businesses often begin small, creating a big impact quickly in the Martech ecosystem. Taking the time to incorporate a CDP into your business process will reap your organization awards in the short and long run.

Want to learn how other organizations are building their CDP? Reach out to me at cdp@hgsdigital.com.

June 12, 2019

The difference between real-time and instant data is the difference between personalisation success or failure

By Joe Cripps, Celebrus

In the context of technology marketing, the term real-time has been ubiquitous for as long as I can remember. And for those who are responsible for evaluating and making decisions to procure technology solutions, real-time has become a concept to be wary of. So why the scepticism? Real-time is perhaps the most over used and certainly the most abused phrase in the history of technology. Quite simply, the number of technology vendors who have laid claim to being real-time and the vast range of their capabilities, has made the term meaningless.

Perhaps we should draw a parallel with another industry whose marketers are obsessed with speed – the car industry. This highly regulated market obliges manufacturers to publish auditable performance figures relating to the speed and economy of their products, and these figures are a significant/important evaluation consideration to would be customers. Ironically in the tech industry, where purchases involve much higher levels of investment, and in theory more sophisticated and professional evaluation and procurement processes, there is no such objective performance data available in many cases. If the car industry was like this, budget low powered shopping cars would be claiming supercar performance.

In a market where anyone can claim to have real-time, it is very hard to accurately establish whether a software vendor’s solution is actually capable of enabling your intended use case. Of course, many uses of CDP technology does not require real time capability. For example, most analytics projects do not require data instantaneously because the insights from analytics are generally not applied in-the-moment. Similarly, if your customer data is being used to personalize email or social media campaigns, the data simply needs to arrive before the campaign is launched rather than within milliseconds of the data being captured.

However, if you are aiming to activate customer data for use in-the-moment, it’s hard to deny that you will need the data instantly. For example, if you are using your data to personalize the content of a web page or mobile app, the data needs to arrive within milliseconds, in significantly less time than it takes for the page to load. Even if the data arrives in one second, this in-the-moment personalization, because the data needs to arrive within a decisioning solution which generates a next best action and personalized content must then be created and served to the CMS. All of this needs to happen in significantly less than a second, because the page content will load in less than this time.

Now, it seems reasonable to assume that a vendor claiming real-time capabilities would be able to achieve what I have described above, but not everyone’s definition of real-time is quite this exacting. In reality, the marketing departments of many vendors make bold ‘real-time’ claims for solutions that take multiple seconds, minutes or even hours to connect data. That is why we at Celebrus have found a better term to describe the speed of our data connections. Because true real time capabilities should involve zero latency. In-the-moment personalization requires data to be available instantaneously, and Celebrus is one of a very exclusive selection of solutions that can deliver instant data.

Celebrus instant data is connected within less than 500 milliseconds of the customer interaction taking place. Celebrus’ unique tagging free technology captures the interaction data, identifies and profiles the customer, detects key signals of opportunity or threat within the customer behavior and connects either these signals or the entire data stream within the customer profile to the chosen campaign end point or decisioning solution.

We think the instant data term makes our capabilities a little less ambiguous for anyone considering which CDP to implement. But ultimately it’s not the name that the CDP vendor uses to describe their capabilities that’s important. It’s about defining your use case and determining how quickly you will need data to be available in order to achieve your desired outcome. If you want to achieve genuine, real-time personalization across digital channels, you will need a CDP that can capture and connect customer data within significantly less than a second.

Find out more about Celebrus instant data capabilities. Click here to contact our expert team.

June 10, 2019

3 Tips for Achieving Customer Data Quality

By Ariella Brown, Zylotech

You may have heard the expression, quality doesn’t cost -- it pays. A more precise formulation applies to business in the form of the 1-10-100 rule of data quality. The idea is that while it could cost you $1 to corroborate the data upon entry, it costs $10 to clean it later and $100 to leave it uncorrected due to the various losses that will result from it. How to prevent that happening? Adopt a CDP solution.

Losses due to poor data quality cost the US economy $3.1 trillion annually, according to IBM’s 2016 estimate, and concern about data quality has risen since then. According to Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report, it grew from 75 percent in 2016 to 89 percent this year. It also found that only half of those surveyed express confidence in their own data.

As we discussed here, the CDP is not merely a customer database. A CDP enables businesses to automate best practices for data quality, assuring complete, integrated, updated, and cleansed data. Essentially, a CDP puts into action the core best practices for maintaining data quality: cutting through silos, synthesizing data for strategic marketing, and eliminating the headaches that result from outdated or duplicated data.

1. A CDP solves the silo problem. Bhavesh Vaghela was among the experts whose customer data management tips were shared in an article on best practices. He identified his top customer data management concern as “siloed internal structures.” The problem it poses is not just a lack of convenience in one centralized repository of data but a setup that “stifle[s] collaborative learning and prevent[s] organizations from getting a better understanding of the customer journey.”

Vaghela expounded on why breaking through silos is so crucial: “With a myriad of touch points connecting customers to brands, it’s now more important than ever for these different touch points to connect and form a cohesive brand experience. But when data is not shared between departments, this cripples the ability for companies to make the most informed decisions about their customers.”

2. Getting all the data streams to run together is the first step toward what Joe Pino identifies in the same article as his primary concern: “to focus on centralization, particularly with the fast, furious, fragmented nature of cross-channel customer data.” Achieving that gives you a much more complete picture of what your customer is about. Pinto adds, “In turn, this approach allows marketers to become more strategic, gain greater understanding and have greater engagements in order to deliver relevant messaging.” Ultimately, your centralized data enables you to “start generating powerful results from it.”

3. There is such a thing as too much data that can actually cost you in terms of wasted resources and a less informed look with your customers. Accordingly, Mathew Boaman’s top tip for the article is to “avoid duplicate data.” Even if the bit of digital bandwidth is of no concern, it is a source of “frustration for employees using the data and also potential customers who are receiving inquiries as a result of the duplication.”

Boaman explained why the problem is such a common one:

Duplicity happens very frequently as companies change business names, move addresses or transfer phone numbers. Imagine that both Company A and Company B are both in the same outbound marketing campaign and are simultaneously receiving letters in the mail. This is a waste of money, and also makes the company sending the letter look bad.

To combat that problem, the CDP ascertains that the data is clean and resolves any duplicates that will impede optimal results for your marketing team.

The Zylotech platform assures data quality because it collects and merges both anonymous and known user profiles back to a single record through an automated process in real time. AutoML comes into play to probabilistically match two seemingly different records and create truly complete customer profiles.

On that basis, it gathers together unified customer data, drawn from all relevant sources to build highly intelligent and centralized segments that can be delivered downstream to all your marketing channels. As activations happen across all your connected marketing tools, new events are picked up in real-time by Zylotech, creating the most up-to date 360 view of the customer from which to derive insight through the embedded analytics engine.

A CDP solution makes it easy to avert the $100 losses and even the $10 cost of data quality cleanup. Like quality in general, the investment in data pays.

June 7, 2019

Open the Door for Customer Exploration: Uncovering CDPs’ Irrevocable Powers

By Amit Levkovich, Optimove

Turning your ideation to execution process from a long, stressful, multitouch point one into a smoother, self-sufficient, result-oriented procedure is not a fantasy. Learn what Optimove can do to help you uncover data insights.

I often encounter situations at work that bring me to this invaluable conclusion: Even the smartest marketer’s hands are frequently tied by the lack of technology and shortage of execution tools, that prevent her from achieving her goals and showcasing her skills as an experienced professional.

I want to share an interesting story: One of my closest friends currently works for a B2C enterprise as a marketing manager. We recently met up during the long Easter weekend. While catching up, she shared about the 10-step journey she had to go through to send her Easter promo campaign.

First, she needed to ask the Business Intelligence team to query the database and export the list of customers who fit her desired target audience. It took just two hours to receive the list. Unfortunately, the audience size was smaller than expected, so she needed to rethink and redefine her audience.

“If only I was able to query the DB myself,” she told me. “I could have been way more productive without having to depend on the other team.”

That’s just one aspect of being dependent on others - the data analysis team will then need to analyze the campaign, share the result in an Excel file, and who knows if it will take a day, two days, or arrive by sometime during the following week. From my experience - it’s usually the latter.

You can probably identify with her frustration. How could she turn the ideation to execution process from a long, stressful, multitouch point process that requires having to push most of her work to the last minute into a smoother, self-sufficient, result-oriented procedure, she could have full control over.

Untying the marketer’s hands

In recent months, Customer Data Platforms (CDPs) nabbed the spotlight they deserve in the martech conversation. These solutions allow marketers to collect, analyze and act upon all customer data within one interface among the “must have” tools in the current marketer’s toolbox. And one application inside the CDP I’ve benefitted greatly from is the customer insight discovery.

Marketers face immense pressure to increase their company’s revenue. Many times, we fall into the trap of focusing on acquiring new customers in order to meet these goals. Other times, marketers put an emphasis on maximizing the value of existing customers.

No matter which of these groups you fit into, much of your success will be tied to your ability to uncover insights about your targeted customers. Below, I offer two of my favorite examples (with short videos made with the help of my colleague) on how to leverage your CDP to uncover valuable insights.

1. Which promotion should you offer valuable customers at risk of churn?

As marketers, there’s a fine line we need to tiptoe around when creating promotions to stave off churn. On one hand, we want to provide customers with a juicy offer they can’t resist. On the other hand, we don’t want to cannibalize our revenue with an aggressive offer. By storing all customer data in one place, you can easily access your segment’s history and confidently answer this question:

Video no 1

In the example above, the average order value for this “High-Value High Risk” customer segment was $331. The average price paid per item was $116.3, and the average items per order was 3.4. The marketer who can easily find and act upon this information will probably offer either a 10% discount for orders above $380 (for a total order value just above the segment’s average), or a quantity discount for orders that contain more than four items. As dealing with high-value customers usually involves outliers, exploring the groups key characteristic and reviewing their range could also determine whether this group should be split to more granular groups. In this example, the average and median are relatively similar, meaning this group is pretty homogenous.

2. Are you acquiring valuable customers?

Sometimes, marketers aren’t interested in discovering insights to create promotions, but rather to understand if different actions or strategy changes had a positive impact. One example of positive impact that could result from a strategy shift could be whether customers acquired after the change had higher future values than before. With Facebook ad spend projected to continue growing in 2019, we imagine marketers are trying to understand if new strategies are improving their results. Leveraging a combination of historic and predictive values from your CDP could answer this question in minutes:

Video no 2

As seen in the example above, marketers can quickly compare the future value of customers acquired before and after a Facebook acquisition strategy change. In this case, the average went from $48.3 to $54.2, a massive uplift.

Focusing on insight discovery

Insight discovery is not a new obsession for marketers, but only CDPs allow marketers to do this on their own terms. Your discovery could focus on identifying the ideal segment to engage with, the precise manner in which you should do so, or even what you should offer. And once you’ve run your campaigns, it should focus on seeing whether your efforts impacted company revenues in the short and long term.

This can’t be mastered after quickly glancing at a few how-to articles, which is why our webinar offers in-depth, easy to grasp examples that prove the benefits of customer exploration capabilities.

June 5, 2019

An early look at CDPs in the APAC market

By Susan Raab, CDP Institute

Demand for customer data platforms has grown internationally in the last two years, particularly in Europe and the UK, so this seemed like a good time to look at the APAC market. Leaders there report Asia-Pacific marketers are acutely aware that they need the functionality of Customer Data Platforms but knowledge of CDPs as a distinct class of software is still in the very early stages, according to local experts interviewed by the CDP Institute. We spoke with executives at Knowesis, Lemnisk, Manthan and MarketSoft to learn more about the state of the market and trends they see evolving.

As in the U.S., the UK and Europe, the main industries to watch for CDP are retail, telecommunications, ecommerce and finance along with government and travel. These industries are feeling pressure to move rapidly to find new ways to meet customer and corporate expectations for improved identity recognition, personalized customer service, increased marketing efficiency, and ROI.

“We are seeing emerging awareness [in Australia/New Zealand] with early adopters of genuine CDPs coming on board,” says Daniel Cummins, CEO of Marketsoft, a consultancy based in Sydney. “At the same time, there are a number of players claiming to be CDPs, but who have products that only offer a portion of the functionality, or without the underlying strategy, so there’s inconsistency about the concept’s definition itself.” 

Amit Agarwal, Senior Vice President at Manthan Systems, concurs that “awareness isn’t there, and there’s a big need to evangelize about CDPs and be more aggressive.” Manthan is headquartered in Bangalore with offices in U.S., Singapore and UAE, and serves global retailers and restaurant businesses, hosting over 250 million customer profiles worldwide. Agarwal said Manthan has had significant success making inroads, including with a large-scale implementation for Future Group, a top food, fashion and consumer goods conglomerate with 1,800 stores across India and sends 200 million customer communications every month.

Nathan Rae, chief business development officer for Knowesis, agrees that “customer awareness is still very low,” but he says, “awareness is building quite quickly, and vendors are pushing” the technology. Knowesis is a Singapore-based CDP company with offices in Thailand, Malaysia, Indonesia, Australia, India and the UAE, and 13 enterprise customers. “We still don’t see a lot of RFPs asking for ‘CDP’,” reported Ajith Kumar, Knowesis’ vice president for professional services. Kumar said he’s seeing a lot of analytics providers using some CDP terminology and believes that, “once Gartner and Forrester begin talking more about CDPs, it will have a big influence with enterprise customers here.” He expects this to make it easier for marketers at companies to categorize the RFPs and to tell higher ups why they are shopping vendors in this specific domain.

Rahul Thomas Mathew, Director of Marketing for Lemnisk, observed that the recent Salesforce and Adobe announcements on their CDP plans have sparked a lot of curiosity. Like the others, he sees this as the right time to broaden understanding about CDP in the APAC market. Lemnisk is a CDP company specializing in financial services, headquartered in Bangalore with offices in the U.S., Singapore and the UAE.

On the sales side, there is an additional challenge in APAC companies because while marketers recognize the value having a CDP, IT departments play a major role in decisions and often have other priorities.

“The best case studies and projects are when the decision sits with marketing and is made with customer use cases in mind” says Marketsoft’s Cummins. “If we can get involved at a company supporting CDP from the top down, that’s ideal. We try to position CDP as a service as much as possible via marketing, and then allow IT to assume ownership once they build the capability.”

Corporate decision makers tend to be CMOs, and in some companies, the chief digital or chief experience officer, said Cummins. Pricing in Australia ranges from AU$150,000-300,000 for technology spend, and from AU$100,000 on the low end to AU$300,000-600,000/year for services on the upper end.

All agreed that privacy is a small factor in APAC countries at present, but thought it was likely to grow as a concern. Cummins observed, “GDPR is more of a perception than a reality here, but one of the biggest benefits for us has been that it has pushed awareness. There’s definitely a new level of IS [Information Security] awareness in terms of risk, adoption, and best practices, but there’s a long way to go.”

Rae and Kumar said a lot of governments in their market are cautious when it comes to PII (Personally Identifiable Information) data being stored on cloud, especially for local companies of significant size.

APAC locations mentioned as most engaged in CDP were Australia/New Zealand, Hong Kong, India, Singapore, and the UAE.

As for trends to watch for, Agarwal at Manthan believes educating the market via the demarcation of CDP and RealCDP, as planned by the CDP Institute, will help to offset efforts by other marketing automation players to position other types of products as Customer Data Platforms. Those often sell customers short by just addressing two or three data sources and channels, rather than providing true unified customer data.

Mathew at Lemnisk believes that “CDP’s tightly coupled with other vertical focused offerings can drive significant traction and value in vertical markets.”

Finally, Rae sees a key trend in the movement evolving in APAC companies from on-premises data stores to the cloud, since that will require “refreshing platforms and being willing to look at new tools.”

June 3, 2019

How AI and Machine Learning Are Impacting B2B: 3 Great Use Cases for CDPs

By Chuck Leddy, Zylotech

An earlier Zyloblog post described the multiple benefits CDPs offer technology companies, benefits that go way beyond “just” the marketing function. This post will explore why so many B2B companies are now choosing CDPs in the noisy marketing technology/martech landscape (now with over 7000 vendors), what CDPs offer them, and how they’re implementing CDPs for three important, marketing-related purposes: Account Based Marketing (ABM), ID resolution, and GDPR compliance.

Unlocking the value of data: 3 key questions

Collecting raw data, by itself, provides almost zero value in B2B or anywhere else. Data needs a strategy and a structure to unlock its massive potential. How should you begin?

Start by defining exactly where your B2B company wants to go (i.e., map your goals), and define how you’ll navigate to get there (i.e., defining your key performance indicators/KPIs aligned with those goals). You must then answer 3 key strategic questions: (1) what data is most relevant to the business outcomes (goals and KPIs) you seek to drive (hint: it’s usually connected to ROI/return on investment) and (2) how you can leverage data to drive organizational decision-making, including around what products you make and how you engage customers?

Answering that second question will have you converting prospects and building customer engagement/lifetime value, while creating great, full-funnel customer experiences. Only by answering these two “data-needs-a-strategy” questions above can you begin asking the third, “data-needs-a-structure” question: (3) what particular technologies, tools and processes can help our B2B company reach our strategic goals? That’s where a CDP comes in.

3 great B2B use cases for CDPs

CDPs with machine learning give you complete, actionable visibility into your customers’ behavior: you can engage them across multiple channels in real-time, plugging leaks in your funnel (i.e., driving customer retention), segmenting customers to drive ROI, and leveraging predictive customer analytics to identify (and take advantage of) cross-selling and up-selling opportunities. An automated CDP helps you become truly customer-centric in how you run and market your B2B business. What follows are 3 of many great B2B use cases for CDPs:

1. Account Based Marketing. ABM is a B2B strategy that concentrates sales and marketing resources on a defined set of target accounts within a market and leverages personalized campaigns designed to resonate with each account. It’s a top trend in marketing today, and your CDP complements and enables it, helping you deeply understand your key accounts and key personas to drive more ABM revenue.

Your CDP provides actionable, timely account intelligence, customer profiling, persona matching, and ongoing data enrichment of target accounts that will drive ABM success. You can leverage a comprehensive view of each account, while your contact data updates throughout the process. Learn who is connected to who, and how, with key account data that tracks your account’s different business units and organizational hierarchies. Know how they make purchasing decisions that impact your revenues, then influence those decisions through ABM.

2. ID resolution. The first rule of sales and marketing is “know thy customer,” but that can be challenging when it comes to digital channels. A CDP can help by organizing collected data points, making them actionable throughout the funnel. A CDP’s data enrichment function will simply fill in missing data fields such as customer names, email addresses, mailing addresses, phone numbers, and more. You can better know your customers through their unique identifiers, which helps you engage them.

A CDP’s data enrichment function can also sift through your web traffic and transform anonymous customer data into actionable assets. It does this by resolving identities and enriching relevant information through the use of cookies, device IDs, or IP addresses. With now-relevant customer data in hand, you can more engage with known customers, not strangers.

3. GDPR compliance. When B2B marketers gain a deeper understanding of who customers are and what they need, as CDPs enable, those customers tend to give you “permission” to market to them. This “permission” approach to marketing, pioneered by Seth Godin, is necessary in a world where data privacy has never been more important (especially to customers).

The General Data Protection Regulation/GDPR means you’ll need customers to actively opt-in, giving you permission to market to them. Marketing to customers you don’t know with offers they don’t find relevant will ultimately lead to customers refusing to stay “opted-in.” Know your customers and their needs, or risk violating a growing regulatory framework around data privacy, of which GDPR and California’s Data Privacy law are two recent examples. A CDP lets you manage and maintain customer permissions, so you retain them.

May 30, 2019

The Differences Between Attracting Customers into Your Store, and Catering to Them While They’re in: Combining Fast and Slow Data

By Shai Frank, Optimove

Luring the right people with the right offers requires a rich set of data and sophisticated customer modeling – but it’s a different set than what is needed to react to their signals when they’ve stepped in the door. The magic happens when you combine.

In today’s digital realm, we should always aspire to attract the most relevant people to our business and guide them to the right sections with the right offers to maximize the chances they’ll make a purchase. In order to do so, we need to understand their preferences and their willingness to buy, to learn best what makes them tick.

Some customers always look for bargains and sales, while some get most excited about new arrivals. In order for us to identify these preferences and tendencies, we need to have a broad set of data about each customer, such as their demographic characteristics and purchase history. This data must be integrated from multiple systems, cleansed and matched, in order to allow for segmentation models, predictions and recommendation engines to produce reliable results.

That said, we should make sure we know the difference between the sets of data we work with, to come to the holistic, 360° view of our customers. Most data used for profile creation, segmentation and predictive modeling is historical data, and therefore these processes are non-real time by nature. We call them “Slow Data.” Real-time engagement using live signals is what we call “Fast Data.” Both kinds serve us for different purposes, and they go hand-in-hand. In this piece, we will clarify how to use these two sets of data.

Nice and slow

Look at this example: When a user clicks the purchase button on a website, it doesn’t necessarily mean that this purchase will be completed, and it does not indicate whether or not the customer is happy about the newly purchased product. We know that a transaction could be declined, that the customer may return the item or even submit a negative review.

Because we do have these scenarios happening, and more often than not, we cannot rely solely on engagement data obtained in real time. Doing so, we will misclassify customers who returned their last purchase and placed a bad review as “repeat customers.”

The more accurate data required to create reliable customer profiles can only be obtained from “systems of record,” and then this data must be processed and modeled. More than that, some insights can only be generated and validated after a customer’s actions were compared to that of a different customer. All this does not happen in real time.

Once we have the right data, we can create multiple customer segments and apply predictive models that will help us tailor the right message to the right audience.

Going faster than a roller coaster

There’s an important point that needs our attention here: We’ve talked about how automation without orchestration leads to chaos – and the more granular our customer segments are, and as we add more execution channels, the harder it becomes to effectively manage all these communications.

In many cases a single customer could be eligible for multiple campaigns, and if these are all sent out on the fly, without considering what other campaigns were already sent or what other campaigns are going to be sent later that day, the same customer could get contradicting offers or a less-relevant campaign that could result in a missed opportunity. We should always plan our marketing campaigns in advance and set a framework of priorities and exclusion rules, so the system can orchestrate all these scheduled campaigns and optimize the communication to each customer.

Once we manage to get our customer into the store, we want to switch to a different mode of operation: We still have all our pre-processed customer profile, but now we also have live activity feed from the store that we need to use in order to respond in real time to specific signals coming from the customer. For example, if we know that a certain customer is a deal seeker and we see her currently looking at non-discounted products, we can point her to the clearance section of the store. Another example could be based on our modeling of the customer’s preferred product category: We can welcome a customer in our store by highlighting a promotion for a product in that specific category.

The tortoise and the hare

So far, we talked about how in-store engagement is a real-time process, while profile creation and smart orchestration are not real time. But there is also a third option in between, in which the marketing plan is built in advance but adapts itself to short-term updates. Consider the following scenario: We plan a campaign for some of our one-timer customers, trying to encourage them to make a second purchase, and we schedule this campaign to be sent this afternoon. Let’s also assume our system already completed the modeling and prioritization and has identified the optimal list of customers which should receive this campaign today, with their specific product recommendations. But right before execution, we find out that a couple of these customers just returned their last purchased items – which significantly changes their profile and the way we want to approach them. For them, we are no longer trying to activate one-timers but rather save them from churning. In this case, and even though we already established the optimized list of the one-timer campaign recipients, we need to re-evaluate the audience just before the actual execution time in order to make sure we exclude any customer that no longer matches the desired profile. This example shows how a combination of fast and slow data processing and modeling can help reach the optimal result.

In conclusion

Identifying a customer’s profile is based on different data sources – integrating and cleansing them, aggregating, and running various predictive and AI models on top of them. But to get the full picture and cater to the ultimate goal of reaching the right customers with the right offers at the best time requires not only to know the difference between slow and fast data, but more importantly, that they always go hand in hand.

May 27, 2019

Encouraging Customer Loyalty - Making the First to Second Purchase

By Roisin Evans, RedEye

Research from National Express suggests that, on average, Britons book their next holiday just 37 days after returning home from their previous one. If they booked the previous one with you, that’s a golden window of opportunity to turn that first purchase into a second. It shouldn’t be one you waste.

The ability of a business to retain its customers is crucial for its growth and success. In order to do so, many include in their business objective decreasing the percentage of single purchasers, while increasing the number of multiple purchasers and therefore the overall customer lifetime value (CLTV).

Knowing that people returning from holiday are keen to book their next trip so soon (and also knowing from personal experience how the feel-good factor of a great break can make you anxious for the next one) it’s easy to see how you might encourage that next booking.

You may start, for example, with a “welcome home” message – hoping they enjoyed the holiday, but sympathetic to the truth that it’s Monday again (ugh). Then, a few days later, you can start suggesting new destinations based on where they’ve been before, maybe with a discount code to use in their next booking? Little nudges that show how much you care can encourage your customers to come back, again and again.

But – oh yes, there’s a “but” – not all sectors find one-time purchasers thinking about their next purchase in the same way as holidaymakers. After all, if I buy a new pair of trainers, I’m not immediately thinking about buying my next pair having worn them a couple of times (unless they’ve fallen apart, in which case, I’ll be looking for a refund...)

Let the numbers guide you

For a first to second campaign to be profitable, the best you can do is to maximise your resources, distinguishing between the customers that are worth pursuing to those that are not. How? Not based on your gut instinct, that’s for sure! You have something in your hands that’s much more powerful and precise: your customer database.

The insights from customer data are your best chance to get to know your customers on a deep level and, therefore, to create communications that will engage and interest them.

Data can help you build a profile of the customers most likely to make a second purchase, analysing details such as how much time passes between the first and the second purchase, or how much discounts and peer reviews influence them. Of course, all of this is the more efficient the less your silos are separated, but more on that in a future blog post.

Say you have the optimal database at your disposal, one that can deliver you a single customer view on all channels for each one of your customers. Let’s take a look at the next steps:

• First, you need to define the timeframe for when a customer typically will purchase again from your brand.
• Then ask yourself, does the purchase value or product, customer type, time of year, lifestyle profile or any other factor affect whether the customer will purchase again? Look carefully for these correlations.
• Now you can build upon your previous analysis and create a full profile of first to second purchasers.

Congratulations! You have now a complete understanding of your customers. The final stage is to move to a predictive model, whereby customers are assigned a likelihood to become first to second purchasers, allowing you to target those with high and low likelihood with more advanced tactics.

Automation in action

Once you have a picture of likely second purchaser built by data, you don’t gain just a deeper understanding of your customers and of how your business works. You can use these insights to create marketing automation strategies which will further maximise your resources. One way of increasing customer loyalty, for instance, is to make sure you maximise the excitement of their first purchase with you. That’s something we’ve been working on with Travelodge, and you can read more here.

Ultimately, the devil is in the detail. The more you get to know, and we mean really know, your customers, through data insights as opposed to “gut feelings”, the more likely you are to build a strong and lasting relationship with them and, therefore, increasing their CLTV and your revenue.

For some tangible advice, take a look at this infographic our in-house strategy team have put together, with tips on converting your new customers into loyal multi-purchasers.

Originally posted on the RedEye blog.

May 23, 2019

How Mobile Marketing Works in 2019

By Evan F.P., PostFunnel

Mobile marketing has become a key part of modern digital infrastructure. How does this industry operate in 2019, and what strategies are currently at play?

For almost two decades, tech experts claimed the future would be mobile. With an estimated 2.7 billion smartphone users around the world, it seems that future has finally arrived. Advertisers and marketers have clearly adapted to this new reality, seeking out new ways to turn traditional marketing strategies into effective mobile campaigns. Even major brands like Amazon have introduced customizable ad services that are gaining momentum in online and US retail spaces.

In 2019, mobile marketing has turned into a multi-channel discipline that supports massive segments of our online ecosystem. Yet, this future arrived so quickly that it can be challenging to explain or even grasp many of the seismic shifts this field experiences on an annual basis.

For that reason, it’s important to take a step back and broadly discuss mobile marketing in 2019, the various techniques at play, and the effectiveness of some prominent strategies.

In this new PostFunnel series, Nuts and Bolts, we’ll delve into the Martech world in 2019, trying to shed some light on main tools and best practices being used by you, our fellow marketers, in your day-to-day strategies. Every month, our experts will sink their teeth into another aspect of this fascinating field, with hope to inspire you to elevate your business through some smart marketing.

Check out our features section with special projects and articles for your reading pleasure.

What is mobile marketing?

As the term implies, mobile marketing is a technique where advertisers deliver communications to users via smartphones and tablets. As simple as that description sounds, mobile marketing encompasses a broad range of delivery channels including email, SMS messaging, push notifications, in-app advertising, QR codes, and many more.

Thanks to the prevalence and accessibility of today’s smartphones, mobile marketing has a higher potential to target specific audiences than perhaps any prior marketing discipline. Advertisers can deliver personalized messaging, deploy ads based on time of day or location, and design interactive ad formats that effectively engage specific demographics.

Why use mobile marketing?

Mobile phones are so globally ubiquitous that you’d be hard-pressed to find a more effective marketing platform. The overwhelming majority of adult populations worldwide own some kind of mobile device, while the global median for smartphone ownership is 43%. Customers use mobile devices to play games, watch movies, and communicate via social media — all fertile ground for marketing opportunities.

The significance of mobile devices is even higher in emerging economies, where cell phones have become the easiest method of gaining internet access. Meanwhile, in the developed world, the volume of online content accessed using smartphones has eclipsed traditional platforms such as desktop computers.

What is in-app mobile marketing?

In-app mobile marketing, sometimes referred to as app-based marketing, refers to the deployment of advertisements directly within an app itself. Since over 90% of time spent on smartphones is used to view apps, this is perhaps the most effective and cost-efficient marketing technique available to advertisers today.

The easiest way to deploy in-app marketing is through one of the titans of the mobile advertising space, namely Google’s AdMob or Facebook, or through a specialized in-app advertising network like Tapjoy. In order to monetize their apps, developers often integrate ad network SDKs that display ads when certain conditions are met. Some app publishers like Facebook even use Promoted Post services that seamlessly integrate ads into news feeds across all devices.

One important variant of in-app mobile marketing is in-game marketing, where advertisements are deployed directly within a mobile game. While there are certain ad formats and deployment considerations when delivering messaging to gaming audiences, marketing SDKs function in fairly similar ways to in-app mobile marketing on a technical level.

What is SMS mobile marketing?

SMS mobile marketing is the earliest form of the technique, first implemented when SMS and shortcodes launched in the early 2000s. It requires advertisers to obtain or capture mobile phone numbers and directly communicate with users via SMS messaging services. SMS mobile marketing can refer to both inbound marketing strategies for lead generation and outbound strategies to communicate promotions and events.

While SMS mobile marketing has been overshadowed by in-app advertising, it still remains a powerful strategy. On average, SMS marketing ads have a 98% open rate, a 45% conversion rate, and are typically read within three minutes of deployment. That makes it an impressively effective strategy for rapid engagement with a large volume of potential customers.

More importantly, SMS mobile marketing is widely used internationally, especially in regions like Europe and Southeast Asia. This broad reach is largely thanks to compatibility with non-smartphone cellular devices. SMS marketing is more strictly regulated than other marketing channels, but tends to benefit from having clearly defined best practices that are standardized through cellular carriers.

What are push notifications?

Push notifications are a type of message displayed on mobile devices by third-party apps that aren’t currently running. These notifications serve a variety of purposes, most commonly to inform users of incoming messages from social media apps. From a marketing perspective, push notifications are an ideal format for keeping users in the loop about new promotions or app features.

Above all else, the primary driver behind push notifications is customer retention. It’s easy for users to install and forget about an app, but push notifications let publishers and advertisers continue to communicate once the app is closed. Studies consistently show that push notifications can increase 90-day user retention from 3x to 10x depending on the effectiveness of your messaging.

What are QR codes?

QR codes are a type of matrix barcode that can be scanned by a mobile camera, usually activating a web link in the process. In mobile marketing, this allows advertisers to combine physical and digital marketing techniques by displaying QR codes in the real world. For example, a retail chain could place unique QR codes on receipts to link a customer’s online and offline identities, or a viral marketer might leave codes in public places as part of an augmented-reality game.

In the hands of mobile marketers, QR codes are unique tools that appeal to human curiosity can be placed anywhere, and are easy to track. Unfortunately, QR codes are also not as intuitive as other marketing strategies on this list, and tend to be used by a smaller subset of mobile users. That said, QR codes can be useful when deployed effectively, and are especially popular in regions like China.

What are mobile search ads?

Mobile search ads are standard search engine advertisements that are indexed and optimized for mobile devices. They can be displayed through a web page or search engine like Google, and typically integrate with smartphones to use features like “click to call.”

When search ads are optimized to match Google’s search interface, they have a higher chance of appearing when users search for related products or services on their mobile devices. Depending on the advertised business, a smartphone’s location service can also narrow down the search to relevant local companies. Google search ads can also feature a click-to-call button or click-to-install button as a call to action for your customers.

What are some mobile marketing best practices?

Always keep your audience in mind. A mobile marketing strategy that’s effective on social media won’t necessarily carry over to mobile games.

Be concise. Smartphones have limited screen space for deploying your message, and there are literally thousands of things users could do instead of viewing your ad. Get straight to the point, and give them a reason to engage with you.

Optimize websites for mobile devices. Much like our last point, transferring desktop-optimized web pages to mobile devices usually means your marketing efforts are lost to clutter and noise. Design mobile-specific versions of your sites that are optimized for on-the-go smartphone users, and build your marketing campaign around them.

Adopt multiple marketing strategies. There are many mobile marketing strategies available to advertisers in 2019. Don’t be afraid to experiment with models that show potential and reflect your brand.

Benchmark your results. Keep track of how users interact with each of your mobile marketing strategies. Follow conversion, retention, and engagement metrics to maximize your ROI.

Mobile marketing is a far more complicated field today than it was in 2000, but there are also far more ways to engage with your audience than ever before. By adopting the strategies listed above, your business will be well underway to expanding your reach across a variety of active channels.

May 20, 2019

Behavioural and Engagement Data Key to Success for Predictive Analytics

By Matthew Kelleher, RedEye

At a recent event I presented six case studies about the successful (i.e. they made money) application of Predictive Analytics. All the case studies were based on the application of Predictive Analytics to help target customers or prospects at various points along the customer journey, for instance, identifying the single buyers most likely to make a second purchase or which VIP customers were at greatest risk of lapsing.

At the end of the presentation I was asked, ‘What is your definition of Behavioural Data?’ I had repeatedly talked about the importance of accurate and complete data to drive Predictive Analytics and described 1^st party customer data as falling in 3 types: transactional, engagement and behavioural. But I had fallen into the trap of failing to explain what I meant by each of the definitions I was using. So, very briefly:

1. Transactional Data. Or RFM. Or RFV. What a customer has bought, how much, when, where… Data that is used to build RFM models (i.e. identifying prospects, single or multi buyers, VIPs etc.)
2. Engagement. Points of interaction with the brand, from opening an email to receiving a catalogue to visiting the website.
3. Behavioural Data. Usually related to website activity, what the prospect or customer has browsed, their recency frequency across devices, what they’ve clicked, if they visit the website direct from social, if they click on a discount offer, etc.

Behavioural data has always been critical. It is the core of data-driven personalisation. By building up, and allowing marketers to react to ‘behaviour’, from marketing the products someone is interested in through to identifying if the consumer is an offer junky or full price buyer, this is what underpins RedEye’s approach to Predictive and AI. Using this information to work in combination with engagement and transactional data identifies prospects and customers in terms of what someone will do next and when.

But despite presenting six case studies all showing conversion and revenue improvements, we can’t escape the fact that there is a little bit of market weariness to the subjects of Predictive Analytics and AI! Gartner are now stating in their Hype Cycle that Predictive Analytics has fallen into the trough of disillusionment! And with so many marketing tech businesses out there are talking about it, but not many are able to demonstrate the real value it can bring for retailers.

Back in 2016, Forbes research showed that 89% of marketers had Predictive Analytics on their roadmap. Fast forward to 2018 and 93% of consumer-facing businesses are unable to use Predictive Analytics. This really shows the disparity between the desire to implement Predictive Analytics vs. the actual implementation.

The aim of the presentation was to try to reinforce the potential value of these tools to the market, but the key is to start with data. Customers are getting more and more difficult to understand with the proliferation of marketing channels. Just recently WhatsApp was added into the fold, yet another channel that marketers can target their consumers through.

Brands are losing touch as they struggle to track all their customer’s moves, which in turn leads to a decline in customer loyalty. Consumers can feel their favourite brand just doesn’t understand them. As humans we just can’t keep up! This is where AI comes in!

Start by understanding your data. Where is it coming from? I recently joined a panel with the Head of CRM at Domino’s; he told me their journey began by creating a Single Customer view by collating their offline data and combining it with their online channels.

He was right: putting in the leg work at the beginning and creating a true Single Customer View was key. A Single Customer View means you can tie together transactional, engagement and behavioural data, allowing you to paint the full picture of your customers.

Finally, it is key to apply AI and Predictive Analytics to something tangible. At RedEye, our predictive models are based on the customer lifecycle. By making incremental improvements at each of the key customer moments, you can see substantial increases in overall customer value.

You can find more about how we’ve driven revenue increases for our clients using Predictive Analytics here.

Originally posted on the RedEye blog.

May 2, 2019

Customer Experience: A Tale of Two Opposing Views

By Jeff Teugels, Crossroad Consulting

One may not assume that a customer perceives a ‘customer experience’ as was intended by the provider. Conversely, an experience that is favorable for the customer does not always contribute a company’s performance. The challenge is to unite both perspectives.

Anno 2019, Customer Experience or ‘CX’ is omnipresent. The CX concept was introduced in 1982 by Holbrook and Hirschman as a holistic construct. In the meantime, both academics and practitioners believe that a favorable customer experience not only positively impacts customer satisfaction, customer loyalty, and word-of-mouth behavior – something customers themselves have known all along – but that it also is a compelling precursor of the much-coveted competitive advantage.

Despite this consensus, the CX concept remains foggy because the holistic construct has diverged into two mostly unconnected schools of thought. The main reason is that academics and practitioners tend to look at customer experience through one of two opposing lenses. One is the organizational lens and the other is the customer lens. This is one of the conclusions of a review of customer experience research since 1982 drawn by Kranzbühler et al. (2017).

The one who looks through the organizational lens assumes that experiences can be designed and that all customers will perceive stimuli alike. The one viewing through the customer lens ascertains that firms cannot deliver value since the customer is always a co-creator of value. While the former focuses on organizational structure, strategy, and customer-employee interactions, the latter considers individual customer journeys, cognition, affect, and senses.

Static and Dynamic Customer Experiences

A distinction is made between static and dynamic customer experiences. A static CX describes how an individual evaluates one or more touchpoints with an organization on a cognitive, affective, and sensory level at one specific point in time. A dynamic CX considers the evolving cognitive, affective, and sensory evaluation throughout the entire customer journey.

The organizational lens points to the design of static CX and to the management of dynamic CX, yet the focus on static CX tends to dominate. The customer lens analyzes customers’ perceptions in three planes: the static CX itself; how dynamic CXs are formed; and how cognition, affect, and the senses impact both static and dynamic CXs.

• The organizational lens focuses on what is within a company’s control; the customer lens looks at the whole picture.
  

As one can see, the famous customer journey is a key component of the customer lens. The ultimate goal of a customer journey is to “teach companies more about their customers in order to market better, sell faster and serve more effectively” (Milbrath, 2019). Being taught requires a willingness to learn and to shift to an outside-in view. Only then can one look inside one’s own processes and, with lessons learned from customers, improve them to match expectations.

Yet most organizations fail to truly master the art of customer journey mapping. Three reasons account for this fact. First, looking through the wrong lens sets one up for failure. Very few answers can be found in one’s navel.

• The one who’s usually left out of the customer journey mapping process is the one with the answers: the customer.

Secondly, the proliferation of touchpoints, channels, and offerings makes a customer journey non-linear, unclear, and unpredictable.

• The VUCA-world leaves most of us wandering around blind. Yet, nearly all answers are in plain sight outside.

Thirdly, in this day and age of big data, data still is unable to capture the emotions and feelings of customers.

• Organizations must develop, hire, or acquire the skills to turn an abundance of data into actionable behavioral insight and ditto foresight.

A study from the CMO Council published in 2016 among the top 150 senior marketers throughout Europe and the U.S. revealed that “only 5% of marketers say they have mastered the ability to adapt and predict the customer journey and what actions will derive maximum value.”

It can be done, and it takes two things to start

Among practitioners, the growing consensus is that one must look at the dynamic CX and not just at the static. Moreover, it takes the entire organization to support the customer journey. Two precursors to CX success are:

1. You must know who your primary customers are
2. You must develop a customer mindset

The first is the select group of people who unlock most value for your organization. The second is “vital to providing a consistent service that meets customers’ changing needs” (Brown, 2015).

• The two lenses need to merge: A successful, agile organization is interdependent from customer success. And customer success comes from deep behavioral insight and foresight.

To end with my mantra once again: “Customer behavior defines the organization; technology enables it, makes it efficient and scalable. Yet only hyper-relevance engages both customers and employees.”

References

Brown, L.R., and Brown, C.L., 2015. The Customer Culture Imperative: A Leader’s Guide to Driving Superior Performance. New York: McGraw-Hill Education.

CMO Council, 2016. Predicting Routes to Revenue: Identifying Real-Time Decisions for Business-Driving Engagement (White paper). San Jose, CA: CMO Council.

Kranzbühler, A-M., Kleijnen, M.H.P., Morgan, R.E., and Teerling, M., 2017. The Multilevel Nature of Customer Experience Research: An Integrative Review and Research Agenda. Oxford: International Journal of Management Reviews, British Academy of Management and Jon Wiley & Sons Ltd.

Milbrath, S., 2019. The fundamental flaw in customer journey mapping—and how to fix it. [online] Vision Critical Communications, Inc. Available at <https://www.visioncritical.com/blog/customer-journey-mapping> [Accessed 22 March 2019].

April 29, 2019

Data Lakes and B2B Customer Data Platforms: A Win-Win for Marketing and IT

By John Hurley, Radius

We’re hurtling at warp speed toward a time when customer experience will reign supreme. In fact, according to Walker, customer experience will supplant product and price as the key differentiator by 2020. In 2014, Gartner said in five years customer experience would be how 89% of businesses differentiate. It’s 2019 – time’s up!

The buyer’s expectation is to have a friction-free customer experience, even as that experience spans an increasing array of channels and touchpoints that rely on complex back-end business operations from vendors. Coordinating all touches along the journey is known as delivering a unified customer experience.

And it starts with unified data, which helps explain why solutions that help unify data are top of mind. Two of those solutions are Data Lakes and up-and-coming Customer Data Platforms (CDPs). Our team is increasingly seeing enterprises that aren’t sure how these two technologies relate and differentiate. It’s a matter of understanding each and how to take advantage of them to best serve the enterprise.

The Shortcomings of Data Lakes to Deliver Customer Experiences

The best place to start when assessing technology is always with a defined use case. Let’s take a look at some of the most common needs we hear from enterprise business teams.

A Data Lake can unify data, but it’s typically geared to serve enterprise-wide data and IT. In other words, it’s not tailored for the needs of go-to-market teams, like marketing and sales. Because it’s a repository for vast amounts of raw unstructured and structured data, a Data Lake can prove difficult to work with for those outside of IT.

They don’t include core capabilities needed to address business growth and the customer experience, like identity resolution or audience management tools. Yet these are essential for analyzing disparate customer and prospect data and combining them into a unified view.

Moreover, they lack out-of-the-box integrations with go-to-market systems and digital channels. Take the integrations for Snowflake, a leading cloud Data Lake. They do not integrate with the channels and solutions a marketer would need to run an effective campaign. Simply put, it’s challenging – and can be quite resource-intensive – adding new data sources and connecting new channels to a Data Lake, requiring coding and integration to enable new data feeds.

Since data is dumped into the lake without any up-front restructuring, resources are needed to apply advanced technologies to explore and gain insights from the data. This forces revenue teams to request data and reports from the IT department. Because these requests are not an IT priority, they often linger for weeks and sometimes months.

Go-to-market teams then have to prep the data for activation in its downstream systems – enriching and restructuring the data, such as validating email addresses and phone numbers – so it can be used effectively by sales and marketing.

This isn’t to deny the power of Data Lakes. They’re just not tuned to the needs of teams focused on driving revenue, customer engagement, and business operations. If the purpose of unifying data is to enable real-time decisions that help orchestrate a unified customer experience, then Data Lakes fall short. There’s no concept of creating – and easily acting upon – a single unified view of each customer in what is essentially a huge unstructured data repository.

CDPs to the Rescue: The Choice of B2B Go-to-Market Teams

That’s where CDPs come in. They make it possible for organizations to ingest and link customer and prospect data and all its detail from virtually any source – including third-party sources – in real time. As such, CDPs help unify siloed data around a customer view, yielding a persistent “golden record” of all knowable data about customers. Plus, CDPs make that record easily accessible on demand so marketers, sales, and revenue ops can ensure personalized and highly relevant customer interactions at every touchpoint.

No wonder Forrester predicts that 70% of B2B marketers will choose CDPs over data lakes in 2019.

As a system that creates a persistent, unified customer and prospect database accessible to other systems (as defined by the CDP Institute), a CDP better serves revenue teams. It’s a packaged platform that comes with prebuilt components and data models, enabling marketers and other business stakeholders to segment, analyze, and activate their data – no significant IT involvement required. In fact, go-to-market teams can easily share and activate data – and change or add system sources – without disrupting the CDP.

A Powerful Duo

Customer Data Platforms aren’t a replacement for Data Lakes. Instead, they’re a perfect complement to them. And, in many cases, enterprises already have – or need – both.

Both Data Lakes and CDPs are persistent stores implemented by IT that host customer data as part of big data infrastructure. But the similarities end there. IT-managed Data Lakes ingest enterprise-wide data – typically first-party data from internal sources – without altering the data form. On the other hand, marketer-managed CDPs unite first- and third-party data and enable a real-time flow of data into and out of the system.

With easy integrations to all channels along with built-in tools for even business users, a CDP makes it easy to view, pull, and analyze data and arrive at audience insights. Because of this – and the addition of third-party data – CDPs help enterprises improve their targeting and customer experiences. Moreover, easy channel integrations that yield net-new data pave the way for faster time to market and expanded customer reach.

In fact, Data Lakes are a key source of data for CDPs. At the same time, CDPs can help improve the quality and completeness of data in a Data Lake.

In essence, CDPs enable the alignment between revenue teams and IT when it comes to an enterprise’s data and technology ecosystem. While revenue teams can use CDPs to capitalize on data to drive business growth, IT teams can enjoy the benefits of CDPs and Data Lakes working in harmony to serve the business.

If you’re convinced your enterprise can benefit from a CDP, understand the pros and cons when it comes to building or buying one. And then download the Innovator’s Guide to B2B Customer Data Platforms – written by David Raab, founder of the Customer Data Platform Institute – and learn how to capture, unify, and activate scattered data.

April 25, 2019

Bad Sherlock and the Case of the Great Data Deception

By Jaeson Middleton and Dave Wardell, Intent HQ

Here’s the theory: if we take all the data we possess about customers, we could deduce a great deal and draw powerful insights. We could use this information to better look after our customers and sell better products and services, tailored to their needs.

It’s the Sherlock Holmes School of Marketing: stunning insights and conclusions drawn from the observation of disparate data. The first time Holmes and Watson meet, the detective deduces from looking at Watson that his new acquaintance is a convalescent army doctor freshly returned from the fighting in Afghanistan.

He explains: “Here is a gentleman of a medical type, but with the air of a military man. Clearly an army doctor, then. He has just come from the tropics, for his face is dark, and this is not the natural tint of his skin, for his wrists are fair. He has undergone hardship and sickness, as his haggard face says clearly. His left arm has been injured. He holds it in a stiff and unnatural manner. Where in the tropics could an English army doctor have seen much hardship and got his arm wounded? Clearly in Afghanistan.”

Clearly?

Well, maybe in those simpler Victorian days. But in our modern world, Sherlock might not be able to make sense of all of the data and may very well get it wrong:

Here is a gentleman of a medical type, but with the air of a military man.
[“I just like the look and the style. I’m not a medic – I work as a barista.”]

Clearly an army doctor, then. He has just come from the tropics, for his face is dark,
[“I just got back from a week in Ibiza.”]

and this is not the natural tint of his skin, for his wrists are fair.
[“Where I was wearing a wristband and a watch.”]

He has undergone hardship and sickness, as his haggard face says clearly.
[“It was a very heavy week, I am dehydrated and I seriously need to detox.”]

His left arm has been injured. He holds it in a stiff and unnatural manner.
[“I fell off a table during a drinking game.”]

So much for the famous detective’s showy parlour games. But there is more to Holmes’s view of the world than his logical pyrotechnics. His creator, Arthur Conan Doyle, knew a thing or two about information, as he shows in his books.

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.”
- Sherlock Holmes and The Adventure of the Copper Beeches

He knew the vital importance of possessing all the information, not aggregating or cutting out any data and being able to understand the patterns and connections.

“It is of the highest importance in the art of detection to be able to recognise, out of a number of facts, which are incidental and which vital. Otherwise your energy and attention must be dissipated instead of being concentrated.”
- The Adventure of the Reigate Squire

Moving from the nineteenth to the twenty-first century, we have radically changed the way we gather, analyse and interpret data. Modern police detection uses DNA profiling rather than Sherlock’s analysis of different cigar ashes, but twenty-first century marketing has failed to grasp the challenge and the opportunities.

Telcos in particular have not kept pace with the expectations of their consumers to provide them with a deeply personalised experience. There is plenty of effort, but much of it is wasted.

Most telcos still operate a marketing and customer experience culture embodied by the question: “What can we do next for our customers?” Campaign managers fight for the next 100,000 customers to put in the audience for their next offer. Contact centre colleagues are asked to sell the next best offer to broad segments of customers who simply aren’t interested. Customer journeys are mapped out at great expense to describe the customer experience for 10 to 20 different customer types.

Telcos do this because the data they have about their customers is limited. And most of the time it is also aggregated. They have CRM data, loyalty scheme data, third-party socio-demographic data, customer care contact data. This data tells the telco teams how much a customer has spent, what products they have, what they last complained about, or their propensity to churn. Laughably, some telcos call this their 360-degree customer profile.

This level of data used to be okay. But the world has changed. Customers’ expectations have massively increased, pushed up by the FANGs (Facebook, Amazon, Netflix and Google) of the world.

Let’s for a moment remove ourselves from big corporation mentality and consider how we think about and interact with our friends, family and neighbours. Humans interacting with humans. Let’s be more Sherlock.

Sherlock doesn’t think about Jane next door as a “Young Professional Mum”. He doesn’t think of her as a part of a segment. He thinks about Jane as a unique human, someone who works in the city, has a young daughter and enjoys running; she’s a fanatical cook and however much she tries, she can’t get on with the latest technology.

A Sherlock who gets his deductions right would look like a man of authority and intelligence – someone you could trust. A Bad Sherlock who ignored the data or didn’t understand the data would look like an idiot – a figure of ridicule.

Telcos face the same problem. They can deeply understand their customers, but they can (and have) alienated them by making broad assumptions based on data aggregation rather than personalised knowledge. Like a poor detective who keeps deducing wrong outcomes based on misinterpreted information. Customers lose confidence in the telcos, become frustrated and even start to mistrust them.

It’s paradoxical, my dear Watson.

“I had,” he said, “come to an entirely erroneous conclusion, my dear Watson. How dangerous it always is to reason from insufficient data.”
- Sherlock Holmes and The Adventure of the Speckled Band

April 22, 2019

How to Build the Case for a CDP in Your Retail Organization

By Kristen Carlson, QuickPivot

Interest in CDPs continues to accelerate, with more retailers realizing that traditional IT-led data management processes aren’t keeping pace with trends in customer engagement, including customers that are demanding a seamless experience across channels. In fact, according to Gartner’s Market Guide for Customer Data Platforms, in 2018, Gartner client inquiries pertaining to CDPs doubled when compared with the same period the previous year.

If you are a marketer that understands that a CDP will bring significant benefits to your organization, that it should be the center of a modern marketing stack, collecting data and feeding it to your organization’s multiple channels, how do you build the case for CDP implementation?

Make a Compelling Case to Senior Management

With so much to gain from a CDP deployment, it makes good business sense to spearhead the initiative within your organization. But how do you convince senior management to take that next step and implement a CDP?

We know that presenting a new budget item to senior management can be a hard sell, especially when they may not be familiar with CDPs, so we created a plan to help. In our guide, Building the Case for a CDP in Your Organization, we lay out four steps to help you demonstrate the marketing, operational and financial benefits of a CDP to your senior management.

In the guide, we suggest the following steps:

1. Highlight the three big benefits of a CDP
2. Demonstrate how a CDP will benefit multiple departments
3. Show use cases with results
4. Address the financials

Demonstrate How Performance Lift and Cost Savings Will Improve the Bottom Line

The first step involves demonstrating how a CDP will improve the bottom line by creating greater efficiencies in personnel and resources, and allowing for more automated, higher velocity marketing. It’s important for senior management to understand that a CDP will enable them to derive more value from customer data that they already have.

Next, make the case by showing how, even though a CDP will be owned and managed by marketing, it can positively impact other departments in your organization. Departments like IT, Finance, Merchandising, and even the C-Suite itself will realize improvements in effectiveness and reductions in cost.

Further make the case by introducing real-life case studies which show the positive results realized when retailers implement a CDP in their organization. Our guide provides examples of two such cases.

Finally, address the financials by demonstrating that many retailers see a return on investment in a CDP in under a year and some even see increased ROI in less than six months, depending on organizational goals.

A CDP’s value can be hard to argue against when you focus on the proven benefits: more effective and high-velocity marketing, increased efficiencies, lower costs and more satisfied customers. To learn how to make the case for moving forward with a CDP implementation, read our guide, Building the Case for a CDP in Your Organization.

April 18, 2019

Expected Excellence - The Growing Demand for Personalisation

By Sherrylyn Heise, IntentHQ

According to Forrester, “A laser-like focus on Customer Experience (CX) is the best path to business success”. Telcos need to up their game if they are to keep pace with the expectations of their customers. The key to this is the development of a more meaningful and relevant personalised experience.

Over the last 20 years, there’s no piece of technology that has revolutionised the way we live and work more than the mobile phone. It has transformed from a clunky curiosity to a powerful pocket computer that is all things to all people.

During that time, we have grown accustomed to the benefits of personalised products, services and messages. We receive traffic updates during our daily commute, listen to automatically generated playlists and travel with an umbrella because Alexa warned that rain was likely. No matter where we are, we expect our technology to cater to our individual needs.

Personalisation is not just a great selling feature, but a fundamental expectation customers have towards any provider. However, it is time to expand our thinking beyond features powered by personalisation and start a discourse on how we can make this the next big differentiator in the telecommunications market.

The fine line between convenient features and personalisation has not always been this clear. We remember a time where phones started offering T9, a method to predict text input, to make typing text messages faster, followed by the ability to send two texts, exceeding the character limit without having to press ‘send’ twice. While these features were milestones of the mobile generation, customers still did not expect their mobile phone provider to consider them as an individual./p>

Continuingly growing expectations represent a challenge that the FANG companies (Facebook, Amazon, Netflix, Google) have met. They never see customers merely as users of their technology. Instead, they aspired to make their products part of their customers’ lives. This required them not just to know whether John had trouble finding the right product online or the next series to watch, but rather knowing that John has two sons, the name of his favourite football team and the fact that he believes ski vacations are just too expensive for the whole family.

To achieve this, they need data and the ability to predict choices, spending patterns and future behaviour. They certainly didn’t get it right the first time, and every time, but customers initially accepted the existence of unrelated junk mail or sales calls for products that were irrelevant to their needs. As their marketing reach grew more powerful, we came to rely on things like Facebook birthday reminders, targeted shopping advice from Amazon and the fact that Google really does know where we live.

FANG companies base their success on three key building blocks:

1. Access to unfiltered, day-to-day life action data
2. The ability to extract insights from this data that creates a concept of individuality but respects the customer’s privacy, and
3. Rewarding customers’ trust by providing actual value.

Since the introduction of GDPR and international laws to follow, data sensitivity and a transparent correlation between data provided and returned value is fundamental to successful customer relations. Judging by these building blocks, telcos have the same, if not greater opportunities to create personal customer experiences.

Next Generation Customer Service and the Path to Successful Business

Successful companies have created truly loyal followers, not just through the superiority of their products and services, but by building highly personal relationships with their customers right from their first interactions. Companies such as Apple, Nike and Instagram have created loyal followers, guarding the business from bad publicity and supportive of the idea that not every new product release will be superior to the competition. Marketing and Customer Service lead a crucial role in establishing not just professional, but highly personal relationships with their customers. That said, customers are - of course - fully aware of the fact that a company with millions of users is not able to create a one-on-one personal connection. They do, however, expect an engine of intelligence smart enough to respect the customer as an individual with complex needs and unique circumstances.

If automated intelligence in marketing and support is paired with an informed customer service agent, customer interaction can create highly personalised experiences. The customer journey can be narrowed down to a few key experiences. We can all recall situations of calling customer service because we cancelled a service too late, we thought we were billed incorrectly or we finally picked up the phone after receiving 13 unrelated marketing communications. What customers remember is not necessarily the problem, but how it was handled by the business they engaged with. If these crucial situations are handled not only poorly but also impersonally, most positive experiences throughout the customer’s journey will be tainted and will put your business with this customer at risk. Lastly, in times of digitally shared knowledge, negative as well as positive experiences with customer service can be spread rapidly and at large, potentially risking your relationship with the rest of your customer base.

A Transformation to “Customer First”

Telcos are facing an opportunity impossible to ignore - the market is demanding personalisation and most telecommunication providers are using little to no technology to address this need. We consider three ways in which telcos can transform their operations and improve their performance. Tackling these issues will enable telcos to compete in the market going forward.

1. They must stop storing data as a random asset without returning visible, significant value to their customers. Most users are aware their data is being stored by their mobile providers, but it is questionable whether the value and reasoning is obvious.
2. Telcos need to lower their reliance on raw, plain data. Instead, they should apply methods of intelligence and machine learning to their datasets. Imagine how your customer service could change if you understood that a customer was actually paying for a phone used by his daughter. For example, would you offer him a better plan, knowing his wife lives abroad and streaming via FaceTime is a crucial element of his daily life?
3. They should evolve their internal processes. They must train their customer service and marketing staff to understand when an exception is appropriate to the rule. They need to be able to provide safe management of data and allow service agents to understand customers as individuals.

The challenge is significant and impossible to ignore. Download our white paper “Customer First Transformation” to learn how you can begin to overcome these barriers.

April 15, 2019

Using AI Intelligently, or How to Avoid the “Zombie Marketer” Apocalypse

By Monica Landers, StoryFit

All marketers are storytellers. You’ve heard this a million times because it’s true. As a marketer, you identify key audiences, get to know them, then craft a narrative that helps each of those audiences picture themselves using your product or service and being happier as a result.

It’s no different when the thing you’re marketing is actually a story. It may seem like the marketers on the front lines of Hollywood and the publishing industry have it easy — after all, how many potential ways are there to sell people on a new superhero movie? But the truth is they’re dealing with a lot of the same market pressures and limitations as the rest of the marketing industry, and their job is only getting tougher.

The Plot Thickens

Entertainment is one of the most oversaturated markets in existence right now, with mass amounts of brand-new content competing for audiences’ time and money every day. And with digital marketing ushering in a new era of targeting capabilities, marketers are able to take a much more surgical approach to reaching the people who will ultimately be interested in their content.

The new challenge is getting those audience profiles as accurate and detailed as possible. Anyone who subscribes to Netflix has probably found themselves laughing at some of the categories the streaming service identifies as “interesting” to them. Maybe you were surprised to find out you liked “strong female leads in stories of revenge” or films about “wine and beverage appreciation,” but however the service categorized your interests, they went way beyond just recommending something because you were a “black female” or a “male under 35.”

Most of us would agree we’d rather be targeted based on our actual personalities and preferences than broad stereotypes. And this is how entertainment marketers want to approach their jobs across the board.

But it’s challenging. Because unlike Netflix, marketers do not have built-in algorithms. They don’t see every movie or TV show that comes out each day, and they certainly don’t read every book. They’re also likely to have biased views on the content they do consume, making it hard to “categorize” things properly. (Is “The Handmaid’s Tale” appealing to people with strong feminist ideals or fans of dystopian dramas? The answer is probably both, but if a marketer only identifies with one of those groups, they may ignore or downplay the other category.)

So if a marketer hasn’t seen “Beginners” (or seen it through the eyes of a diverse audience), they may not realize that it shares thematic and cinematic similarities with the project they’re currently promoting. And therefore, they may totally miss out on some valuable audience profiling data.

How AI Can Help

You may see where this is going already. To help entertainment marketers be more effective, we can equip them with the data and algorithms they need to make informed, surgical marketing decisions.

My company StoryFit has a database of millions of film scripts, teleplays, and novels, with new ones being added every day. Our AI breaks down those scripts to make them easier to categorize across detailed metrics like the personality profiles of the characters, thematic elements, the type of story arc (rags to riches, etc.), and even things like whether they pass the Bechdel Test.

Marketers can find comps to their current project, see exactly where the similarities exist, and use that information to target audiences who responded well to the comparable content. They can also look back at marketing campaigns for those properties and see, for example, how they were structured tonally and what aspects of the story they focused on to make more informed decisions about their own campaigns. Comps can even be helpful when determining — and defending — marketing budgets.

Can StoryFit’s database help in the development process as well? Of course, but it’s a tool that has to be wielded wisely. Producers who find themselves reverse engineering scripts from scratch based on what has worked in the past are likely to find the results disappointing. The human brain is well-tuned to identify unoriginality, and playing it too safe will come back to bite studios and publishers over time.

Ideally, if producers or writers are using the tool, they’ll come to it with a list of questions or benchmarks. Are my characters registering as complex, and are they sufficiently different from each other? Do female characters have at least 50% of the dialog? Do they have characteristics that are usually reserved for male characters (are they trailblazers)? Which famous character is my leading male most similar to, and am I happy with that comparison, or should I rethink a few things to make him fresher? In other words, it’s a tool that can help creatives see their stories objectively and decide whether they like what they’re seeing.

As AI starts to become more of a fixture in entertainment marketing and marketing as a whole, it’s important that we use it to augment and improve our decisions, not make them for us. AI is one valuable voice in the room. It should not be the sole decision maker or sole opinion we listen to, any more than we’d listen to one team member at the exclusion of all others.

Machine learning should help marketers across industries become superheroes, not zombies. So as you start to craft your marketing stories, look to AI where possible for data that makes you smarter. Then let your humanity take the wheel, with clearer knowledge of the best routes and the lay of the landscape ahead.

April 11, 2019

The 360° Customer View – Dispel the Myth!

By Mark Hiseman, IntentHQ

Telco Customer Experience and Marketing teams are failing to make emotional connections with their customers. They are frustrated because they are unable to use all of their data to develop rich customer insights. To add to their frustration they see other companies using data in a more personalised and engaging way than they are - we call this the Great Competitive Paradox.

But do telcos have the key to find the Holy Grail - a complete view of the customer?

Local shopkeepers know their customers personally because they meet them and speak to them regularly. They build relationships based on familiarity and trust, and that makes doing business easier.

For the telcos, the challenge is to build a deeply human connection by generating insight about customers’ behaviour based on all the available data, not just the basic CRM and service data.

Making an accurate prediction of what customers want next might be difficult - but it’s not impossible. Let’s look at John, a customer. He’s a 44-year old man with an iPhone X and is on the 10GB data tariff. He spends $60 a month and has been with his service provider for four years. He lives in New York and has a low propensity to churn.

That tells us something, but it’s light years from a complete customer view.

Add a little more data and you discover he also has another contract for an iPad and a prepaid account. His iPad contract is the 1GB tariff and he tops up the prepaid account $10 every month.

Is this a ‘complete’ view?

What about other product lines? He doesn’t have any fixed-line services, but the address does. Third-party data tells us the household details and that John is married to Jane. Our data tells us that these services are in his wife’s name. So now we know John is married and the household view shows associated fixed and mobile services. This additional data indicates they have a child. Do we have a ‘complete’ view now?

What about adding some more data in our search for crystal clarity?

• Has John contacted his service provider in the last 12 months, visited the website or used the app?
• Has he responded to a customer satisfaction survey?
• Maybe he’s raised an issue, complained to the regulator or been on social media?

Let’s add all this data in, too.

So now we know his family and domestic arrangements, the phone he uses, how much he spends, and we have a somewhat blurry picture of his relationship with his service provider.

We can learn more. What about service usage? Perhaps John experiences higher than average dropped calls or maybe has a poor data experience. We should include this quality of service information because that will provide a view of his experience of the network. We have some good propensity models too - such as propensity to churn or Next Best Offer models. They are pretty accurate, so let’s add those in.

Have we yet got to the ‘complete’ view?

We know John’s participation in previous Marketing offers and also in our Loyalty or Reward scheme. We know which offers he clicked on and accepted or declined. He’s given us some personal information in exchange for Loyalty Scheme offers. We still haven’t reached the ‘complete’ view.

This level of customer insight is usually the best it gets for a typical telco. In the past, this level of understanding was ok, but the world has changed. This customer’s expectations have massively increased, pushed up by the FANGs of the world.

We need to add a lot more insight in order to gain a deeply human understanding of John and in turn give John a highly personalised customer experience.

We have the details of who John calls and messages and who calls or messages him in our billing records. We can use this information to identify key interests. We know what websites and apps he visits, so we can use that, too. There’s so much data here that could provide even more insight into John as a person rather than a commodity.

Data is fine, but understanding is the key to greater wallet share. You have to add meaning to the data; otherwise, it is useless. The fact that John frequently visits a website tells us little. Is it a coffee shop or a clothing retailer? If he calls a number, is it a ticket line for the cinema or is he calling a friend?

To really make sense of this cumbersome data, you have to be able to give it meaning; otherwise, it’s just a bunch of words and numbers. The key to all this is to process the data in a way that allows you to create a micro-database for each and every customer. You can add meaning to the data which in turn delivers a depth of insight never experienced before. Instead of knowing John frequently visits the usual suspects in terms of websites, you now know he has interests and brand preferences. He likes coffee, football, dogs and Formula 1 racing. You know he’s interacting with your competitors and he’s looking to move house.

Once you understand the behaviour, you can also infer attributes. Remember John has a prepaid account? The interests associated with this account are tennis, cosmetics and university. Targeting a football-related loyalty offer to this customer is not going to be successful. The behaviour suggests an 18-22-year-old female, probably their daughter, and John pays for her account.

Now you have a human-like understanding of your customer, for all your millions of customers.

You can micro-segment like never before, create tribes of customers with similar behaviour and target with enormous precision to deliver a 1-2-1 personalised experience. This means no more spam. Now you are processing your data and giving it meaning. The profile is as complete as your data allows. Maybe now you can say you do have a 360° view of your customer, and maybe now you can say you have a deeply human understanding of them.

See how O2 have started to challenge the Great Competitive paradox link here.

Contact me at mark.hiseman@intenthq.com to learn more about human-like 360° profiles.

April 8, 2019

The Evolution of the Independent Data Driven Marketer, in 3 (and a Half) Motivating Steps

By Rony Vexelman, Optimove

In today’s demanding marketing orb, every marketer’s quiver has to be armed with the right tools that will allow access to customized reports filled with the most recent data. Here’s the starting point of your quest.

Ever come into your office, look at your calendar, and see right there in printed black and white that today is the day of your big presentation? You walk into the meeting with the higher-ups, sharing how the marketing campaigns are doing great, feeling confident about the progress. And then, someone in the room asks you to prove your work’s impact. “How does it impact revenue or order amount?” Things that move the needle, they call them.

I know what I used to do. I’d go right to the finance team and ask for sales numbers from the day before. I’d have the marketing team put together a report on our campaigns, so we could attribute sales to each campaign. And I would sit down in front of my Excel and start stitching data together to prove the campaigns’ effectiveness.

By this point, much of the morning – not to mention lunchtime – would pass by with little progress. I knew a change was due. And that’s when I began my evolution into an independent data-driven marketer.

Answer Their Questions

The first step? Standardized Autonomous Reports. The type you can find inside your ESP or marketing automation platform. A report provides the exact number of customers who performed a given action. If the system is really advanced, you would even have a dollar amount attached to those numbers.

Standardized reports helped me identify which campaigns had better CTRs and which generated a strong uplift in revenue. They took much of the manual work out of my day-to-day. But I felt we could take this further. There are many articles out there on why marketers need next-best-action recommendations or insights into missed opportunities and potential optimizations. None of which is apparent from standard reports. Additionally, with a small team, it eventually became too difficult to measure our entire operation.

The second step of my journey led me to Smart Reports that could help marketers detect customers at risk of churn and which campaigns could bring them back from near abyss. Ones that provide insights into which campaigns had a significant impact on customer lifetime value or lacked any sort of impact at all. They could even help me see the parts of my database that weren’t being targeted at the required frequency, solving the all too common case of marketer tunnel vision.

And to better answer those company-specific questions that await at the management meeting, we have step three: a Consultant or External Supplier who could help create custom reports with the insights I needed: impact of my marketing efforts for my VIPs, aggregated campaign reports filtered by customer lifecycle and the like.

The Final Ingredient

I’ll admit this solved my problem. I was now an independent data-driven marketer, armed with the tools to prove how campaigns impact the business in any metric we could come up with. But, if you’ve read this far, you know something is still off. I felt too dependent on this external team to send me the report at a certain cadence. Like most marketers, I want access to customized reports filled with the most recent data whenever I want.

Enter integrated BI Dashboards. Customized and dynamic, integrated within the marketing solution of my choice, and always up to date. Reports that support today’s omnichannel marketing efforts and allow me to visually analyze revenue from different lifecycle stages, broken down by device type. Reports that aren’t static, that could filter dates, devices or stages from the total with the click of a button and see the revenue distribution at any given point.

This solution allowed me to slice and dice the data in a visual and intuitive manner. This was the final step for me, the stamp of approval for my personal growth as a modern marketer.

This journey did not take a day or a week. It began with understanding if the team were to achieve marketing agility, then we needed to own the data. From customer lists to sales results, we required access under one interface. We had to truly understand what the most important metrics were. We had to acknowledge the limitations, know when help is needed to uncover the deepest insights from marketing efforts either through smart reports or an external consultant. And finally, dream big. Think hard about the most dynamic report that could answer the toughest questions in an interactive way and find the BI tool that could deliver this promise.

April 4, 2019

AgilOne Welcomes Adobe and Salesforce to the CDP Party

By Gangadhar Konduri, AgilOne

This week we saw interesting and exciting announcements from Adobe and Salesforce. They both announced their Customer Data Platform (CDP) offerings, which seem to be in different stages of development. We will discuss our detailed thoughts on these product announcements in future blog posts once they share more details. But, to start with, we want to extend a hearty welcome to Adobe and Salesforce to the Enterprise CDP party -- a party AgilOne started and been at the forefront of for several years already.

Both announcements acknowledge the excitement about CDPs, the legitimate need for CDPs, and that many brands are in the midst of their CDP initiatives. Both announcements acknowledge the definition of a CDP to include, at a minimum, the following features:

• Bringing transaction and interaction data together from multiple data sources
• Identity management as the critical core of the CDP
• Customer intelligence powered by AI is a key part of a CDP
• Driving customer engagement based on this rich data and insights in a CDP

In fact, we at AgilOne have been saying this all along. We worked with analysts (the CDP Institute, Gartner, and others) and the market to arrive at this definition of a CDP based on our exciting and growing enterprise customer base.

Contrast that with how less than a year ago, these and other vendors dismissed CDP as a passing fad, while at the same time they made attempts at partial solutions that were not well received by customers or analysts. The market forces were too strong for them to continue this approach. Now they are acknowledging and building their own CDP offerings, perhaps finally with an understanding of why CDPs became a “marketing category that enjoyed instant attention,” as referenced in Salesforce’s announcement.

AgilOne’s enterprise-grade CDP has been used by brands for many years as they reaped the benefits of a CDP, and we evolved our platform based on real client usage. Other brands, still grappling with what a CDP is and trying to gain customer adoption, don’t have this same benefit of experience and platform maturity that is a response to real world use cases. Because of this, we have a unique perspective:

• We have learned that brands have their data sources spread across enterprise applications from Salesforce, Oracle, Adobe, IBM, SAP, applications from many other vendors, plus custom and homegrown applications. For most brands, a pure walled garden doesn’t exist.
• During these years, we developed the AgilOne enterprise-grade CDP, hardened it in production with usage and massive data processing for all our clients, and enhanced it with additional modules based on client feedback (see our case studies for more details).
• We’ve learned that no two brands are alike and configurability is critical to a CDP. (Read about AgilOne’s unique approach to configurability and how this supports enterprise use cases).
• Enterprise CDP goes far beyond adoption by digital marketing teams. A CDP is also used by, and provides benefits to, Customer Service, Customer Analytics, Data Science, and all teams that benefit from having access to unified customer intelligence.

All these new entrants to the CDP party will need to go through the same growth curve as they move from this announcement stage to actual customer usage. However, we are happy that these announcements could help accelerate the market adoption of CDPs and thus increase the adoption of the AgilOne CDP. So, Salesforce and Adobe, we welcome you to the enterprise CDP party and to a growing and exciting market!

P.S. We will be back with our thoughts on their product announcements in another blog post (though we are not holding our breath for those vendors to develop and publish more details any time soon...). Until then, here are some interesting articles for your reading enjoyment:

• Case studies on how our clients leverage the AgilOne CDP
• Our blog posts last year on Gartner’s definition of a CDP and how to choose a CDP
• CDP Institute summaries on Adobe and Salesforce announcements

April 1, 2019

If You Can’t Beat Them, Join Them: Adobe & Salesforce Join the CDP Club

By Tasso Argyros, ActionIQ

3 Questions Every Brand Should Ask About Their Own CDP Investments

The Customer Data Platform market officially expanded this week with both Adobe and Salesforce throwing their clouds into the CDP ring. For those of us who have been building this category for the last 5 years – we want to say welcome to the club! This is a huge acknowledgment from the two biggest players in marketing technology space, so we wanted to provide some context and clarity around the announcements and how we view this shift.

What Do These Announcements Mean?

Adobe and Salesforce’s expertise is not in customer data. Their pedigree is in content (think Adobe Photoshop) and business applications (think Sales Cloud). Both “built” their marketing product portfolio (Cloud) through a multitude of channel-focused acquisitions (think Campaign and ExactTarget – both ESPs). Fundamentally, these are execution-first products with very limited data capabilities.

These announcements mark Adobe and Salesforce’s attempts to pivot down from the execution and content layer into customer data. This is BIG. Not just for the marketing clouds, but also for the Customer Data Platform market. For the last two years, we at ActionIQ have been evangelizing the value of CDPs for enterprise marketers. In the last week, we’ve had two very large voices join the club and validate the market need – welcome, Adobe & Salesforce!

Why Are Adobe & Salesforce Pivoting to Customer Data?

Let’s just say it’s been a journey. Their first response was to say, “We already do that!” But as the CDP market picked up momentum, they were forced to respond. Salesforce called CDPs a “passing fad” and Adobe announced a new data layer they described as “more than CDP”. Fast forward to just a year later, Salesforce is finally committing to check-the-box CDP, and Adobe announces a (another?) CDP. What changed?

Ultimately, the market and their own clients forced them to make this change. We know for a fact that in the last 6-12 months both Adobe and Salesforce have lost out to smaller startups with CDP capabilities that power personalization at scale. We also know that once the honeymoon wears off from an Adobe or Salesforce marketing cloud purchase (think 12-18 months), enterprises start to realize that data integration and unification is not their core competency, nor the reality of their offering. Both the market and clients have succeeded in pressuring the marketing clouds to acknowledge their existing data gap and promise a future solution.

How Should Brands Think About This?

Now for the most important question, how should brands evaluate these announcements against their marketing strategy and capabilities? This can be summarized in 3 simple questions all brands should ask themselves:

1. Do you believe Adobe and Salesforce can build a CDP?

This goes back to an organization’s core competencies – do they have the pedigree and proof points to make you believe they can pull this off? Building a CDP requires deep expertise in data integration and architecture which becomes the foundation of the entire platform. Data must be a core competency and this can’t be solved through yet another acquisition.

2. If you believe they can build a CDP, can you afford to wait?

This won’t be a quick fix. At ActionIQ, we spent the first two years of our existence building out this data layer (and we didn’t have to deal with decades of legacy technology). Building foundational data infrastructure takes time. Big companies have a major force working against them that hyper-focused startups don’t – momentum. And not the good momentum, but the kind that makes doing anything new extremely difficult. Change is hard, and when it comes to something as foundational as core data infrastructure, it takes a long time.

3. If you can afford to wait, does it make sense for you to go all-in on a single cloud?

To answer this question, you have to ask another question – do you expect Adobe or Salesforce to be the only vendor you ever use, i.e., is it reasonable for your business to live completely within their walled garden?

Even if you believe Adobe or Salesforce can build their own CDP in a reasonable amount of time, you would still be constrained to only the channels that live within their cloud. Vendor lock-in is a major risk a company must consider, and in this case, it’s lock-in of your entire marketing tech stack. At ActionIQ, we believe that brands should have the flexibility to choose best-in-class channel solutions for themselves and reduce switching costs when their strategy changes or the vendor falls behind.

In closing, we see the last week as a massive win for the Customer Data Platform market and, more importantly, for brands wanting to be more personalized and customer-centric. CDPs will be a foundational layer in the enterprise marketing stack going forward – powering personalization at scale while optimizing marketing performance and efficiency. With these announcements, everyone has now acknowledged that single view of the customer is an unsolved problem and we can all move on to evaluating who has the best solution to solve it not just in the future, but right now.

March 28, 2019

What’s Missing from Salesforce Customer Data Platform?

By John Hurley, Radius

After Salesforce denounced the need and longevity of CDPs, they’re announcing one of their own. The claim is that there is a need for "Enterprise-grade CDP” – one platform to rule them all. By defining their vision, Salesforce does in many ways help clarify where CDPs fit in the tech stack and how they could be used to ultimately create compelling customer experiences for consumers. Customer Data Platforms may be the technology category with the most variance across vendors, with one product looking a lot like BI, one like marketing automation, and the next bordering on master data management (MDM). So I welcome any large cloud provider like Oracle CX Unity or Salesforce raising awareness of the category and guiding the market toward a standard definition.

However, Salesforce’s vision statement also adds to the confusion. I see several gaps, or details that were left out or intentionally overlooked, that will mislead the market toward the CDP that Salesforce wants you to see.

Definition of Enterprise Grade

Salesforce claims to “deliver the first real enterprise-grade CDP.” But what makes a platform enterprise-grade? It is scalability, flexibility, and trust.

First, scalability is critical to a customer data platform. Customer data growth is exponential across volume, variety, velocity, and veracity. The platform must be able to handle massive loads of customer data at once and respond as the data changes. Data storage, matching and validation pipelines, and APIs all must be built for scale. Imagine the data from several enterprise CRMs, marketing automation platforms, dozens of third-party data providers, and data lakes all synced and managed by a single platform. That’s enterprise scale.

Second is an open platform that’s flexible to the customization large enterprises require — purpose-built interfaces, APIs with broad endpoints and detailed documentation, and transparent processes. While the use cases can be generalized to terms like ‘data management’ and ’segmentation,’ each enterprise is a snowflake with unique needs.

Lastly is trust built from a focus on security, privacy, and responsibility. Enterprises across every industry often claim they are becoming technology and data companies. Customer data has become their most precious asset. Platforms must have security and privacy as a pillar to both their technology and ethos to work with enterprises. They also must showcase the values enterprises so desperately as trying to encompass. Is positive impact part of the pitch? Are you using data for good versus evil? How are you giving back?

No knock on Salesforce here. They’re gods of the enterprise cloud. They have enterprise solutions and have mastered selling them. I look forward to seeing if their CDP lives up to the criteria of enterprise-grade.

Where’s the “Data Platform”?

Breaking down a Customer Data Platform into its various parts and where it sits across your technology suite is a worthy exercise. Salesforce lists “Insights Platform” and “Engagement Platform” as the two things marketers want. They understate the foundation to both insight and engagement – and that’s data.

CMO, CIO, and CDOs’ first phase is operational efficiency. How can we clean up and unify the data we have, augment it with critical data we don’t have, and then use our mastered data to improve our decisions as an organization? These actions can be very tactical, like enriching CRM data, or very strategic, like migrating from on-premise data lakes to cloud data management. Next is differentiating with rich customer experience. How do we use the master customer data to create faster, more personalized, delightful experiences for our customers?

Building platforms on top of a shaky foundation puts big promises for customer experience at risk. End-to-end Customer Data Platforms must fully commit to their role in data transformation.

Revenue Operations Inefficiency

Let’s piggyback on the importance of enterprise-grade characteristics and “data platform” specific capabilities. CDPs that address fundamental data accuracy and unification open up a smattering of other use cases under the umbrella we call Revenue Ops. Data is essential to the revenue-focused responsibilities of operations experts who:

• Formulate go-to-market strategies
• Optimize sales and marketing processes
• Implement technologies and manage their workflows
• Deliver data across organizations and systems

A sales ops team with a CDP that offers a robust data management toolset can do better routing, territory planning, forecasting, and strategic planning. Data teams can use CDPs to find data redundancies across internal and external sources that lead to significant cost savings. Strategic groups concerned with data portability can use the open, scalable data infrastructure of a CDP to break down enterprise-wide silos for a broad set of needs. We see customers using CDPs to migrate from one system to another (CRM to CRM), manage M&A go-to-market integration (i.e., customer overlap, joint-market planning, system consolidation), and even analyze shared opportunities with partners.

Is Salesforce definition of a CDP too narrow? They could make the argument that all these technical solutions are in pursuit of better customer experience. We believe the applications and gains for revenue ops are so significant that they deserve their own call out.

Nothing B2B-Specific

As usual, solutions built for B2B are always an afterthought. While some technologies can serve both those targeting consumers and businesses, Customer Data Platforms do not translate well. An email to a consumer is not all that different than an email to a business buyer. A consumer website visitor who converts in a shopping cart is tracked similarly to a business buyer that converts on a demo request landing page. Both B2C and B2B CDPs are in pursuit of a single view of the customer, but the tech required to handle the complexity of B2B data is wildly different.

Data architecture in B2B is built to ingest, match, resolve, and manage a variety of data types: business records, hierarchies, buying centers and locations, contacts, departments, intent data, behavioral data, and countless others. To create a high-performing platform tuned to business data requires years of dedicated work from expert data scientists and engineers. Moreover, unlike their empty-box B2C counterparts, B2B CDPs often come with external data accessible within the platforms. This data is used for enrichment, total address market analysis, segmentation, audience activation, and prospecting lists.

Experts in CDPs generally agree that B2C CDPs are weaker on:

• Third-party data on-boarding
• Proprietary prospect data sourcing
• Data stewardship (hygiene, enrichment)
• Rich top-down profiles of business and contacts

Salesforce’s vision for their CDP isn’t wrong. However, it lacks detail and does not encapsulate the broad applications and technologies that have become known as Customer Data Platforms.

March 25, 2019

Build or Buy Your B2B Customer Data Platform?

By John Hurley, Radius

Looking to reduce your total cost of ownership and accelerate time-to-market? Don’t build a customer data platform from scratch for data management and integration — buy one and build an innovative solution that drives competitive differentiation.

“Organizations investing too much in building or customizing systems of record have less funding available for differentiating applications.”

This statement from Gartner has proven all too correct for many business struggling to create their own customized B2B data collection and distribution systems from scratch. While you might understandably wish for a bespoke solution to meet your exact specifications, there’s a much smarter way to get there — by buying a powerful, out-of-the-box platform that is also open for customization. Then use the money you’ve saved on total cost of ownership (TCO) and time-to-market efficiency to build differentiating applications as needed.

Buy Customer Data Platform (CDP) to Standardize, Build to Compete

Creating systems of record that integrate data flawlessly and connect readily to outside applications is no small feat. This is especially true in the B2B space. Any organization hoping to build a B2B data management system from the ground up must cope with time-consuming, laborious issues:

● B2B Data Architecture: You can’t onboard and manage your B2B data until you’ve created a reliable, well-tuned infrastructure to handle business records, hierarchies, locations, contacts, departments, intent data, behavioral data, and countless other details. Creating a reliable architecture that can actually do all these things requires years of dedicated work from expert data scientists and engineers. That’s talent and brainpower you could (and should) be devoting to more specialized applications.

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● Aggregated 3rd-Party Data: External data allows for data enrichment, net-new data sourcing for prospecting and reaching out to new audiences, and more advanced analytics such as total addressable market analysis, territory planning, and risk assessment. But aggregating a robust 3rd-party data ecosystem internally is very costly and impractical, especially when you consider the issues of maintaining that ever-evolving data.

● Pre-Built Integrations and APIs: Designing and developing the “pipes” to connect and integrate your various customer and prospect data systems is a major investment. Why re-invent APIs when many vendors offer ready-made, flexible, open, well-documented APIs and native integrations built specifically to onboard, register, and catalogue B2B data sources? Don’t forget the additional need to integrate your data with other key go-to-market systems such as ad platforms for activation of your data. You’re better off purchasing a pre-built solution and then focusing on putting that data to work for your business operations, and marketing and sales channels.

The ROI of Buying a Customer Data Platform

Buying an established, pre-built B2B data management platform frees up your engineering resources to focus on your core product and equips go-to-market teams with the integrated tools and data they need to drive meaningful customer experiences. By leveraging a CDP’s economies of scale and expertise in B2B data integration, management, and sourcing, you can create your customizations on a trusted, continually improving platform that is built on best-of-breed tech. The benefits of the platform, along with access to expert teams and communities of peers, can yield dramatic returns as opposed to building:

● Reduce Total Cost of Ownership (TCO): Access existing data, tools, and integrations as your foundation. The solutions on the market may be best-in-class for your needs, or good enough when looking at the cost to build exactly what you want. Your costs can be 3-5x if you choose to hire internal teams, commission technology-enabled service providers to build a custom solution, or buy the entire big data tech stack. Case in point: Comcast Business reduced their TCO by 75%, meaning the solution they were building and maintaining was four times as expensive!

● Accelerate Time-to-Market: While the goal is to eliminate data silos, a risk for new centralized data sources is that they often end up creating a new, sometimes larger silo. For instance, the promise of Data Lakes for marketers was that their teams could do granular segmentation and analysis on audiences from this wealth of data. The problem was data requests required IT, were not fulfilled for weeks, and the data then had to be re-prepped for activation since the data lakes were not connected to sales and marketing channels. Customer Data Platforms enable both IT and revenue teams, and connect to the systems critical to reach more buyers.

● Extensibility & Innovation: Achieve data unification and data stewardship across your existing systems with a Customer Data Platform, and benefit from their innovation and yours. These platforms are well-funded, growing businesses that are constantly releasing new capabilities that you will benefit from. If you’re an enterprise, look for open, flexible options that will allow you to customize and scale to your needs. Beyond the core capabilities of the Customer Data Platforms, other bleeding-edge technologies required holistic, accurate data like BI and analytics, customer experience engines, AI-driven applications. CDPs with robust APIs can make trusted, unified data accessible to these powerful systems.

Forrester’s Steven Casey predicts of the companies investing in data unification, 75% will choose a CDP over Data Lakes and custom build solutions. Packaged Customer Data Platforms and other data management solutions open the door to more than new code, apps, API, documentation and integration. Increase your business agility, experience the ease of maintenance, and access expert teams. You’ll be glad you bought these proven, expertly-crafted, fully-integrated solutions instead of trying to build them yourself!

Want to learn more about CDPs? Watch my webinar “CDP 101” and download the slides. If you’re currently assessing CDP vendors, I highly recommend our Action Brief 5 Key Steps to Streamline Your CDP Purchase Process. If you have any questions or comments, feel free to reach out to me directly – john@radius.com.

March 22, 2019

The CIO and CMO Are Natural Partners: Here’s Why

By Ariella Brown, Zylotech

Different strengths are involved in the various C-suite roles, which accounts for different personalities in the CMO and CIO roles. While that could sometimes bring up opposing points of view, they need to come together to work on the martech. The CMO provides the customer data needed for driving revenue, and the CIO has to set up the IT infrastructure that can extract value from the data. When they both work together, they realize greater efficiency and impact.

The great divide between CMOs and CIOs

Not all CIOs and CMOs are succeeding in working together, according to a pair of recent Deloitte surveys. When surveying CIOs, they found that over 50 percent ranked their relationship with CMOs as “weak or very weak.” That exceeds the number -- 42 percent -- that said they are collaborating with marketing. CMOs don’t have a much rosier take. The majority of CMOs surveyed considered their relationship with their company’s CIOs far from strong. Less than a quarter of the CMOs identified “the CIO as a key alley and champion.”

It’s possible that the small sample size of under 200 CMOs may not be large enough to represent the groups. However, Liz Miller, senior vice president of marketing at the CMO Council, offers an explanation for the two feeling out of sync. CMOs sometimes fail to consult with CIOs in the planning stage. “That isn’t involving technology in the strategy; it’s making IT an afterthought,” she asserts, and not giving those involved in planning the tech the consideration due “valued strategic partners.”

Likewise, in a CMO.com article, Cindy Jennings cites CIOs complaining that they aren’t brought in to set clearly defined aims with input from both sides. That translates into “unrealistic schedules, reactive and poor planning, and shifting requirements that broaden a project’s scope and complexity.” When the marketing department tries to find its own solutions, it can also end up bringing in things without IT’s knowledge, as is often the case for shadow IT.

Jennings also presents the CMO’s point of view that the primary causes of failed partnerships they identify include “a shortage of technically skilled resources, insufficient funding, and a lack of support from IT.” They also find that the tech is not always up to the speed demanded by responsive marketing. In addition, she observed, she has noted “lack of effective governance, a dearth of relevant data, and resistance to organizational change” obstructing collaboration.

Then there is also the fundamental difference in outlook between CMOs and CIOs. As Jennings puts it: “Marketing thinks in campaigns and outcomes. IT, on the other hand, approaches projects from a process standpoint.” But it is possible to bridge across this divide to achieve the ends needed in today’s data-rich and responsive marketing.

Getting the two to come together

In “Successful Digital Transformation Starts With A Beautiful CMO-CIO Relationship,” Peter Horst lists three leadership behaviors that got the CMO and CIO on track to work together. The first was “collaborative decision-making,” which demanded that both sides were brought to the table. The second was “embracing ambiguity,” which entailed accepting that not everything was going to be clearly set from the start, and they would not lose sight of the forest in ticking off every individual tree. The third was “empowered decision-making, whereby choices were generally made by the people doing the work rather than at an executive level.”

Horst’s approach is consistent with Deloitte findings dating back to 2015. The key points that emerged from the survey of C-Suite executives. One of the key findings was the primacy of “shared vision.” Like Horst’s “collaborative decision-making,” this criterion called for being sure both sides agreed to “clear marketing goals, collaborative technology selection, and a defined governance framework for data and technology access.”

The collaboration then had to extend to implementation teams, which relates to the question of who makes the decisions and how they would work through the details. “Shared responsibility helps build stronger cross-teams.” With teams made up of people from both departments, it’s possible to bring in the strengths of each in a complementary way. Marketing’s growing demands of efficiency, speed and availability then push the expectation for IT, which is there to be sure that the demands can be met with their insight into the capabilities of the technology.

As Jennings explained, the marketers can plan their campaigns around data and realize the IT department, which will “know where it is stored, how privacy is protected, if or how it’s integrated with marketing systems, the integrity of the data, and whether it can be delivered in the right format at the right time.” She likened it to the CMO’s saying what a building should look like, and the CIO coming in to be sure it can be engineered.

It is that difference of perspective between the CMO and CIO which makes them natural partners. You need the expertise in marketing vision to be counterbalanced by the expertise in technological capacity. This is the way to build the marketing campaigns of the future.

As Henry David Thoreau said, “If you have built castles in the air, your work need not be lost; that is where they should be. Now put the foundations under them.” The partnership allows the CMO to plan the castles in the air and work with the CIO to set up their foundations.

March 20, 2019

Give your Customers the Chance to Fine-Tune their Preferences

By Eilon Morgenstern, Optimove

Your customers want more control. Present them the subscription preferences option and they’ll repay you with their loyalty. While a one-click unsubscribe option limits your ability to communicate with customers, a preference center gives them more opportunities to remain in touch with your brand.

As marketers, one of our main responsibilities is to continuously distribute personalized content, creating a win-win situation: captivating our customers and earning more engagement for our business. One way to achieve both end-goals is by asking our customers, our subscribers, to share their preferences, giving them the ability to choose. Out of this desire, the preference center was born. The email preference center is a function that allows subscribers to pick and choose which emails they receive from you and the frequency at which those emails arrive.

Customers unsubscribe from our emails for several reasons. In most cases, the culprits are high frequency and irrelevant content. If you send an email to the wrong customer at the wrong time, they’ll point their cursor right at the unsubscribe button and sever their relationship with your business. The email preference center solves this issue.

Email Preference Center Types

There are three main types of preference centers:

1. Time-based - customers can choose the frequency in which they receive emails
2. Content-based - customers can choose which type of content they want to read
3. Hybrid - a combination of time-based and content-based types

1. Time-based

If your business sends daily communications that aren’t varied to a significant degree, we recommend brands present customers with the option to choose the email frequency. Maybe you have an engaged customer who’s happy to receive an email a day, while someone else prefers communication only once a month. We suggest building your time-based preference center around the following three parameters:

• Email frequency (twice a week, once a week, or once a month)
• Receiving emails on a specific day during the week
• Option to unsubscribe from a specific time period

Or, maybe you’ll opt for the next preference center type:

2. Content-based

If your content consists of several different topics, build a preference center around your categories. There are a few ways you can categorize the content-based preference center:

The product/service type - Maps all your services and products and divides them into clear categories. For example, a travel company could categorize their offerings as:

• Flights
• Hotels
• Car rentals
• Day trips

Or if you’re a fashion retailer, you could group items into:

• Men
• Women
• Kids
• Accessories

Based on this method, the customer can choose the content that best serves their objectives. This method is especially helpful because you can use the data from the email preference center and apply it to your segmentation model.

Medium type - According to this method, we’ll create categories based on the mediums we use in our marketing. The most common mediums are:

• Weekly promotions
• Weekly newsletter
• Blog
• Announcements
• Recommendations
• Reminders

Unlike product or service type, the disadvantage here is that we don’t have the information on our customers’ product preferences. We can, however, assume that there is a high correlation between the number of mediums the customer chooses and their engagement level with our brand.

Content-Based Hybrid - Using this method, we’ll divide selections into medium and product type. As in the example below, if we choose personalized recommendations, we’ll further enable customers to specify what they’re seeking:

• Hotels
• Flights
• Cars

3. Content- and Time-Based Hybrid

The benefit of combining the content- and time-based solution is that we’re able to provide our customers with a robust list of preference options. The risk is that a hybrid solution may appear overly complicated to the customer and instead of sifting through all the options, they’ll opt for the easiest solution, unsubscribing from our communications altogether. If we choose this type, it’s important we keep the design simple. Below is one example of the content and time-based hybrid:

Structuring the UI

After we define the content categories, we need to build the email preference center page. A few tips:

• All of the content must be on one page. Customers should not need to use extraneous navigation to refine their preferences.
• Avoid using links that take customers off the preference center page or tooltips that hide the information from plain sight.
• Keep the text short and concise.
• Include the customer’s entire email for easy identification and validation.
• Yes, you need an unsubscribe option; it’s required under CAN-SPAM, CASL, and other spam laws.
• Make sure your preference center is mobile-friendly.

Where to Put Your Preference Center

Sticking your preference center in any old spot won’t do. To maximize your preference center’s potential, make it accessible to all your customers through the following tips:

• Present your preference center to subscribers right after they register, so you can immediately begin catering to their needs and show them you’re dedicated to listening to what they want from the very beginning.
• Add the center as a link to your marketing emails to provide subscribers with easy access to adjust their preferences whenever they wish. To reduce the number of unsubscribers, include the unsubscribe option inside the link to the preference center. By using only one link, you’ll likely decrease the number of customers who opt out of your communications.
• Every so often, go over your unsubscribe list and see who hasn’t been active for a set period of time. Your next step is to give them the option to update their preferences. Sometimes, this kind of mail can create reengagement with subscribers who might’ve lost interest.

In Conclusion

Take a look at your offerings and determine which preference center will best serve your customers. Are you regularly sending out emails where the content isn’t drastically different across the board? Or do you have segments of customers who each gain something different from your communications? It’s time to reap the rewards from the mutually beneficial preference center. Matching your content with customers’ needs increases their satisfaction with your brand and reduces the likelihood of unsubscribers. Think of the email preference center as a gift for you and your customers, one that’s tailored to their needs and will result in the long-lasting relationship you desire.

March 19, 2019

CDP Industry Confusion Doesn't Invalidate CDP Industry

David M. Raab, CDP Institute

Technology analysts comment on the CDP industry all the time.  I usually don't respond.  But since the Winterberry Group's recent report criticized the CDP Institute by name, some comment seems appropriate.

The main critique of the report is that the CDP Institute includes too many vendors in its list of CDPs and applies too inclusive a definition.  We recognize the confusion in the marketplace.  But we feel that a narrow definition of CDP would not solve the problem, since there's nothing to prevent vendors from calling themselves CDPs whether we agree or not.  Rather, we believe buyers are best served by including any vendor whose system solves the problem that a CDP is intended to solve -- building a persistent, shareable customer database -- and then helping buyers understand how these systems differ. 

We have recently been using a division of the market into three types of CDPs: those that primarily build the database, those that offer analytics in addition to the database, and those that provide personalization in addition to the database and analytics.  Our reports, including the latest industry update (download here), specify which vendors fall into each category.  This, along with the more detailed information in our Vendor Comparison Report  (download here), is a good place for buyers to start sorting out which vendors they should consider in depth.

There's plenty more work to be done to reduce confusion.  Indeed, there are several projects under way at the Institute to do this.  I don't want to disclose them prematurely but will note that it has recently become clear that we need to subdivide the "data CDP" category between vendors that primarily collect data and vendors who are expert at building and distributing the customer database.  So look for the number of CDP categories to grow. 

This may sound like a recipe for still more confusion, but our plan is to avoid that by making it easier for buyers to understand which use cases are supported by each category.  That will let them start by considering all possible solutions and then quickly narrow their consideration to a suitable subset.  Educating buyers and arming them with relevant information will be the real key to reducing confusion.  Ignoring the crowded reality of the CDP marketplace won't help at all.

March 18, 2019

Three Things Retailers Need to Properly Segment Customer Data

By Kristen Carlson, QuickPivot

Most marketers, especially those in the retail industry, understand the importance and value of customer segmentation and personalization. However, that doesn’t mean that most marketers are successfully executing it. The reality is that segmentation and personalization can be overwhelming and dizzying when you start to look at the sheer amount of data, all the possible sources of data and the potential technical barriers.

Yes, retail customer segmentation takes some pre-planning and foundational work, but it doesn’t need to be headache-inducing. Here are the three things retailers need to put into place before they begin to segment customer data.

Task 1: Data Integration

The first step is to identify all channels, vendors and sources of customer data. This could be catalog, online, in-store, email, social media and mobile, just to name a few. Before you even address the technical component of compiling this data, we know that there may be organizational barriers to overcome. All too often the marketing department is divided by channels, reducing opportunities for collaboration and making it more difficult to gain a 360-degree view of the customer.

The key is to identify an executive sponsor, ideally someone who oversees the entire marketing department and can understand the value of bringing together data across marketing channels. The second step is to identify technology that can aggregate all of the data into a flexible and elastic contact database. Look for a tool made for marketers, rather than one that provides marketing insights but still requires a technical team or SQL specialists.

Task 2: Trust in the Data

Once you have all your cross-channel data in one place, the question is whether you trust it or not. Duplicates, old data, wrong data, empty fields and more can wreak havoc on the success of your customer segmentation strategy.

Look for a contact database that includes data cleansing, normalization, matching and merging to ensure the highest data quality. You should also develop a proper data cleansing regimen to maintain data cleanliness levels.

Task 3: A Well-Defined Goal

You have all of the data possible on your customers and prospects. You trust that the data is accurate. Now, you need to create an actual strategy for what you want to achieve with your customer segmentation. “Better personalization” or “more specific targeting” won’t cut it. Instead, think in terms of outcomes such as reduce shopping cart abandonment, increase average customer spend, and increase store traffic.

Once you have your goals in place, you can begin to brainstorm the segments and lists that you need to create and market to. Marketing should be able to own the segmentation process from start to finish, rather than relying on an analytics or technical team, as it’ll make your team faster and more creative.

It’s undeniable that we are living in the era of Big Data, and retailers will either sink or swim. Retail customer segmentation doesn’t need to be overwhelming or scary. If you have ideas for what your marketing team could do with cross-channel data, QuickPivot wants to make them a reality.

Request a demo today to see QuickPivot in action and discuss whether or not it could be a fit.

March 15, 2019

Self-Learning in Customer Analytics: A Primer

By Chuck Leddy, Zylotech

Self-learning is now a key marketing technology and a major trend for 2019 and beyond, according to Gartner. While self-learning technology may be neither widely recognized nor well-understood, the trend is already having a broad, burgeoning impact on marketing. This post serves as a self-learning primer, explaining what self-learning is and why Chief Marketing Officers (CMOs) need to care about it.

What is self-learning?

Put simply, self-learning helps any system get smarter over time, based on insights generated from collected data. It’s a key component of machine learning, which is a subset of artificial intelligence. All these terms refer to software that performs a particular function based on fixed rules, typically provided through an algorithm written by a human programmer.

In classical software development, the inputs or data that provide the foundation for the software are fixed, and so the rules are also fixed (by a human programmer). Machine learning systems are programmed/trained not by human programmers, but by the data the systems collect: this data, filtered through self-learning software, teaches the system to get smarter as more data is collected.

To illustrate, let’s look at one major challenge machine learning is tackling today: the maintenance of high tech industrial machinery (wind turbines, industrial robots, etc.). General Electric is as much a software company as an industrial company. Each piece of GE equipment has sensors attached that collect performance data from the equipment. All that collected data gets poured into self-learning software that “learns” how to maintain and optimize each piece of equipment. So GE’s high-tech equipment maintenance system, called Predix, is being “trained” to optimize its equipment in real-time with data provided by the equipment itself.

Self-learning should thus be seen as a software-enabled version of our cognitive skills. These self-taught systems have the ability to “learn” and adjust from aggregated data: they can grasp complicated concepts and gain insights from the data.

How does self-learning fit into machine learning systems?

It gets a bit “data science-y” here. All machine learning systems have three interrelated parts: (1) a model, which makes predictions; (2) parameters, which are the factors used by the model to inform its predictions/decisions; and (3) self-learning, which dynamically adjusts the parameters and the model by harmonizing differences in predictions versus actual outcomes.

The initial model, developed by a programmer, may be quite accurate/predictive (or not), but the self-learning software adapts the system over time according to how the model is performing in the real-world. Where there are gaps between what the model predicts and what actually happens, self-learning intervenes to dynamically adjust the model. Put simply, the system adapts based on the data it collects.

Why should CMOs and marketers care about self-learning?

Self-learning can be applied to the entire customer experience (CX) through a self-learning customer analytics platform, one that enables marketers to collect multi-channel data about the CX and dynamically tailor content delivery. Marketers can deliver relevant content by “learning” (1) where the customer is in their journey; (2) what behavioral factors drive that customer; and (3) what content they might need at each step along the way. Self-learning enables marketers to deeply understand customer behaviors, enrich customer profiles, make predictions about customer behaviors, map the entire CX, and then scale customer personalization and precision in ways that simply didn’t exist before. That’s powerful.

Self-learning can also help marketers identify new customer segments and create a more unified marketing and analytics system to constantly drive relevance and ROI. With the help of a self-learning customer analytics engine, marketers can truly influence purchasing decisions, drive upselling/cross-selling, and proactively enhance brand loyalty over time.

Finally, customer analytics provided by a self-learning system enable marketers to identify (and predict) areas where customers might be experiencing friction (i.e., places where your funnel is leaky and prospects are abandoning you), allowing marketers to respond proactively to improve customer engagement and conversion rates. With self-learning, your funnel becomes less leaky over time.

What’s the final takeaway here? You should think of a self-learning customer analytics platform as always delivering real-time signals (data) from your customers for you to drive enhanced CXs and revenues. Now if that isn’t relevant for CMOs and marketers, then nothing is.

March 13, 2019

Make Your Customer Data Platform a Corporate-Wide Asset

By Tom Treanor, Arm Treasure Data

Three Rules to Keep You From Building Yet Another Customer Data Silo

Who “owns” the customer experience at your company? For companies like yours – seeking to deliver outcomes that keep customers engaged and loyal – few questions are more important, and unfortunately, more complicated.

The problem is that responsibility for the customer experience is often diffused across different business units that interact with customers across their complicated journey and collect different types of customer data: point-of-sale, online browsing experiences, mobile and other customer apps, Internet of Things (IoT), customer service – and the list goes on.

How Customer Data Platforms Can Boost Effectiveness Across Your Business

Let’s say you head up one of these business units. You want to be effective. You need to understand your customer. And you understand that this requires data visibility. So, you set out to solve this problem by either stitching together multiple systems or trying to build your own customer data platform (CDP) for a single source of truth for all customer information.

Remember, though, that you’re not alone. Chances are there’s someone at your company in another department trying to do the same thing as you read this. Databases of customer information are everywhere. Most of them are homegrown, best-of-breed databases that someone, somewhere attempted to piece together to finally solve this ‘single view of the customer’ only exacerbated the problem with yet another data silo.

How, then, do you implement a solution that actually serves as the single source of truth for all your customer information? The key is to approach a CDP as a valuable corporate-level asset that is sanctioned by IT and the business, yet owned and operated by the marketing team. Make it a system designed to yield visibility into your customers for the entire organization, regardless of the data source. From what we’ve seen at Arm Treasure Data implementations – and in other organizations as well –successful CDP-using organizations usually follow these three rules.

Rule #1: Get support from the top.

It’s important to start at the highest levels of your organization. The objective is to get everyone working together, aligning with the same goals of customer intimacy, loyalty and ROI. Many customer data platforms on the market today emphasize quick and fast, out-of-the-box functionality – but that just won’t cut it now, with the explosion of customer data sources. No way!

And unless the entire organization is on the same page, you’re looking at another data silo that gives you half-measures and half-results.

This is an important insight, and companies are already grappling with these problems. Recognizing the need for a holistic approach, some organizations have even developed C-level positions such as Chief of Customer Experience. Whatever your specific approach, executive engagement is key. If your business is to be truly customer-centric, it needs to be customer-centric from top to bottom, and you need to measure meaningful KPIs that cut across your teams.

Rule #2: Involve IT to empower users with self-service.

Data projects that don’t involve IT are problematic on multiple levels. Chief among them is security. If you’re striving to improve the customer experience, the last thing you need is a security lapse that will put your customers – and your company – at risk. Best to have IT on board from the ground up.

The IT department is also important because of its reach. A customer data platform extends across the entire enterprise – and that’s the whole point. Leaving IT out of the picture will be an exercise in futility.

Not that IT needs to do all the heavy lifting. Rather, they should be your supporter and champion because you have done your homework and given them the peace of mind to let you run your business, engage with your customers in meaningful, holistic ways. Your customer data platform should be cloud-based, which alleviates IT of the complexity involved in an on-premise implementation and maintenance. Be sure to look for offerings with a comprehensive library of data connectors that help speed data consolidation. Also critical: professional services for tasks like building custom connectors to proprietary sources such as customer apps.

Ultimately, your goal is to empower business users throughout your organization with powerful self-service capabilities. The role of IT is that of checkpoint to validate the technical approach taken. Once you’re up and running, IT should feel confident enough to take a step back and allow people in marketing and elsewhere to take it away. Even connecting new customer data sources as they emerge should be the responsibility of business-level data stewards because you have done the heavy lifting early on and implemented a system built for scale.

All of this requires that you strike the right balance between IT and business users. Security is critical, as is proper integration into your environment. But platforms that require IT involvement at every twist and turn simply will not keep pace, and your project will fail.

Rule #3: Start with a proof of concept.

An enterprise-wide customer data platform does not mean that you have to boil the ocean by doing everything at once. To get off the ground (and get support from the top per Rule #1), start with a focused proof of concept (PoC) to identify low-hanging fruit: anything likely to yield high value quickly and visibly.

To demonstrate value, a PoC should unite a minimum of two data sources such as web site interactions and in-store point of sale transactions. This will help jump-start the data-driven, cross-business collaboration required to deliver a holistic customer experience.

Good, quick PoC projects – virtually limitless in variation – tend to focus on a key KPI. Here are some examples for inspiration:

• Increase revenues by identifying top customers and providing them personalized offers
   
• Upsell with improved engagement
   
• Develop better customer profiles and segmentation for targeted marketing
   
• Bridge the online and physical point of sale
   
• Enhance engagement by improving product features
   

Whatever PoC you choose to pursue, don’t underestimate its importance as a showcase. It will show what’s possible with a proper enterprise-grade customer data platform. It will demonstrate to your colleagues the power of a consolidated, holistic view of the customer. It will generate more than buy-in; it creates enthusiasm and inspires collaboration.

March 11, 2019

Going Direct-to-Consumer? A Primer for Marketers

By Kristen Carlson, QuickPivot

Brands selling direct to consumers (D2C) is not a new concept; however, in the past few years we’ve seen explosive growth in this strategy. For decades, large consumer brands have executed hybrid selling strategies by operating both traditional distribution channels and direct-to-consumer channels such as ecommerce websites and self-branded stores. What has changed in the past few years, though, is the ease and speed in which a brand can now create a direct-to-consumer channel, and how modern logistics make it easier than ever to get a product into a consumer’s hand.

These digital and logistic advances have harkened in a new generation of disruptive brands that are changing the retail ecosystem. Although these new brands are admittedly small, they are growing at an amazing speed, while many of the legacy incumbents who have been slow to adapt are facing slowdowns in growth. Just look at the grocery business. While grocery store growth is projected to be about 1 percent annually through 2022, growth for meal-order kits is expected to grow 10x over that same period.

The advantages of D2C are stark. Not only does a company assume more control of their brand and how it’s represented, but it also reaps the instant benefit of higher margins.

Executing a D2C strategy, however, is not without its challenges. The onus is now on you, the brand, to understand your consumers, connect with them and create the best possible experience for each user, every time.

We’ve outlined the fundamental elements for any company, big or small, considering ‘going D2C.’ These steps will prepare your marketing department to brace against the challenge, while supporting a vibrant direct-to-consumer channel.

Get your customer data in one place – and make sure it’s owned by marketing, not IT

Even if the marketing department is well-aligned, if IT owns and manages master customer databases, the procedural hang-ups can significantly hamstring your efforts to engage with customers quickly, which is the heart and soul of D2C.

When building lists of customer segmentations is contingent on an IT ticket that takes days to fulfill, or specialized data management skillsets not typical of marketing professionals, the resulting delay makes even the most basic marketing campaigns that much more difficult to produce and the speed required for well-oiled D2C virtually impossible.

Learn the difference between useful and ineffectual customer data – clear the noise

Your business most likely already has a CRM system that houses a lot of customer data, but how much of that information is useful and organized in a practical way for marketing to use? CRMs were built to manage and track customer transactions, and while some of this information is useful, other data fields, such as SKU numbers and payment cards used, just get in the way of marketers being able to do their jobs.

Marketers looking to implement a D2C strategy need to review their entire marketing tech stack and first identify the systems that are generating customer data. From there, they should review each of the systems to pinpoint the data that can be beneficial for marketing purposes and ignore what can’t.

Break out of the channel mindset – your customers are not thinking in terms of channels and neither should you

When it comes to reviewing all of the systems in your marketing stack, rather than having each marketing group review the technology specific to their group, create a cross-functional team to perform this exercise and look across the entire marketing department.

To date, many marketing teams have been organized by channel, but modern marketing teams need to tear down these silos and instead begin to think in terms of the customer journey. As a team, marketers should consider the following questions to develop their cross-channel marketing strategy:

• What are the behavioral pathways and attitudes people take at each stage of a purchase journey? Where do they get inspiration and information, and where does that flip over to actual shopping?
   
• What kinds of products and product attributes do customers prefer across all product-lines and categories? These insights come from analyzing website touches and product-purchase history and can help you develop key preference indicators for your products.
   
• What are the incremental results from various types of offers?
   
• How does a customer behavior change during life moments (such as having a child, getting a new job, or relocating) and seasonal events? This analysis provides a better understanding for what’s underpinning purchase frequency.

Make sure your entire marketing team has access to the same data – knock down the silos

Marketing teams cannot work in harmony to expose the above insights if they do not have access to the same information or the means to communicate openly among one another. Meaningful collaboration starts by spreading data access, along with appending privileges, evenly across the team.

Finally, future proof your technology

Technology is evolving at such a rate that in five years, a brand’s most valuable source of customer data could be a channel that we can’t even fathom right now. D2C marketers need to prepare for all types of data, data sources and data formats, and have technology in place that is able to ingest that information.

Additionally, evolving technology like machine learning can continuously analyze purchase trends and call out a wide variety of behavioral patterns. These systems can prompt the marketing team to take appropriate action, whether it means preventing a customer from going away, predicting a follow-up purchase in the near term, or targeting consumers that look like they’d be an ideal customer.

No individual can ever know for certain what’s coming next, and sustained D2C success hinges upon having as much information about current and future customer inclinations as possible.

Learn how a Customer Data Platform can bolster your move to D2C. Contact us and we’ll connect you with one of our CDP experts.

March 8, 2019

4 Tips for CMOs Wanting to Build a Data-Driven Culture

By Chuck Leddy, Zylotech

All Chief Marketing Officers know that collecting data isn’t enough. The real value of data is in leveraging it to develop behavioral insights about your customers and using those data-driven insights to inform better decision-making in real time, driving marketing relevance. To perform this essential work of deeply understanding your customers you need a customer analytics platform that accesses, analyzes and renders your customer data fully actionable.

As the amount of data accelerates, poor data quality will make it harder to implement a successful data-driven culture. Your marketing stack and marketing team exist for just one reason: to drive an understanding of (and engagement with) your customers that supports more relevant customer experiences. What are the capabilities that CMOs need to execute on a strategy that improves marketing’s efficiency, agility and customer engagement?

1. Integrate your “data house.” The challenge that many CMOs confront in leveraging their customer data is that it’s often trapped in various silos. In pursuing the latest “shiny object” or hot trend, companies proliferate tools and platforms that end up becoming “data spaghetti.” Another problem is data becoming outdated if it’s not continuously updated in real time. So the starting point is to get a platform that brings you agility and decentralization with governance and centralization of your customer data.

You should look to standardize your customer data, remove duplicates, and enhance it further with missing information towards getting a single source of truth about your customers across their entire organization. By doing so, you also open up the opportunity to connect specific marketing activities to ROI, enabling the type of marketing attribution that enhances your credibility in the C-suite (where “prove it” is the mantra).

2. Self-learning benefits: drive memorable customer experiences with deep, actionable customer insights. A self-learning customer analytics platform analyzes customer data in near real time to give marketers deep, actionable insights that support strengthened customer engagement, the type that fosters cross-selling, upselling, and higher lifetime value. By uncovering valuable behavioral trends and patterns in your customer data, a self-learning customer analytics platform enables you to map the customer’s journey and reach out to your customers with relevant, personalized messaging across the web, email, and any other marketing channel. The beauty of self-learning is that the platform learns as it goes, unlocking and offering deeper insights about customers as it collects more data. A self-learning customer analytics platform can thus be seen as a 24/7, “always-on” customer engagement/nurturing engine.

3. Automate key drivers of your CX with AI. Autonomous, data-driven decision-making at scale is fast becoming a reality, especially as artificial intelligence continues to emerge. Let’s explore one simple example to understand the enormous possibilities.

The global B2B telecommunications industry, which generates over $30 billion annually, is renowned for promotions, as firms like Verizon, Vodafone, and AT&T battle for B2B customers. A B2B customer might call their existing telecom provider and say, “I have a better offer for my business from a competitor/other provider. What can you do for me?” A telecom company can now create an end-to-end automated process where those business customers are asked to take a photograph of the offer or send a text message. The AI-enabled system would then read the offer and, since the system maintains an ongoing database of B2B telecom offers, confirm that it’s real.

Then it looks at the customer’s lifetime value and history and, based on that, computes a response: “Here is the updated deal we can offer your business today. We want to keep you.” All this takes place in real time, with no humans involved, except in setting up the data/AI system.

4. Democratize your customer data. Key customer insights gleaned from data were once accessible only to the few: the leadership team and data scientists. In today’s marketing landscape, everyone in your organization impacts the customer experience and therefore must have access and use of relevant customer data. It takes a clear, cross-departmental approach to sharing and leveraging your customer data across the organization, something a customer analytics platform enables.

The culture, mindsets, and capacities of your people are as important as, and sometimes more important than, any technology. Today, human and technological capacities are increasingly blended into hybrid solutions. In fact, technology won’t be of much use unless you mobilize your organization around creating a data-centric and customer-centric culture. Meeting and improving the customer experience is a key priority for CMOs, requiring that your people have the tools and capabilities needed to succeed.

March 6, 2019

Personalization: Going Beyond Demographic Data

By Korina Velasco, Lytics

There’s a reason personalization is marketing’s holy grail. When you reach the right customers with the right offers at the right time, the results exceed expectations. Instead of centering marketing efforts around content, future-proof companies are centering their efforts around individual users and their unique needs, then developing content and experiences that are tailored to those unique needs.

Download our latest guide, "Personalization at Scale: 1:1 Marketing to the Millions," and learn how to implement true personalization to the millions.

March 4, 2019

How to Assess Your Data Requirements When Implementing a Customer Data Platform

By Sanjay Kupae, Manthan

Customer data is no longer just an analytics requirement, or something used to send out marketing messages – it is the source of critical competitive advantage for a B2C business.

Truly customer-centric organizations use customer data for all aspect of business – from guiding product roadmap and categories, designing rich experiences, pricing their offerings right, creating the right customer service interfaces, deciding sales channels and even to make the right hiring decisions. Modern marketers use these customer insights to form a unified understanding of every customer and create contextual experiences that strengthen the relationship with the customer, ensuring loyalty and higher word of mouth.

Every company today collects more data about their customers that they use. They can also easily acquire additional, reliable data from data companies, and each byte presents insights that were not available before.

As a first step when deploying a Customer Data Platform (CDP), know what use cases you want to enable – this decides what data needs to be brought together. At this stage, assess your present data and what you need to acquire. Controlling customer churn, lifecycle marketing, and marketing attribution all require certain unique data sets.

The use cases and user types will dictate the format to store data in, the frequency of refresh and ingestion (real-time or batch) data, transformation required and responsiveness.

For example, in an e-commerce retail business like Amazon, purchase history is needed to provide personalized recommendations to customers. While digital data such as web-browsing/ app-browsing, items added to cart, saved for later, searched or viewed is captured, there is value in plugging in additional information such as income bracket, size of family, lifestyle, nature of work, types of vacations taken. This provides deep insights into what the customer would like, and the customer is pleasantly surprised with the retailer’s next-best product recommendations.

Organization owned customer data from the CDP foundation

Let us start with the data you have. If you have a Customer Relationship Management (CRM) program, provide any level of customer service or have a functioning loyalty program, then you have a solid foundation for your customer data platform. The CRM and loyalty systems can provide insights about demographics, purchase behavior, loyalty, and customer lifetime value and also contain the basic identity details.

But to do this right, it’s necessary to have a single customer record across all touchpoints by removing duplicate entries and resolving mismatches, that are created due to multiple reasons: incorrect data entry, different name variations of a customer, customers with multiple loyalty cards, change in their address etc. (Read more about solving the customer identity puzzle here.) After de-duplication and linking customer records, you know the real size of your customer base and have more accurate profile snapshots.

Transactional and digital data generates actionable insights for strategic gains

If your business engages your customers in more than one channel, for example through the store and e-commerce, or over mobile apps, that’s a new set of data streaming in, which can enhance your understanding of your customers. Again, it’s important to link customer IDs across different channels, functions and customer devices to have a holistic customer view, that does not vary based on who is accessing it and where.

Most businesses use outbound digital communication data for pointed interventions, but information such as search, browsing patterns, offer engagement, wish-listing, abandoned carts all tell you something about the customer.

An insight-driven organization focuses on this knowledge, rather than the data itself. If along with the CRM and loyalty data, you have these astute observations, it can power better marketing and experience strategies. Storing every click of every customer is not feasible and honestly, not required. The signals you get by analyzing these clicks, cookies, customer location, and device information during the digital engagement should be part of your CDP strategy.

External data sets to enrich customer attributes

Finally, bring in 2nd and 3rd party web, demographic, psychographic, location, buying intent data to further enrich what you know about your customers. This is the ‘test-use-evaluate and iterates’ part of your data strategy. Not all data providers are the same, they all start their data journey differently and may or may not work for your brand and your customer base. It’s also important to note that data degrades quickly – the data was good a few quarters ago might not be valid anymore. Customer interests and intents may have evolved – they may have changed jobs or had a major life-event. To find out what works for you, do small experiments with a good set of data partners and choose the one that works best for your current marketing and strategy needs.

If you are poorly utilizing the data you have captured, or not capturing certain essential data, you are losing important revenue opportunities. If data is the new oil, accurate customer insights are high octane racing fuel for your B2C business!

March 1, 2019

Who Controls the Marketing Stack in 2019? The CIO or the CMO

By Ariella Brown, Zylotech

The position of CIO is synonymous with taking charge of the technology in a business. But recent shifts in marketing demands means CMOs may actually be spending more on technology than CIOs. Who’s in charge, and how should organizations set up the defined lines of responsibilities for selecting, implementing and managing the use of these technologies? The key is building a partnership between marketing and IT.

How the CMO leads

CMOs’ IT spending is said to already outpace that of CIOs, according to a Gartner report. The reason for that is much marketing efforts have become a lot more data-driven, which translates into increasing need for IT services. However, not all those needs are going to be met by an organization’s IT department. The report also noted a movement toward externally sourced away marketing tech, often through SaaS.

In fact, according to the figures in Infotechlead, CMOs in the US alone are expected to invest more than $122 billion in martech and associated services by the year 2022, up from the estimated $90 billion spent in 2017. The marketing automation market is estimated to contribute $26 billion to that spending within the next three years.

CMOs already determine which martech to buy. Business.com offered some figures that show the current impact of tech driven by marketing needs:

• Marketing bears responsibility for selecting and managing marketing service providers at 83 percent of enterprises.
  
  
  
  
  
  
  
  
   
• Marketing chooses technology providers at 71 percent of companies.
• Marketing has control of the budget for consulting and design of marketing-related software at 75 percent of companies and for purchasing software-as-a-service at 47 percent of enterprises.
• At 43 percent of companies, marketing also controls the budget for external hosting of technology required to run any marketing-related software.

Generally, having the CMO direct the marketing tech works out quite well. According to the survey cited in Infotechlead among organizations that allow the CMO to set the pace for digital transformation, 62 percent report “double-digit growth, compared with 50 percent of respondents whose firm’s digital transformation is run by the CIO.”

The CIO’s role today

Allowing the CMO to lead in this way doesn’t mean that the CIO becomes less important, though. On the contrary, a Forbes report entitled “The Ascent of CIO” found that over 80 percent of CIOs consider their role to have become more important now. But they also realize that their role has evolved. No longer is their value defined strictly by their “technology know-how,” but by what they bring to the table with respect to strategic planning.

While CIOs still rank leadership as a primary attribute of the role, “partnering with others” is also up there.

One of the areas in which they have to partner is marketing. Current trends indicate that the successful companies are ones that will have the CMO leading the search for martech. They deliver the best experience for the firm’s customers and work with the CIO to find the most efficient and effective solution. It’s not a matter of the CMO making a unilateral purchasing decision, but of communicating what the marketing team needs to work with the CIO on making it happen.

Better together

The smart money appears to be on a collaboration between the CMO and CIO that brings them together through a shared interest in data and analytics while also capitalizing on the distinct strengths of each role. That means that while the CIO still works on the integration and implementation of the technological solution, the CMO would be the one who sets the goal for the martech. Working together, they can break through silos to effectively integrate data to extract the maximum from it and derive more accurate insight to inform marketing strategy. That will be the direction for successful companies in 2019.

February 27, 2019

CDP: The Data Foundation for FinServ Marketers

By Pedro Rego, Lemnisk

Effective marketing is all about understanding your customers: their needs, their behaviors, their relationship to your organization. Customer Data Platforms (CDPs) simplify the process and expand the potential of organizations and their marketers using actionable information to build and a better sense of who is your customer, what they want, and improve performance metrics such as cost per action (CPA), customer lifetime value (CLTV), and ultimately drive brand loyalty.

What is a Customer Data Platform (CDP)?

As defined by the CDP Institute, founded by David Raab, a CDP “is packaged software that creates a persistent, unified customer database that is accessible to other systems.”

Raab emphasizes three key components of that definition: a CDP must be understood as a pre-designed piece of software that can be tailored to the needs of an individual customer (packaged software); is designed to collect and archive customer information regarding things such as purchasing history or site behavior, collected across multiple channels and attributed to one customer (persistent unified customer database); and is able to sync with other tools and systems that can analyze the collected data to the benefit of marketers (accessible to other systems).

These three essential features define what makes CDPs so popular among companies looking to optimize their marketing: flexibility, the ability to curate massive amounts of customer information, and their compatibility with a number of other powerful marketing technologies.

What Can a CDP Do For My FinServ Institution?

CDPs are the best way to build communities of customers, and the best way for institutions to connect with those customers on a personal basis.

For FinServ organizations, it’s often essential to constantly track and manage customer loyalty, to keep an eye on what your customers need and how they’re engaging with your business. Patrons are creating relationships with banks, insurance companies, and financial services mainstays across multiple platforms, and a CDP guarantees that these organizations are able to check in with their customers on the platforms they prefer - whether that be email, telephone, or mobile device app - providing the offers that will pique customer interest the most.

With the data collected via a CDP, FinServ marketers are able to build marketing that is both hyper-personalized and omnichannel, connecting with customers on an individual level across every single device that they use to interact with your brand.

Why Does That Matter?

Financial Brand published a fantastic analysis of the June 2018 study of how banking marketing is evolving, published jointly by Adobe and eConsultancy. Surveying marketers and executives working at major banks, the study sought to understand where these individuals felt their industry needed to focus in the wake of the rise in popularity of FinTech. The results were instructive.

The survey shows that 28% of those spoken to consider optimizing the customer experience to be the highest priority moving forward, which is especially interesting when compared to the fact that, as the report contends, only 19% of marketers working most other industries would consider that the highest priority. The study further asserts that its results show that 23% of respondents listed as their highest priority “data-driven marketing that focuses on the individual.” A later excerpt shows that when asked what they believed would be “very important” for digital Customer Experience (CX) over the next few years, 81% of banking providers the majority selected options such as “optimize the customer journey across multiple touchpoints,” or “ensuring consistency of message across channels.”

Personalized marketing, consistent across channels: the perfect task for a CDP.

FinServ Marketers Must Adapt to the Demands of Their Clients

As customers become comfortable interacting with businesses via multiple devices, and as they’re trained to expect content that specifically responds to their individual wants and needs, marketing teams must build strategies that take these realities into account. The alternative is being left behind.

FinServ Customer Data Platforms, like Lemnisk, offer marketing teams a versatile tool to address these very demands: to better understand their customers across various touchpoints, and to design marketing collateral and sales campaigns that are molded to these individualized preferences and behaviors.

As FinServ organizations look to reinforce customer loyalty and attract new clients, they must find the tools that not only suit their unique needs, but those of the people they are targeting. The careful, strategic use of customer data platforms have revolutionized how organizations in myriad industries evolve their marketing.

For FinServ marketers, it’s a sterling investment.

Do You Want to Create a Unified Customer Experience for Your Prospects, Leads, and Clients?

Learn more about how Lemnisk's CDP Empowers Smarter Marketing for FinServ Institutions or visit their profile page.

February 25, 2019

The Identity Puzzle in Customer Data Management

By Sameer Narula, Manthan

In Hindu mythology, Ravana, the great scholar and demon king, has ten heads, symbolizing his various powers and knowledge. The heads were indestructible with the ability to morph and regrow. In their battle, Rama, the warrior god, thus must go below Ravana’s heads and aim the arrow at his solitary heart to slay him for good.

In modern times, the consumer is a bit like Ravana, not in terms of his evil designs but his multiple identities. Research states that an average consumer in US today is connected to 3.64 devices. With proliferation of a host of new age devices like smart speakers, wearables, connected homes and automobiles etc., it is projected that she could be connected to as many as 20 devices in not so distant future. Like it did for Rama, this poses a clear challenge for today’s marketer – how to navigate through the maze of these devices to identify and recognize THE consumer so she can be singularly, consistently and contextually engaged across her addressable touchpoints.

Industry research suggests that only a small fraction of consumer businesses can currently accurately identify their audience – hence the advent and rapid rise of Identity management solutions that help businesses resolve the identity of their audience into individual consumer identities and profiles. The size of the Identity solutions market is estimated to grow from $900 Mn currently to over $2.6 Bn by 2022, outpacing overall marketing investments growth.

A recent Winterberry research survey indicates that about 50% of consumer businesses have intensified focus and plan to increase investments on Identity solutions. While segmentation and targeting on paid media remain the predominant use cases for consumer brands, cross-device and channel personalization plus measurement and attribution are expected to become areas of focus in the near future.

Identity Solutions: The past, present and future

At its core, an identity resolution solution’s job is to continuously gather audience activity data from a disparate set of data sources, platforms and services to derive a cohesive, omni-channel identity and profile of each individual audience member. However, the approach has been largely siloed so far with marketing channel specific identity platforms and strategies. CRM databases as custodians of first party customer and contact information, have been the mainstream identity platforms for direct marketing activations, primarily over email or direct mail.

With the growth of digital marketing spend, Data Management Platforms (DMPs) that store digital audience behavior data to primarily support display ad buying use cases have come into prominence. However, their relevance is now questionable with walled gardens like Facebook and Google closing doors on them. The other growing channel of influence has been mobile data platforms to support mobile device and location-based engagement.

To overcome the limitations of a disconnected, multi-channel approach that current Identity solutions like CRM databases or DMPs are constrained with, the focus is shifting to emerging modern solutions like Customer Data Platforms (CDPs) and Identity Graphs. These offer a unified, cross-touchpoint and omni-channel approach towards identity resolution and linking, enabling a fully harmonized, single view of the customer to the marketer.

The Mechanics of Identity Resolution

Identity resolution system’s key job is to continuously collect audience related data from a variety of sources and put it through an ongoing process that resolves, generates and updates this data into discrete consumer profiles, which are then used by the business for various forms of marketing or other activations.

The process comprises of 3 key steps:

1. Data Management – Includes ingestion of disparate set of consumer data, both identity and activity related, followed by processing and storage of this data into organized repositories.
2. Identity Resolution – This is a crucial and complex mix of a deterministic and probabilistic process of deriving identifiers, matching, cross-referencing and linking to unique consumer identities followed by a validation mechanism to maximize the accuracy of the resolution process.
3. Consumer profile generation – This associates all identifiers, attributes and activities into a harmonized, holistic Identity Graph of the consumer, an individual or a household.

What makes an Effective Identity Management Solution: 5 Mantras

1. Ensure the Identity system is fed with data from a wide array of data sources. Not just device activity but also the applications behind to help drill past the device, cookie or pixel and reveal the real people behind them and their behavior.
2. As part of data management, ensure meeting consumer privacy rights and compliance requirements of industry norms like GDPR, CCPA etc.
3. Identity resolution should include a consistent, rule based deterministic match process to ensure high accuracy critical to support contextual, personalized engagement in direct marketing use cases.
4. The deterministic process must be supplemented with machine learning driven probabilistic matching to expand the data set and meet the requirement of use cases like social media or display ad marketing that expect a wider net but relatively less 1:1 personalization.
5. The generated consumer profile, in form of an Identity graph, while having the requisite accuracy and timeliness, should go beyond the linkages to identifiers and attributes by including the desired insights to optimally enable marketing activation use cases.

February 22, 2019

What's Driving the Booming Demand for Customer Analytics

By Chuck Leddy, Zylotech

Forbes recently highlighted the top five B2B marketing trends for 2019. First among these trends is the burgeoning demand for real-time and enriched customer analytics: offering key insights that enable B2B marketers to deliver experiences meeting the individual needs and wants of their customers.

Let’s explore what’s behind this trending demand for customer analytics, and how analytics platforms will help shape B2B marketing and the customer experience in 2019.

Marketers are frustrated with their customer data

Many B2B marketers are profoundly frustrated with the state of their customer data. For some, it’s segregated into siloes, and this segregation inhibits effective multi-channel communications. For others, their data is available but it’s unstructured and un-actionable. And, there are cases of data and key insights being accessible to few (perhaps senior leadership) but not the near-the-customer people/marketers who need it to deliver better, real-time customer experiences. The bottom line is that marketers can't access their data in an efficient way to meet the expectations of their customers.

With such fragmented and disconnected customer data aggregation and analysis, the customer insights that do get delivered to marketers are - it comes as no surprise - fragmented and disconnected. Marketers are skeptical about trusting those insights, and their lack of trust is fully justified.

What these B2B marketers want instead of fragmented, un-actionable data is customer analytics via accessible and actionable insights that allow them to better understand and engage their customers (for example by anticipating cross-selling and up-selling opportunities). With customer analytics, marketers can target the right customer with the right message at the right time. That’s ROI-driving relevancy.

B2B marketers can’t separate the signal from the noise because they’re drowning in raw, unstructured data. The noise is everywhere, but they’re listening for that one special signal, that one piece of relevant customer data that can help inform decision-making and drive a better CX.

Why customer analytic platforms are “in” for 2019

The emerging solution to these data management woes are customer analytics platforms that provide B2B marketers with crucial insights about customer behaviors throughout the CX, while connecting customer data across various systems and channels. You have full access to the signals you want, and the noise gets eliminated.

A great customer analytics platform needs to do two things: first, aggregate your customer data and second, render it actionable.

One layer of an analytics platform might pull all your organization’s collected data into one platform. It would then standardize your customer data, remove duplicates, and enhance it with missing information. B2B marketers would thus get one crystal clear view (a single source of truth across the organization) of the customer that’s as close to a real-time representation as possible.

Then there’d be another layer that analyzes customer data, in near real-time, to provide deep, actionable insights that support smarter marketing to drive ROI. That’s the leveraging part. By uncovering valuable trends and patterns in your customer data, you can map the customer’s journey and reach out to them with the most timely, relevant, and personalized messaging to enhance customer engagement. You can even do so in automated, AI-enabled ways that serve as an “always-on” customer nurturing engine.

Customer analytics will drive B2B marketing success in 2019

Caroline Robertson of Forrester Research is quoted at length in the Forbes article. Ms. Robertson puts things simply: “Most B2B marketers have low confidence in the quality of their data and their data management skills. To reconcile their numerous data silos – and enable the customer-driven engagement the modern B2B buyer demands across the entire lifecycle – many B2B marketers” are turning to customer analytics platforms for answers.

As explained above, these platforms offer massive advantages over fragmented, unfiltered (raw) data, most notably time-to-value. You don’t waste your time drowning in unfiltered data, and searching for relevant customer data. You get the signals that customers are sending you, and you can then leverage them in a timely, relevant manner, even through automation. Make 2019 the year you find better ways to access and leverage the relevant data you need to inform better B2B marketing.

February 20, 2019

The Great Competitive Paradox: Telcos’ Defining Moment

By Alex Holmes, IntentHQ

The biggest problem that telecom operators face is keeping up with the demands of the connected customer.*

Just 4% of executives felt their customers were happy with their current customer experience. The vast majority (96%) of Chief Marketing Officers from telcos said that non-telcos do a better job on customer experience.

The bar isn’t being set by traditional competitors, but by the renowned FANG (Facebook, Amazon, Netflix and Google) companies that have stolen wallet share and commoditised telcos’ offerings. FANG companies are winning a bigger wallet share because they understand their customers and they use that knowledge to predict their behaviour and treat each customer differently. Personally.

In perhaps the ultimate snub to telcos, most of that wallet share gained by FANG companies is being transacted via telco customers’ mobile devices on their network! Telco CMOs know they need to do something, but it seems like a huge mountain to climb and they are struggling to get going.

But there’s a paradox. Amazon and others have learned to understand their customers and predict their behaviour. But telcos have just as much of the raw ingredients to do the same thing. Amazon knows every customer’s browsing and purchase history, searches and ratings. However, telcos are in a position to create an equally - if not richer - view of every single customer. So what’s stopping them? What is so difficult?

FANG businesses have invested millions creating a machine to mine the data, look for patterns and find meaning. For the most part, telcos have invested incredible amounts in creating and analysing data about their network - to improve network quality. The customer understanding and personalisation machine that the telcos have is thin in comparison. FANG companies are using customer insights to create stronger affinity between their brand and their customers; telcos feel nowhere close. Despite having rich data in the business, the majority of it remains unused - it’s difficult to access and virtually impossible to work with using conventional ‘big data’ tools.

Creating a deep understanding of customers to coordinate activities in a unified way across channels still feels like a hopeless aspiration. Their customers often feel as though they are being treated as a commodity: faceless, spammed, irritated and not cared for. CMOs know that they are missing out but fixing it is like trying to turn around an oil tanker. We call this The Great Competitive Paradox because the telcos are losing this battle despite have plenty of powerful ammunition.

It doesn’t have to be like that.

Imagine you could understand your customers more deeply and more personally than Amazon does. What if the digital picture you had of your customers wasn’t just data points? What if it was a solid human picture of their interests and desires; a picture of where they’ve come from, why, and what they will do next? More than that, though, you need to make sense of the network data to paint a rich picture of the world your customers live in. It’s no good just knowing your customer is young and female. If you know she’s a single mother of a young child, she works on shifts and has strong affinities to specific brands, you can really engage her with relevant messages and create a positive experience.

The only way to win back wallet share from Netflix and Amazon lovers is to have a better understanding of your customers than they can. Because you’ve got millions of customers and billions of events, you need to do that at scale.

You also need the same rich human story of your customer in every channel at once. That means moving each unique customer story around to everyone that needs it at incredible speed. Achieving that would be a challenging, but amazing journey. If you could, you would be the ones raising customers’ expectations. And that would make you the stickiest brand in a very slippery world.

See how O2 have started to challenge the Great Competitive paradox here.

REFERENCE:

• CMO Council: CMO Council Predicts Brand and Telco Partnering Will Drive Omni-channel Marketing Effectiveness Globally, 2017. Source: https://www.cmocouncil.org/media-center/press-releases/cmo-council-predicts-brand-and-telco-partnering-will-drive-omni-channel-marketing-effectiveness-globally

February 18, 2019

Build or Buy a Customer Data Platform? Here’s the Answer

By Varij Saurabh, Manthan

A packaged, industry focused CDP provides ‘people ready data’ and quick time to value

Any marketer looking to invest in a Customer Data Platform (CDP) will have to grapple with the build vs. buy conundrum. The points of view are more entrenched in this space than any other. This is primarily because the end deliverable is a customized data platform, which resembles a services engagement. Let’s compare the build and buy options.

If you know exactly what you need, can describe it in exact requirements and have a service partner or an in-house team who can deliver to the requirements, build might be a path to consider. You will still have to wait for 6 to 12 months to get access to the solution. If you have a stop gap technology that meets your requirements while you build, or time to market is not critical consideration, you can opt for the build route.

The build option

Organizations with substantial IT budgets and large development teams with complete range of data, design and IT skills might opt for building a CDP in-house. The people investments required to deliver a custom CDP and maintain it are considerably higher than the buy option.

Done right, there could be competitive advantages with a custom solution. However, be cognizant that at every stage there are very real challenges – scope creeps, failure to accurately define specs, cost overruns, overheads of vendor management and staff attrition.

Basically, a large-scale IT project is often lost in translation.

The buy option

A pre-packaged CDP that is crafted for a specific industry offers the best of both worlds – quick time to value of a plug and play solution and close fit of a bespoke solution. While you would need some technical resources for the set-up and upkeep, the time and costs involved are much lower, and there are no harsh surprises.

This means you can run pilots and POCs quickly before you go all-in, you can examine and are sure that the solution really works for you. A much lower upfront cost and quick onboarding takes away risks, and still provides a solution that is tailormade for your needs.

CDP that delivers people ready data

A well-designed solution must cover scenarios and requirements that haven’t been thought of and encountered before while being cost-effective.

A CDP should serve multiple user groups that have unique needs. It should be scalable to serve data scientists who work with large data sets and require clean, well-organized data. It should be flexible to serve marketers and campaign planners who want to promptly analyze metrics and decide targeting strategies or perform customer segmentation. Senior executives form another user group and would typically be interested in trend reports and business dashboards.

Recognizing the distinct personas in the value chain, a differentiated CDP must have distinct interfaces and functionality for each. It’s time you addressed the unique data needs of different people.

February 14, 2019

Gearing Up for B2B Marketing in 2019

By Ariella Brown, Zylotech

CIOs at successful B2B companies are planning for a 2019 marketing stack roadmap with a focus on #CustomerTech. That means not just collecting data, but using it to address the business needs of their customers to achieve an unprecedented lift in KPIs defined around understanding customer behavior.

Next year is set to be one for businesses to fully optimize their customer data quality and insights by setting up the right technology platform for marketing to business customers. As you plan your 2019 budgets, the essential thing to think about is the customer aspect of #CustomerTech. Approaching tech from that perspective results in better customer data and analytics -- the prerequisites for account based marketing (ABM).

Thanks to the rise of digital transformation, marketing models have evolved. The same demand for personalization that has arisen in the B2C realm now applies to the world of B2B marketing. As Gartner put it in "2019 CIO Agenda: Secure the Foundation for Digital Business" [PDF], ”organizations are shifting their focus from what they sell to how they sell.” In fact, Gartner found that 49 percent of CIOs surveyed said they had already transformed their approach or were in the process of doing so.

The lifeblood of digital transformation is data, and that is why we’re seeing B2B marketing evolve now. We’ve arrived at the point of capability to tap into the ubiquitous streams of data to compile B2B marketing data that is rich and robust enough to trigger advanced customer analytics.

Accordingly, Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report found that concern about data quality has grown over the past three years from 75 percent in 2016 to 89 percent this year. However, the numbers are lower for those who can say that their own data meets their expectations; only 50 percent express confidence in it.

And there’s the rub. No matter how much data there is out there or how sophisticated your analytics may be, ABM won’t work if the data is wrong. This could account for why the majority of businesses have still not adopted it.

Only 38 percent of people said they were using it, according to Dunn & Bradstreet. The report attributes the low rate of adoption to the lack of “quality data – specifically strong firmographic and demographic data – to identify key accounts and targets, reach them across a variety of channels, and deliver relevant content that accelerates their buyers journey.”

This is why CIOs have to direct their tech efforts to consolidating quality customer data for analytics that will enable B2B ABM. The key to that is defining metrics around your business customers to determine the ROI of the tech.

As Gartner reports, the overwhelming majority of top performers (89 percent) “favor consumer metrics as indicators of success.” Such companies pay less attention to the time they save from the use of tech and instead focus on results the time savings can deliver to their customers, as well as that most important goal of attaining a “deeper understanding of consumer behaviors and needs.”

Achieving such understanding calls for high quality data and accurate customer analytics. Those form the basis of ABM, which needs to be in sync with the customer experience throughout the buyer’s journey.

This is the equation for CIOs seeking to fuel their business initiative this year: set up the #CustomerTech that will get you the right data for accurate analytics that will enable your marketing teams to tap the great potential of ABM. This will in turn allow your marketing teams to tailor their messages to the needs and preferences of the targeted business contact. If they do that then they will see measurable ROI for tailored B2B marketing in 2019.

February 11, 2019

Smart Audience Segmentation and Suppression With a CDP: A Primer on Segmentation and Suppression

By Korina Velasco, Lytics

67 percent of customers say brands need to automatically adjust their content based on a customer’s current context. And 42 percent say they get annoyed when content isn’t personalized.

Which is why more and more companies are turning to the data to personalize their marketing, to target the right customers with the right messages at the right time and to make sure they aren’t targeting the wrong customers.

In the world of Customer Data Platforms, we call this segmentation and suppression. And here’s how it works.

What is Segmentation and Why Does It Matter?

Chances are, your company has a lot of customer data on its hands. The challenge for most companies is in filtering and using that data to drive real campaigns. Which is where segmentation comes in.

Segmentation is the act of filtering the data to identify a target audience. Maybe that target audience is people who have abandoned their shopping carts (and who you hope to convince to come back and complete a purchase). Maybe it’s your most engaged customers. Maybe it’s customers within a 30-mile radius of an event. Or maybe it’s a combination - shopping cart abandoners who are highly engaged and within a 30-mile radius of your event.

One of the powerful features of a good CDP should be the ability to segment your data and let you identify your target audiences quickly, simply and in real time.

And then there’s suppression…

What is Suppression and Why Does It Matter?

On one hand, you want to target the right audiences for your campaigns. But what about the wrong audiences?

If you chase your customers around with ads for things they’ve already purchased, they’re going to get annoyed. And 66 percent of those customers who you annoy with irrelevant ads? They say that annoyance makes them far less likely to purchase.

Which is where suppression comes in.

A good CDP should allow you to not only target your ideal customers, but also suppress audiences you don’t want to include in a campaign. Don’t want to advertise to customers who just bought a similar product from you? What about customers with open customer service tickets? Or customers who’ve stopped engaging with your brand? You should be able to identify and suppress those audiences in your CDP as needed for each campaign.

Smart Segmentation and Suppression Leads to Big Wins

Smart segmentation and suppression are how The Economist grew digital subscriptions by 300 percent.

Through segmentation and suppression, they identified and targeted users who were not yet subscribed and who were likely to subscribe based on Lytics behavioral scores. Not only did they increase digital subscriptions exponentially, but they decreased acquisition costs by 80 percent as well.

Dr. Martens’ story is similar. They used segmentation and suppression to identify customers who had an affinity for their museum collection, who lived in the UK, who were likely to engage and who had not recently purchased a satchel. They targeted this audience with ads for a museum collection satchel…and saw a 60 percent increase in conversions and a 20 percent increase in average order value.

Ready to Start Segmenting Your Customers?

A CDP like Lytics is the answer. We’d love to show you how. Schedule a demo today to see the product and chat with our expert team.

February 4, 2019

Is Your Organization Ready for a Customer Data Platform?

By Korina Velasco, Lytics

Data-driven marketing is proven to increase engagement and sales. But before you commit to a CDP, it’s important to assess your organizational readiness.

Data-driven marketing is vital in today’s economy. Or so say 64 percent of marketing executives.

So, it may come as no surprise to learn that Customer Data Platforms and data-driven marketing strategies are growing - and fast.

And while speed is important in an always-changing industry with customer expectations that keep on rising, it’s also vital that we put strategy before tech. Because it isn’t just new technology that makes a company data-driven. It’s also about the people, processes and strategy behind the scenes.

In fact, in a recent panel discussion, some of tech’s biggest names agreed that the biggest challenge to MarTech success is people and process.

So, before you tackle the technology side of becoming data-driven, we think it’s important to assess your organizational readiness. Is your business ready for and committed to a CDP? Here are three questions to help you figure it out.

1. Have You Identified Your Goals?

Every tech platform on the market, including CDPs, is a tool meant to help you achieve real business goals. This means you need to know what those goals are and make a plan for reaching them before you choose and implement a CDP.

If your goal is to identify the content each user responds best to, you’ll need a CDP with a content affinity feature. If personalization at scale, like that demonstrated by Netflix or Spotify, is your ultimate goal, you’ll want to look at CDPs with built-in machine-learning and artificial intelligence (like Lytics). If your goal is simply to centralize and unify your data, you may be able to get away with a less feature-rich option.

Whatever your goals, both short- and long-term, identify them up front and use them to assess the technology. Because it’s much easier to buy the right tech the first time than it is to replace it later.

2. Is Your Data Accessible?

For a CDP to really drive business results, it needs to have data coming in. Before you start looking at platforms, take a look at your existing data. Where does your data currently live? In how many places? Is it accessible to you? Is it organized? Are there process or tech restrictions that keep you from accessing it? How will you get around them?

The more you know about your current data situation, the better prepared you’ll be for both choosing a CDP and understanding what you need to do to get the data into it once you have it.

Read more about making the most of your first-party data.

3. Are Your Teams Ready and Willing to Work Together?

If siloed data is bad, siloed teams are even worse. If you’re committed to data-driven marketing, that means your teams are going to have to work together. Are the IT, marketing, sales, PR, customer service and other customer-facing teams already connected? If not, are you willing to make changes to connect them?

According to the aforementioned industry leader panel, the biggest point of failure in this process isn’t technology. It’s communication. For the best results, every team that touches the customer service experience will need to be aligned. The earlier you start thinking about how to align those teams, the better (and faster) you’ll start seeing results.

What Now?

Understanding the people and process challenges and changes ahead will help you choose the right CDP and hit the ground running. We also recommend choosing a vendor that understands that the tech isn’t the only important piece of the data-driven puzzle. Look for vendors with robust services, workshops, learning resources for your team and great customer service reputations.

Want more help assessing your organizational readiness and choosing a CDP? We just re-launched our CDP Buyer’s Guide. We’d also be happy to give you a demo and chat about how Lytics can help you reach your business goals.

January 28, 2019

Everybody’s Talking About Customer Data Platforms

By Matt Cannell, Lytics

With customers demanding greater personalization and privacy regulations demanding the companies control their data, marketers are talking about CDPs more than ever before.

According to CMS wire, CDPs will be worth $1 billion by 2019.

If you’ve been paying attention, this probably comes as no surprise. After all, the customer demand for personalized content is at all all-time high. Data-driven companies are exponentially more likely to be profitable year-over-year. And in 2017, 83 percent of companies who exceeded their revenue goals were using personalization.

If your company doesn’t already have a Customer Data Platform, chances are you’re on the hunt.

And you’re not alone.

According to Forbes, about 78 percent of Enterprise companies have or are planning to implement a Customer Data Platform.

Which is probably why so many guides are emerging to help companies through the process of selecting the right CDP.

CDP buying guides recently published include:

• The Relevancy Ring’s CDP Buyer’s Guide
• MarTech Today’s Enterprise Customer Data Platforms: A Marketer’s Guide<
• Real Story Group’s CDP report
• The ongoing CDP Explainer series by MarTech Advisor
• How to Choose a Customer Data Platform (CDP) (our own newly relaunched guide!)

The number of guides, published one right after the other, just goes to show how serious marketers are getting about their customer data. With privacy regulations, customer demand for personalization, and stats that consistently show that personalized marketing leads to big business wins, the search for a data platform is becoming more and more of a priority for businesses as the year progresses.

If you’re on the hunt for a CDP to centralize, stitch together, and help you analyze and use your first-party customer data, we’d love to schedule a demo and show you what Lytics can do.

There’s a reason we took top marks in The Relevancy Ring Buyer’s Guide above. Let us show you.

January 24, 2019

Apply Business Use Cases to Evaluate CDPs

By Tony Byrne, Real Story Group

When comparing or evaluating Customer Data Platforms, people tend to default to feature comparisons. This seems logical, but can risk missing the business forest for the technical trees.

For Real Story Group vendor evaluations, we do examine features, but focus more on business use cases, or "scenarios," to find more meaningful contrasts for prospective enterprise technology customers. While you should always consider product functionality and vendor predilections, the key to comparing technologies lies in how well they fit your particular scenarios.

Nine CDP Scenarios

Explicitly or not, different CDP vendors target different scenarios. This is usually because the product's flagship customers wanted to fulfill a specific subset of use cases. The platform might have broadened its scope as it matured, but typically the initial roots remain visible — and powerful.

Nine CDP Scenarios. Source: Real Story Group

In our recently-debuted CDP vendor evaluations (get free sample evaluation here), RSG isolated nine distinct business scenarios against which we assess platform fit. Some will be more or less germane to you. In some cases CDPs support a business capability, rather than deliver the service itself.

Vendors will of course claim broad applicability for their platforms, but we find in reality they typically specialize in three or four of the nine. Let's look at each in turn.

1. Advanced Customer Data Management

This scenario might seem like an oxymoron because the primary purpose of a CDP is to provide data management services. But some vendors focus intently on this set of back-end capabilities, and less on the more activation-oriented services below. It's a kind of purist approach, but your enterprise might want to focus here if you anticipate having to support multiple data sets, create virtualized data store, expect unusual challenges reconciling identities, anticipate using the data for non-marketing use cases, or have other advanced data munging needs.

2. Outbound Marketing Campaign Support

Support for outbound marketing campaigns is almost foundational and therefore, most CDPs enable this case in some form. However, the extent of that support varies. Some tools enable varying degrees of integration with outbound marketing and campaign platforms, most notably delivering segmented lists for activation. Other CDP vendors provide some inbuilt campaign capabilities to activate that data themselves, while others have built multi-step, journey orchestration features. Your mileage will vary.

3. Predictive Analytics

Predictive Analytics helps you answer hypothetical questions by predicting some aspect of an overall customer journey. CDP tools that offer this service will typically apply machine learning and artificial intelligence-based techniques. With time, and more data, the system is supposed to keep learning and improve its recommendations.

Of course, this also adds complexity to these platforms. Several CDPs can get you up and running within days, but most products that support predictive analytics will need you to do your homework well. Also, you may already possess more advanced predictive analysis services in other platforms and repositories (e.g., data lakes) within your organization that could prove more mature and adept at this.

4. Online Personalization & Experience Optimization

CDPs can potentially address two key dimensions of personalization:

1. Understand Individuals: Gain a better understanding of customers, apply them to segments, and then...
2. Personalize Experiences: Adapt content, options, and digital experiences based on that understanding

Most CDPs stop at the first dimension. They will integrate with downstream marketing / experience management / targeting systems for actual experience personalization — principally by pushing segments or individual records forward. Many large enterprises prefer this approach as it neatly separates data and experience concerns.

Some CDPs also offer built-in personalization engines. Also sometimes known as "recommender" systems, these platforms try to recommend content based on different attributes.

5. Ecommerce Recommendations & Optimization

Some CDPs offer specific services to target ecommerce scenarios. These include support for features to handle shopping cart abandonment, repeat purchases, next best offers, promotional campaigns, and so forth, leveraging diverse product attributes. These platforms typically feature tighter integration with common ecommerce environments, as well as heavier reliance on machine learning-based techniques for product recommendations. Here again let's distinguish between providing the data for your ecommerce engine to act on and actually providing the recommendation service itself.

6. Omnichannel & Offline Aggregation

While most CDPs can handle data and campaigns for digital marketing, not all of them are as good for integrating the following types of data:

1. Offline customer data from stores and point of sale (POS) terminals
2. Data from mobile apps
3. Data from other internet-connected devices such as Apple TV, gaming consoles, smartwear, and so on

Platforms that excel at this scenario offer at least some of these capabilities, along with services like managing physical address formats, and SDKs for different computing environments like IoT devices.

7. Realtime behavioral analysis

Most CDP vendors will claim they have machine learning capabilities. But very few can apply machine learning to streaming data in real time. CDPs that excel in this scenario offer more advanced algorithms that can work against existing data as well as live streaming data. Mind some significant architectural issues here around performance, reliability, and security.

8. B2B Marketing Support

In this environment, a company becomes a first-class object and not just a facet of an individual's record. CDPs that support this scenario can map customers to an account or organization object and vice-versa. Then ideally other CDP services — like data integration, segmentation, and analytics — can get carried out at an account or organization level as well.

9. Digital Advertising Support

Adtech and martech are slowly converging. Most CDPs can integrate with DMPs and other tools to get second-party and third-party data which they can use to enrich their first-party data. A few platforms also provide specific capabilities for advertising scenarios by integrating with ad servers and other adtech platforms.

Putting Use Cases to Use

You can apply scenarios to find out what a CDP really does well...and not. Consider the case of IBM's Universal Business Exchange (UBX). Many observers would argue it's not really a CDP, though Big Blue will try to sell it to you as one. A deeper, scenario-based evaluation suggests that UBX is reasonably broad but not deep, and, importantly, relies on numerous other IBM platforms to achieve business value.

Evaluating IBM's UBX as a CDP. Source: Real Story Group

Summing Up

To be sure, these scenarios are abstractions. In practice, your own efforts here are likely to represent variants or a hybrid combination of scenarios. The cases overlap somewhat, but they are useful for understanding what types of platforms tend to work better for different types of projects.

So a savvy customer will prioritize their needs and select technology accordingly from among the dozens of choices in front of you. Feel free to ping me with any questions or leave a comment below.

January 21, 2019

Wolf Tales: Observations from the Road

By Ed Wolf, Zylotech

Luxury: the very word evokes images of elegance and extravagance, and an elite and posh lifestyle that all should aspire to. Well known designer brands such as Coach, Gucci, Rolex, Louis Vuitton, and others understand that the lure of the luxury lifestyle is at the heart of their appeal among their clientele. Those customers, in turn, expect white glove service and royal treatment with every interaction. They spend a lot of money on their brands and have very high standards for customer service and personalized communication. In today’s big data, multi-channel world, however, this lofty ideal of high-end red-carpet treatment is more wishful thinking than reality. The truth is that even the most expensive and exclusive retail brands struggle with their customer data, which is the foundation for the personalized treatment that these customers expect and demand.

Luxury brands often have difficulty using data for personalization. These traditional businesses, significantly rooted in an historic past, are now competing with savvier labels. Born in the analytical age, they inherently understand how to leverage such modern technologies as real time data, machine learning, and artificial intelligence.

We met a mix of these luxury brands at the Luxury Interactive event in New York in October. Much of the discussion there centered around the importance for marketing executives in luxury to think analytically and luxury-minded at the same time. Luxury is just now getting a feel for one-to-one marketing, which offers a significant mining potential to capture new luxury consumers, as well as to cross-sell and up-sell existing ones, all while maintaining their exclusive reputations.

The main issue preventing these brands from reaching this holy grail of white glove personalized attention revolves around the use of customer data for insight into shopping behavior, product affinity, and brand loyalty. The problem is not lack of customer data. On the contrary, like most retailers, luxury brands have been collecting voluminous amounts of customer data for years. Whether it’s through ecommerce transactions, brick and mortar stores, department store sales, catalogue orders, or even social media sites, these choice companies have vast amounts of customer data with which to mine unique insight into customer behavior.

Instead, the key is understanding the full picture of everything the customer has ever done regarding the brand. This becomes challenging because the various sources of data often don’t speak. The ecommerce data is usually in a different silo than the email marketing data, for instance. And both are separate from, say, the data showing customer service calls, or POS data from instore purchases. The bottom line is that without these disparate sources becoming unified, luxury brands will find it nearly impossible to understand the behavior of their clientele. Sure, they may know that a customer purchased a pair of shoes and a handbag, but will they also know that the customer had to call the customer service line because of a repair issue on the bag? Or that the same customer follows the brand on Pinterest? Do they have a sense of the customer’s external demographics?

Without this data unification, and the data cleansing that accompanies it, brands will be in the dark when it comes to a thorough understanding of customer behavior. This makes segmentation and messaging that much more inaccurate. This comes back to haunt the brand when they send a marketing campaign that purports to be personalized but misses the mark because of outdated or incomplete data. This results in luxury customers not feeling the individualized love and attention that they crave, and that the brands promise to deliver.

The good news is that, as evidenced by the conversations at Luxury Interactive, these exclusive brands recognize this shortfall and are trying to address the problem. The emergence of Customer Data Platforms (CDPs) is a potential light at the end of the tunnel. These platforms seamlessly integrate and unify ALL customer data (some like Zylotech even enrich this with third party data), making it much easier for brands to communicate to their high-end customers. This is crucial for all brands of course, but even more so for luxury retailers whose reputation means everything to them, and any misstep in marketing messaging or communication can be damaging to their brand image.

To conclude, even though in many ways luxury brands evoke an old-fashioned sort of elegance, they need more than ever to embrace the forward thinking methods of customer engagement. Today’s brand conscious luxury shopper demands it.

January 17, 2019

Enhancing Your Customers’ Shopping Experience

By Hadas Tamir, Optimove

Every holiday season causes the increased need to better identify and treat your shoppers. How should you win their attention? Entice them. Here are some ideas.

Lately, I have been participating in a lot of conversations around how customers should be targeted and with what offers. The conversations focus on which offers are most effective on specific customer segments and how marketers can save money by identifying customers who only return during the holiday season, but don’t stick around for much longer - causing an actual negative ROI for the business in the long-run.

While offering discounts during the holiday season is accepted and implemented by many, some companies are using a different method to prepare their customers for the holiday shopping.

Let’s start out by saying that curiosity goes a long way. Yes, it did kill the cat, but within the marketing industry, it has proven to be an efficient and cost-effective way of getting through the holiday season. Here are some examples of how companies used simple customer curiosity to promote their brand:

The Teaser

Take Black Friday, for example. Most brands publish their deals either the day of or the day before Black Friday. As a consumer, I can attest to the fact that this practice drives me crazy! I’m a planner, and the only way I know how to plan my shopping spree is by knowing what's available, at what price and where.

This is where the teaser comes into play.

Two or three days before Black Friday and before customers are bombarded with Black Friday emails and campaigns from other brands, consider sending your customers a teaser campaign. The campaign template should be the same coloring, creative, and type of wording as the main Black Friday campaign so that customers (on the day of Black Friday) can easily recognize your brand’s campaign. This will make your campaign stand out, since your customers will already know what to look for.

Timing is key since we don’t want to send the campaign out too early (to prevent customers from holding out on shopping until Black Friday), but also leave enough time before the commercial holiday so customers know what they are looking for and remember to shop with your brand.

As for the content, it’s up to you how much you want to share, but note that you don’t need to specify what items will be on sale. You can give consumers just enough information to spark their curiosity and search for your campaign in their inbox.

The Pleaser

Let’s admit it, we all want to feel like we’re special and that we’re not “part of the pack,” and so do our customers. Loyal customers that have been with a brand for a long time and/or VIPs want to feel appreciated and acknowledged for their loyalty.

Reward them with a unique experience such as a special “Just for you” offer, a sneak peek into the upcoming holiday deals, a pre-sale event, etc.

The idea is to give customers a unique and special experience, not necessarily discounts or special offers unavailable to other consumers. The content/timing/creative of the holiday season sales will include copy that acknowledges this segment as your VIPs and/or most valuable customers.

This also works well with shoppers who have churned, but were previously VIPs. It’s a great way to remind them that although they haven’t purchased in a while, they are still appreciated and the relationship they previously had with the brand is still valued.

Another way that brands create uniqueness is in the actual shopping experience. Make it fun! Competitive! Enticing! Gamification campaigns turn the customer shopping experience into a game or a competition. Some examples of this are “Spin the Wheel” or “Scratch Cards.”

Lastly, make the shopping experience as easy as possible. Click2Cart, for example, allows customers to add items for sale into their cart directly from the email campaign. This is much more than just a trip to your cyber mall, but rather a whole new experience for your customers.

And Julius Caesar

AKA the Optimove way: segment your customers, segment your offers.

Define holiday offers by what your customers have previously responded to. Churned or dormant customers may need a bit more nudging, so send them a stronger incentive, vs. an active customer who, regardless of discounts, is continuously purchasing from your brand.

One type of segmentation is by ‘previous categories purchased’ by individual customers. If Julius Caesar only purchases sleepwear and Marc Anthony only purchases jewelry and accessories, then we would want to send them offers that compliment their past purchasing behavior. It seems that Julius is more of a sleepwear consumer and Marc is more interested in day-to-day style. For Julius, you might try to cross-sell socks, slippers, more pajamas. For Marc, you might send a campaign for dress-wear, shoes and more accessories (because you can never have enough). You get the gist. Recommendation models are most effective for these segmented campaigns.

Another type of segmentation is offer based. Send offers based on average purchase amount of each customer. If you know that a customer is spending $100 on average per order, structure your campaign accordingly. If you want to save resources and/or don’t have a full view of what each individual customer’s AOV is, try laddering your offers/templates:

• 10% off purchases of $100 or more
• 15% off purchases of $200 or more
• 20% off purchases of $300 or more

In summary: Stand out from your competitors. Show your customers that you are not like all the rest, not in your offers, not in your campaigns and not in the experiences that they will have with your brand. A customer’s shopping experience combined with valuable products will pave the way for not only this holiday season but will help you acquire long-term, loyal customers. Happy holiday, all. May the shopping commence.

January 14, 2019

Where Does #CustomerTech Fit into Martech? It's at the Center

By Chuck Leddy, Zylotech

Oh no, you might be saying after reading the headline above. Not another new category in the crowded Martech space, one already filled with thousands of vendors and multiple technologies. Yes, #CustomerTech is a relatively new definition, but it’s here to stay and may well be the future of marketing.

What is #CustomerTech?

Marketing legend Doc Searls defines #CustomerTech as “standard ways for customers to engage with companies at scale -- for customers.” So #CustomerTech puts customers first and places them (not marketers) in the driver’s seat. What’s driving the growth of #CustomerTech? Customers want control over their data, and big regulatory changes are giving them more of that control.

What Customers Want Most: Ownership of Their Data

Customers want decision-making power over their data. They want to opt in, to grant marketers permission to reach out to them with messaging that the customer both invites and values, rather than getting spammed by marketers with irrelevant content and being forced to opt-out/unsubscribe. Marketers may talk about “capturing target customers” and “driving them through the funnel,” but unsurprisingly, customers don’t like being viewed as cattle to be corralled and driven through funnels. In fact, customers want to use technology to control how, when, and by whom their data gets used.

This is where #CustomerTech comes in. It flips traditional Martech on its head, because the customer gains the capacity (and tech tools) to decide how they’re marketed to. As a marketer, you can no longer market “at” or “to” customers, but you’ll increasingly need to market “with” customer permission, mediated through #CustomerTech which enables customers to decide the who, what, and when.

Regulatory Changes Support CustomerTech

On May 25, 2018, the new General Data Protection Regulation (GDPR) came into effect across the European Union and for all EU customers, radically altering the way marketers collect, store, and leverage customer data. By the way, just because you don’t do marketing in the EU doesn’t mean you can disregard GDPR. The state of California, for instance, passed a new law quite similar to GDPR. Other U.S. states and nations will likely follow suit, transforming the way companies can market to customers.

Why Marketers Need to Care

The consequences of more customer control over their data and the arrival of new regulations supporting said customer control are simple: (1) more #CustomerTech will emerge to enable customers to “own” their data and control how you market to them, and (2) different approaches and technologies will be needed by marketers based upon gaining customer opt-in/permission and keeping it, as well as supporting compliance with emerging GDPR-like rules. So for marketers, the days of “spray and pray” spamming or consent authorizations buried deep within standard licensing agreements (that nobody reads) are disappearing, much to the delight of customers.

These major changes mean that marketers have no alternative but to take more care in “earning” and retaining customer consent. Marketers need to offer customers value in the content they share, and will need to be hyper-aware that customers always have the right to deny you permission to market to them at any time.

The customer is at the wheel now, and may permit savvy marketers to come along for the ride. But those marketers had better earn their place in the car by offering customers valuable conversations that inform and entertain. New regulations and #CustomerTech are enabling customers to assert control. Marketers don’t have to like it, but it’s the new reality that #CustomerTech helps enable.

January 10, 2019

Drive Smarter Experimentation and Personalization with Fully Integrated Data

By Julie Graham, Tealium

When it comes to your experimentation program, your experiments are only as powerful as the data you use. When customer data is siloed and fragmented, it impacts the user experience and internal operations. You need to ensure your team is leveraging the most comprehensive visitor and event-level data to build better cross-device user experiences and create highly targeted experiments and personalization strategies for audiences.

Kyle Brierley, Director of Global Integrated Solutions, Tealium, recently did a webinar on “Driving Smarter Experimentation and Personalization with Fully Integrated Data.”

In this webinar, Kyle took us through key ways on how to fuel better user experiences with centralized, comprehensive and enriched data. He also showed attendees how to stitch together anonymous and known visitor data to then leverage those audiences to generate more consistent cross-device experiences. You can find key takeaways below.

Experimentation Is Easier Said Than Done

Some factors that can limit being able to do so:

• Lack of a culture of innovation
• Legacy systems hinder innovation
• Processes are complex and time consuming
• Internal resources are limited
• Organizations may be risk-averse

When Universal Action Is Possible Through Universal Data

• Marketing is less reliant on engineering
• Data teams have clean data from all sources
• Businesses and Execs have accessible and actionable insights
• Product teams can make informed testing decisions and rollouts
• The end user gets a unified experience across all devices and contexts

3 Key Components Of A Powerful MarTech Stack

1. Collect and standardize data in real time
2. Integrate data across end-point solutions and contexts
3. Translate data into actionable insights

To get more key takeaways and knowledge around all things personalization and experimentation, watch the on-demand webinar and learn:

• How to leverage both online and offline data to deliver a seamless user experience across devices
• How to connect client-side and server-side integrations for increased performance and real-time experiences
• And so much more!

Watch the on-demand webinar and start personalizing and experimenting with your campaigns today!

January 7, 2019

How Marketing Is Better With AI

By Ariella Brown, Zylotech

It is a truth nearly universally acknowledged that a comprehensive marketing strategy must include AI. Most marketers know that AI is the wave of the future. Yet a disconnect persists between that aspiration and the actual number of AI users. Nevertheless, current trends indicate that CMOs and CIOs will have a meeting of minds on the benefits of committing to AI, and businesses will soon reap the benefits.

Recent research from the IBM Institute for Business Value found that the overwhelming majority -- 91 percent -- of marketers at successful companies believe AI plays a central role in their continued success. Yet, the same research shows that only around 25 percent say they currently make use of it.

While a quarter falls far short of the 91 percent, it still is better than four percent, which is the number that appeared in Gartner’s 2018 CIO Agenda Survey. The survey found that only four percent of CIOs have implemented AI in 2017.

However, that pitifully low number does not tell the whole story. The same survey reported that 46 percent of CIOs said they have plans to implement AI. So adding up ones who already have it with the ones who are indicating a commitment to adopting AI yields 50 percent getting on board for the tech in 2017.>

That number appears to grow on the marketing side to 60 percent in 2018, according to a BrightEdge survey of enterprise marketers. Clearly, interest in AI is on the rise both for the CIO and the CMO at forward-thinking companies that have demonstrated that it contributes directly to their success.

In fact, the McKinsey Global Institute Study, Artificial Intelligence, The Next Digital Frontier devotes a full 80 pages to exploring the different ways in which it can be implemented in various business operations. It found that many businesses have already reaped the advantages and pointed out “that serious adopters have significantly higher projected margins than others.”

What’s key to the improved profitability, though, is a full commitment built on “a strong digital starting point,” the report warns. This is why it is necessary for the CMO to get the CIO to embrace the digital transformation that needs to be in place for effectively leveraged AI.

What’s holding them back? McKinsey’s survey found that 41 percent identified “uncertain return on investment” as the most significant obstacle they faced in getting their firms on board for AI.

But if done right, the return is not uncertain at all, and it’s up to marketers to demonstrate their business case. They can cite some of the figures in the report, for starters.

Sales will go up with AI-enhanced marketing. “Insights-based selling, including personalized promotions, optimized assortment, and tailored displays, could increase sales by 1 to 5 percent,” the report says. Even more impressive is the potential 30 percent growth in sales that can result from “this kind of personalization, combined with dynamic pricing” for internet sales.

The McKinsey report cites the example of Stitch Fix, the personal shopping service that can figure out what a customer wants even if they fail to describe it. Its algorithm reviews the images they favor on Pinterest to get a sense of their taste.

Even retailers who sell in physical stores stand to benefit from AI-enhanced marketing. The report anticipates that “virtual assistants could identify repeat customers using facial recognition, analyze their shopping history to make suggestions, and communicate in a conversational way using natural-language processing and generation.”

Some establishments are already exploring these possibilities, tapping into the power of machine learning and computer vision to deliver responsive, personalized experiences to virtual assistants presented as holograms or to dynamic shop windows. With AI, it’s possible to tap into digital data while presenting an impressive in-person experience that’s a far cry from the static setups associated with old-school retail.

Whether a business faces its customers primarily in-store or online, AI can effectively turn out data in real time making its marketing a lot more personalized and a lot more effective. With the CIO and CMO both on the same page about full commitment to AI, they are set on the path to greater profitability that all successful companies will be taking.

January 3, 2019

Building a Foundation for ML/AI Readiness – Part 1

By Catherine Ballantyne, Tealium

After a day of mountain biking in the Kosiosko Royal National Park in Australia’s Thredbo Valley, I found myself in the depths of a serious conversation around Artificial Intelligence with a fellow cyclist, whilst hurtling down a mountain trying to avoid falling off the side of a cliff.

Laughing as I realised he knew far more about data than I ever will know, one of his first comments was, "All this talk about Artificial Intelligence is way off – Machine Learning will come first. Those talking about AI are literally doing just that, talking. AI will follow long behind."

It is not often I find a kindred spirit, especially one who rides a bike like that, but there you go, that’s what they call serendipity.

This conversation got me thinking, and doing so has lead to this 4-part blog series, where we will explore what successful organisations who work with Tealium in Australia are doing to prepare for Machine Learning and Artificial Intelligence beyond.

We are currently having a lot of conversations with customers and prospects who want to know about Artificial Intelligence and whether Tealium is going to solve the challenges of scale and automation through AI. The answer to this is yes … and no. Do we believe, Machine Learning and Artificial Intelligence will eventually enable much of the long sought-after nirvana where our electronic relations with people interested in our brands will be as fluid, relevant, and consistent as those we have created in the face to face world? Yes. Is Tealium embracing that as part of what we offer? Most definitely yes.

However, we would be negligent if we simply told you to go out and buy another tool (what is another one in an average stack of 90) that you can solve the problems of scale and automation with. Unfortunately, it doesn’t work like that.

If you are puzzled over how to bring together the many growing sources of data you have, to unify that data, understand it and act on it, all in the same time it takes your customer to click the purchase button, then read on, Macduff.

At Tealium we have spent ten years understanding what makes data fast and useful to all, and whilst we are backing the capabilities of ML and AI to ultimately solve these issues, we don’t foresee it will happen by some magical shake of a wand. Anyone seriously embarking along the path to the world of ML and subsequent AI is preparing now by consciously building a neutral, cross-channel dataset. One that is ‘Machine Learning Ready’ so that when they are ready to start utilising the power of Machine Learning, their data will be ready, too.

When developing something truly unique that maps unchartered territory, it helps to have some guiding principles upon which to make decisions.

We find our most successful ML pioneers are building a neutral dataset which is anchored in the following principles:

• The customer at the center
• Real time
• Data readiness/access ownership
• Privacy, governance, compliance and security by design
• AI/ML Ready

These principles form the basis upon which brands are consciously designing a unified, customer at the center, first-party dataset. Not just giving lip service to the concept of data as an asset, but actually treating it as such. The principles and their outcomes can be summarised as follows:

Customer at the Center: This concept has many facets, but when it comes to communications, the underlying dataset must be one primed for delivering what the customer needs next, not what an organisation wants them to receive. All decisions on how, when and what to communicate should be based on the state of the customer (or potential customer) with respect to the organisation at hand. That can only be achieved if there is a centralised understanding that can be activated as it is generated. To be effective this must be:

• Responsive: Data must be responsive and fluid to provide that edge we call ‘experience’
• Data-Centric: As we have moved into a world of distributed trust, data is needed to reliably bridge the digital relationship gap
• Invest in Content: Data is part of it, content is also a key and needs investment
• Continuous Testing, Learning and Adjusting: Continuous learning and adjustment will become the new business as usual

Real time: The moment matters in the electronic world just as it does in face to face relationships. Ask any shop assistant how they make the most sales – it is by watching and listening to their customer’s needs and responding appropriately. There is no campaign, channel or device. Data is the language of relationships, so to capture the moments that matter most, data must be activated in real-time by design.

Access and Ownership: As the volume of data grows, organisations must enable data democracy. For many it is also about ownership of first-party data in a consolidated manner:

• Organisational Data Freedom: To scale its use, businesses must ensure data is accessible, understandable, and usable by all.
• Omnichannel: The key to this starts with a data layer. This concept is born out of digital which has become omnichannel. It consists of standardising and unifying all data, from all places at the point of collection, in the language of the business, thus creating a fluid dataset in a language that everyone understands.
• First-Party Data Focused: First-party data is a brand’s greatest competitive asset as no other organization has that dataset. Control, ownership, and activation will be key to fully realising its value. It is not only the history of previous transactions and loyalty but also the behavioural data that provides the insight to intent with first-party data.
• Internal Alignment: This is not just a technical requirement but will satisfy business strategy as well. It is important that all internal groups participate in both the definition and implementation of internal alignment.

Governance By Design: Whether you call it governance, privacy or simply respectful, sustainable relationships and the complexity of communications, combined with the growing understanding of the value of data, means organisations are now architecting this into their datasets and tools.

• Consent: Ensuring consent is stated before any data is captured is key as is the ability to review or apply this as required.
• Transparency of Use: To maintain a sustainable relationship where data continues to be freely given will require its use be transparent.
• Consumer Rights: With this transparency, the consumer will increasingly expect the ability to review, delete and be notified of the data a brand has about them and organisations will need to provide real-time capabilities to do this.
• Control and Audit: Finally, to facilitate all these capabilities requires fundamental data control and audit. It will be imperative to concisely articulate the data you collect and how and when it is distributed.

AI/ML Ready: As the volume of data grows, so will the need to automate it using the power of Machine Learning and beyond. Having built a dataset that is customer-centric, real time, accessible, and properly governed means organisations will be ready to utilise the data. The core processes to enabling this mandate are:

• Valid and Consistent Streams of Data: First up is the development of a framework which enables the efficient and consistent onboarding of new data streams which are ready for consumption by Machine Learning and the subsequent activation. This includes engineering the needs of Machine Learning into your organisation’s omnichannel data layer.
• Listen and Respond to That Data: Once the framework is in place, working with what the data tells you is important. Interpretation will be as important as generation.
• Hire, Train, Enable, and Prioritise: This is new, not all the capabilities will exist in your current team – you will need to hire, train and enable employees to achieve these goals. As this happens, prioritise your projects and use these to form the basis of your training and recruitment.
• Brand: Marketing is an art and a science, ultimately data and Machine Learning are still tools to enable the scalable delivery of your organisations brand and the messaging and engagement is the art of this piece.

Tealium helps our customers build a strong data foundation first, one firmly anchored in the bedrock of an owned, accessible-to-all, neutral, governed dataset that will be fluid in nature. We believe this will enable capabilities such as ML to flourish, but without the principles as fundamentals, there will be a constant need to rebuild the underlying architecture. A process that is costly both in time and resource.

The first step towards ML readiness is building a strategy and roadmap for uniformly onboarding data from any source in a state that is immediately ready for use by all.

This is a concept we will explore in part 2 of the ML/AI Data Readiness blog post series.

December 21, 2018

Why You’re in Desperate Need of a CDP

By Tabi Yoo, Simon Data

What makes a marketing campaign effective? Personalization.

What’s essential to drive effective personalization? Customer data.

That said, just collecting customer data doesn’t mean you’re at the end of your journey to successful marketing; it’s just a step in the right direction. How you utilize this collected data is incredibly important.

First and foremost, your data is not stored in one place. Email engagement data, on-site/in-app activity, transactional data, etc. all live in different places. And what’s worse, they all have different data structures with no common identifier, meaning linking all of your disparate data sources requires painstaking manual work. In other words, if John Smith were spending time on your website and separately opened an email, it’s often difficult to tie both actions to him.

Point is: it’s very hard to get a unified customer view when all the customer data you’ve obtained is scattered, and it’s even harder to build a relationship with your customer without this cohesive, single customer profile. The difference really comes from having one coherent conversation with your customer instead of multiple rudimentary ones that prove both ineffective and a little disorienting.

This is where a Customer Data Platform, or a CDP, comes into play. By aggregating all of your customer data into one place, a CDP allows you to fully understand customer behavior across all touchpoints. But the real difference between CDPs and other CRM or data management platforms is that CDPs are designed specifically for marketers.

Let’s take a peek into what managing the customer lifecycle without a CDP look like.

If that sounds difficult to you, you’re right. This is where CDPs come in.

Since CDPs are meant to be marketer-managed, you no longer need to rely on engineering resources to create and deploy personalized marketing campaigns. Further, the most advanced CDPs support both advanced customer segmentation and actual marketing execution, giving marketers a one-stop-shop for building and targeting audiences across channels.

This kind of power has real revenue impact. A recent McKinsey study focused on the value of CDPs cited a multinational retailer that leveraged customer purchase data alongside on-site activity to trigger targeted messaging to specific segments of customers. The result? The company doubled its email open rate from 10–15% to 25–35%. Similarly, the same study highlighted a travel company that employed the same personalization in their marketing strategy to drive a 10–20% boost incremental boost in conversion rates and customer lifetime value.

A CDP isn’t strictly necessary in order to build this crucial relationship with your customer, boost relevant KPIs, and drive an increase in revenue, but going at it alone isn’t easy. Moreover, there’s a real opportunity cost in doing things the old-fashioned way. Time lost due to manual processes isn’t just a quality of life issue for marketers. Moving slowly here means running fewer tests, optimizing less efficiently, and losing out on the ability to capture opportunities as they arise, all of which have significant revenue implications.

Better personalization is rapidly changing from a nice-to-have into a necessity, but building out these capabilities doesn’t have to be painful. After all, that’s what a CDP is for.

Sounds too good to be true? Give us a shout at hello@simondata.com.

December 18, 2018

Designing a Data Supply Chain for Data-As-A-Service

By Julie Graham, Tealium

Having a clearly defined data strategy is a necessity in creating exceptional moments with customers and ensuring long-term brand loyalty. Yet most organizations aren’t sure which strategies and methodologies need to be implemented to ensure accurate and seamless alignment of data within the overall business strategy.

Ted Sfikas, Director of Solutions Consultants, North America and LATAM, Tealium, recently did a webinar on “Designing A Data Supply Chain For Data-As-A-Service.”

In this webinar, Ted showcased why data is so critical to an organization, what Data Center of Excellences are, and what the requirements are of data today. You can find key takeaways below:

Data Requirements Today

• Data must be up-to-date, trusted and available in real time
• Every department needs the same first-party data set, at exactly the same time
• Privacy and regulations will require a central, governing approach that applies to all
• Implementation must be flexible and modern – data must flow freely throughout the enterprise and must be orchestrated centrally at some point
• Future proof: Machine Learning (ML) is upon us and without a central, governing entity to orchestrate, the business will be adversely impacted as competition moves ahead

3 Keys in a Data Center of Excellence

The CoE can be a virtual organization or a dedicated one. It covers the “people”, “process” and “technology” needed for a data supply chain:

People

• Subject Matter Experts
• Solution Delivery
• Service Administrators

Process

• Data Strategies
• Best Practices
• Data Chargeback Model

Technology

• Data Collection Technology
• Data Enrichment Technology
• Data Activation Technology

What Is Data-As-A-Service?

• The technology delivery method for a Data Supply Chain
• The most cost effective and lowest risk approach for modern organizations
• Delivers on the promise of built-in, unified capabilities for every stakeholder, regardless of platform or department

To get more key insights and takeaways on Data-As-A-Service, watch the on-demand webinar and learn:

• Why a data supply chain is critical in creating a high performing data and analytics program
• How to build a data supply chain
• 4 ways data-as-a-service becomes a necessity for organizations
• How to standardize how data is requested and received

Watch the on-demand webinar and start designing your Data Supply Chain for your Data-As-A-Service strategies today!

December 14, 2018

Decrease Risk Through Holistic View of Data

By John Nash, RedPoint Global

Many data-savvy marketers and martech professionals think about how they can use data to create a competitive advantage. That’s crucial in today’s environment, where the more you show customers that you understand and value them, the better your marketing outcomes will be. But that’s not the only benefit of building the type of holistic view of customer data that allows marketers to deliver contextually relevant experiences and communications. There’s another, equally important, benefit: risk avoidance.

Privacy is paramount and ensuring it can be expensive if you have the wrong processes or systems in place. Whether put in place by federal, state or industry regulators, regulations such as GDPR loom large for marketers, as well as their entire organization. There are other business and technology risks that can be avoided by having robust customer data platform (CDP) capabilities. Decreasing risk by creating a comprehensive and accurate single customer view is a prudent and potentially profitable approach.

There are four primary ways to decrease risk through a customer data platform:

• Improve compliance through strong closed-loop processes, e.g. for GDPR,
• Increase data quality, and lower costs of maintaining customer data through advanced matching and streamlined Master Data Management (MDM),
• Improve cross-team collaboration, through connecting all data and making it accessible across the enterprise,
• Future proof your enterprise to enable innovative customer experiences via emerging technologies, through an open garden approach.

Think about how you currently access the data you need to optimize customer engagement. You may have robust demographic and purchase data in one system and rich online behavioral data in another. Your primary goal in bridging those silos may be to provide your frontline staff with access to the real-time data they need to provide the most relevant interactions, offers, or communications at each customer’s moments of truth.

Certainly, it’s essential to provide that access. But without the right tools and processes, you may be setting up a data free-for-all that leads to non-compliance with regulations, poor data quality and maintenance practices, and siloed operations.

Creating a holistic view of customer data that solves these issues is easier, and more cost-effective, than you might think. The solution is a customer data platform. A CDP is a marketing technology that enables brands to integrate all types and formats of customer data (batch and streaming) to build a golden record of a customer or consumer — including anonymous-to-known behaviors, preferences, and purchase history. This not only enables you to hyper-personalize cross-channel customer interactions in real time, it also helps you to reduce the risks associated with siloed data.

Currently, only 48 percent of executives polled by systems integrator Dimension Data for its “Global Customer Experience Benchmarking Report” have a customer analytics system, and only 36 percent have a big data analytics solution in place. Companies embracing analytics and big data have a distinct advantage when it comes to both personalized customer experiences (CX) and risk avoidance.

Data access helps us to transform and innovate our marketing and CX, and as a result, reach new levels of marketing performance. But that access comes with its share of risks, including privacy, data quality, and fiefdoms. Using a CDP can help you reduce these risks. The key reason: CDPs connect all types and sources of customer data in real time, which creates a unified customer view that is accessible across the enterprise. This level of data access may at first seem too broad if it’s not what you’re used to. On the contrary, it provides a single point of control to effectively guard against privacy and quality issues. It also serves as bridge across data silos, which encourages data sharing and collaboration instead of guarded fiefdoms.

One of the biggest concerns in terms of managing the vast stores of customer data most marketers have at their disposal today is compliance risks. A CDP can lower these risks by helping you achieve compliance with regulations and industry mandates such as GDPR, HIPAA, PCI, and GLBA that focus on personally identifiable data.

Linking a CDP to a customer preference center is one way to decrease compliance risks. Providing a consent-based preference center for individuals allows them to select the exact ways they want their data accessed, processed, and forgotten. It should also enable them to set their preferences for communication across all interaction points along the customer journey. This creates a closed-loop process between data capture, preferences, and use. Along with reducing compliance risks, this approach will ensure that you’re delivering an excellent customer experience, one that’s personalized and delivers high value to consumers, so they opt-in rather than opt-out of your communications. That’s a marketing hat trick.

A CDP can also lower costs and reduce the efforts of maintaining and managing customer data. For example, using a CDP to automate manual data processes, Xanterra Parks & Resorts reduced the number of hours required to prep data by 80 percent. That efficiency not only reduces marketing costs, it also increases marketing performance by allowing Xanterra to get new communications and offers in market faster and enables its marketers to focus on higher-value activities.  This overcomes traditional silos and takes the risk out of the complex process required to turn data into business value.

CDPs further help to guard against data quality issues. A CDP creates a singular, accurate, and continuously updated golden record of each customer through probabilistic and deterministic matching algorithms that is maintained with a persistent key. CDPs with streamlined MDM capabilities also ensure that as data is curated in creating precise customer and household views, there is proper data stewardship and governance in place to ensure quality. This solves customer identity challenges that are essential to risk avoidance and will quickly surface any data quality issues.

At a time when most data scientists spend 50 to 80 percent of their time prepping data, according to estimates by The New York Times, maintaining data quality to cut that prep time is more important than ever. And, as it turns out, is profitable: Research from Dun & Bradstreet and NetProspex shows that companies that regularly maintain their database can see 66 percent higher conversion rates than those that don’t.

The most important risk avoidance may be using CDPs to future proof your business, so you can continually innovate customer journeys in ways that take advantage of emerging technologies. This requires CDPs with an open garden approach that allow you to connect to new sources of data and new touchpoints in the future, including real-time engagement through mobile, social and Internet-of-Things (IoT) touchpoints. This effectively avoids the risks of becoming locked into technology that may otherwise prevent you from developing customer experiences that are competitively differentiated.

As an enterprise solution, a CDP can help you bring data together from across your organization, allowing you to know all that is knowable about your customers. You can then deliver relevant, personalized engagement across channels and interaction touchpoints. And just as importantly, it enables you to decrease the risks associated with managing volumes of customer data, improve data quality and maintenance, ensure compliance, and enhance collaboration — all while improving the customer experience and building a profitable competitive advantage.

There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is risk avoidance. The others are:

• Innovation – Increased market value through breakthrough innovations
• Revenue lift – Increased revenue through improved customer experience
• Operational effectiveness and efficiency – Decreased costs through improved operations and customer understanding

You can read more about innovation in “Increase Market Value Through Customer-Led Innovation,” about revenue lift in “Increase Revenue Through Improved Customer Experiences,” and about operational effectiveness and efficiency in “Decrease Costs through Improved Marketing Operations.”

December 12, 2018

CDP Institute Workshops Set for January in Brussels, Utrecht and Munich

By David Raab, CDP Institute

The CDP Institute will run half-day workshops in Brussels, Utrecht, and Munich this January, working with local partners in each city.  Each workshop will be presented by CDI Institute Founder and CEO David Raab.  Format will include a mix of lecture, discussion, and exercises to ensure that each participant achieves a working understanding of key issues and opportunities. Topics will include:

* Why CDPs matter• How CDP fits into the larger marketing data architecture• Key benefits provided by CDPs• An overview of the CDP industry and trends

* How CDPs create value• How CDP relates to business and marketing strategy• Developing CDP use cases• Uncovering requirements for CDP success

* Selecting the right CDP• Requirements definition• Key differentiators• Specific features to look for• Running an effective selection process

* CDP deployment planning• Readiness checklists• Overcoming organizational roadblocks• Project planning• Finding the right deployment sequence

Check out this very nice video overview.  Then, use the links below for details and to register.

January 22, Brussels.  Hosted by NGDATA and Business & Decision.  Register here by December 24 for €100 discount.

January 23, Utrecht, NL.  Hosted by Squadra NL and DDMA.  Register here by December 24 for €100 discount.

January 29, Munich.  Hosted by b.telligent.  Register here by January 7 for early bird discount.

December 11, 2018

CDPs Aren’t Just For Marketers

By Mike Anderson, Tealium

There’s still a common misconception that CDPs are just a marketing tool, but in reality, their scope extends much further than that.

While it is true CDPs can help marketers take control of disparate data and deliver consistent customer experiences, there is no reason they can’t do the same for other departments within an organization. As technologies that build unified, persistent, and accessible databases, these platforms offer equal value for sales and business intelligence teams. And in fact, the CDP Institute recently amended the definition to ‘packed system’ as a reflection of broader applications.

To prove that CDPs aren’t just for marketers, let’s explore four examples of their use in wider business:

Upgrade the Customer Service Experience

A complete view of the individual — both the ‘known unknown’ and existing customers — is vital to fuel great service and results, and this is exactly what CDPs provide. By collating event-level data streams from a multitude of channels in real time and combining it with offline legacy data, these tools give sales teams in-depth knowledge about the activities of specific consumers: such as purchase history, items they are browsing, and abandoned shopping carts. Using this insight, call center staff can engage in highly personalized conversations with customers and deliver exemplary customer service.

For example, say a customer calls about a delayed delivery. By logging into the easily accessible and consolidated system, call center staff can quickly retrieve specific order details, such as order placement and expected delivery date. This information can then be leveraged to personalize the conversation and provide a resolution that suits the particular customer — be that free delivery on a frequently recurring order or a partial refund — and ensure their company relationships stays strong.

Smarter Business Intelligence

Customers should always be at the center of business strategy and CDPs generate the granular and constantly refreshed intelligence needed to ensure company goals align with customer needs. By orchestrating data held about individuals across the business, translating it into unique profiles that are continuously updated with new information, organizations can achieve a clearer picture of who their customers are, how they behave, and what they want. This comprehensive information can become the foundation for better decisions, from which markets to invest in — based on the location of current customers — to pricing or future product development. Plus, fast access to feedback about products and services also means adjustments can quickly be made to resolve concerns, keep customers happy, and stay ahead of rivals.

Opening the Door

CDPs can facilitate greater data control across the organization; some have an integrated privacy and consent management tool allowing data leaders to permit and revoke access to data, pending employees’ roles and the business purpose. This enables data controllers the chance to ensure there is a legitimate business interest for employees having access, the data has been collected with consent, and consumers are made aware of how their data is being used.

Similarly, tools such as these might allow some data handlers to view customer information, but block them from editing; meaning that a customer’s “master file” cannot be inadvertently deleted or changed, which could cause problems – such as delivering inappropriate communications and ruining the rapport.

Expanding Horizons

By employing a CDP to re-order data sets and enhance their customer understanding, businesses can obtain valuable insights that benefit the entire company. From addressing customer pain points to adapting products and strategy, these versatile tools give firms the information needed to fuel consistent growth and revenue.

Ultimately, organizations looking to optimize consumer insights need to stop restricting use of CDPs to just marketing teams. Their capabilities extend far beyond one department and can have a tangible impact across wider business objectives, if given the chance.

December 7, 2018

Decrease Costs through Improved Marketing Operations

By John Nash, RedPoint Global

Personalized customer interactions and communications do more than deliver engaging and profitable customer experiences. They also allow marketers to optimize campaign performance and omnichannel marketing, which reduces the costs of customer interactions. In fact, marketers who use personalization can reduce their acquisition costs by as much as 50 percent and can increase the efficiency of their marketing spend by 10 to 30 percent, according to Harvard Business Review.

A customer data platform (CDP) is the engine that can help you decrease costs through fine-tuning operations based on deeper customer understanding. As a result, you’ll be able to drive growth by monetizing your customer data and save money by improving operational effectiveness and efficiency. The insights and actions you can drive from a CDP will help you to:

• Lower customer fatigue, churn, and service costs,
• Shift your channel mix to lower-cost channels,
• Reduce the number of customer interactions required to drive results, and
• Improve marketing, IT, and data science resource utilization.

CDPs support these outcomes by creating a single source of true information for each customer. A CDP ingests customer data from all sources across an organization in real time to create a holistic, always up-to-date golden record that supports effective and profitable data-driven marketing activities such as personalization.

Simply put, using a CDP to get a clearer understanding of customers’ channel and interaction preferences enables you to personalize the customer experience and, as a result, reduce the number of customer interactions required to drive results. Further, by using a CDP to access cross-channel data at the customer level, you can vastly improve attribution and channel mix. By assigning truer values to your marketing activities, you’ll be able to better assess channel performance and then shift activities and budget to the most effective and cost-effective channels.

Additionally, by using a CDP for data-driven marketing techniques such as advanced matching and householding it’s possible to achieve outsized results even within specific channels. These techniques, for example, can put an end to over-mailing, which, in turn, reduces physical direct mail budgets by 20 percent on average.

Optimizing marketing can help reduce costs in other ways. For instance, using a CDP to support progressive profiling and dynamic customer journeys provides a better customer experience by allowing you to respond to customers in their moment of truth, no matter where they are. As a result, customers are more likely to convert. The ability to be wherever customers are as they consider a purchase is not only prudent, it’s essential. According to McKinsey & Company, 50 percent of customer interactions today happen during a multichannel, multi-event journey.

A CDP also reduces operational costs by creating privacy-compliant persistent customer records that are accessible throughout the enterprise, and available in real-time when needed. CDP’s provide a single point of control over data, empowering both applications and users to access it from across the organization to ensure the best customer experience, which improves efficiency. IT, data science and marketing resources are then able to spend their time creating powerful analytic models and customer experiences rather than struggling with inconsistent, inaccurate and inaccessible data. A CDP should also provide a single point of control over its data to achieve the best ROI. This allows you to leverage your existing IT investments without having to rip & replace systems that are in place today, another cost-saving operational benefit for the organization.

Overall, using a CDP to support personalized marketing can lead to double-digital savings. According to McKinsey, data-driven personalization generates a marketing-budget saving of 30 percent.

One company seeing operational improvements and related savings through personalized marketing is Xanterra Parks & Resorts. The company consolidated dozens of sources of customer data from across its properties and systems, including transactional and management systems each with unique characteristic and complexities. Xanterra used that integrated data to create rich 360-profiles for nearly all of its customers and prospects. Using those profiles, Xanterra’s marketing team was able to identify key customer segments, craft detailed personas and journey maps for each, and use them to deliver personalized communications at scale — throughout the entire customer lifecycle. The company reduced its marketing costs by an astounding 40 percent through personalization and now sees percentage improvements in marketing performance that routinely reach three figures.

Even when you consider the direct costs of implementing and running a CDP — software license, professional services and training, infrastructure, and support, maintenance, and data enhancement — you still clearly see a return on investment from the revenue growth and cost reductions you’ll gain by using one.

Industry leaders are capitalizing on this opportunity. Currently, 22.4 percent of executives polled by systems integrator Dimension Data for its “Global Customer Experience Benchmarking Report” say customer experience is a top strategic indicator of operational performance. Those companies and others like them have an advantage because they understand the cost-savings potential of CX and the data-driven marketing that underpins it.

Plus, increasing personalization across channels to deliver that stellar customer experience not only reduces operational costs, it also drives revenue — a potential 500 percent overall boost in consumer spending when marketers take an omni-channel approach, according to The eTailing Group and MyBuys. The research also found that 40 percent of consumers buy more from companies that personalize their experience across channels.

Investing in a CDP not only reduces costs by improving operational effectiveness and efficiency, it also drives additional revenue. The result is a double dose of profitable marketing practices.

There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is operational effectiveness and efficiency. The others are:

• Innovation – Increased market value through breakthrough innovations
• Revenue lift – Increased revenue through improved customer experience
• Risk avoidance – Decreased risk through a holistic view of data

You can read more about innovation in “Increase Market Value Through Customer-Led Innovation” and about revenue lift in “Increase Revenue Through Improved Customer Experiences.”

December 4, 2018

6 Ways Customer Data Platforms and Personalization Drive Retail Revenue

By Erik Archer-Smith, Arm Treasure Data

Smart retail executives know they’re under pressure due to changing consumer retail behavior, new technologies, and the influence of digital-first companies like Amazon and incumbent retail giants with massive digital transformation budgets, such as Walmart.

But several recent reports suggest that maybe it’s not enough just to understand what consumers say they want; the trick lies in anticipating the things that they might want, for the right offer and the right appeals. In other words, data analytics and personalization, powered by customer data platforms (CDPs) might just make the difference between those who can’t survive, and those who thrive, in the current cutthroat environment.

Are Data and Personalization the Keys to Survival?

One thing that makes innovators like Amazon so successful in driving profits is their strategic use of technology to build loyalty through positive, personalized experiences. Using customer data brilliantly is the secret to this personalization effort.

And while it has long been known that about half of all shoppers report having bought a product that they had not originally intended to buy, it’s also clear that a ham-handed approach drives people away. Smart personalization blazes a clear path to revenue growth. But uninformed recommendations are ineffective, according to recent reports indicating that 79 percent say they’ll only engage with brands that personalize accurately based on their previous interactions and behavior.

Omnichannel Retail Understanding of Customers Is a Crucial Survival Skill

As the retail world has become omnichannel, customer journeys are in turn becoming increasingly sophisticated, as customers navigate their way to purchase their product of choice. Customers leverage online data before, during and after their purchase, often switching back and forth between online and offline channels, on a single buyer’s journey. More than two-thirds of consumers use smartphones for product research while in a physical store, mostly for price comparison with other retailers, product information, and/or online reviews.

Personalization of the Buyer’s Journey

True omnichannel personalization of “the customer journey” – one that spans online and offline channels – has eluded retailers for years. Part of the problem is that marketers, in an effort to do the best with the data they can access, try a one-size-fits-all to customer personalization. What we really should be discussing are “customer journeys,” plural, because most of the time, people not only come to experience your product through their own unique personalities, but through their own unique trajectories of successive experiences of the product, often through different paths and even totally different channels.

But it’s often tough to manage – or even be aware of – all of these different paths, experiences, customers, and prospects. That’s because most retailers keep their online and offline activities separate and stored in different databases. The data is siloed, because these activities are managed by different teams using entirely separate systems that don’t ‘talk’ to one another, making anything other than a cookie-cutter approach to modeling consumer behavior virtually impossible.

Here’s a clever way to use data to personalize your customer experience. First, gather as much data as you can on your customer. Next, enrich your first-party data with second-party data (partners) and third-party data (DMPs, data marts, etc)  to help to fill in the gaps. That’s a good start at creating a clear picture of each customer’s activity and interests, updated in real time.

But how do you go about it? Here are some smart, real-world steps you can take to get started:

1. Start with the purchase histories and customer data that you already have. You already have data from receipts, loyalty programs, email campaigns, and where customers live, to name a few important types of information that can help you analyze habits and behavior.
    
2. Add tracking data from mobile devices. The analysis of mobile device location can help you understand your customers’ commuting patterns: for example, knowing how often they are near your store, what day of the week, and what time of day. If you have a mobile app, this data can usually be pulled directly provided the user has location services enabled. If not, you can look into partnering with location service partners to get this data.
    
3. Add “in-store” data from sensors, beacons or in-store WiFi. Information such as when a customer is inside your store, which aisles or departments they’re spending time in, and what they’re buying is the next step. In-store sensors or beacons are an easy way to do this. But if you haven’t deployed those yet, you can get started by pulling data from customers who connect to your in-store WiFi. If you do, just make sure you update your terms and services to inform people that location data can be tracked while they’re connected to your WiFi.
    
4. Combine the mobile device data with the in-store data into one unified customer profile. After these are combined, you can create customer segments around frequency of visits, in-store paths, and even the “halo effect,” where one customer’s behavior might influence another’s.
    
5. Use the newly created segments to deliver customized coupons to customers while they’re still shopping. Many retailers are experimenting with this location-based approach, with some even tracking the aisle the customer is in and conditionalizing the offers to in-store customer location. Doing this avoids a common pitfall that some retailers suffer when their in-store and digital data is siloed: They only analyze mobile data to deliver mobile coupons and analyze in-store data for paper or email coupons. Muji, for instance, increased in-store revenue 46% by delivering coupons based on in-store shopping behavior, but delivering them in real-time on a mobile device when a customer was near a target item.
    
6. Tie in weather and seasonal trend data to predict behavior and create personalized offers. Customer behavior is often profoundly influenced by weather. That’s why it pays to conditionalize offers based on what you know (or can predict) about the retail behavior of your customer segments. You might have to experiment some, with A/B testing on different offers, to tease out the details for your particular customers and product lines.

Customer Data Platform Technology Helps

A customer data platform (CDP) is key to marketing success as it collects and unifies data across the enterprise for a single, actionable view of your customer. Increasingly, these platforms are used to create and manage complex data sets that represent every detail needed to map a customer’s brand experience and create one source of truth for every customer profile. These profiles can then be shared with other marketing systems for the purpose of personalized marketing and/or predictive analytics.

Now more than ever, CMOs should be efficiently harnessing the data — providing their teams with tools that can measure customers’ propensities, frequencies, and rates to engage and purchase. Within a CDP, machine learning (ML) can be useful in finding your ideal customer segments based on a deep analysis of first, second and third-party data, personalized customer journey data, advertising/retargeting data, web personalization and recommendation engine data. It can also be used to pick the best creative and messages for each segment.

The sooner CMOs have the power to collect, aggregate, process and use big data more effectively, the better prepared they will be for success in the marketplace. The forward-thinking leaders in the industry are already moving towards a future of retail innovation, one where online and in-store will work together to generate loyalty and a smarter, more integrated shopping experience. CDPs, and the deep customer understanding they generate, might just be what saves some of these embattled companies.

November 30, 2018

Increase Revenue Through Improved Customer Experiences

By John Nash, RedPoint Global

“We’re customer centric” is little more than an all-too-common refrain today. The reality is that more companies talk about being customer focused than are actually doing it well enough to create a competitive advantage. Although 81 percent of executives polled by system integrator Dimension Data for its “Global Customer Experience Benchmarking Report” say they recognize customer experience as a competitive differentiator, only 13 percent rate their company’s CX as a 9 or 10. The challenge? CX is too difficult, according to about 44 percent of respondents.

Yes, completely overhauling your customer experience is difficult. However, it’s still possible to increase revenue through improved CX while leveraging existing engagement systems. The not-so-secret formula for doing so is by using a customer data platform (CDP) to create uniquely personalized, innovative customer experiences that spurs sales. You can see revenue lift using a CDP in three primary ways:

• Improved revenue per customer through intelligent cross-sell and upsell
• Improved customer retention through frictionless experience
• Improved customer acquisition through relevant targeting

These approaches are so effective because customers today want to feel both understood and valued. Nearly 80 percent of U.S. consumers say they expect brands to show they “understand and care about me” before those consumers will consider making a purchase, according to the “Wantedness” study by marketing agency Wunderman. Additionally, 56 percent of consumers polled in that study say they’re more loyal to brands that “get me” as a segment of one; in other words, showing a deep understanding of customers’ preferences, needs, wants, and past purchases.

Delivering on those expectations is a tall order for most marketing and martech professionals. Despite the advantages of using CX as a competitive differentiator today, few marketers have the ability to deliver truly personalized, relevant experiences consistently across all channels. The reasons? What marketers know about customers is limited, and marketers are unable to keep up the cadence of today’s always-on consumer.

That’s where a CDP comes in. A CDPconnects data from across a company in real time to enable an always-on, always-processing holistic view of the customer. Marketers can use that unified and complete golden record to deliver unique and highly relevant experiences.

Marketing professionals who use a CDP have easier access to more robust, timely data than their peers who don’t use one. Which means that they’re more likely than those peers to deliver a customer experience that improves revenue per customer, as well as increases retention and acquisition rates.

Using a CDP, you can perform advanced predictive modeling and enhance your customer data with information such as propensity scores to help determine customer intent and anticipate needs and preferences. This enables tactics that are individualized to each customer, such as next-best actions and dynamic customer journeys; for example, by presenting personalized, relevant communications and recommendations for the content and offers most likely to convert. Because the data is available in real time, marketers can now react at the speed of the customer — essential for delivering the experiences customers expect today. According to Boston Consulting Group, “brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers” see revenue increase by 6 to 10 percent, at a rate of two to three times faster than those that don’t.

The Impact of Transforming the Customer Experience

The business results of using a CDP to support your CX efforts are numerous and invaluable. They include increasing upsell and cross-sell, wallet share, average order dollar value, purchase frequency, and customer lifetime value because interactions and communications are contextually relevant and personalized. And, personalization can deliver five to eight times the ROI on marketing spend and can lift sales by 10 percent or more, according to Harvard Business Review.

Now is the time to start using a CDP to build the customer engagement that marketers covet — because not only will it deliver game-changing ROI, it also could provide more of a competitive advantage than you think. Although 43 percent of organizations claim to have a single view of the customer, a mere 12 percent say they’ve implemented the required technology to achieve that holistic view, according to Econsultancy. Further, 64 percent of respondents to the Dimension Data study say they have no big data capability that combines data from across channels.

Marketers who use a CDP can bring data together from across their organization to know all that is knowable about their customers. In doing so they have a distinct advantage. Companies considered leaders in data-driven marketing are about six times more likely than laggards to achieve a competitive advantage in increasing profitability and five times more likely to do so in customer retention, according to a study by Forbes Insights and Turn. The reason: The customer experiences these leaders deliver by using data-driven marketing are competitively differentiated. And there’s no greater advantage than using the robust, continually up-to-date customer data that a CDP provides, delivering a deep understanding that informs the interactions, messages and offers that comprise your CX.

There are four primary ways marketers and martech professionals can use a CDP to drive immediate and long-term business value. One is revenue lift. The other three are:

• Innovation – Increased market value through breakthrough innovations
• Operational effectiveness and efficiency – Decreased costs through improved operations and customer understanding
• Risk avoidance – Decreased risk through a holistic view of data

You can read more about innovation impact in “Increase Market Value Through Customer-Led Innovation.” We’ll cover the other two topics in upcoming posts.

November 27, 2018

Not All VIPs Are Created Equal: Get to Know Your Most Valuable Customers Better

By Chen Ofir, Optimove

Investing time and money on your VIPs is never a wrong tactic, but you can make it even more personalized, and profitable. A new experiment by Optimove tells the tale of the Resellers and the Enthusiasts.

When exploring e-commerce client data, we often run into different active customer personas from which we might benefit.

The most common separation is between standard active customers and VIPs. As the Pareto Principle states, roughly 80% of the effects come from 20% of the causes. When applying this principle to the eCommerce industry, we see the same effect time after time: 80% of the revenue is generated by 20% of the customers.

Recently, during an interesting chat with one of our clients, we discussed the possibility of going deeper into our VIP breakdown: what if these VIPs could be split into many diverse groups?

In this case, the separation into having Resellers and Enthusiasts made a lot of sense, but not every e-commerce business is built in a way that supports the existence of different kinds of VIPs, and the terms, of course, may vary between one brand and another.

In our case, and after exploring dozens of e-commerce brands, we came up with a unique separation – to Resellers and Enthusiasts with the following approach:

What is the difference between an Enthusiast and a Reseller?

The Reseller is a pretty common definition in the e-commerce industry. It refers to customers who make high volume purchases of cheaper items, in order to make a profit when selling these items to their own clients for a marked-up price.

The Enthusiast is a client that loves your product and is willing to pay a high price for it. The definition of the Enthusiast may vary among different brands, for example: a brand that sells art might call them ‘Collectors', whereas a photo equipment brand might call them ‘Professionals’, etc.

Both Reseller and Enthusiast have different behavior from the standard Active customer, but there are no similarities between the two:

How can we distinguish Enthusiasts from Resellers?

When trying to identify customers belonging to these two persona types, there are two attributes for each that we should look at. In this case, we used cluster analysis to identify these customers.

Resellers: The two main attributes that we should take into consideration when we try to identify Resellers:

• Number of Orders
• Number of Items Per Order

The combination of these two may help us detect a reseller early on in their journey as a customer. It might be enough to see that a customer ordered a huge number of items in their first order to flag them as a Reseller. But, if the customer ordered many items, although not a significantly high number of times, it might take a few orders to identify their Reseller pattern.

Enthusiasts: The attributes that correlate with Enthusiast’s behavior:

• Average Item Price
• Max Item Price

An Enthusiast might not buy as frequently as a Reseller, but their purchases are very valuable. In this case, the attributes above might do the job perfectly. Please note that your brand’s SKU has a crucial impact on the existence of Enthusiasts, in the case that your catalog’s range of prices is short, you shouldn’t expect to find Enthusiasts within your customers.

The example below shows how a simple cluster analysis discovers Enthusiasts based on Max Item Price and Average Item Price:

Each dot in the plot represents a customer. The X-axis represents the maximum item price, while the Y-axis represents the average item price. In this plot, we created five clusters based on these attributes. Our Enthusiasts, those who have high Max Item Price and high Average Item price, are the dots within the purple area on the right.

That being said, the CRM professionals might have insights that the data can’t detect, such as personal relations. If a client is known as either a Reseller or Enthusiast, you should always flag them manually, in addition to using the cluster analysis.

So, we can define two types of VIPs, how can we benefit from it?

We know that Resellers and Enthusiasts have different behavior, and we know how to detect each. The next step is to use this knowledge to target our VIPs better and boost their value.

When developing marketing and campaign strategies, we should take into consideration the type of VIPs we might encounter. For example, contacting an Enthusiast with a new line of luxury items makes perfect sense, but the same interaction will go straight to the Reseller’s Junk folder. On the other hand, when trying to push a surplus product at a low price, we might want to reach the Resellers, rather than the Enthusiasts.

Another crucial aspect is the churn indication. As Resellers buy much more frequently than Enthusiasts, we should consider raising the flag of possible churn much earlier. The ordering activity varies between different brands among the e-commerce industry, but the pattern is clear, as stated above.

In the chart below, we can see the frequency distribution of Resellers and Enthusiasts belonging to one of our clients. In this example, we might consider a Reseller as churned after 90 days of inactivity, as only a small fraction (16%) of the Resellers’ frequency exceeded this period. On the other hand, for an Enthusiast, this is a very short period of inactivity and should not indicate churn.

In conclusion, defining different personas within your customer database may be very valuable for your marketing strategy. Look into your stock, asses your products and think about your clients; who they are, what they do and how they achieve their goals, to help you segment them better. In our example here, understanding the different behavior patterns of a company’s VIP segment allows the brand to communicate with its customers and offer them the best offers, with the most suitable content, at the best timing.

November 20, 2018

Art of the Possible – The Role Data Can Play in Unlocking Marketing Creativity

By Aaron Brennan, RedPoint Global

According to Astro Teller, head of moonshots for Google X, “It is a lot easier to hit a 10x number improvement than a 10% improvement.” The reason for this is when you set your sights on small improvements you tend to look for incremental ways to improve rather than big ideas to reach toward the stars.

The concept can easily apply to marketing teams that adopt a 10x strategy to reach major growth objectives, and the rise of NoSQL and document databases helps marketers to deliver on that strategy.

In traditional marketing campaigns the time and effort required to access data and easily query it has been a major obstacle to success and has limited marketing creativity. The need for a data scientist or an IT manager to assist has also added to the burden.

With NoSQL databases, marketers have a new way to easily access data and unlock marketing creativity to get a multiplicative level of improvement instead of incremental improvement. NoSQL document databases offer more flexibility in accessing all data types, cost savings by reducing the overhead and expense of data wrangling, and allow marketers to scale and grow their efforts more quickly and easily.

Following are a few ideas of how marketers can now utilize customer data to uncover the art of the possible:

• A picture says a thousand words:  We all know that messaging isn’t the only reason people click or engage with marketing campaigns. Often there are beautiful images or videos along with the campaigns, but it is hard to measure the impact of images on customer engagement. With a NoSQL document database, image content becomes searchable by engagement: who bought what, when, where and with that you can pull together a timeline of highly effective customer images. This allows for smarter and more contextual retargeting strategies in digital and traditional channels. With this capability you always can bring forward each customer’s favorite image to help boost conversions and drive revenue.
   
• Turn your frown upside down:  A bad customer experience doesn’t have to be the end of the customer journey. What if it was possible for you to run a marketing retention campaign more quickly and efficiently for people who returned items and had a bad experience? The ability to access product information in a timely manner from a NoSQL document database means that your marketing team can easily find and target all the people that have faced certain challenges with a product or didn’t connect with the messaging. You could also re-engage with customers who have return items or stopped services.
   
• Data defines you: Data is one of the most powerful assets for today’s business and the ability to derive insights from customer data is what differentiates market offerings. Metadata has long been important for indexing and ranking, but there is marketing potential in metadata as well. With NoSQL document databases, you can simplify and reduce the time it takes to create audiences based on buying trends and metadata tags associated with them versus segmentation based on traditional age, gender, and affiliation demographics. Campaign development on similar metatags would be a simple process and could have potential to reach segments with much higher engagement and conversion rates.

These are just a few examples of what is possible with the right tools. By enabling marketers with a new level of connectivity and access to data you unlock the potential of the data. Furthermore, with a NoSQL document database such as MongoDB or Cosmos DB, all this information lives in one place. Then the question becomes more about how you encourage your team to embrace the art of the possible and think more creatively around the data and the customer journey.

November 14, 2018

From Customer Data to ROI: How AI Unlocks Cross-Channel Success

By Marina Ben-Zvi, Blueshift

Data and AI are today’s power couple. While customer data holds a wealth of insight, marketers have reached a point where their ability to capture data has exceeded their ability to take action on it. But coupled with AI, marketers have a way to make sense of the volume, velocity and variety of their data and use it to adapt marketing actions in real-time to drive relevance and impact across the customer journey.

Yet, there is a lot of confusion around AI because even though it’s been hyped with promises of breakthrough transformations, it’s remained largely black box. As a result, marketers are questioning AI’s credibility and are unsure where AI belongs in their marketing strategies.

AI: Unlocking ROI from Customer Data

To separate the hype from the real ROI, Blueshift set out to quantify the impact brands implementing AI-powered campaigns have realized in customer engagement, conversion and revenue. We analyzed 3.8B marketing interactions across channels and verticals, including e-commerce, media, consumer finance, and travel. Results of the benchmark study, published in the report, ROI of AI Marketing: 4 Levers for Cross-Channel Success, show that AI-powered marketing campaigns achieve:

• 3X revenue relative to their use in the marketing mix
• 3.1X-7.2X lift in customer engagement
• Nearly 2X greater impact on engagement for mobile push compared to email
• Continuous improvement in campaign performance as the AI engine learns from customer interactions, driving an additional 50% lift over initial results

Brands across industries are experiencing performance gains from incorporating AI into their marketing campaigns. For example, LendingTree saw a 35% lift in revenue per email by sending “Savings Alerts” that are personalized to each user based on their credit history and optimizing its send times for engagement. Tradera saw gross sales per session increase by 125% when deploying AI-recommended content.

Optimizing the Customer Experience with AI

People today want relevancy and expect immediacy, but marketers face challenges delivering that experience in today’s world of always-on consumers, channel proliferation, and fleeting consumer attention spans. Marketers need to act fast to deliver tailored, timely, cross-channel experiences and that requires a tool that scales insights, decision making and cross-channel automation. AI is that tool. AI is also the ingredient that advances Custom