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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.
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.
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.
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.
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 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:
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:
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
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:
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.
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:
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:
When Universal Action Is Possible Through Universal Data
3 Key Components Of A Powerful MarTech Stack
To get more key takeaways and knowledge around all things personalization and experimentation, watch the on-demand webinar and learn:
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:
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:
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:
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.
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:
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 firstname.lastname@example.org.
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
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:
What Is Data-As-A-Service?
To get more key insights and takeaways on Data-As-A-Service, watch the on-demand webinar and learn:
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:
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:
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.
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:
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:
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:
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:
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:
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:
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:
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:
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:
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 Customer Data Platforms from a system that unifies and distributes customer data to one that truly activates that data by using it for real-time decisioning and dynamically orchestrating customer engagement.
At its core, AI helps marketers be smarter and faster about how they engage customers along the customer journey by optimizing the “Who, What, When & Where” of marketing:
Activate Customer Data with AI
Data can be a powerful competitive advantage when harnessed effectively. Companies that are able to successfully unify their customer data and translate it into improved customer experiences will be the winners in today’s dynamic landscape. But realizing data’s potential requires a tool built for its increasing volume, speed and complexity. Realizing data’s potential requires AI.
For the full set of findings of AI’s impact on the core components of marketing and real customer examples of revenue gains achieved by implementing AI-powered marketing, read Blueshift’s report The ROI of AI in Marketing: 4 Levers for Cross-Channel Success.
November 9, 2018
Why Every Marketing Organization Benefits From Having Their Data in Order
By Albert McKeon, Zylotech
You're probably tripping over an abundance of marketing data: behavioral, historical, demographic, lead generation, social media. But at any moment, can you harness all of that information into an accessible, easy-to-discern stream? If not, you're just like any other marketer who won't realize your ROI.
After acknowledgement, action is the next step to getting your data in order. So here are four strategies you should consider if you want to realize the full potential of your data.
Knock down all silos
Many marketers face a seemingly insurmountable hurdle: organizational silos. But it's not insurmountable. All you need is a golden hammer, basically the promise to turn data into gold for the entire organization. That should tear down the walls.
Make data unity a constant goal by establishing a company-wide data management strategy. Charge leaders from each department to ensure that all information is shared. Product development, sales, shipping and just about every department matters in your pursuit to market effectively. Know what's going on across the company.
Clean up dirty data
Having dirty data might be even more frustrating than having siloed information. After all, disjointed data is at your fingertips but it's far from perfect to be used effectively. This is where an organization without silos has a huge head start.
When departments are aligned on the value of sharing data, they're also of the same mind on the value of the data itself. Have those data representatives occasionally meet to prioritize data. This will help departments decide which data needs to be made whole and which data can simply be discarded. Thankfully, data analytics tools help departments easily collaborate and make joint decisions on data.
Interpret the data
Once you decide which data is pertinent to operations, it can't just sit there waiting for the sunny day of exploration. Your organization needs to consistently interpret data to find patterns and discern its meanings. You'll want to know if preliminary findings can lead you down unexpected paths, or whether it's necessary to redefine the parameters of a data-driven project.
Not every organization can afford to employ a team of data analysts. Fortunately, data analytics technologies do the heavy lifting of learning. Actually, some platforms today are self-learning. Here, the analytics platform learns on its own, finding patterns and anomalies without first having to be instructed what to look for. This leads to unimaginable and exciting possibilities because human programmers don't always think of everything.
Choose the right technology
Speaking of technology, it's critical to have the right technology that will collect, coalesce, clean and analyze your data. Your marketing team should choose a data analytics platform that will identify, unify and enrich all of your relevant data points. You'll know it's the proper platform when you see that it's thoroughly aligning clean data with your marketing strategies. The ROI will be obvious.
November 2, 2018
Four Ways AI and Machine Learning Enhance the Customer Experience
By Chuck Leddy, Zylotech
While everyone has heard of artificial intelligence and machine learning, not everyone understands how these technologies work and how they support successful marketing. In this blog post, we’ll demystify these technologies and outline four important ways they can help you better know your customers and enhance revenues/ROI.
AI and ML, demystified
The type of artificial intelligence now commonly in use (called “narrow” AI) is a computer program humans teach to do one particular task, like identifying spam email, monitoring customer behaviors, or routing drivers through rush-hour traffic. AI is built upon data: you take data about the past, analyze it, and make predictions about the future.
AI is most effectively used in situations where historical patterns uncovered in the data are predictive of future actions. Machine learning/ML is a field within AI that uses statistical techniques to give computer systems the ability to "learn,” to progressively improve performance on a specific task, without the need for explicit programming (the data itself does the “programming”).
4 ways AI and ML are changing the marketing game
Here are four ways that AI and ML can help your marketing efforts. They are in no way exhaustive but are intended to offer you an idea of what’s possible with this emerging martech:
1. AI can create highly-personalized customer experiences, the kind your customers increasingly demand. As you collect data about what your customers do (and where they go), AI allows you to understand the behavioral factors that drive your customers’ behaviors. With this “actionable intelligence,” you can then send just the right message to the right customer at the right time, leveraging AI. In the same way that Pandora uses machine learning to suggest songs to listeners (based on prior listens), you can use marketing technology to send the right content to the right customer at the right time.
2. AI can better engage customers. No marketing team has enough members to engage all your customers all the time, but AI and machine learning can be used for customer segmentation, allowing you to send them more personalized messaging resolving their particular issues. Through combining “human + tech,” you can have chatbots perform simpler levels of engagement and then escalate more complex engagement tasks to your human marketers, optimizing efficiency and ROI.
3. Predictive analytics to promote customer retention. Every marketer knows that focusing on turning customers into repeat buyers can positively impact your bottom line. AI and ML can be used to identify patterns among those customers who are most likely to “leak out” of your funnel before they convert. By understanding why they leave, you can take proactive/anticipatory action to keep them within your funnel until ROI and beyond. This is what “retention marketing” is all about. When you keep your customers, you also grow your business.
4. Actionable insights for better performance. In the past, marketers were often blind to the customer’s journey. When might a customer purchase, and why then? These key questions matter for speculation and “gut” thinking. Now, we have data about every step of the customer journey. We can leverage that data to understand our customers and develop actionable insights that inform what we do. Data, the “oil” fueling AI and ML, simply gives us more visibility into the entire customer experience. In the end, AI allows us to market at scales not humanly possible before.
October 26, 2018
Let the Numbers Persuade You: The Success of Multi-Channel Campaigns is Indisputable
By Jonathan Inbar, Optimove
Are you still hesitant about investing more time and money upgrading your marketing plan with multi-channel campaigns? Let this research by Optimove show you exactly how more effective they can be.
In today’s digital-focused world, multi-channel marketing is important for the simple reason that you must be able to reach your customers where they are.
The number of devices per user is growing and it is important to have an orchestrated strategy of offers sent through several channels, so you won’t miss any potential touchpoints.
The following research comes to show exactly how effective multi-channel campaigns are compared to single channel. For this research, we looked at over 20 gaming and e-commerce brands from around the world and measured the percentage of multi and single-channel type campaigns with statistically significant results. Then we tested the best combination of email, SMS and push notifications, and which single channel leads to a higher percentage of campaigns with significant results.
Go Multi-Channel Already
Before we get to the part of measuring the percentage of significant results, we first examined the role of multi-channel campaigns in the entire marketing plan.
On average, the percentage of multi-channel campaigns represents only 15% of the entire marketing plan, a surprisingly low number given the fact that these multi-channel campaigns yield more significant results. In terms of the most common combination – the email & push combination won with 60% of the multi-channel campaigns observed in the research:
Significant Enough for You?
Next, we looked at the difference between multi-channel and single channel in terms of the percentage of campaigns with statistically significant results. It’s important to note that determining whether the campaign is statistically significant or not indicates how likely it is that the marketing action was responsible for its recipients’ behavior. The numbers speak for themselves: On average, 24% of all multi-channel campaigns ended with statistically significant results, while only 12% of the single channel campaigns ended with significant results:
We also looked at specific channels for single and multi-channel campaigns. We checked different combinations for multi-channel campaigns and saw which ones have the highest percentage of campaigns with significant results.
While none of the single channel campaigns passed the 15% mark, we can see an obvious leader from the multi-channel combinations chart. A third of all campaigns sent through email and SMS resulted in significant results:
When checking the biggest difference, comparing email only to email and SMS, we see that the addition of the SMS channel increased the percentage of campaigns with significant results by 250%.
What Should You Do?
Here are three key components to succeeding at multi-channel marketing:
1. Create and maintain a single customer view: Customers often interact with your brand in a variety of ways that involve more than one touch point. Create a single customer view with a centralized marketing data hub that consolidates your customer data in one place. This data must be dynamic and constantly refreshed and updated.
2. Launch a multi-channel marketing platform: Multi-channel campaigns involve synchronizing your messaging across different channels. By establishing a multi-channel marketing platform, you will simplify the creation and execution of cross-channel campaigns. Here are the capabilities you’re looking for:
3. Generate consistent customer experiences across all channels: The quality of the customer experience is greatly influenced by its consistency. By managing campaigns across multiple channels, you’re creating a presence for your brand in your customers’ life. If you treat each channel separately, you will fail to deliver consistency and damage the customer experience. Gone are the marketing days when a single message through a single channel sufficed to reach your complete customer base.
This research again shows why you should invest resources into multi-channel campaigns, either by making an integration with a certain type of channel or by creating the right strategy that will send the right content through all the channels together. The benefits of applying multi-channel strategy are great and could help substantially increase campaign uplifts.
Advancements in data and technology these days will help you achieve this goal quite easily, and there’s little choice but to join the new game. Luckily, tools like Optimove are designed particularly to help you win. Your customer base is out there, waiting for you. The right technological platforms will make your brand’s voice louder and much clearer.
October 23, 2018
Five Success Factors for Implementation of DMPs and CDPs
By Rutger Katz, EY VODW
Data Management Platforms (DMP) and Customer Data Platforms (CDP) are both technologies that have the potential to uproot the internal organization due to their cross-departmental function. Both technologies enable a link between online and offline customer information and allow marketeers to track, automate and orchestrate large parts of a customer journey. This requires that online (e.g. social media) teams have to collaborate with offline (e.g. CRM) teams. Mature companies would have cross-departmental and multidisciplinary teams that focus on key aspects of the customer journey such as sales funnel conversion optimization. Unfortunately, there are still many organizations either in their digital transformation or have not started it yet to such an extent that it can sustain such teams. Because the DMPs and CDPs are such disruptors of the organization, companies should start a transformation program to facilitate the transition that enables cross departmental and multidisciplinary teams. An important starting point is to create a strategic vision of initial focus areas and ultimately a total transformation goal for the entire company. To assist companies in establishing a strategy and adoption of these technologies we wanted to create a maturity model which could also be used as a benchmark in a company’s growth towards being a CDP/DMP enabled organization.
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A pinch of old with a dash of new
To structure this maturity model we used the model of M. Lippit from 1987 for successful business change. M. Lippit’s model was focused purely on project management for the successful implementation using five success factors. However, when focusing on the maturity of an organization we should also look at the maturity of each of those success factors. It is through the combination of M. Lippit’s success factors, including a slight adjustment, with our proprietary maturity levels, that we could create a maturity model that fits extremely well with the issues we experienced in companies struggling with gaining value out of their CDP/DMP.
The maturity model consists of three maturity levels, Explorers, Adopters and Leaders, each with their own goals over each of the five success factors of M. Lippit’s model: 1. Vision & Strategy | 2. Organization | 3. Roadmap | 4. Resources & Skills | 5. Processes.
Getting a running start and growing from there
Organizations that are exploring or have just acquired a DMP or CDP would do well to start with creating a Vision & Strategy and establish a core team (including all the necessary roles) if they are not already available. Make use of the platform suppliers or external expert consultants to assist in establishing these goals as they are of high importance for a running start. The next priorities are ensuring that the initial roadmap is impactful and is aligned with IT should their assistance be required for any integrations. Again the supplier should be able to provide a shortlist of easy to implement use cases and customize them to your business. At this point the business and IT management agreements come around in order to facilitate the core team to be able to work on the platform. The core team should establish a delivery process (e.g. Scrum) and pick a selection of productivity tools (e.g. Jira) which works well for them and the organization. Now the organization should have its Explorer goals reached and can start creating value out of the platform while the entire organization learns what it is to work with a DMP/CDP.
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In our maturity model the entire organization grows along while each goal has been accomplished. The achievements of earlier goals, when communicated widely enough in the organization, will release a ripple effect through the departments and colleagues to facilitate the changes in behavior and thought that make it easier to complete higher level goals. For example, it will be difficult creating a set of aligned CDP/DMP related KPI’s (a Leader goal) in most organizations without first getting agreements with the managers (an Explorer goal) and establishing a steering committee which influences priorities for each business unit’s resource (an Adopters goal). The managers first need time to see the value of the platform and realize that in order to motivate colleagues from different departments to collaborate on a CDP/DMP, they should also have KPI’s that focus on that cross departmental work.
As companies grow along their maturity they will realize that the earlier level goals have been the stepping stones that to pave the way for the higher level goals. The time required to progress from an organization without any of the goals to a fully mature DMP/CDP enabled organization differs per organization and situation, but on average would be 2-3 years. Using two real life examples we will demonstrate the worth of using our maturity model in overcoming roadblocks related to the absence of achieving success factor goals and that the sequence of the goals is of importance.
Case 1: Creating your internal experts and processes
The first case was at a FMCG organization that implemented a DMP one year ago and was working on their vision of a global rollout of the product (Explorers goal in Vision, working on Adopters goal). They had made management agreements (Explorers goal in Organization), a shortlist of easy to implement use cases that could be done in each country (Explorers goal in Roadmap) and a core-team which worked on the DMP part-time (Explorer goal in Skills & Resources) without any real structure in their approach (no Explorers goal in Processes). However, due to a lack of skillsets and knowledge abroad they hit a wall trying to establish the DMP in new markets. The local team and agencies did not know where to start and the global team had just enough knowledge on how the tool works, but not enough maturity to guide other local teams.
We decided to train the global team to be experts in the tool and the guiding light for the rest of the organization. After a large set of in-depth training days to create our center of excellence (Adopters goal in Skills & Resource) we went to work with the global team to establish standard and structured delivery processes explaining the entire process (Explorers goal in Processes) of a use case from idea to measuring it’s success (Adopters goal in Processes). Furthermore we created a standard country rollout plan that they could use for each new country that would be onboarded on the platform. From then on each country could use the delivery process to get a running start and contact the global expert team to resolve questions and issues.
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Case 2: Too fast for your own good
The second case was at a large car importer for multiple brands that had a very strong strategy of where the company should go in terms of customer experience the next few years (Explorers and Adopters goal in Vision & Strategy), complete with a high level roadmap of the next three years (Adopters goal in Roadmap). The company also had a core team with full time members that was able to establish use cases (Explorers goal in Skills & Resources, advancing into Adopters goal). Furthermore, they had some agreements with the different car brands to work on the DMP platform and was implementing KPI’s for every department to spend at least a few hours a week on the DMP after convincing senior management that this was the way to go (Explorers and Leaders goal in Organization).
However, most colleagues and managers from other departments had no idea what they should do to achieve their DMP KPI’s or even what a DMP was. The KPI’s were only vaguely formulated as a 10% time spent on DMP activities requirement. Furthermore, the use cases that they executed were small in impact due to a lack of certain technical integrations (e.g. CRM integration with a weekly change upload) and not prioritizing higher impact use cases. And because the impact of these use cases weren’t communicated, the brands had no way of knowing how the DMP platform could influence their core business.
In short, the company had already gone ahead and reached some higher level goals on some success factors while ignoring intermediate and beginning steps on others. To reground the organization we first took care of all the basics, getting the technical integrations in order which were crucial for some easy high impact use cases. We created a short term roadmap with technical integrations and use cases that build on them (Explorers goal in Roadmap). We also started to include the impact measuring of use cases and sharing of successes as a step in the standard process in order to spread the word of what a DMP can do to inform other departments of our capabilities (Explorers and Adopters in Processes).
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Not a one trick pony!
Organizations should not underestimate the amount of coordination and effort it takes to successfully implement a DMP/CDP. Unfortunately, it also takes time. Colleagues and managers from different departments have to experience the success of the platform before they are convinced to want to try it themselves, before committing fully. This is exactly why it is important to follow the sequence of the maturity model, to let the earlier level goals pave the road in order to achieve the next ones effectively. Using our maturity model, organizations can either prepare for an implementation by strategizing when to accomplish which goal or use it to further their implementation using our achievable goals in order to increase the impact the DMP/CDP has on their daily business.
The model has been made specific to DMP/CDP implementations, but it can also be adapted to different technology and platform implementations into organizations. I have used it for data science maturity within organizations and plan to use it for Robotic Process Automation. If you are struggling with such an implementation yourself and you could use my insights or clarification on the model, feel free to reach out to me through Linkedin. I would also love to hear your thoughts and experiences in the comments below.
October 19, 2018
Strictly Private: The ABCs of CDP and ePR
By Susan Raab, CDP Institute
Last May, when Europe’s GDPR law went into effect, it was clear the rules of customer engagement would change significantly for consumers, and for companies both in the EU and for those outside whose customer engagement included sales within Europe. But GDPR, which obligates companies to protect the privacy of personal data and adhere to consumer preferences for engagement, was not intended to be the whole story on ensuring consumer privacy. A second, even larger regulation, ePrivacy, was planned to become law along with GDPR, but has been delayed in a prolonged editing process and it currently exists as the EU’s ePrivacy Directive.
This is due to change, and while a firm date has not been set for ePrivacy Regulation to become law, many believe it will do so before the end of 2020. According to Matthias de Bruyne, legal counsel for the Data Driven Marketing Association (DDMA) in Amsterdam, “the legislation currently sits with the Council of the European Commission and there’s a lot of pressure right now from those who have been working on it for the Council to reach an agreement by December, so it can go into trilogue with the European Commission and European Parliament and finalize the law by April before the European Parliament’s elections.”
As with GDPR, companies will be given a grace period to comply with ePrivacy regulation, but will need to prepare well ahead to ensure they are in compliance on the allotted time schedule. “Right now,” de Bruyne explains, “the ePrivacy Directive allows a lot of interpretation and countries have their own implementation of the ePrivacy rules – for example Germany and the Netherlands are very strict, whereas other countries such as Ireland, are much less so. As a result an organization can currently look for the minimum standard in each member state as opposed to having to adhere to an EU-wide standard. After the ePrivacy Regulation enters into force, the differences will be much smaller, so there will be less to choose.”
ePrivacy, which has been called the “Cookie Law,” will extend beyond regulating the direct relationship companies have with their customers by regulating further how all EU consumers can be marketed to, including via advertising, telemarketing, email, and social media.
Compliance with this will mean ensuring that companies must have a clear picture of the state of their customer data, ways to ensure their data is consistent and up to date, and an ongoing knowledge of consumer preferences as they evolve. Customer Data Platforms are well positioned to take a lead helping to ensure that is possible.
According to MarTech Advisor, “there are two laws because they are derived from two different rights in the European Charter of Human Rights….The GDPR covers the right to protection of personal data, while the ePrivacy Regulation encompasses a person’s right to a private life, including confidentiality.” IAB Europe, in its FAQ on ePrivacy states the regulation would “introduce rules allowing users to set general privacy preferences in their browsers and other software, which would be binding on and enforceable against any other person.” Additionally, as Pravin Kothari, CEO of CipherCloud states in CMSWire, “the regulation is expected to include specific language ‘with respect to the confidentiality of communications data such that listening, observing or monitoring a user specifically on a website is prohibited.’”
According to CMSWire, experts advise companies to, “take inventory of your current data, work with the teams that have the best insight on data infrastructure and finally adopt a data-privacy program that adheres to the most strict laws…. Andrew Frank, vice president distinguished analyst for Gartner for Marketing Leaders, said in the same article, “Trying to keep up with different privacy laws on a case-by-case basis will be a nightmare to try to stay on top of for marketers. It becomes much more costly and risky to try to maintain a hodgepodge of separate privacy policies rather than have one global policy that works everywhere.”
The CDP Institute recognizes this a very important and evolving area, so welcomes input from readers, and we will continue to explore ePrivacy and GDPR topics as they relate to CDP technology and use.
October 12, 2018
Three Reasons Marketers Should Be Enthusiastic About Machine Learning
By Janet Wagner, Zylotech
Most marketers today are aware of machine learning (ML) and how many companies are using it for a wide variety of use cases- from automating business processes to creating highly personalized applications. Some marketers may be concerned that machine learning will leave them without jobs. However, the core tasks of marketing involve creativity- an attribute machine learning does not have. Machine learning can’t take over every marketing task, but it could be used to assist marketers in a wide variety of ways.
Automate repetitive marketing tasks
According to a recent Workfront survey, U.S. marketers spend 38% each workday on primary job duties. Marketers are spending 62% of their time on tasks many of which are repetitive, monotonous, and aren’t directly related to marketing at all. Emails and wasteful meetings are among the top three things the marketers surveyed said decreased their productivity.
Thousands of marketing technology solutions are available today, and most of them leverage machine learning. With the help of ML-based solutions, companies can automate and streamline a variety of marketing workflows- emails, marketing campaigns, data and documents, and analytics to name a few.
Boost marketing productivity
Machine learning allows repetitive marketing tasks to be completed at a speed and accuracy that humans can’t achieve. When repetitive marketing tasks are automated, marketers can spend more time on brainstorming marketing campaign ideas. They can spend more time on building strategies aimed at finding, attracting, and retaining customers. Marketers can also spend less time on testing their ideas. Some testing solutions leverage machine learning and AI so that marketers can try out all their ideas all at once instead of only a few at a time.
Automating repetitive tasks leaves marketers more time to spend on the core marketing tasks they were hired to do.
Enable self-learning for analytics
Analytics is a crucial part of marketing. Analytics helps marketing teams find potential customers, understand customer purchasing patterns, and even predict and prevent churn. Analytics involves many processes including collecting and preparing data, building and training ML models, and creating visualizations and dashboards.
Machine learning along with artificial intelligence (AI) enables platforms to be self-learning and allows many of the processes needed for analytics to be automated. For example, the Zylotech platform features sophisticated self-learning algorithms for embedded analytics. Our platform is also designed for the whole automation of customer analytics. It features built-in data analytics tools, including predictive analytics, for creating highly personalized 1:1 customer experiences.
Machine learning can help marketers
Automating repetitive marketing tasks, boosting marketing productivity, and enabling self-learning for analytics are just a few of the ways machine learning can help marketers. Thanks to machine learning, many companies are including in their digital marketing strategies chatbots to engage customers, highly personalized loyalty programs, predictive analytics, and so much more.
Marketers should be enthusiastic about machine learning because it can enable marketing innovation, boost creativity, and increase marketing campaign successes.
October 9, 2018
5 Top Quality Tips to Transcend Your Welcome Email Stream
By Shefa Weinstein, Optimove
As I continue with my “Top 5 Tips For…” articles, I thought, what better topic to kick off the series than the Welcome Stream. When I began building my first Welcome Stream years ago, I first had to familiarize myself with the concept of the email journey and how to tell a story in just a few emails. Today, the streams are much more complex and may include conditional sends, real time data, and high-level personalization. But we are nowhere near the end of the innovation train – there is so much more we can do to grab the users’ attention and turn them into brand ambassadors. Here are 5 tips for stepping up your Welcome Stream game.
1. Start with a cohesive experience:
Congrats! You’ve convinced the user to sign up for your newsletter. You’ve shown them that you have something to offer that they want. As soon as the user provides their email address, you begin your journey towards building a trusting relationship with that customer. Users will subscribe to your newsletter from various avenues and for various reasons, and it is your job to give them more of what initially attracted them. Using dynamic content, scrape the main banner from your website and add it to your email, showcasing the same experience that attracted your customers to begin with. This has two great outcomes: 1. You are building a cohesive experience from your site to your emails. 2. This keeps your emails fresher longer. The scrape will continuously update itself and by using this method, you can buy time before you redo the stream.
2. Make sure your stream has offshoots:
If you are already doing conditional streams, you rock! If not, we are here to help you rock it. You want to think about the different journeys your users have embarked on, and where you want them to go. For example, if they signed up for your newsletter in-store, you should consider sharing more store sales. If they signed up on your site, show them online offers. Be mindful of your goals; at times your main objective might be to get them to download your app instead of continuing to shop online – make sure your stream directs them to your chosen destination. You can also add different flow offshoots to your stream. If they complete a purchase, they might receive a different sequence than users who haven’t. If they haven’t completed another purchase in X days, you might want to send only these users a coupon, instead of sending offers to all customers.
3. Get more data:
Everyone knows that knowledge is power. The more data, and therefore, knowledge you have, the stronger the connection between you and your users. The Welcome Stream is a great time to dedicate an email to customers who have not filled out their profile – this gives you an opportunity to get this information while developing your relationship with your customers. There are two ways you can collect data from emails: actively and passively.
Actively collecting data involves asking questions in your emails. Here, think carefully about the two most important profile questions you have – make them simple with multiple choice answers. Asking your user to fill in the text will result in a lower response rate and it’s harder to compile.
Passively collecting data is a bit sneakier. You might be tracking clicks to gain knowledge of what intrigues your users, or you might grab their location over time to understand their patterns and drive customers to the closest store. But remember, always be super careful of your users’ privacy, avoid violating GDPR, PII, and all those other scary, yet very important acronyms.
4. Get them to the store:
If you have a brick-and-mortar location, you obviously want to get as many people as possible in the door. Our suggestion? Geo-target them. As mentioned above, the Welcome Stream is your first opportunity to begin building that relationship and teach them about your brand. This is also your chance to reveal your closest location– and take it to the next level by adding more useful data for your users, like opening hours, local events, and news that might be attractive to them.
5. Entice them:
I am a big fan of Nir Eyal – the father of the hook theory. In short, his theory posits that for users to keep sticking with the brand, you want to create habits for them. You should create the possibility for them to make small investments and then reward their efforts, so that a habit is formed in how they interact with the company. It is vital that during the welcome process, you get users to make another purchase and habitually start using your brand. Remember, you are not looking to maximize the order value – you just want them to take out their credit card again. We suggest starting off light, reminding them of the cool items you offer, maybe showing a few sale items. Then, you can start adding a coupon or a discount. As you continue with the stream, you want to keep messaging them until either 1. it’s been too long and it’s time to give them a communication break, or 2. they make another purchase. Once they complete the transaction, you want to keep rewarding customers with further coupons and discounts, showing them the value of their loyalty to ensure they get to that next purchase. Get them into the cycle of thinking about your brand whenever they want to buy what you sell.
In Conclusion: Before you start building (or revamping) your stream, you should lay out your KPIs very clearly. Make sure your message is focused on getting those numbers higher. While it is important to say welcome, remember that the purpose of the Welcome Stream is to begin fostering trust with your users, and to initiate the journey toward a long-lasting relationship between consumers and your brand.
October 5, 2018
Predictive Analytics: A Powerful Tool for Marketers
By Janet Wagner, Zylotech
Many companies today are using a variety of analytics tools including predictive analytics. Predictive analytics can be applied to many areas of a business. But for marketers, predictive analytics is an extraordinarily powerful tool.
What is predictive analytics?
Predictive analytics involves applying statistical algorithms and machine learning techniques to historical data (and sometimes real-time data) to make predictions about future events. Predictive models estimate the probability of a specific thing that will happen in the future. For example, a marketer could use predictive analytics to figure out which customers are likely to buy a particular product soon or to forecast and prevent customer churn.
It should be noted that businesses must feed predictive models and analytics tools high quality, contextual data. This blog post highlights several essential components of quality data.
How marketers could use predictive analytics
Predictive analytics can be used to optimize and personalize just about every type of marketing campaign including web, mobile, and email.
Predictive analytics makes it possible for marketing teams to create highly personalized web marketing campaigns. For example, predictive analytics could be integrated with an e-commerce site to optimize onsite ads. If a customer is logged in, historical data could be used along with real-time data to make predictions about the products that customer is likely to buy. The site would then automatically display ads offering personalized discounts based on the customer’s propensity to purchase specific items. Predictive analytics could also be used to enhance the cross-selling and up-selling capabilities of the e-commerce platform.
Businesses could use predictive analytics to re-engage past customers via mobile marketing. For example, an office products company could send customers timely 1:1 personalized sales offers via mobile phone push notifications or SMS text messaging. Retailers could leverage predictive analytics along with location-based technologies (e.g. GPS, BLE Beacons) to create marketing campaigns based on geographic data and shopping behavior. Retailers could send sales promotions to mobile customers who are near specific store locations and most likely to buy the sales items. These location-based promotions would help increase local sales revenue.
Using predictive analytics businesses could optimize and personalize email marketing campaigns. Some companies use a one size fits all approach when it comes to email marketing. For example, a clothing retailer may send an email every August offering a 50% summer clothing discount to all customers to get rid of excess stock. Using predictive analytics, the retailer could tailor that email marketing campaign based on the propensity of customers to buy. Instead of emailing all customers a 50% discount on summer clothing, customers who are more likely to buy would instead receive an offer of 25% off. By reducing the number of 50% discount offers and emailing each customer a discount they would likely respond to, the retailer could unload the excess inventory and significantly boost revenue.
A powerful tool for marketers
This post highlights just a few of the ways marketers could use predictive analytics to create effective marketing campaigns and drive customer engagement. When given high-quality, contextual data, predictive analytics is a powerful tool for marketers.
October 2, 2018
5 First-Rate Email Tips to Deepen the Relationship With Your Customers
By Shefa Weinstein, Optimove
When Optimove acquired DynamicMail (the company I worked for), I was super excited. I really believed it was a great fit for the company. When two businesses have different offerings, yet share the same tagline – deliver the right message to the right person at the right time – they are sure to produce great things.
But what I initially loved most about Optimove—and deeply connected to—was its commitment to helping marketers deepen the emotional relationship with their audience. I’ve learned over the years that a strong relationship is the basis of a good email campaign. The more relevant an email is to the user, the higher the open and click rates. Building these relationships with your customers will not only help increase their engagement (resulting in a rise in sales), it will also help generate brand ambassadors.
So, to deepen my relationship with our community, I want to share 5 great email tips to help you and your users form a meaningful, mutually beneficial relationship.
1. Start with the basics – how often are you emailing your customers?
We’ve all had days when we open our inbox and see four emails from the same brand. When I see a brand clog up my inbox, I can’t help but wonder “what on earth are they thinking?” Last week, after experiencing this very nuisance, I snapped.
In that moment, I hunted for the unsubscribe button. I filled out the form with a feeling of glee and satisfaction, but I also harbored negative feelings after the act. Even though I know they didn’t mean to annoy me, I felt that they weren’t thinking about their customer enough, about me as a user. My advice? Whenever possible, think like a user. Would you want to receive so many emails in such a short time span?
This type of consideration can ensure that you are actively working on your relationship with the customer. Using automated rules and segments can help you take this step. For example, if you sent out a birthday note, there is no need for them to receive the weekly promotion as well. Using automation, you can dictate that a user who received an email during the current week will not receive another email on the same subject, or that a user who received the birthday note will not receive a promotional email a few hours later.
2. Do what you say you will
Recently, we began working with a client who placed product blocks in their email campaigns. Each block had an image, name, and price. When the user clicked on the box, however, the action directed them to the category page rather than the product page. To me, this felt like false advertising. The user saw a product and was interested enough to click on it, but instead of giving them what they wanted, the brand redirected them to a completely different page. Take a minute to think about all the links you publish – do they all connect to the content you promised? If you haven’t been mindful of this, now is the time to start. Building trust with customers takes time, but with an increase in trust comes an increase in open rates, engagement, and loyalty.
3. Personalize the email – and there are many options
It feels like personalization has been ‘one of marketing’s biggest trends’ for over a decade. Personalization started as a small concept, just adding the user’s name to the email. As time went by, the options for personalization have evolved tremendously. From the users’ name to their loyalty points on demand, to what type of content they want and should be seeing, everything can be personalized. True, you can always tell your customers how to find your closest branch, but wouldn’t it be better if you automatically showed them the closest store? In every section of your email, consider how you can take it a step further and personalize, personalize, personalize.
4. Contextual clues and behavioral triggers
Contextual clues and behavioral triggers come hand-in-hand with personalization. Instead of deciding ahead of time what to show your customers, let them “pick” their own content. By using information that you’ve gathered when they open their email, you can determine what users want to read. For example, you can use contextual clues to determine which assets to show based on the email capabilities – iOS users get a full video, while Outlook users see a single image. Behavioral triggers are even more exciting. If the user hasn’t purchased anything in over 60 days, you can give them a reason to come back based on their previous purchases or sales within their geographical location. If your customers left your site without checking out, you can show them what they left behind the next time they open their inbox. Using these clues and triggers, you show users that the email was tailored specifically to them, turning your emails into ‘must-reads!’
5. Realtime content
More than ever, users expect a lot from your marketing efforts. The offers within an email need to be enticing and dynamic in realtime. If your email advertises a fantastic deal, but it suddenly isn’t valid when the user clicks onto your site, you can forget about that customer opening your emails again. Make sure the content in your emails is updating in realtime as soon as the users open the email to ensure that you won’t disappoint your customers, and that clicks will lead to conversions rather than another disgruntled customer. Can you imagine the excitement on a customer’s face when they open your email and see “15% OFF, and we're only 0.6 miles away from you”? Using up to date content creates a unique and exciting email experience for each customer. Likewise, if a user opens your email and sees his coveted item on sale only to click and see it is out of stock, their frustration will have a tremendous effect on whether they continue using your brand.
These 5 tips are just the beginning. As Optimove and DynamicMail continue to grow together, the benefits of cutting-edge marketing technology and love for visionary email tools will help our clients take their email marketing to the next level. Stay tuned for our future blog on updating your welcome stream, birthday emails, and weekly promotion emails.
September 28, 2018
The New Role of Marketing Operations and Technology
By Chuck Leddy, Zylotech
The role of the marketing executive has changed dramatically in the last decade. With the accelerating growth of marketing automation and other emerging marketing technologies, a key component of the executive’s role has become dynamically matching the organization’s marketing needs with just the right technological tools, while ensuring that the martech stack remains integrated.
Marketers have turned to digital channels because that’s where customers are and new technologies to track them. Marketing executives are using their marketing technology stacks in so many ways, including: (1) collecting customer data, (2) creating customer personas and segmentations, (3) communicating with customers, often in automated ways, (4) distributing and scheduling content across multiple channels, (5) nurturing/managing leads, and much more.
No more genuflecting to IT
Years ago, marketing executives might go hat in hand to IT, explaining their technological needs and asking IT to select and install the right marketing technology. In those bygone days, marketing executives waited for implementation, prodding IT leaders who were managing multiple projects from multiple business units (HR, Finance, etc.). Today, marketing executives are deeply involved every step of the way, typically taking the lead from the beginning (choosing the martech) all the way through implementation and far beyond.
Wearing many hats
In terms of managing marketing operations (MO) and driving technological change, today’s marketing executive requires new skills and new roles. Marketing executives act as data-driven process optimizers, work as data scientists who garner key insights from data in order to drive better decision-making, and serve as portfolio managers who deeply understand ROI. As MarTech advisor Debbie Qaqish explains, marketing executives are tasked with analyzing how their marketing operations are performing, much “like they are looking at a stock portfolio. Analysis and optimization of all current [MO] programs is an on-going and agile practice.”
Martech’s accelerating pace
A new set of processes has come into play for acquiring marketing technology. Perhaps the biggest challenge marketing executives face today is curation, finding just the right technology tool (among thousands) for just the right marketing task. There are currently 6,242 martech vendors (compared to 150 in 2011) in 48 categories offering a booming number of solutions. And since martech is evolving so quickly, it’s becoming harder to simply keep pace with what’s even possible, let alone implement (and integrate) new, cutting-edge martech capacities and solutions.
4 habits of highly-successful marketing executives
What makes a successful marketing executive today? Well, it helps to be an open-minded, fast learner. Marketing executives must also be masters of integration, data analysis, customer experience/CX, and marketing attribution (connecting activities to ROI). Here are four required qualities a great marketing exec needs:
1. They are team builders. Great marketing executives attract, assemble, manage, and retain talented teams. Of course, in a landscape of accelerating change, an organization’s talent needs can shift over time. Great marketing executives are continually assembling and tweaking their teams to align with evolving business needs.
2. They blend talent and tech. The marketing solutions of today and tomorrow blend people and technologies in ways that get optimal results from each. As technologies like AI and machine learning continue to evolve, “human + tech” blendings will become more dynamic and essential.
3. They are “business-first.” Great marketing executives think far outside their departmental silo, collaborating across functions as strategic partners in the C-suite in order to grow the business. They know that marketing isn’t about marketing, but about growing the whole business.
4. They learn to remain relevant. Great marketing executives constantly stay up-to-date, not only with technological change but with the behavioral trends of customers. The truth is, companies don’t even know what specialized skills or emerging technologies they may need to leverage in ten years. Great marketing executives know that agility and continuous improvement are the keys to success today and tomorrow.
September 12, 2018
PWAs: What They Are and Why They’re Important
By Ty Gavin, Tealium
What is a PWA?
The ‘Sort-of’ Online State
Google is using a term describing the state of “sort of” online called “Lie-Fi.” Sometimes your phone’s Wifi reading is lying to you that it is online. The strange state of having poor Wifi connectivity is something we’ve all experienced and we typically blame our network administrator (at home or in office) or our cable company. But it could be the web server itself to blame — for example, this can happen when retailers have a sale and their website speeds are slower than usual due to increased online traffic. The Wifi lights may be on, but nothing seems to show up after clicking refresh.
According to Google, “Lie-fi” is when the device is connected but the network connection is extremely unreliable or slow and the network request drags on and on before eventually failing. Users end up wasting precious seconds just waiting for the inevitable.
Cache and Service Workers to the Rescue
Retailers and other web content providers (anyone who has a website you visit infrequently and will never install their app) now have Progressive Web App technology to solve for this offline state. The solution is simple: a modern web browser will run something in the background that will display content from a previous visit to the website when the web app determines that the connection is off or just really slow. The underlying technology is a ‘service worker.’
What is a service worker? A service worker is extra logic that intelligently decides what state you’re in (offline or online) and if the app should get new content from the network or get content from the cache.
You’ve most likely heard of cache before and may be thinking, “Doesn’t the browser cache files already? If I go to clean out my browser file storage, I see hundreds of megabytes to delete.”
While the browser has been doing some optimizations for speed improvements, this cache was not designed to help with the “no network” experience. The difference with traditional browser cache and service worker cache is that the service worker cache is available when offline.
For typical websites, when you’re offline (and don’t have a service worker), you don’t get to see what is in the cache, and instead, see the famous dinosaur image in Chrome (pictured below).
Web developers can now handle this situation by building service workers to pull content out of their cache. The experience (for the most part) is now just the same for the person browsing the web high up in the mountains where Wifi may not be plentiful.
Web Analytics When Offline
If you need to report analytics data on website usage, you may be wondering how to report usage to a web analytics vendor(s) when there is no connection and how to track some of the new events that happen in PWA.
For the offline state, this is rarer in our current 4G world. Of course, you won’t know how rare until you attempt to measure it.
If you have a large client base with intermittent internet, your analytics vendor tracking libraries are already ready to queue up tracking when offline.
You can even set up your PWA to store/retrieve the utag.js itself from your service worker cache in an offline scenario. If your utag.js has loaded and has the Adobe Analytics tag bundled (often the best practice) then you’re ready to go. There is no need to reinvent the wheel here.
New Events are Now Ready to Track in PWAs
The following events are introduced with Progressive Web Apps that are likely interesting metrics when determining the success of your PWA-building efforts.
Service Worker Events:
NOTE: At the date of this article being drafted, most of the app-like events are not-yet-supported in iOS (Safari), but they do have service workers.
Now the Code to Listen for and Track Events
While there aren’t any necessary changes needed in your PWA when working with Tealium’s Tag Management (TMS), we do recommend making the following updates:
While PWA technology is still in the early stages and things may change, it is only a matter of time before you’re reporting on your user’s interactions with your updated Progressive Web App.
September 10, 2018
How Does a Customer Data Platform Solve These Common CMO Challenges?
By John Nash, RedPoint Global
Chief marketing officers (CMOs) are often tasked with executing the organization’s customer experience strategy. Meeting this expectation requires a deep understanding of customers, the technical infrastructure to personalize interactions across channels, and visibility into updated customer data in real time. Many CMOs lack any or all of these capabilities, with a recent survey of senior marketing executives finding that 47 percent of marketers have selective personalization at best, 37 percent still have siloed customer data, and 49 percent lack real-time customer data.
These are not new problems, and marketers have attempted to solve them for years. The issue is that traditional marketing technologies such as data lakes and campaign management solutions are ill-suited to meeting marketing’s needs at the real-time pace that is required. To truly achieve their goal of meeting customer expectations, CMOs need to adopt a solution that can break down data silos, leverage that insight to personalize omnichannel orchestrations, and access cleaned customer information in real time.
Only a customer data platform (CDP) solves all these challenges, empowering CMOs and marketing departments with unified data that can be leveraged to orchestrate personalized messaging and is accessible at the cadence of the customer. For this reason, a CDP is a powerful tool in the CMO’s toolbox – especially as their customer-centric responsibilities increase over time.
Customer Data Platforms Eliminate Data Silos
Siloed customer data is often a major customer experience issue. When customer data is kept in silos, marketers are unable to recognize loyal customers across different channels. In a practical sense, this means a customer who visits the brand’s website and then interacts via social media is often treated as two separate entries in the database. This happens because most marketing departments added customer engagement systems piecemeal over time, and each point solution collects and stores data a little differently.
CMOs need to break down these data silos, especially because customers expect a consistent experience from brands across all engagement touchpoints. Brands that retain data silos are unable to meet this need. A customer data platform is designed to solve this problem by ingesting data from all the marketing point solutions into a single portal, regardless of structure, cadence, or volume. The end result is a unified customer profile, or “golden record,” that collects all that is knowable about customers throughout the organization.
The better customer data platforms maintain this golden record over time. Keeping the golden record consistently updated is key; recent research found that organizations who maintain their database have a 66 percent higher conversion rate than their peers. This makes a golden customer record key to long-term customer engagement success.
Improve Personalization with a Customer Data Platform
The modern omnichannel customer expects a personalized experience across all digital and physical touchpoints. Many brands lack the ability to deliver on that expectation, whether because of data silos or a lack of other capabilities within the organization. With a CDP in place, the CMO gains access to a centralized source of clean, updated data for better personalization. Brands who leverage a CDP also become able to progressively profile their customers and alter personalization strategies over time.
This means a CDP can have a powerful impact on long-term business results. A recent survey found that data-driven marketing organizations 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). As personalization improves through better data, consumers notice the improvement and stay loyal. It is far less expensive to retain an existing customer rather than acquire a new one, and the behavior and preference insights of a CDP streamlines that process dramatically.
Customer Data Platforms Provide Real-Time Data Availability
One of the most significant challenges CMOs face is gaining access to updated data in real or near-real time. The average customer’s path to purchase is often so dynamic and fast-moving that brands need to be able to respond in the moment of need. Right now, only seven percent of marketers can consistently deliver real-time experiences across all digital and physical touchpoints. Traditional data management solutions like data warehouses and data lakes only update at a batch cadence, and that is too slow for the quick-moving nature of customer engagement.
Customer data platforms are designed to accept batch and streaming data equally, and update the golden customer record accordingly. A CDP’s ability to maintain the golden record in real time puts all relevant information at marketing’s fingertips, which allows marketers to more easily personalize customer interactions and respond on the fly to signals that they may otherwise miss.
CMOs are increasingly responsible for delivering contextually relevant, impactful customer experiences. They need the right tools for this job, and a customer data platform is one of the most powerful. With a CDP deployed in the organization, the CMO can eliminate internal data silos, more easily personalize customer interactions, and have clean customer data accessible in real time. With these three challenges solved, CMOs are better able to meet the demands of their customers and power revenue growth in the organization.
September 6, 2018
Personaliz(s)ation: Where to Focus?
By Greg Morton for CDP Institute
Is the EU a good place to invest and focus if you are a provider of personalization software and services?
Personalization is becoming a standard that all companies need to adopt to survive moving forward. It is a game changer that will shift $800 billion of revenue to the 15% of companies that get it right over the next five years, according to Boston Consulting Group1. In addition, 98% of marketers1 agree that personalization positively impacts customer relationships with the principal areas of benefit coming from increased visitor engagement, improved customer experience and increased conversion rates.
The quest for personalization is becoming an important capability for marketers globally and a high growth marketplace for organizations who offer personalization software and services. But, North American-based brands are twice as likely as EU-based brands to be more advanced in their personalization journey, according to Monetate1. With all this value driven from personalization, why is the EU not as advanced as the US? Should personalization software and services vendors focus on the EU? The answer to these questions depends on the state of personalization in EU. This report looks into that.
What is Personalization?
Gartner defines personalization as "a process that creates a relevant, individualized interaction between two parties designed to enhance the experience of the recipient. It uses insight based on the recipient's personal data, as well as behavioral data about the actions of similar individuals, to deliver an experience to meet specific needs and preferences."
What is the EU consumer’s attitude towards personalization?
Let’s start with customer behaviors. Personalization is proving to be a lucrative opportunity for EU retailers. It has proven to lead to impulse buys. In a recent EU personalization market report from Segment2, 59% of EU shoppers said in the past three months a personalized offer led them to purchase a product that they didn’t initially intend to buy. In addition, 20% more EU shoppers were likely to make an impulse buy based on a personalized recommendation than shoppers in the US. Overall, 40% of EU consumers say they have purchased something more expensive than they originally planned to because their experience was personalized and 65% report being satisfied with their last-minute purchases.
Despite the opportunity, only 34% of EU consumers expect highly personalized experience when they shop. More consistent personalization should improve results, so long as recommendations are accurate.
How do EU marketers compare with marketers elsewhere in their approach to personalization?
One way to understand the EU personalization market potential is to compare EU marketers to others elsewhere. We’ll look at this from angles including (1) the sophistication of the current martech stack, (2) level of Machine Learning, (3) level of Attribution, (4) level of data analysis sophistication and (4) adoption of buy online and pick up in store (BOPIS) and (5) and level of martech spend.
· Marketing technology. EU marketers are less confident in their tech stacks than US marketers. According to Salesforce3, over 70% of marketers in US say their current tech stack is extremely or very effective at increasing productivity by delivering better analytic insights, providing more cohesive view of customer data and delivering more efficient marketing spending. Just 60% in EU of marketers make similar claims. A 10% difference is significant.
· Machine learning. Monetate’s1 research has shown that UK companies are several months behind their peers in the United States in adopting machine learning. This is likely a reflection of different data privacy policies and consumer expectations, but also the adoption stage of personalization in the United Kingdom and elsewhere in the EU. European marketers are less likely to have a documented plan or dedicated budget than their counterparts in US. We expect the gap on machine learning to close over time. This provides a unique opportunity in the EU with a high propensity for growth in personalization adoption.
Monetate, 2nd Annual Personalization Development Study
· Attribution. Marketing attribution is another indicator of personalization readiness. A recent Adroll attribution4 study found that North American and EU marketers agree that attribution allows better allocation of budgets across channels. But 51% of American companies were actually using attribution on most or all of their campaigns, compared with just 39% of EU companies. The study also reports that North American marketers and analysts are more than twice as likely to say that their systems are ‘very flexible’, suggesting greater confidence in their attribution tools.
The good news is that Europeans see the value of attribution. In fact, 71% in EU vs 55% in US believe that a better understanding of how digital channels work together is a benefit of marketing attribution. This is more evidence that the EU is a ripe marketplace for personalization.
AdRoll, State of Marketing Attribution 2017
· Analytics. One the main capabilities required to drive personalization is the ability to analyze the data an organization collects from all channels. This is another area where EU companies lag the U.S. In a recent Gemalto study5, 42% of North American respondents believe their organization can highly effectively analyze the data that it collects. Figures from EU countries ranged from 29% in Germany to 19% in the UK, with France and BeNeLux in between. Clearly there is room for improvement for the majority of organizations when it comes to analyzing data – if they can’t analyze it effectively, then they may as well not collect it at all. The EU region’s inadequacies to effectively analyze data pose another obstacle to expanded personalization and a good opportunity for personalization services and software firms to drive value.
Gemalto, Data Security Confidence Index Report, 2017
· Omni-channel retail. Omni-channel personalization grows revenue. The EU is actually ahead of U.S. retailers in the pocket of omni-channel retail capability known as BOPIS (Buy Online and Pick up In Store). A recent Order Dynamics Global Omni 10006 study found higher levels of BOPIS capability in UK and Nordic countries than in the US and Canada. BOPIS can be an important revenue driver because it provides consumers with greater flexibility and options. These positive results show the potential value from further personalization investment across all sectors in the EU.
Order Dynamics Research, Omni-1000 Research: Global
· Technology spend. Another reason the EU is has a great potential for growth is that the EU marketers are actually spending a larger share of their budget on marketing technologies. A recent study by Moore Stephens7 found that martech consumed 18% of the average EU marketing budget compared with 14% in the U.S. This is mostly due to a higher propensity to outsourced marketing spend rather than build in-house. This is good news for personalization software and services wanting to invest in the EU.
The EU provides a growth opportunity for personalization software and services vendors. Although the EU marketer is behind the US in many areas, the time is now for EU brands to start really driving personalization initiatives to surprise and delight their customers with accurate, one-to-one personalization. EU brands that adopt personalization have proven it to be extremely effective.
There are a few obstacles. The EU lags behind in marketing technology, machine learning, attribution, and analytics. These findings may seem to show there are foundational obstacles that may delay the market in taking off. However, those that have adopted omni-channel retail have seen very positive results. In addition, the EU shows a willingness to invest in martech. All of this points to the EU having a potential for high growth in personalization.
With so much proven value that can be driven from personalization, this should be the immediate focus of all EU marketers. Thus, the EU personalization market has a higher propensity to likely grow faster than the US over next 12 – 18 months. There is already a precedent of a higher percentage of marketing dollars spent on martech solutions. This budget will be focused on personalization technologies which will become higher priorities than some of the other supporting marketing capabilities.
September 4, 2018
CEO Jeff Lunsford Describes Long Term Guiding Principles
By Matt Parisi, Tealium
Read below for part 1 of an ongoing interview series with leaders from Tealium describing how we approach today’s data challenges. Our first interview is with CEO Jeff Lunsford to delve into Tealium’s mission and guiding beliefs:
Matt Parisi, Interviewer: When comparing software and vendors out there, it can be easy to lose sight of the vendor’s purpose as you check off feature boxes. But with how fast tech and behavior changes, it’s arguably more important the direction a company is headed than the specific feature that exists today.
What is Tealium’s vision in the market? What was the initial opportunity and how did it evolve to where we are today?
Jeff Lunsford, Tealium CEO: The world is awash in data. We started Tealium back in 2011 because of this explosion of customer data. Companies saw an opportunity to leverage these new digital touchpoints that were creating all of this data and wanted insights about their customers from it…but they didn’t have the technology to manage it. This great opportunity with so much data became a problem having so much data, which then created a very large data fragmentation problem that we’re tackling today.
We, as software engineers, found an opportunity to create a standard data layer across the enterprise that defines who a customer is and what’s important, where all important attributes could live and be accessed in one place. We called this platform Tealium’s Universal Data Hub (UDH) and we set out to build it in 2011 when we founded the company.
We built it step by step. The first component was that we saw 80% of the big data problem was from interactions happening on a website. We started with tag management (Tealium iQ), then advanced to creating mobile capabilities. In 2014, we created a Customer Data Platform (CDP), called AudienceStream, where we take visitor-level data, store it and route it to other vendors. Then we created DataAccess, which offers the capability to store that data and filter/stream it to a data warehouse. And finally, because of the advent of IoT and mobile, we added EventStream, a cloud-based API hub. Those 4 components make up Tealium’s UDH.
Our UDH allows companies to listen to all their different data sources, normalize it and leverage that data across all of their best-of-breed apps. Rather than picking up a software company only at the customer experience layer, we allow a customer to have a flexible, vibrant and real-time data model that is leveraged across all packages.
MP: Interesting to hear how the needs of customer data management have evolved from tag management in the early days to the capabilities offered by customer data platforms today. When Tealium built its customer data platform, the category didn’t exist, so I’m wondering what guides Tealium’s decision-making.
What does Tealium view as its guiding mandate? What is Tealium trying to solve and how should people out there researching Tealium view its approach?
JL: Digital Transformation mandates. All companies are going through digital transformation initiatives – so how will they be competitive in this new world, where there’s all these data sources and customer touchpoints?
We believe there are 5 mandates for successful digital transformation:
Companies that go through this Digital Transformation and incorporate these 5 key requirements into their processes will be more competitive in the future.
We believe Tealium is the only company in the world that has built an end-to-end platform that covers the whole data supply chain and lifecycle from point of data creation to final resting place, at a global scale, that meet these 5 requirements of Digital Transformation. No other company has built that (yes, of course there are other point solutions here and there and in other categories that cover some portion of the data supply chain) – but no one else has built what we’ve built.
Why Tealium? We’re the innovators; we invented the real-time CDP concept. We’re the first ones that knew data collection and activation had to happen in real time.
Why Tealium? We’re pioneers, thought leaders, and listen to our customers religiously. We listen to their pain points and have built products to solve those pain points. We stretch ourselves and we build for the future and we invest in our customers future.
Why Tealium? We have a model where you can build it yourself or use Tealium.
Why Tealium? We’ll be here for decades to come, still listening to our customer.
MP: Tealium’s first mandate of putting the customer at the center of everything you do brings the current CDP category buzz to mind.
There is a very diverse collection of tools in the space, and as a pioneer in the space, how did Tealium’s vision lead down that CDP path before there was an even a CDP category to build against? And what does that mean for a Tealium customer? What advantages does our approach give over other tools?
JL: Prior to Tealium there were companies that tried to solve this problem with an advertising approach by way of Data Management Platforms (DMPs). They were combining third and first-party data. In our approach, we were the first vendor that said we’re going to help you manage all of your first-party data – as well as all of the other data you have about your customer. We’ll help you capture the data and you decide what you send to your DMPs and what you don’t, in real-time.
That first mandate is customer data – your data. We’re first party data, in real time, and you can control that data geographically to follow privacy laws. And we added ML and future-ready data and always ensure data is freely flowing between the four walls of your business.
MP: Speaking of the future, how do you see that evolving and how will Tealium’s vision keep its solutions in tune with the market? What’s coming out now that you’re excited for that our vision is leading us towards?
JL: The most exciting thing is the continued addition of data sources and digital touchpoints for customers. There are only 24 hours in a day and now we have smart devices like Alexa and Google Voice and Siri…listening to us all 24 hours a day with these different voice interfaces. We have smart IoT devices, smart homes, cars and TVs.
What’s going to happen is that every company we work with, depending on their industry, will only continue to grow their number of data sources and type of data they’re collecting (ie: healthcare having doctor’s office visit data, sports teams having sports venue data), and we will help them seamlessly collect, correlate, and act on that data in a way that satisfies the first mandate. There will be a Darwinian explosion of companies that help with these touchpoints, and Tealium will be the leader in how brands are leveraging this by way of providing the capability for companies to manage their data from the point of collection all the way through delivery, and back again.
I’m excited about bringing in atmospheric data (i.e. weather, sports scores) like things that allow our customers to deliver a more contextually relevant experience to their customers because we have access to that data (i.e. body temperature, sports data).
Building a massive engine that can pull customer data from all sources and allow you to act on it and correlate it in real-time – Tealium is the engine. You set it up, you do with it what you want – Tealium is the engine that helps you drive your data to where you want it to be.
MP: What are your thoughts in the Artificial Intelligence (AI) realm? What kinds of advantages does Tealium have with working with AI and the data, based on the approach Tealium takes?
JL: AI is obviously one of our 5 mandates with ML-ready data. We’re very excited about customers having the ability to take this data and use whatever algorithms they want (Tealium will package some), and they’re becoming more accessible. Tealium will be open in our architecture. You can use whatever AI models you want or you can use ours. We believe these models are required to do things like predictive modeling, next best action, product recommendations and pricing algorithms. No matter what companies are doing with AI, it’s essential to have a substantial amount of data that has been managed for this purpose— that’s what we do.
Tealium is not an AI company – we’re an AI enablement company.
With Tealium you get clean, structured data that will get you results. We’ll get you clean data which is the first step in successful AI.
Thank you Jeff!
August 30, 2018
B2B Marketers Fail to Act in Time
By Sandeep Koul, Zylotech
Even with a sophisticated marketing stack, b2b marketers are inefficient in using data for decision-making due to delays between insights and action.
There is an ever increasing pressure on marketers to have KPI’s directly linked to revenue. Despite this, marketing teams are failing to demonstrate their impact. A recent survey in a white paper by Marketo shows that almost 61% of marketers say they are either fair or poor in this area.
It is puzzling to see almost two-thirds of marketers fail to show their impact despite having cutting edge technology at their disposal. Modern marketing stacks constitute tools capable of capturing customer data, analysing it, and automating marketing operations.
One clue to this puzzle can be obtained by a recent survey of marketers by MIT Sloan Management Review. In the report, only 49% responded that they were able to use data to guide marketing strategy.
This means that though marketers have access to technology that is capable of capturing and analysing data, they still fail to use insights for taking action. But then what is stopping them?
The culprit: Delay in data activation
Analysing current marketing stacks points to a gap: there is a delay between the times when insights are generated and when actual action is taken—insights and action are out of sync. Gartner provides a power framework to make sense of this. The contemporary analytics continuum looks something like this:
The feedback loop you see in the above diagram causes a delay between insights and action. Most of the analysis done by b2b marketers is reactive in nature, inhibiting marketers who actively use prescriptive analytics with decision automation. This means that though they know answers to:
They still cannot use this knowledge to act in time for results that are impactful.
Marketing stack that can help
As in most cases, technology can save the day for b2b marketers by having a marketing stack with not only a Data & Decision layer, but also an automated Delivery layer.
Here is our take on this solution:
The above diagram shows an ideal stack that can considerably shorten activation time. This delivery layer would capture insights by the decision (analytics) layer and, with the help of tight integrations via APIs, could deliver insights back to campaign and CRM tools for timely action. For example, the system could maintain automated delivery of suggested content to a suggested segment by delivering insights directly into campaign workflow.
August 29, 2018
10 Questions to Ask Your Customer Data Platform Vendor
By Buck Webb, RedPoint Global
Does this sound a bit too familiar? Articles and whitepapers espouse the benefits of a holistic view of the customer. You agree, but your data is in purpose-built silos that aren’t being dismantled anytime soon. You’ve determined through research that a customer data platform (CDP) is the right solution to help you create that coveted single view of the customer and you’re ready to talk to vendors to see which CDP is best for your organization. The problem is that numerous companies you’ve encountered claim to provide a CDP, yet they all handle customer data differently.
The confusion in the marketplace about what a customer data platform is — and isn’t — stems largely from a lack of an agreed-upon definition of the solution. Even analyst firms such as Forrester and Gartner, and organizations such as the Customer Data Platform Institute, all define a CDP somewhat differently. As a result, some vendors have branded their product as a customer data platform when they perform adjacent functions or only some functions of a robust solution.
A true CDP is an enterprise solution that ingests all sources and types of enterprise customer data to provide an always-on, always-updating golden record that is continually available at low latency to all touchpoints and users across an enterprise.
It should help you…
There are 10 questions you should ask the vendors on your short list to ensure their platform meets that criteria before you invest in their CDP:
If you’re ready to invest in and implement a customer data platform, it’s probably because you’re ready to build a single view of the customer without having to dismantle your current data structure. It’s likely that you also want the ability to easily add data to enhance those customer profiles, as well as reap the benefits of artificial intelligence and machine learning to use that data to personalize each customer’s experience. And you should. Consider: 79 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 a study by marketing agency Wunderman. And, 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, businesses that show a deep understanding of their customers’ preferences, needs, wants, and past purchases.
You’re only going to show that understanding by responding to customers at their moments of truth with a real CDP. Nothing less will do.
August 20, 2018
How to Maximize the Value of Your Customer Data Platform
By Steve Zisk, RedPoint Global
A customer data platform (CDP) is a powerful way to gain visibility into the customer’s path to purchase. Like any robust technology, however, you can’t just turn it on and expect results. Rather, a CDP presents an opportunity to improve processes, such as the way you handle customer data, which you must do if you’re to gain the most value from the solution. In addition, because a customer data platform is an enterprise solution, it also helps you bolster cross-team collaboration to provide a better enterprise-wide customer experience.
Brands who deploy a customer data platform will be able to:
Once you implement a customer data platform, there are five steps you can take to fully leverage its power:
Let’s take a closer look at each.
Build the single customer view: You likely have purpose-built silos of customer data that serve specific areas of the business, such as accounting and marketing. These will need to stay in place, and that’s fine, of course. But you must unify the data from those siloed data sources in the CDP to get a complete picture of the customer. Doing so is the only way to provide truly personalized experiences.
To streamline building a single customer view, your customer data platform should be data agnostic and support all data sources: batch or streaming, internal or external, structured or unstructured, transactional or demographic. Advanced CDPs also allow you to integrate first-, second-, and third-party data and function equally well with Hadoop, NoSQL, and traditional data warehouse technologies. Develop a detailed project plan to prioritize how you’ll start connecting these data sources to your CDP and you’ll see that single view of the customer come to life.
Add data to enhance each customer’s profile: One of the most powerful aspects of a customer data platform is the ability to build and maintain a golden customer record at the speed of the consumer. The golden customer record should include every touchpoint or proxy identity the consumer presents, such as a cookie, a social media handle, or even a smartwatch. Each customer’s golden record should also include transactional and behavioral data, as well as other triggers. The transactional data should include the history of all transactions and interactions that a person represented by a proxy identity has had with the brand. And the behavioral and transactional data should integrate into the golden record in real time to ensure that analytics, personalization, and engagement are relevant and up to date.
Add in-line analytics and machine learning: Build a plan to take advantage of the advanced analytics, data processing, and matching algorithms a customer data platform enables. Take a crawl, walk, run approach. For example, start with features such as name, address, and phone standardization. Next, use the CDP to perform deterministic and probabilistic identity resolution and to build customizable groupings such as individual, household, segment, and business. Then add advanced parsing, normalization, and validation rules. After that test geocoding and spatial analysis. Using your CDP to do this intelligent data cleansing will reap huge benefits in analytics, both by reducing the time data scientists and marketing analysts must spend cleaning up bad data – currently data scientists spend more than half of their time cleaning data – and by improving both data quality and timeliness to produce better customer engagement. Ultimately, aim to use advanced analytics to understand individual and segment-based customer behavior to improve your marketing and customer engagement decisions based on predictions and goal-based optimizations.
Personalize experiences across channels: A staggering 87 percent of consumers say brands need to put more effort into delivering a consistent experience across channels, according to research by customer feedback platform provider Kampyle. The single customer view that a customer data platform provides will give you a deeper understanding of a customer’s preferences and behaviors. Use this insight to power messaging and actions that will align with the customer’s expectations. For instance, create cross-channel campaigns with personalized real-time messaging that respond to customers in their moments of interest and need as they move through the buyer’s journey.
Optimize the CDP’s use across the enterprise: Set up the customer data platform to maintain customer profiles in real time, while also allowing marketers and other line of business users to access that real-time, unified data when they need it. One of the core benefits of a CDP is the ability to continuously maintain clean, current customer profiles created from unified data. Without access to that updated customer data at the moment of need, marketers and other line of business users can’t act at the speed of the customer.
Customer data platforms are a powerful solution that solve a new version of an old problem: how to gain visibility into the customer’s path to purchase throughout the organization. The challenge is how to leverage the power of the solution properly to gain the greatest value once you’ve deployed it. Following these five steps will set you on the path providing a better customer experience and building revenues through the benefits your CDP provides.
August 15, 2018
ABM @ Scale: Meet Lattice Atlas
By Chitrang Shah, Lattice Engines
This is part 2 of a 2 part blog series. In this post I describe how Lattice Atlas, our latest release, enables marketers to successfully scale their ABM programs. In Part 1, VP of Product Marketing Nipul Chokshi highlights reasons why marketers are challenged with scaling their ABM programs and how that hinders their ability to drive impact.
At Lattice, we are obsessed with helping marketers realize the promise of delivering personalized experiences and growing relationships at scale. We thrive on solving the biggest customer challenges in an unconventional and radically simple manner by leveraging cutting edge AI technologies. Today, we are continuing this mission by launching Lattice Atlas, the industry’s first customer data platform for ABM at scale.
As we discussed in Part 1 of this series, marketers are struggling to scale their ABM programs to focus on more accounts, more segments, more product lines, etc. The challenges they face center around getting customer data and insights in a unified way.
Let me give you a simple example. One of our customers wanted to create an ABM program to acquire net new customers that looked like their existing customer base (“lookalikes”). This seemingly straightforward program requires a highly manual effort using ad-hoc tools and workflows supported by a large ops team. Why? Well, let’s take a look the long list of tasks they have to do:
All this work to support one narrow ABM program for one solution. Imagine the level of effort required if they wanted to run separate programs for various stages of the buyer’s journey or if they wanted to run programs based on customer lifecycle stage across each of their product lines. It is almost impossible to do so with the fragmented data and disparate applications glued together with ad-hoc tools and processes.
So, unless we wanted our customers to limit their ABM efforts to a small scale, we needed to innovate - at a massive scale. We realized that our customers needed a Customer Data Platform (CDP) that unifies all data, enables AI-driven audience creation as well as omnichannel activation & personalization all in one centralized place, and provides enterprise-grade marketing governance. That’s how Lattice Atlas was born. This next generation AI-platform helps marketers scale their ABM programs by removing four major bottlenecks.
Unified Customer Data
Lattice Atlas is architected on open platform principles and the data model is designed to aggregate a wide variety of data, enabling customers to create 360-view of their prospects and customers. It has a standardized data ingestion interface to import 1st-party data from internal systems quickly & easily. In line with our tradition of building everything to scale from day one, the platform is designed to process billions of signals everyday.
Our patent-pending Adaptive Match technology then does the hard job of resolving identities, matching the 1st-party data to 3rd-party data in Lattice Data Cloud with over 20,000 curated insights and automating the data unification. This automated process dramatically reduces the time and effort needed to create a 360-view, eliminating the first bottleneck to enabling ABM at scale.
Radical simplicity is in our DNA. We decided to build Lattice Atlas with the goal of enabling marketing teams to build highly targeted audiences with simple point and click in a matter of minutes - not days, or weeks. With self-service modeling and automated rescoring embedded in the platform, every account and every contact is automatically rescored as the new signals are detected, creating AI-driven insights for the next best actions and personalized engagements every step of the journey.
The product experience is designed to let the marketing teams do what-if analyses on the fly, define criteria for the next best actions and “right size” the audiences with just a few clicks. For the always-on campaigns, marketers can define the criteria once and the platform creates the audiences for them automatically and keeps it up to date, enabling personalized engagements at scale. The platform also lets marketers manage brand reputation by assigning engagement thresholds and removing targets that may have reached marketing fatigue.
The AI-driven insights and simple marketer-friendly way to create and manage targeted audiences remove the second bottleneck in enabling ABM at scale.
Omnichannel Activation and Personalization
According to McKinsey, the average B2B customer engages with 6 channels prior to purchase. So how do we support these disparate channels? Well, we opened the Lattice Atlas to integrate with all of them. The platform has real-time REST APIs so our customers can pull audiences and recommendations in any channel in real-time and enable programmatic personalization at scale. We are also investing heavily in building out-of-the-box apps for the most common channels so that customers can scale their ABM programs quickly and easily.
Enabling marketing teams to programmatically deliver the right message at the right time via the right channel eliminates the third bottleneck facing marketers.
“Informatica knows that creating an interactive and personalized buyer’s journey is critical for the success of account-centric programs,” said Steven Shapiro, VP of Digital and the Buyer’s Journey at Informatica. “We’d seen previous success with Lattice and knew that the Lattice Atlas platform would create the personalized experiences we needed across all channels. Our vision is to use Lattice as the AI brain that powers all next-best-actions.”
Lattice has been the pioneer when it comes to meeting the security needs of our customers. Lattice Atlas continues to carry the baton forward with new data governance capabilities. With GDPR on the horizon, we knew that ABM programs couldn’t get off the ground unless there was an automated and simple way to remove the opt-outs. That’s why we built a self-service interface for marketing teams to remove opted out accounts and contacts from their campaigns. This centralized system to manage inclusions and exclusions globally, for specific channels and/or for specific campaigns makes it easier than ever for driving privacy-compliant ABM programs at scale, and eliminating the fourth and final bottleneck facing scalable ABM programs.
Lattice Atlas was a natural evolution of our platform. Since day 1, our approach has focused on being deeply integrated with each execution application and managing all data under one platform. Because of this, we not only capture the largest amount of data, but also all that relevant metadata that describes it. Lattice Atlas is built on our understanding of these applications and their data to create the first CDP for enabling ABM at scale.
The first application debuting on the platform is Playmaker, which delivers prescriptive recommendations to sales teams adopting play-based selling. Our customers sell many solutions across many audiences. Playmaker lets them quickly identify top products to sell across all audiences and programmatically deliver those recommendations to the sales teams. It also has built-in interactive dashboard to track the engagements (or lack of it) and its impact on the pipeline, enabling out-of-the-box visibility into play ROI measurements and the ways to improve it.
Lattice Atlas is currently in private beta and is expected to be generally available in Q4. This release marks the next big leap in Lattice’s evolution as a marketing AI company. This is just the beginning, so stay tuned for more exciting updates.
August 13, 2018
ABM @ Scale: The Challenge
By Nipul Chokshi, Lattice Engines
This is part 1 of a 2 part blog series. In this post, I explore the key reasons why marketers are challenged with scaling their ABM programs and how that hinders their ability to drive impact. In Part 2, our VP of Product Chitrang Shah describes how Lattice Atlas, our latest release, enables marketers to successfully scale their ABM programs.
Account-based marketing (ABM) has rapidly gone mainstream in the last year. According to ITSMA research, marketers are now allocating 28% of their budget to ABM, up from 20% last year. Yet only 1 out of 5 marketers are realizing the benefits of ABM (Forrester Research study on ABM).
As practiced today, ABM is hard work. It’s especially hard to scale beyond the first pilot program into production across all target accounts. Scaling ABM programs has many facets, each of which is challenging:
In the pursuit of consistent and personalized experiences across all channels, marketers have purchased many marketing and sales applications. Each of these applications has its own data, segmentation engine, activation layer, and reporting structure. Since the applications don’t share data with each other, and segmentation is often built on the limited data they have access to, the end-user experience is completely broken.
Marketers get around this by challenge by either running very manual campaigns using email and SDRs or relying entirely on diffuse channels like display or retargeting. The disconnect between different channels (and applications that don’t talk to each other) leads to a fragmented customer experience.
Figure 1: Comparing ABM @ Scale with Typical ABM Programs Today
In this post, I will dig into the root cause that prevents scaling and propose the emerging solution to this problem. In our next post, our VP of Product Chitrang Shah will describe this solution in greater detail.
Why B2B Marketing Can’t Scale ABM
The good news is that we are capturing more data than ever about our prospects and customers in a range of applications – CRM, marketing automation, web visitor logs, third party intent, DMP, ERP, support systems, etc.
The bad news is that all this data is fragmented and disconnected. Ops teams and IT spend weeks on end cobbling together the data needed for audience creation, activation and measurement. Most of this work is done in spreadsheets or data-lakes. Spreadsheets are error-prone, hard to maintain, and don’t scale. Data-lakes require IT involvement across the campaign lifecycle which impacts agility.
What’s an ABM practitioner to do?
David Raab, founder of the Customer Data Platform Institute, makes the case for a new technology called a Customer Data Platform (CDP) specifically built to help marketers drive personalized marketing. According to David, “Modern marketing requires a unified view of customer data to support coordinated, optimal treatment of each customer and prospect across all channels throughout the customer life cycle. Customer Data Platforms allow marketers to create this unified view.”
Similarly, Gartner says a CDP is an “integrated customer database managed by marketers that unifies a company’s customer data from online and offline channels to enable modeling and drive customer experience.”
Our point of view is that a CDP solves the “data problem” for scaling ABM. Because whether you call it a CDP or not, marketers ultimately need a solution that does the following in order to scale ABM.
ABM practitioners have not had a packaged solution that meets the criteria laid out above ...until now. Read our next post to learn why we are announcing Lattice Atlas, the industry’s first platform for driving ABM at scale.
August 9, 2018
What Does a 360° Customer View Really Mean?
By Hadas Tamir, Optimove
Taking all the variables and data and combining them together for a better view of your customers may sound like a no-brainer. But to ensure personalization, the effectiveness of your campaigns, and real money savings, it’s a must. And it’s doable.
Let’s talk owls for a second… Did you know that owls can’t move their eyeballs? It’s because owls don’t have eyeballs at all. Instead, their eyes are shaped like tubes, held rigidly in place by bones.
Since owls can’t roll their eyes around the way we do, they have to move their entire head to get a good look around. They frequently twist their head and “bob and weave” to expand their field of view. Owls can turn their necks almost 360° in either direction and 90° up-and-down without moving their shoulders.
This flexible movement along with their enormous eyes, binocular vision and ability to hunt in the dark (when they are invisible to their prey) makes owls some of the most efficient night hunters on the planet.
So how does this tie back to marketing? I’m not suggesting that you think of your customers as prey and hunt them during the night; however, I do suggest positioning yourselves as marketers with a 360° view of what your customers are doing before defining your marketing strategy.
More variables, more personalization
Most marketers are turning the necessary corners and understand that today’s consumers expect brands to anticipate their needs, understand their preferences and only send them relevant offers and communications. Smart marketers need to “use” the consumers’ expectation to their advantage, tying together all aspects of a consumer's behavior into one beautiful personalized journey, or to be more precise, infinite journeys.
What is this 360° view constructed of?
On an analysis front, any of the above variables can be mounted together and analyzed to understand a customer’s behavior. Is a specific customer more inclined to purchase one type of item online and another in store? Can we identify strong cross-selling opportunities if a customer is only purchasing from a certain department but viewing items from another? If a customer is clicking through a campaign, adding an item to cart but not purchasing, this shouldn't be viewed as a loss, but rather an opportunity to understand what additional incentive the customer may need to complete his purchase.
The more variables we can utilize in creating our customer journeys, the more personalized we can get as marketers – hitting the exact spot that will bring our customer to not only make a purchase but fulfill their purchasing potential.
The all-inclusive view
Let’s take a few cases that can help us understand the usage of the above variables:
1. Churned Customers:
a. Purchase activity variable would show that these customers have not purchased for a long time (i.e.: in the past year) and are considered churned with a low likelihood of reactivation.
b. Web / App Activity – These customers are active on your website and/or app.
The idea is to identify these customers as “currently interested” because they are actively searching through the site rather than “previously interested” because they have not made a purchase in over a year. It would be up to the marketer to decide what level of communication they would want to include in their campaigns – for example target customers based on past purchases (including items from departments that the customer has previously purchased from), recent products/departments viewed online or web (items that we know the customer is interested in today) or just a generic “10% off just for you” (to all active churned customers) – all are viable options and relatively easy to implement with the relevant systems.
If we want to take this campaign a step further, we can add in the campaign engagement variable. Let’s say that after we sent out our campaign:
• Customer A has opened and clicked through to the website but has not completed a purchase. Our follow-up campaign would most likely include a bit of a stronger incentive since we know that the customer is indeed interested.
• Customer B has neither opened nor clicked the email. He/she may not have seen the email or may have ignored it. Our follow up campaign would most likely be either a reminder campaign (give our customer a 2nd chance to view the email) or a slightly different campaign that might appeal to the customer more in terms of content and graphics (different subject line / different template, etc).
2. One Timers:
a. Purchase activity variable would show that these customers only made one purchase. Consider how many days passed since their purchase. This will determine if they should be regarded as potential or at risk of being one-timers. If a customer completed their 1st order 10 days ago and we are seeing that the average customer places their 2nd order after 30 days, this customer should not yet be regarded as a potential one-timer, but rather a new customer who probably deserves a welcome series of campaigns.
b. Web / App Activity – Are these customers currently active on your website and/or app? Before they became customers, what items were they searching for?
The problem with customers who only purchase once is that the visibility into their line of preferences is extremely limited. The more items they purchase, the more we can personalize our campaigns and our strategy, nudging them to make that second purchase. By adding the variable of web activity even before they became customers, we can identify if they searched for any items/products/categories that didn’t end up in their final cart – thus enabling us to get a more inclusive view of what their preferences are and targeting them with cross-selling campaigns that they are more likely to respond to. In the same manner, we can utilize any current online or app activity to achieve the same results for cross-selling only relevant items.
3. Active Customers:
The same methods for cross-selling that we saw above can be used for determining up-selling opportunities. If a customer had formerly purchased items that average between $10-$20, but now is viewing items that sell for $30-$40, this is a clear invitation for marketers to send out an up-selling campaign that includes items/products that cost a bit more than what the customer is used to paying.
That being said, I wouldn’t recommend up-selling campaigns for inactive customers since our goal with them is to convert/reactivate them. First, let’s get them to start purchasing, then we can get them to start purchasing at a higher AOV.
One more aspect of cross-selling is the platform:
• Where are customers purchasing vs. where are they looking? Customers who purchase only in brick & mortar but are active online or in-app should receive an incentive to complete a purchase online or in-app. The more platforms they become comfortable purchasing through, the higher the probability that they will make more purchases.
• What items are they purchasing on each platform? If a customer’s purchasing activity shows that they purchase shirts online but pants in store, your marketing campaigns should be personalized accordingly. Don’t send a customer 20% off all pants online because you know that this customer prefers to try on the pants before buying them. These campaigns might actually have a negative effect on your customer if they feel like they can’t get the discount just because they don’t buy pants online…
4. Multi-Channel Campaign/Most Effective Channel – taken to the next level…
We all know the effectiveness of multi-channel campaigns and the need for increased visibility. Have you considered, however, that not every customer reacts the same manner to every channel?
a. Purchase activity variable shows what channel each customer is purchasing through. Online, in Store, in App?
b. Campaign engagement shows which channels are most effective for each specific customer.
There may be a customer who only responds to push notifications because all of their purchases are done in-app. Or, a customer who responds best when they are targeted with both email and SMS. The idea here is to enable the marketer to target each customer only through the most relevant channel(s) based on responses to campaigns and purchase activity that we see from each customer individually.
In conclusion: if you can take all of the data points and variables that you have as a marketer and combine them together into one holistic view of what your customers are doing now and what they did in the past, you can ensure personalization, relevance, and effectiveness of your campaigns. Oh yeah… and save a lot of money by not sending promotions when they aren’t really necessary.
August 7, 2018
Removing “Marketer-Managed” from the CDP Definition
by David Raab, CDP Institute
Back in April 2017, I raised the question of changing the CDP Institute’s definition of CDP from “marketer-managed system” to “packaged software” or something similar. My decision then was to keep “marketer-managed”, largely because the primary buyers of CDPs were marketers.
Times change and I think we're now due to make the switch. So I’ll hereby announce a tentative new definition of: “A Customer Data Platform is a packaged system that creates a persistent, unified customer database accessible by other systems.”
The main reason for the change is that over the past year I’ve consistently found myself substituting “packaged system” for “marketer-managed” when I try to explain CDP. “Packaged system” relates more closely than “marketer-managed” to the prebuilt components and configurability that give CDP its advantages over custom development. The contrast with custom-built systems is the purpose for that portion of the definition.
“Marketer-managed” has also created some misperceptions that have led to unnecessary criticisms of CDPs. These include:
The change also reinforces a point that’s increasingly important: the distinction between Customer Data Platforms as a class of system (“CDP-as-a-product”) and customer data platforms as a component in a corporate IT architecture (“CDP-as-a-function”). CDP products are stand-alone packaged systems that are one way to build a unified customer database. Other ways include enterprise data warehouses and modules within integrated software suites or marketing clouds. The distinction matters because each approach has advantages and disadvantages and buyers can compare only these if they recognize the difference exists. As developments such as privacy regulations and digital transformation make corporate IT groups increasingly interested in unified customer data, there will be more alternatives to consider and more need to understand how these compare with CDP products. Removing “marketer-managed” from the definition will also help corporate IT departments realize that CDP products are an option they should consider.
The main disadvantage of the change is that it obscures the focus of CDP on marketing applications. It’s not just that CDPs are still primarily bought by marketers. In addition, many CDP vendors have designed their systems with marketing-specific capabilities such as segmentation, predictive analytics, and message selection. Indeed, most of the industry’s recent growth has come from vendors with integrated marketing features. So you might think the definition should be made more marketing-focused, not less. Or, that we should replace “marketer-managed” with a combination such as “marketer-oriented packaged system”.
That’s tempting but I think it would be a mistake. Plenty of software used by marketers isn’t marketer-specific: for example, predictive analytics and business intelligence tools are used by many departments. Other software that definitely is marketer-specific, such as marketing automation or recommendation systems, doesn’t explicitly reference marketers in its definition. In practice, marketers will look at the use cases and features associated with a product and they’ll recognize which CDPs that are aimed at them. The core function of the CDP is creating that unified customer database, which is something that most marketers easily recognize they need without being told it has marketing applications. Vendors who extend that value with marketing features such as personalization need to state that anyway, since those are not core CDP features. In short, putting something like “marketer-oriented” in the definition would add little value and might do harm by reintroducing the misperceptions we’re trying to prevent by removing “marketer-managed” in the first place. So it’s best to leave it out.
This still leaves open the question of exactly what should replace “marketer-managed”. Since the goal is to present a contrast against “custom-built”, the obvious choice seems to be “packaged software”. But that seems to imply on-premises installation, while many CDPs are delivered as a service. So “packaged system” appears to be a better choice.
Some people have suggested that “packaged” itself is problematic because it suggests end-user deployment, which again gives the false impression that IT isn’t involved. A better term might be “productized”, which is how many developers describe a system that started as a custom project and was later converted into something reusable. But “productized system” seems rather vague and “productized product” is just plain silly. “Productized solution” might be a viable alternative, since it implies a contrasts with “custom solution” (to my ears at least). But it’s pretty vague as well.
“Commercial off-the-shelf software” is an older industry term to distinguish custom software from packaged systems. But we don’t hear it much these days and shortening it to “commercial system” might suggest an unintended contrast against “open source system”. “Off-the-shelf” sounds even more packaged than “packaged” so forget that. Other suggestions have included “standalone product” or “dedicated product”. Those are accurate but shift the focus to the fact that CDPs are bought separately from other systems, which isn’t the main point we’re trying to convey.
Given all these considerations, I currently see “packaged system” or “productized solution” as the leading contenders. Of the two, I favor “packaged system” as sounding less like jargon. But I’m still open to alternatives. Please share your thoughts on the subject.
August 2, 2018
The Mechanics of Predicting Customer Churn Series: When the Business Follows a Subscription Model
By Andrew Malinow, PhD and Mimoza Marko, Zylotech
Customer churn is a typical dynamic in any business – for one reason or another, a customer who has previously purchased from a company, no longer purchases. However, to surface potential causes for churn that can inform mitigation activities, we need a more operational definition.
Churn can be defined in several different ways. If a business uses a subscription model (e.g., Netflix, Amazon Prime membership), churn can be defined as those customers who have cancelled their subscription. A subscription cancellation typically exists as an explicit field in a database (cancelled_subscription=True), or it may need to be derived in some way. In either case, there is a specific event, that is either explicitly captured, or can easily be derived from existing data points, that provides the definition for churn.
However, if a business does not use a subscription model, the definition of churn must be derived based on a change in the customer’s transactional behavior (purchases) over a certain amount of time.
There are various methods that can be used to predict churn. When there is a subscription model being utilized, we have a specific indicator available for our analysis – we can ‘label’ those who have cancelled their subscription – the analysis is relatively straightforward. The first step is to take a sample of internal customer data and split into two groups – those who have churned, and those who have not. A number of Machine Learning models (e.g., such as Logistic Regression, Random Forest, or Naïve Baysian) can then be ‘trained’ to learn which ‘features’ are most predictive that someone is likely to churn. A feature represents a piece of information that we know about a customer. Examples of features include age, gender, geographic location, and marital status. A hypothetical analysis might indicate for example, that for Netflix, geographic location is highly predictive that someone will churn, and that zip codes along the Gulf coast are most highly correlated with churn. A possible explanation could be that weather-related issues negatively impacted streaming services in those areas, causing many people to cancel their subscriptions.
So far, we’ve described how to predict customer churn for businesses that have a subscription model, where the definition of “churn” is straightforward – a subscription canceled equals a customer churned. Now let’s look at how to predict customer churn for businesses that do not rely on subscriptions.
The absence of an explicit churn ‘label’ in our data adds an additional level of computational complexity to the analysis – specifically there is need to develop a mechanism to define “churn” (rather than inherit it directly from an existing data element). To this end, we will leverage information about customers’ transactional behaviors to provide us with a definition for churn that we can use for building our model.
The first and most important step in building a model that will accurately predict propensity (likelihood) for a customer to churn is to assign each customer a label indicating whether the customer has churned or not based on their historical transaction data. Since there is not a specific field in the data that indicates if a customer in the database is still a buyer or not, we need to focus on the customer’s purchasing behavior. The most recent six or twelve months of transactions (depending on how much historical data you have access to) should be left out of the initial analysis and model development process and used to test, validate and refine the accuracy of the predictions produced by an initial model.
The remaining transaction data (purchases) will tell each customer’s “story” – specifically, the frequency of purchases and the time interval between purchases. Analyzing this behavior mathematically can be used for a definition for churn of that particular customer. Additionally, we can generate new features regarding customer’s buying attitude that might be helpful in predicting their point of churn in time.
An example of such a feature might be the buying frequency (e.g. customer buys once every 45 days). At the end of this process, the specific event which determines the label will be a statement regarding the frequency of purchases (e.g. "a customer has churned if there are no purchases during the last 45 days"). It is important to switch focus from company churn definition to individual customer level. Doing so will result in higher probability of assigning the right label.
Once we have divided the customers into churned and not churned, we can begin training Machine Learning Classification Models (like Logistic Regression, Random Forest, etc.) and follow the same process as for businesses that follow a subscription model. To evaluate the accuracy of the model, we use the transaction data that we set aside in the first step of our analysis, which allows us to see if the customers we predicted to churn (or not churn) have made any purchases. Finally, the selected model will give us the importance of each feature included in it as a coefficient score, which we can use to determine which piece of information about a customer – either given or derived – is more influential in predicting the churn.
Now let’s focus on how to ‘tune’ your churn definition after building a preliminary model.
When building any Classification or Predictive model, there are always multiple iterations – throughout each, we “tune” the model based on its performance on training data. All of the steps that we take, from segmenting the data, to feature engineering, building the model, and then evaluating the model, are typically repeated several times. For our churn model, it is critical to retrospectively evaluate model performance for each phase of development and identify things that can be modified to improve model accuracy.
The accuracy of a model is simply the ratio of the correct predictions to the total number of cases that have been evaluated. However, to improve model accuracy we need to find where the model missed and why it missed – did it predict someone would churn, but they did not? Or, more importantly for our model, did it predict that someone would not churn, but they did? By rigorously interrogating our model, the data will tell us the missing parts of the story and suggest ways to improve our model.
In churn modeling the first thing we need to check is the misclassified customers, specifically the ones that we were not able to “catch” before they churned. These cases are critical since the purpose of predicting churn is to have this information while there’s still time to do something – customer retention is easier and less costly for a business than the acquisition of new customers.
When we circle back and evaluate how we built the model, we need to look for the unseen – the reason underneath the churning of those customers the model predicted to have a low probability to churn. Performing cluster analysis (e.g. K-Means, Hierarchical Clustering, DBSCAN, etc.) will help us find common patterns for this customer group. It is likely that we will see at least some of the reasons they churned. For example, they might be “irregular” buyers with orders placed in wide and unpredictable time intervals. Therefore, either the mean frequency (e.g. mean frequency = customer buys every 45 days) alone might not be enough to identify the churning point, or it should be calculated differently for these types of customers. Another scenario might be that these customers had bad product experiences or unsatisfactory service. For example, if a group of customers who bought the same product churned, it is likely that customers who recently bought it could be at high risk of churning regardless of their normal buying frequency. Including the data that captures this information into the model will improve the quality of the model.
On the other hand, there are customers that were predicted to leave but didn’t. Although the previous ones are more important to prevent revenue loss, these customers are indeed loyal to the company. Retaining loyal customers is essential. Therefore, a business would not want to target them with aggressive campaigns or too many emails. It would be a poor allocation of resources and even worse, these customers might not like frequent contact at that point, and choose to disengage (e.g. not purchase) in the future. Consequently, loyal customers might need a more flexible calculation for their buying frequency that is leveraged by our churn model.
As we see, there are various aspects to be considered when we investigate customer purchase behavior. While much of this information is noise and complex models should be avoided, we must capture the most important elements in the churn definition.
All these kinds of findings are useful input for model improvement. We go back to the very first step to modify the definition of churn for each customer and assign the zero and one labels again. Then, as in the first time around, we train and evaluate the best model. Finally, we conclude that constant observation and improvement is the key to learn and predict customers’ behavior better. The more we know, the higher the probability is of building a powerful predictive model.
July 30, 2018
How Marketers Can Combine Cross-Device Identification with a CDP to Enhance Their Campaigns
By Anthony Botibol, BlueVenn
It’s indisputable that every customer experience, across every device, should be personalized in real-time! Whilst it once may have seemed to be something of a distant dream for many marketers, personalized interactions across multiple devices is a reality with cross-device identification and a Customer Data Platform.
What is cross-device identification?
These days, marketers are not just looking to connect devices together – they’re looking to manage identity across all platforms and focus on the customer, not on the device. Cross-device identification achieves this by bridging the gaps between a customer’s devices.
For example, let’s say that you are a clothing retailer and a customer is using their smartphone to access your website to browse a pair of shoes. Whilst they may not purchase the item there and then, they may opt in to your mailing list. The next morning they are using their laptop in a coffee shop. Using cross-device identification, the customer will automatically return to the exact same place they were on the website the night before, encouraging them to place an order for the shoes they were looking at.
How does cross-device identification work?
Cross-device identification works by combining data from media companies, publishers, ad exchanges and third party solutions. An identity graph is then built from these data sources and assigns each user a universal ID. This ID links all of the devices, websites and apps used by that user and connects all of the touchpoints within your customer journey. This provides the foundation you need to create a unified, seamless customer experience that is tailored to that specific individual.
However, it is important to note that there are two types of cross-device identification:
·Deterministic identification - This involves recognizing personally identifiable information such as an email address that is used to log in to various apps and websites. This authentication is seen as the more reliable type of cross-device identification.
·Probabilistic identification - This relies on algorithms rather than authentication and tracks a multitude of anonymous data points connected to different digital elements such as screen resolution, device type, Wi-Fi network, operating system and location. However, because these do not contain personally identifiable information, probabilistic identification, whilst more scalable, is less accurate than deterministic.
How does cross-device identification benefit marketers?
Cross-device identification enables marketers to use the data provided by customers to identify and target them across all channels and devices, thereby moving towards a more omnichannel customer experience. For example, a marketer will have the ability to match a smartphone, tablet, laptop, and smart TV to a specific individual as well the information that shows how this customer navigates their way between websites and their purchase journey.
Marketers can activate the insight and information that cross-device identification brings by using it to enhance data in a Customer Data Platform (CDP). Just like other additional data feeds used for enhancements, this improves segmentation and customer profiling capabilities. And when you consider that the majority of website visitors don’t convert, it is highly valuable for identifying consumers.
Why a Customer Data Platform is essential for cross-device identification
A CDP works by blending online and offline data, bringing the information contained in disparate data silos to give you clean, consolidated information. Whilst the real-time information gleaned from cross-device identification can be invaluable to any marketer, it must be stored and handled carefully if it is to be used effectively. You can feed your cross-device identification data into your CDP which enhances what you already know, but will also give you an unparalleled insight to who is accessing your websites or apps, and device preferences.
Cross-device identification will continue to evolve and take centre stage as we enter an age of connected devices and the Internet of Things (IoT). It is in the best interests of every marketer to ensure they know exactly who their audience is in order to execute highly effective campaigns that speak to them personally. As time and technology progress, more and more marketing strategies will revolve around connected devices, meaning that a Customer Data Platform is essential if you’re looking to future-proof your business for upcoming marketing trends and technologies.
July 23, 2018
Foreign Exposure: Reaching the Unreachable Customers
By Moshe Demri, Optimove
The countless ways the world has changed over recent years has totally modified customers’ habits, making them very hard to influence. But the same changes in our world make it impossible for our consumers to be completely anonymous.
It’s just impossible to keep track. The changes are so vast, and they’re affecting every aspect of our daily lives. They cover all fields and industries: manufacturing, medicine, agriculture, automotive, corporate services, energy efficiency or digital economy. The use of deep learning technologies, big data analysis, cloud development, cyber security and many more, has brought a dazzling number of inventions onto our fingertips, and nothing will look the same again.
And the rhythm is gonna get you. Every year tops and doubles the previous one. 20 years ago when Deep Blue beat Garry Kasparov, the world saw an explosion of ideas that touched and changed our habits, day-to-day routines, caused rifts in our attention and behaviors until the point of no return. From the autonomous car to shoes that tie themselves, from the bionic leg and artificial skin to smart umbrellas. This list is just a drop in the bucket, and it is almost too difficult to imagine the forthcoming drops.
One of the more expected result of this era is the rapid change in customer habits. What was done on Sunday becomes outdated on Monday, and where people went during the summer will wind up completely deserted when winter arrives. One of the clear demonstrations for this phenomenon is this startling study by Omnicom Media Group Agency - Hearts and Science published this week: 47% of adults aged 22 to 45 are consuming absolutely no content via traditional TV platforms. Less than one-third of the TV and video watched by people in this age group (Gen X and millennials) is accounted for by traditional measurements. The rest is being watched on smartphones and other channels like Apple TV and Roku.
That’s not to say that the subjects aren’t watching TV content or viewing ads anymore, but the research suggests that the various ways we consume content has changed, which makes it much harder to reach these groups. These days, effectively reaching customers is a significant obstacle.
Anonymous? No such thing
There are multiple ways to connect with the most aloof customers. Whether it’s scratching the surface layer with a chisel to expose the hieroglyphics or scanning the depths of an ocean for the remains of a sunken ship. If Bigfoot does exist, and you’re probing for his footprint in the Cascades snow, just open your laptop. He would have probably left a digital footprint by now.
Customers can no longer be anonymous. A highly personalized approach through multi-channels, which work across every stage of each customer’s journey, will maximize the marketer’s ability to reach those who seem unreachable. Marketers should reach their customers where they are, and much of users’ screen time is spent on Facebook, Twitter, or surfing the Web. The fact that you have customer’s email address doesn’t mean much if that same customer spends hours every day on Twitter. In addition, consumers today have much more control over the buying process than marketers do. As a result, marketers must constantly develop and coordinate highly orchestrated touch points and micro-campaigns that fluidly span multiple channels in a way that the customer finds meaningful and trustworthy. Keep in mind that different types of messages work better over different channels, and what fits an email, won’t automatically translate well for SMS.
Mix and Match
Below are some approaches for using multi-channel marketing to improve campaign results. There are countless others of course, and that is where creativity and resourcefulness come into play.
Sequential Messaging Across Channels – Run campaigns that present a sequence of messages that tell a story across multiple channels. For example, we can use the 97% open rate of SMS messages to deliver a short teaser regarding a subsequent campaign message via email, and thus dramatically increase the open rates of that email when it comes half an hour later.
Message Blast– Send the same offer on every channel at once. This works best for a great time-limited offer that is tailored to a small segment of customers.
Channel Competition – Run campaigns with the same message/call to action via different channels, to different random segments of a target group to see which ones work best. Known as A/B/N testing, this approach can reveal which types of messages work best on specific channels for certain customer segments.
The Pros and Cons
Using all available channels comes with a price. In the table below, you can see the relative strengths and weaknesses of some commonly-used channels, according to these attributes:
Cost – how expensive is it to communicate with each customer through this channel?
Attention – how effective is this channel at grabbing the customer’s attention?
Design – how much flexibility and creativity does this channel provide when crafting the communications?
Annoying – how invasive or irritating do customers perceive this channel to be?
In conclusion - In the ecosystem described in this article, the ever-changing-dynamic era, marketers are required to closely analyze customer behavior, loyalty and retention. They need to communicate with their customers wisely, in all sorts of ways and tools according to their audience’s particular preferences.
Using different, less traditional channels, and combining them with more recent platforms, is really the right path to success. Examining consumer’s level of receptiveness for various communications, not only guarantees you won’t leave a customer behind, but also provides them with a tremendous amount of added value in terms of content, offers, and messages, delivered exactly where they're at.
July 5, 2018
A Touch on The Creative Side: Why It’s Too Soon to Fear AI Capabilities
By Pini Yakuel, Optimove
In one of his last public appearances, Prof. Stephen Hawking expressed real fear that AI improvements will lead to world chaos. But as AI capabilities are still very much limited, it's up to mankind to start harnessing its benefits to our favor. Are we capable of doing so?
In one of his last public appearances, Prof. Stephen Hawking, who passed away in March, expressed a real fear of the possibility that AI will bring disaster upon mankind. In a speech during the Lisbon Web Summit last November, Hawking stated that well-harnessed AI abilities could lead to the end of poverty, eradicate diseases and even stop the damage we’ve inflicted upon Planet Earth. But in his mind, it depended too heavily on people who hadn’t exactly proven themselves in the past. “AI could develop a will of its own," Hawking said in his signature computer-generated voice. “The rise of AI could be the worst or the best thing that has happened to humanity.”
It’s not the first time that scientists have tried to protect the universe from itself. But Hawking wasn’t talking about possible risks a generation or two away – it’s a matter of decades. Despite the notion that AI is on the fast track to rapid evolution, this is not the case. We’re talking about a much slower and more premeditated pace of development. Is this a breakthrough? By all means, yes, but not the kind that is going to change our lives on a large scale any time soon.
Unlike environmental issues, where the impact of near-term solutions on future generations is more concrete – such as reducing the pace of climate change or reducing our over-consumption of red meat and fossil fuels – horror scenarios involving AI originate in places where imagination is high, such as sci-fi movies and books. A brilliant New Yorker cover portrayed a homeless man lying in a New York street surrounded by robots and a cute robo-dog. High impact, definitely, but still science fiction. No less, no more.
The even spookier story around AI – and don’t get AI confused with automation or even ‘computers’ – is about its effect on the labor market. It’s important to stress that this ‘automation’ started ages ago and will continue to happen on a much larger scale as a natural part of the technological revolution. 200 years ago there were cotton pickers, and 50 years ago there were milkmen, and today everyone is a computer engineer. The ‘robots’ are already here. AI and its derivatives will open more job opportunities, while other professions will vanish altogether – perhaps cashiers and taxi drivers will be the first to go. But we’re not talking about a large-scale unemployment crisis for mankind.
The main issue surrounding the development of AI is the fact that computers still don’t have the capability to imagine, to guess, to create. They simply don’t address the creative side of things. Computers can’t build a narrative. Computers don’t even have the intuition of a three-year-old.
Imagination and intuition are at the core of our unique power as humans. In this context – when machine learning scientists are dealing with these generalization abilities – there are mathematical proofs. The most famous is Rice’s Theorem, which expresses the idea that there are absolute limitations on what a machine can learn.
Humanity has disappointed itself many times throughout history. Do the atomic bombs that are well kept in unknown shelters around the world, which many hold responsible to the relative peace in the last decades, say otherwise? Will we know how to channel these massive capabilities to our benefit? We can already see evil forces trying to take advantage of AI’s many shortcomings, and as more decisions and calls will be made by machines, the will of these forces will grow. Another heavy load on the shoulders of mankind. Like we haven’t had enough of that already.
July 2, 2018
How to Design Your Data Supply Chain for GDPR
By Julie Graham, Tealium
While the dawn of massive change approaches with GDPR, it’s critical for brands to be transparent and ethical with the customer data they’re handling. They must ensure that the data being collected is only used for the purposes of transforming interactions and creating better customer experiences.
And as brands scramble to orchestrate and execute their compliance plans before May 25th, many are unsure which tools, processes, internal groups and roles their organization should employ. Sound familiar?
Ted Sfikas, Director of US Engineering at Tealium & Jason Koo, Developer Evangelist at Tealium, recently did a webinar on “How To Design Your Data Supply Chain For GDPR.”
Watch the on-demand webinar and learn:
It’s crucial that data, marketing and analyst professionals focus on overall data governance, rather than just legal ramifications, and the effect that leakage and security events can have on the brand itself when it comes to GDPR compliance.
Watch the on-demand webinar and safeguard your brand and consumer trust by ensuring your compliance today!
June 25, 2018
What To Do When You Don’t Have A Data Layer
By Ty Gavin, Tealium
Tag Management requires a little effort up front, but the returns are worth it. The main bit of work is adding a clean data layer into your web page or app. But what if you can’t? You’ve developed a large set of static content pages and updating each individual page is next to impossible. Or you need to get a data layer into your content but have no one to assist. (Has your entire web development team just decided to use their unlimited vacation days?) Tealium provides a way to make possible what was previously impossible.
The Hosted Data Layer (HDL) feature provided with Tealium iQ was originally called Data Layer Enrichment (DLE). Recently, the Tealium visitor service that provides visitor-specific Data Layer Enrichment was updated to be called “DLE.” Although they both are similar in that they will effectively bring data from an external source (CDN or service) to the data layer on the page. The main difference is that while DLE retrieves visitor-specific data, the HDL is designed to retrieve data about anything except a visitor.
Web Page Usage
The Tealium iQ Extension is called “Hosted Data Layer.” The typical use case on a web page would be to configure this extension with a variable that contains the “MD5” string of the current URL. That means it will lookup/retrieve all the additional data for this URL in one request. This allows for easy ‘deployment’ of Tealium iQ on a site with many static web pages and not a lot of developer resources to update each and every web page with a data layer. Best practice is to only request the minimum number of files per page to reduce HTTP requests and increase the likelihood of tracking with your tag vendors. The more data files to retrieve, the more delays are added to tracking calls that want to fire with the data.
Single Page Apps Usage
While the HDL feature was originally designed for the use case of a website with many static and difficult-to-modify pages, this feature can also be leveraged on Single Page Apps. For this scenario, we recommend that you do not use the Tealium iQ Extension. Instead, create a wrapper function to retrieve the data form HDL service before calling utag tracking functions (utag.view or utag.link.) This allows for your app developer to decide what is required and what is optional data. Required data should “block” (retrieve data and call callback) before executing utag.link or utag.view. Optional data can be asynchronously requested.
For example, if you have currency conversion data, you might want to retrieve that data in order to fire a purchase event with the adjusted amount at the time of the transaction. However, you may also want to pull data around general stock market shifts. This data is interesting, but not required. You would like to send the recent stock trend data to your analytics provider, but it will be OK to send that information on the 2nd or 3rd tracking event in your app.
Choosing to retrieve some data asynchronously allows for increased tracking (firing pixels sooner) and increased information (additional data is eventually retrieved, but user experience takes precedence.)
Tealium’s best practice is to place the data your tag vendors need in your data layer in the page. During your implementation process, the Deployment Engineering team will ensure you have the best data layer in the business. This provides the most powerful, intuitive, and performant solution. However, the Hosted Data Layer feature provides a framework for those cases where this is either technically not possible or just not practical. Many enterprise clients have complex implementations or internal processes.
We’re looking forward to some additional applications of HDL with added support for JSON files hosting. This allows for CORS support out of the box.
We’d love to hear about how you’re using this feature!
June 18, 2018
Refining the Marketers’ Aptitude: Jumping over the Second Purchase Hurdle
By Eilon Morgenstern, Optimove
Lowering the ratio of our one-timers should be a key approach in every company’s marketing strategy. This DIY will help you optimize your business and increase your KPIs.
We all like to think of ourselves as spontaneous creatures, casual, cool, easy like Sunday morning. The kind of people that can hop on a plane at the spur of the moment. Well, that’s optimistic. First try to order your lunch half an hour later than usual, and maybe take it from there?
You get the point. We are, regardless of how much we don’t want to be, predictable beings. For us marketers, that’s a comfort. And an advantage, that we must know how to use in our favor, like in the case of the most important purchase of all – the second one.
The Door is Already Open
In most e-commerce brands, the majority of resources are spent on bringing in new and additional traffic and only afterwards they invest in converting the existing leads. Succeeding in converting the lead to a customer is great, but oftentimes we are stuck with about 70-90% of first time customers who never make it to a second order. That’s a huge waste of potential.
One of our tools to measure true growth and true retention is to lower our one timer rate by growing our second timer rate. And think about it – it should be easy; these customers already like us, they already spent money on our brand. All we need to do is improve our personalized marketing outreach, that is based on their unique behavior, and we can significantly increase our second timer rate.
In this blog we’ll teach you a simple DIY method to help turn these one timers into “more timers”. We’ll learn how to do that by analyzing the patterns of our returning customers and categorizing the relevant marketing campaigns to send to your one timers, based on our knowledge of existing customers.
Every company has their own way of characterizing their products. If we know what characterized a specific first order that led to a second order, we can better understand how to personalize campaigns to first buyers with similar characteristics, that have yet to make a second order. Let’s begin:
Step #1 – Collecting
This DIY process can be applied to various types of companies. In our example we use a fictitious online clothing store.
For this analysis, we will collect data from all customers who made at least two purchases in the last year. The data needed from the first order is: department, category, brand, product name. For example: Shoes, boots, Timberland, light heritage cashmere flat boots.
We need the same data from the second order as well: department, category, brand, product name.
If a customer ordered more than 1 item in either one of the orders, we will create a line for each order, and in part 2 we’ll compare each item from the first order to each item from the second order.
Step #2 – Comparing
For each characteristic, we will create an Excel chart. For each customer, we’ll define 2 columns (column B and C in the example below) in each chart: The first column will be the first order department (B), and the second column will be the second order department (C). In the fourth column (D), enter the formula = IF(B2=C2,1,0)
In this fourth column we’ll see the result of “1” if the value of the first order is equal to the value of the second order. Otherwise, we’ll get the result of “0”. See this example below for the department characteristic:
Step #3 – Schema
As we mentioned above, we’ll create an Excel chart for each characteristic. Then, we’ll divide the total in column D (Is second order equal) by the number of rows in the specific chart, and that answer will be the percentage of customers who have ordered twice from the same characteristic.
Step #4 – Characterizing
For this step, we’ll take a look at the percentage result we got in column D (above) for each characteristic. If the result is between 40%-60%, we’ll recommend ignoring this characteristic when we begin creating our campaigns, since the results aren’t significant enough to create an action plan that will definitively have an effect on the correlation between the first and second order.
Now, we’ll be able to analyze the results we got from the 4 characteristics (department, category, brand, item) and understand the behavior of our customers during their second order. In accordance with our results, we’ll personalize the campaigns we send to customers who have yet to make a second purchase.
Here are a few examples:
Department = 80%
Category = 70%
Brand = 40%
Item = 20%
In this example, we’ll send a campaign with items from the same department and category as their first order, but we’ll recommend different brands and items.
Department = 40%
Category = 30%
Brand = 85%
Item = 10%
In this example, we would send a campaign with items from the same brand, but different categories.
Going the Extra Mile
After mastering that, we suggest you start drilling down into the numbers and charts. Here are two more vital ways to use the data:
1. If your company offers a wide variety of departments and items, we recommend doing this process within the department. For example: Choosing the woodwork department and noticing the category characteristic yields a 15% result, which is quite low, or going with the Gardening department, where the category is 85%. According to this example, we’ll send different kinds of campaigns for those who have purchased woodworking and those who have purchased gardening in their first purchase.
2. Regardless of the items your company offers, we recommend taking the process a few steps forward by creating sub-characteristics that will be able to give you a more granular perspective. For example: Analyzing first and second purchases for customers by looking at their geographical location, sex, age, or other attributes, that would help us have a deeper insight, and converting more first-time customers into second purchasers. Here's a good demonstration:
With Optimove’s vast experience across many ecommerce brands, we’ve been able to understand the importance of learning more about the behaviors of your current customers in order to better approach your new customers. This blog examined a simple way to practically understand the behaviors of your second timers in order to convert more first timers. Applying this method and evolving alongside it will help turn your one timers into loyal, lasting customers. Want to know more? Don't hesitate to give us a call.
June 8, 2018
Interpreting Customer’s Loyalty: There’s Definitely A Logic to Human Behavior
By Maya Shaanan, Optimove
The world of retail shopping is changing rapidly and with it the level of loyalty of your customers. See what are the changes you should expect, and how to deal with them.
These days, consumerism is not only an inseparable part of our lives, it’s an essence within it. The modern way of living and the vast developments we’ve experienced in the last decade make purchasing an item as easy as reaching your hand forward and tapping once with your index finger.
As the assortment grows and the possibilities are endless, the competition raises. It’s a race for survival for every brand. And the purchasing habits of the new generations – the millennials and even generation Z, whose oldest representatives are now in their twenties – are making sure it will be even harder. When you have tons of brands and products to choose from, and trends are changing all the time, it is quite clear why the issue of customer loyalty is becoming more and more crucial for companies, why they are fighting for every customers, trying to keep them content and happy.
In the following analysis, we tested some of the changes that occurred in recent years in customers’ behavior and what these changes mean to a retail company that wants to keep as many loyal customers as possible. The analysis was based on data gathered from 6 brands with more than 7 million customers.
Number of one-timers
The most basic thing from which we can learn about the current state of customers’ loyalty is the rise of one-timers. One-Timers are customers who bought only once and didn’t return to make another purchase. In order to compare between the different years, we looked at the percent of customers who bought only one time in the six months after their first order, out of all the customers who bought that year. It’s easy to notice a trend – the percent of customers who bought only once in this time period goes up with the years.
More than that, the uplift between 2014 to 2017 is 13%. That's a significant number. Another way to notice on this graph is to realize that in 2014 we had 30.6% - almost a third – of customers who bought more than once, while in 2017 it went down to less than a quarter - 22.8%.
The chart below drills down with this data. The left columns show the percent of customers that bought 2 times (all in the six months after their first order), the columns in the middle are customers who bought 3 times, and the right columns show the percent of customers who bought 4 times and more. All, again, by the years.
From this table we can learn how customers have a much higher chance to keep buying from a brand after the first purchase, not only a second time but more than that. This strengthens the point of the previous chart we presented – customer loyalty is decreasing. Even the most loyal customers, who have bought 4 times or more, aren’t maintaining their loyalty – a decrease from 6.6% in 2014 to 4.5% in 2017.
Abandoned Shopping Carts
Another good indicator for customers’ loyalty is the customer’s commitment to proceed to actually making a purchase and not just browse the site. The chart below shows the percent of customers who abandoned their shopping cart, meaning they visited the site and the ‘check cart’ page, but then left and did not buy. The percent displayed here is the number of customers who visited their shopping cart but didn’t proceed to complete an order, divided by all the customers who visited the cart page. The customers are divided into age groups, as the age factor is crucial to understanding purchasing power.
Again, the trend is as clear as can be. In the older segment (Baby Boomers), the chance to abandon the shopping cart is the lowest, and for the younger generation (Gen Z) the chances are the highest. The difference between the two is 20%. There are many reasons for this gap, some from the field of behavioral psychology – from simply the fact that younger people are more indecisive due to the amount of data and options they are familiar with compared to older generations. Another way to interpret this is that the older customers are used to a different, “old time” behavior, when showing your intent to buy really meant something. If someone puts an item into his shopping cart he intends to buy, in compare to the modern way of going into a company’s site to check the options, see what’s new, what’s in style, and then move on to the next site without buying. This is another explanation to how today’s customers and their shopping habits make it harder for companies to maintain customer loyalty.
Customers’ survival rate represents the number of customers that made at least one purchase in the months following their previous order. In the graph below, the numbers on the horizontal axis are the months after the customer’s first order, and the percentage on the vertical axis is the percent of customers who purchased (out of the all the customers who made a first order one month prior).
For example, in 2015, 11.3% of customers made a purchase one month after their first order. This number decreased to 8.5% in 2017. When looking at the time span of six months after the first order, the gap between the years is smaller: in 2015, 3.7% returned to buy after six months, compared to 2.2% in 2017. We see that the trend is quite similar in different years, and it makes sense since the chance of making another order a month after our first order is higher than making an order six months after the first one. But here we also see a consistent gap between the years: each year has a lower chance of having returning customers – the survival rate has gone down.
Nowadays, companies have to work harder to gain customer loyalty. As these indicators we presented here show a clear decrease, the companies are suffering by receiving a much lower share of their customers' wallets. The growing competition, not only in terms of the products themselves but also in terms of buying experience and AI solutions around to make the buying easier and more enjoyable, promises that share of wallet will decrease.
The point here is to make marketers and retailers more aware of these facts and learn how to overcome this obstacle. Companies need to be more attentive to their customers, monitor their actions closely, use campaigns wisely, use more emotional intelligence and basically try harder to get their customers to come back and keep buying.
So, in conclusion, what should you do?
Learn who your customers are - A few things companies can do to keep their customers loyal is making them feel special and treating each customer differently so that the customer won’t be tempted to look in other places, a “we have exactly what you need” strategy. Birthday campaigns, as old as they sound, are usually effective in making the customer feel special and know that you are a company that cares.
Use promotions and offers wisely - Companies tend to give special discounts to new customers to try and make them come back, but returning customers should be rewarded as well, and it is important they are aware of the fact that they are treated differently than others due to their loyalty. One way to do it is to give your loyal customers a special discount – for example, if you are having a big sale, let your loyal customers get the discount one day before the rest, and have “dibs” on the items they want without fearing those items go out of stock. Another way is to give them special offers using promo codes and letting them know they got this discount thanks to their loyalty.
Address their problems - Another example is a customer that is unsatisfied with the order or service he got. It is crucial that the company is aware of that, and is doing their best to handle this customer carefully, and resolves the issue for potential future customers as well.
Find their rhythm – A company must find the right way to communicate with the customer. The first question we need to ask is 'when is the right time to contact the customer?' - what time of day, what day of the week and what frequency. It might be every two weeks and maybe once every three months, depending on the customers' activity. The second question is what channel: some customers never open emails and prefer text messages, others will think text messages are too “pushy”.
June 5, 2018
For Hospitality Brands, Becoming Customer-Centric is Becoming Guest-Centric
By Amy Cross, NGDATA
Hospitality marketing departments face many challenges: a lot of processes are still following a slow batch rhythm, there is a lot of data fragmentation (data silos), guest data is often created and owned separately by marketing, customer service, physical retail and sales departments, guests’ usage of digital channels is growing, and will increase even more in the future with the rise of ever more devices.
Those digital touch points produce a wealth of behavioral data, making the challenge to connect all the data even bigger. But, at the same time, the loyalty of guests is harder to earn, and there is very little time to learn about the guest, find out what he or she is interested in and to react to opportunities or risks in a consistent, timely and relevant manner. Improving the guest experience and being able to deliver new types of integrated services on top of that will be the key differentiators in the coming years.
The “integrated resort” is on the rise in popularity for the complete and multi-faceted experience it offers its guests. This kind of resort is the epitome of convenience and matching expectations – something people want for their valuable breaks from everyday reality. Everything you need is under one roof, steps away from your room. Restaurants, shows, casinos, recreation, spa services and retail shopping – guests don’t have to leave the facilities to have an all-inclusive experience.
With the combination of activities and full services, your resort can collect customer data in all sorts and forms: What are they spending money on? Do they favor room service or dining in the restaurants? Who are night owls vs. early risers? Do they prefer pampering or fitness sessions? Do they plan their activities in advanced or more spur of the moment? What are the trends in how, when and from which devices they conduct their bookings?
Because of the abundance of guest data, marketing and customer experience departments in large hospitality brands face some important challenges:
Guests are becoming more demanding of quality service and their loyalty is harder to earn, forcing you to react to customer opportunities or risks very quickly. The more you know about your guests and their history with you and all your properties, the better you can serve them. This knowledge is not concentrated in one channel (e.g. the online channel, the mobile channel, the call center…) but should be based on insights gathered across all channels and devices.
Improving the guest experience and delivering new types of integrated services will be key differentiators in the years to come. It’s time to take advantage of all the data that you have on your customers, from all sources. You need to connect and engage with each and every guest, based on all of the intelligence that your brand collects, and put it into action.
For further interest:
May 25, 2018
Keep Your Losers Close but Your Winners Closer
By Omer Liss, Optimove
|Marketing System Capabilities|
|execute||web||mobile||call center||social ads||display ads||search ads||POS|
|optimize||web||mobile||call center||social ads||display ads||search ads||POS|
|create content||web||mobile||call center||social ads||display ads||search ads||POS|
|message connectors||web||mobile||call center||social ads||display ads||search ads||POS|
|audience connectors||web||mobile||call center||social ads||display ads||search ads||POS|
|ingestion connectors||web||mobile||call center||social ads||display ads||search ads||POS|
|access connectors||web||mobile||call center||social ads||display ads||search ads||POS|
|campaigns||no-code interface||multi-step||multi-channel||journey framework||schedule based||trigger based||real time interact||rules select messages||scores in rules|
|analytics||segment||BI/ explore||ideal customer||manual models||automated models||recommend products||incremental attribution|
|content||no-code templates||store||workflow||cross-channel||dynamic content||auto-test/ optimize||auto-classify (NLP, video)|
|admin||budgets||project mgmt||marketing plans||simulate results||optimal spend by channel|
|ingestion||structured||semi- & un- structured||no-code set-up||high volume||batch||real time||stream||API||find deltas|
|storage||raw detail||multi-table data model||auto-add attributes||manage PII||in-memory||dynamic scaling||industry data models||B2B data model||read external|
|identity||stitch||offline match||cross device match||identify devices||persistent ID||lead to account||anonymous to known||golden record|
|enrichment||location||intent||personal||postal||B2B data||feature extraction||external device graph||external ID graph|
|access||extract||API||SQL/HQL||analytical data sets||prebuilt connectors|
What I like about the map is exactly that it encompasses all marketing functions, not just what’s done by a CDP. This lets it accommodate any function built into any CDP, now or in the future. This, in turn, lets us identify sets of functions needed to meet particular requirements. For example, the shaded boxes on the map I’ve published here are the minimum functions required to qualify as a CDP. Other collections might define functions that are common or support specific use cases such as online-only marketing or journey orchestration. But I’m getting ahead of myself.
The other big departure that this map makes from my last effort was the boxes contain general functions (“offline match”) , not specific technical features (“name/address standardization, postal address verification, similarity matching”). In some cases, additional technical criteria are probably needed to clarify what would or wouldn’t qualify as having that feature. Those could easily be appended to this map. But in many cases, the general function description is specific enough that it should be fairly clear whether or not a particular system qualifies. To help things along, I’ve added another version of the table below that gives more specific definitions for each box. I still wouldn’t necessarily ask vendors to decide for themselves whether they qualify for a particular box, but I think these are specific enough to make it pretty easy for an objective observer to make that judgement and get the vendors to accept it.
Keeping them somewhat general also fits with the broad purpose of this exercise, which is to help marketers understand which systems have the types of functions they need without trying to provide the details of how each system performs those functions. That more detailed analysis is something that marketers need to do for themselves based on direct interaction with the vendors. In other words, we’re only helping marketers exclude the clearly irrelevant products, not create a short list of the best systems for their purpose.
|Delivery (by channel)|
|execute||send complete messages and audiences in the specified channel (email transfer agent, Web CMS, mobile app, mailing list generation, Adwords bid, programmatic display ad bid, retail POS)|
|optimize||automatically run tests and pick winning message (a/b or multi variant copy testing; possibly find best message per segment)|
|create content||the system provides tools to create content in the specified channel|
|message connectors||prebuilt API or SDK connectors to send message components to specified channel systems (templates & personalization variables inc. customer IDs; no configuration needed beyond providing credentials)|
|- Web||ability to personalize anonymous users based on device, campaign, referrer, location, weather, behavior, etc.)|
|audience connectors||prebuilt API or SDK connectors to send campaign audiences to specified systems (email lists, next best offer lists to Web personalization, advertising audiences, etc.)|
|- display ads||ability to continuously update audience segments, to send suppressions lists, to cookie synch with DMP/DSP; DMP functions|
|- social ads||ability to continuously update audience segments, to send suppressions lists|
|ingestion connectors||prebuilt API or SDK connectors to ingest data from specified systems (e.g. MailChimp, Sitecore, Google Analytics, Adwords, Facebook, BlueKai DMP, NCR POS)|
|- mobile||collection of mobile device attributes and location, batch mobile data collection to save battery|
|access connectors||prebuilt API or SDK connectors to expose data to specified systems (e.g. MailChimp, Sitecore, Adwords, BlueKai DMP, NCR POS)|
|no code interface||users can set up marketing campaigns by filling out forms or drawing a flow chart; no writing of code or script|
|multi-step||one campaign can include a sequence of messages over time|
|multi-channel||one campaign can include messages in different channels|
|journey framework||the system can track customers through user-defined journey stages or states; campaigns can be triggered when a customer changes state; state is available to use in campaign rules|
|schedule based||campaigns can be set to execute on a regular schedule e.g. daily, hourly, weekly|
|trigger based||campaigns can be set to execute when a trigger event occurs; triggers may be recognized immediately or at a given interval (e.g. nightly)|
|real time interact||campaigns can react in real time to customer behaviors|
|rules select messages||rules within a campaign can select messages (i.e., different customers get different messages depending on the rules)|
|scores in rules||rules within a campaign can use predicitve model scores or other modeled values (e.g. cluster membership) as inputs|
|segment||the system provides tools to define customer segments based on all data within the system|
|BI/ explore||the system provides tools to analyze all data within the system, such as cross tabs, profiles, visualization, etc.|
|ideal customer||the system provides tools to identify an ideal customer profile|
|manual models||the system provides tools for skilled users to create predictive models|
|automated models||the system provides tools for unskilled users to create predictive models|
|recommend products||the system provides tools to generate product recommendations (best choice among many)|
|incremental attribution||the system provides tools to calculate the incremental value created by any single marketing action|
|no-code templates||the system lets users apply templates to create content without coding in HTML, etc.|
|store||the system provides a repository to store and access content to use in marketing messages|
|workflow||the system includes workflow functions for content planning and approvals|
|cross-channel||the system can create content that is used across multiple channels|
|dynamic content||the system can create messages that contain rules to select content based on customer data and other variables (time of day, weather, inventory, etc.)|
|auto-test/ optimize||the system can automatically execute tests that find the best content for a given campaign and deploy that content. Tests might compare complete pieces of content, different combinations of content components, and/or different content for different customer segments.|
|auto-classify (NLP, video)||the system can automatically tag content based on subject, tone, images, brand, etc.|
|budgets||the system can track budgets and spending against budgets for marketing campaigns and other marketing expenses|
|project mgmt||the system can track tasks to develop marketing campaigns, including due dates, status, resources assigned, approvals, etc.|
|marketing plans||the system can track marketing plans including campaign schedules and expected results|
|simulate results||the system can simulate the results of a planned set of marketing campaigns over time|
|optimal spend by channel||the system can develop an optimal marketing plan including allocation of spend across different channels and campaign types|
|structured||the system can ingest structured data such as CSV files and relational database tables|
|semi- & un- structured||the system can ingest unstructured and semi-structured data such as text comments and Web logs; specific requirements include nested JSON objects|
|no-code set-up||users can set up a data feed into the system without writing program code|
|high volume||the system can handle high volumes of input data (we need specific criteria)|
|batch||the system can ingest data via batch feeds such as CSV files|
|real time||the system can ingest data via real time feeds such as API calls|
|stream||the system can ingest data via data streams (we need specific criteria)|
|API||the system can ingest data via API calls, not necessarily in real time|
|find deltas||the system can ingest copies of a complete data set and identify and store only changes since the previous version of that data set|
|raw detail||the system can store inputs without losing any details|
|multi-table data model||the system can store data that is organized into multiple tables or data types|
|auto-add attributes||the system can automatically accommodate new attributes in input data (without manual adjustments to the data model)|
|manage PII||the system can manage personally identified information (including compliance with privacy regulations for access and security)|
|in-memory||the system can store data in-memory (for fast access and processing)|
|dynamic scaling||the system can automatically accommodate changes in demand through features such as dynamic load balancing, addition of new processors and storage, etc.|
|industry data models||the vendor has prebuilt industry-specific data models (for retail, travel, financial services, communications, etc.)|
|B2B data model||the system has a prebuilt B2B data model with separate account and individual layers|
|stitch||the system can maintain relationships among personal identifiers that are deterministically matched to the same individual|
|offline match||the system can match offline data (names and addresses) with features including name and address standardization and similar-string matching|
|cross device match||the system can maintain relationships among devices that are probabilistically matched based on usage patterns|
|identify devices||the system can identify devices over time using cookies, device fingerprints, and other methods|
|persistent ID||the system can assign each individual an ID that remains the same despite changes in identifiers (new address, new device, etc.)|
|lead to account||the system has features to match individuals (business leads or contacts) to business accounts (companies or departments).|
|anonymous to known||the system has features to retain identity links when an anonymous profile is linked to an identified cusstomer profile|
|golden record||the system can select the most likely value for personal data (name, address, etc.) and expose it to other systems|
|location||the system has prebuilt integrations with location data providers (data about venues e.g. store traffic; possibly data about individuals e.g. types of stores visited)|
|intent||the system has prebuilt integrations with intent data providers (data about topics or products individuals are interested in)|
|personal||the system has prebuilt integrations with personal data providers (data about individual name, address, phone number, income, education, interests, etc.)|
|postal||the sytems has prebuilt integrations with postal and address data providers (data about valid postal addresses and address changes)|
|read external||the system can connect in real time with external systems to look up specified data (local weather, recent behaviors, etc.)|
|B2B data||the system has prebuilt integrations with B2B data providers (data about companies and individuals e.g. company revenue, CIO name)|
|feature extraction||the system can extract structured data from unstructured inputs (e.g., products mentioned in social media comment, products viewed from Web log)|
|external device graph||the system has prebuilt integrations with external vendors (e.g. TapAd, Crosswise, Google) who provide a list of devices associated with specific individuals|
|external ID graph||the system has prebuilt integrations with external vendors (e.g. LiveRamp) who provide a list of identifiers (in different channels) associated with specific individuals|
|extract||the system can generate batch extract files to share with other systems|
|API||the system has an API that can be called to retrieve data it contains|
|SQL/HQL||the system can accept SQL or related queries and return the results to external systems|
|analytical data sets||the system can create extract files in formats suitable for analytics (e.g., containing aggregates or with multiple tables flattened to a single row)|
|prebuilt connectors||the system has prebuilt connectors for specific external systems e.g. BI tools, predictive modeling tools, etc.|
As you examine the second table, you’ll notice that the Delivery section shows one definition for each row, while the Decision and Data sections show one definition for each box on each row. I think you’ll see that makes sense. But I’ve also allowed a few exceptions in the Delivery section, such as “Web” under “manage connectors”, where I’ve listed specific technical criteria that apply to a single channel. This is simply an efficient way to present that information.
Over-all, I think this approach brings us closer to a workable solution. No doubt there are more items that could be added, although too much detail would be bad. There’s also an objection that function checklists like this are easily misinterpreted to suggest that systems with more functions are better. That’s certainly a danger, but I’m hoping that marketers know enough about their needs to look only at the functions relevant to their situation. This approach does avoid creating a vendor typology, which was implicit in the previous approach and seemed likely to cause confusion.
The next step is to get feedback from the community about whether this approach seems reasonable and about specific items they’d add or remove from the map. After that, we’ll need to assess the functions provided by individual CDP vendors – a great deal of work but something that is probably inevitable. Once that’s done, we can publish a proper guide to the vendor systems, making it easier for marketers to find products that meet their needs.
What do you think?
November 27, 2017
If the Martech Stack Only Had a Brain...
By Amy Cross, NGDATA
Today, we are in the Age of the Customer, an age that differs from its predecessors. The balance of power has officially shifted from the business’ hands into the customers’, with customers increasingly empowered by the amount of information they have access to. Gone are the days of the Age of Manufacturing, where industrial powerhouses ruled; the Age of Distribution, when globally connected supply chains had the power, or the Age of Information, where connected PCs dominated. Today, understanding the customer and anticipating their needs is the way that brands must succeed to remain relevant.
During the Age of Information, the real accrual of customer data started to explode with the availability of first-party data, third-party data and transactional data. Thanks to connected devices and social media, we have seen this explosion in data magnify as we transition into the Age of the Customer.
Data are the breadcrumbs that customers leave behind for brands to learn how to better understand and anticipate their needs. While data itself is invaluable, it’s actually the customer insights that are buried within the data that are the most powerful. As customers interact with your brand, analysis is needed to unearth insights. From there, acting upon those insights is needed to reap any value.
Today, we have so many marketing and customer engagement tools and technologies, and a plethora of customer data. But, what is missing is the “brain.” It’s the central nervous system that talks to all the tools in the martech stack – the critical component that knows what to do and when to do it.
The brain has three central components: data, intelligence and orchestration.
It should know what to do for each customer based on all the data, and have the ability to coordinate across every engagement channel. When you “plug” the brain into your marketing stack, a lot of the guesswork of segments is removed. And, efficiency is increased as you truly target each customer on the individual-level.
So, how does the brain work? First, all available data about your customers and prospects are brought together in order to unlock silos. This comes from internal first-party data sources (CRM, web, customer service), as well as external data sources (second-party partnerships and third-party data providers). This then leads to a holistic, dynamic and living individual profile, or Customer DNA, for each and every customer. But, this isn’t simply all the data aggregated in one place; intelligence is added to that data. The brain needs to be smart enough to know, for each individual, what all that data means for them. Through metrics and propensities, the brain can tell you what each customer wants – how and when they want it.
Once we have the Customer DNA, the final piece is using that DNA to drive customer engagement. This is, of course, achieved through the various tools and technologies typically available in the martech environment. But, how do these tools talk to each other? How do they coordinate? That is where, once again, the brain comes into play.
The brain aggregates the data, has the intelligence to know what all that data means for each individual, and can orchestrate the engagement channels. Thanks to the brain, each customer can have the most seamless, relevant and personalized experiences with your brand.
For further interest:
November 20, 2017
Who Should Be Responsible for Digital Customer Engagement?
By George Corugedo, RedPoint Global
Digital technologies have turned the traditional relationship between brands and consumers on its head. No longer can brands be assured of controlling the customer journey from awareness to sale – consumers now direct their own buying journey, and expect brands to deliver a seamless experience in digital and physical touchpoints alike.
Many brands have not been able to deliver. Legacy technologies, customer data locked in silos, and inflexible traditional processes have hamstrung efforts to adapt for many organizations. McKinsey recently found that digital disruption has shaved 45 percent off incumbents’ revenue and 35 percent off their pre-tax earnings. McKinsey also reported that digital transformation has only currently reached mainstream penetration in one of 10 industries likely to be affected. More companies will need to alter their business models if they want to survive and thrive in the modern age.
Part of this transformation involves translating your brand promise into an engaging brand experience. Digital disruption provides this opportunity in spades; customers increasingly interact with brands through digital channels, which enables greater capacity and opportunity to translate the brand promise into brand experience. To do this effectively, however, you need to first determine who owns the brand experience.
At first thought, it may seem obvious. But keep in mind that enterprise customer engagement covers everything , including sales, service, marketing, and IoT. The brand experience is also everywhere and all the time, which is why it is vital to designate who in the organization has ultimate responsibility to ensure that it is consistent. Who has the right skills for the job? Should a new role be created or new responsibilities given to an existing employee? These questions must be answered if the organization is to succeed at digital customer engagement.
Customer Engagement and the Chief Digital Officer
One possible owner of the brand experience is the chief digital officer (CDO), who has taken on new organizational importance in the face of digital transformation. The CDO has become more strategically important in the wake of broad-based digital transformation, evolving beyond implementing new technologies or changing a few processes. The modern CDO or the equivalent – e.g., senior vice president of innovation – now directs and emphasizes the integration of digital thinking and processes throughout the organization.
The CDO’s role often includes deploying digital technologies and processes that enhance the customer experience. Because of this, it has become increasingly common for the CDO to be responsible for digital engagement. The RedPoint Global team and I have seen this happen frequently at our clients. CDOs and similar roles have wide-ranging mandates to spread digital throughout the organization, and they are often forward-thinking and cross-functional. Because the CDO works with every internal department, they are often a natural fit for managing the customer experience.
For all their technical expertise and understanding of digital systems, however, brand management is not typically a CDO’s core competency. This is a problem because the CDO needs to understand the intricacies of defining and delivering on a brand promise if he or she is to be responsible for the customer experience.
CMOs, CDPs, and the Brand Experience
The CMO is another possible owner for digital customer engagement. Marketing has historically owned the customer experience, and this shows in the marketing focus of most modern customer engagement platforms. The challenge most traditional marketers have is their mastery of technology and the related ability to leverage digital technology to extend the brand experience. This is the exact opposite problem of a CDO. CMOs understand the ins and outs of providing a consistent brand experience; in fact, brand management is one of the traditional responsibilities of a CMO. At issue is whether the CMO is skilled enough in advanced digital systems and process to leverage those new tools effectively. Given these limitations, should the CMO be responsible for delivering the brand experience in a digital world?
Take customer data platforms (CDPs) for example. CDPs are a foundational technology for modern customer engagement, enabling an always-on, always-processing golden record that facilitates a unified and complete view of the customer available at low latency across the enterprise. CDPs are a powerful technology solution to the problem of fragmented technology stacks and siloed customer data that have bedeviled brands’ efforts to provide a consistent customer experience across channels.
Industry analyst David Raab, who has written extensively about CDPs, has consistently defined the solution as “marketer-controlled.” This idea has carried through to other industry analysts as well, with a recent Gartner report placing customer data platforms in the marketing camp. But should marketing really be tasked with owning customer data and thus being responsible for the brand experience? If they are, the CMO needs to have support and authority from other senior staff to pull everyone in the same direction.
Support Your Local Brand Experience Owner
The brand experience is too vital to your success in the age of digital transformation to be without a leader. Someone within the organization must be responsible for driving digital customer engagement and for ensuring the brand experience is consistent across touchpoints. The person who is responsible for doing so must have the capabilities to leverage digital technology and understand enough of the nuances of brand management to be effective. If it’s your CMO, then he or she needs to comprehend the value and function of new digital technologies; if it’s your CDO, then he or she needs to be comfortable with the necessities of maintaining a consistent brand.
Your customers have already shifted to experiencing your brand through digital channels. Now the onus is on you to follow along, ensuring that your organization has the right tools and the right people in place to execute at the highest level. Whether that is a new role or an existing person, it is critically important to meet your customers where they are with a consistent brand promise to succeed in an age of digital disruption.
November 16, 2017
Foreign Exposure: Reaching the Unreachable Customers
By Moshe Dimri, Optimove
The countless ways the world has changed over recent years totally modified customers’’ habits, making them very hard to influence. But the same changes in our world make it impossible for our consumers to be completely anonymous.
It’s just impossible to keep track. The changes are so vast, and it’s affecting every aspect of our daily lives. It covers all fields and industries: Manufacturing, Medicine, Agriculture, Automotive, Corporate Services, Energy efficiency or Digital Economy. The use of deep learning technologies, big data analysis, cloud development, cyber security and many more, has brought on a dazzling number of inventions into the very tips of our fingers, and nothing will look the same again.
And the rhythm is gonna get you. Every year tops and doubles its former. Since Deep Blue beat Garry Kasparov 20 years ago, the world saw an explosion of ideas that touched and changed our habits, day-to-day routines, caused rifts in our attention and behaviors until the point of no return. From the autonomous car to shoes that tied themselves, from the bionic leg and artificial skin to smart umbrellas. This list is just a drop in the bucket, and what will be added to it in just a few months, is too hard to even imagine.
One of the more expected result of this era is the rapid change in customer habits. What was done on Sunday become outdated on Monday, and where people went during the summer was completely deserted when winter arrived. One of the clear demonstrations for this phenomenon is this startling study by Omnicom Media Group Agency Hearts and Science: 47% of adults aged 22 to 45 are consuming absolutely no content via traditional TV platforms. Less than one-third of the TV and video watched by people in this age group (Gen X and millennials) is accounted for by traditional measurements. The rest is being watched on smartphones and other channels like Apple TV and Roku.
While it doesn’t mean that these subjects are indisputably not watching TV content or seeing ads anymore, it does suggest the ways to consume content today have changed, which makes it much harder to reach these exact group. That’s a big problem for marketers these days.
Anonymous? No such thing
Look at it as you will - this is not a philosophical argument, for now – there are ways to connect with the most aloof of a customer. Whether it’s scratching the hard surface layer with a chisel to expose the hieroglyphics or scanning the depths of an ocean for the remains of a hunted ship. If Bigfoot does exist, and you’re probing for his footprint in the Cascades’ snow, look no further – just open your laptop. He would have probably left a digital footprint by now.
Customers can no longer be anonymous. And a more personalized approach through multi-channels, which work across every stage of each customer’s journey, will maximize the marketer’s ability to reach those who seems unreachable. Marketers should reach their customers where they are, and much of users’ screen time is spent on Facebook, Twitter or surfing the Web. The fact that you have an email address for a customer, doesn’t mean much if that same customer spends hours every day on Twitter. Customers are everywhere, and you should always aim to be where your customers are. In addition, customers today have much more control over the buying process than marketers do. As a result, marketers must constantly develop and coordinate highly orchestrated touch points and micro-campaigns that span multiple channels fluidly, in a way that the customer finds meaningful and trustworthy. Keep in mind that different types of messages work better over different channel, and what fits an email, shouldn’t automatically be sent using SMS.
The Mix and Match
Here are some ways marketers can use multi-channel marketing to improve campaign results. There are countless others of course, and that is where creativity and resourcefulness come into play.
Sequential Messaging Across Channels – Run campaigns that present a sequence of messages, like telling a story across multiple channels. For example, we can use the 97% open rate of SMS messages to deliver a short teaser regarding a subsequent campaign message to come via email, and thus dramatically increase the open rates of that email when it comes half an hour later.
Message Blast – Send the same offer via every channel at once. This works best for a great, time-limited offer that is highly focused to a small segment of customers.
Channel Competition – Run campaigns with the same message/call to action via different channels, to different random segments of a target group to see which ones work best. Known as A/B/N testing, this approach can reveal which types of messages work best over which channels for your various target segments.
The Pros and Cons
Using all available channels doesn’t come without a price. In the table below you can see the relative strengths and weaknesses of some commonly-used channels, according to these attributes:
Cost – how expensive is it to communicate with each customer via this channel?
Attention – how effectively does this channel grab the attention of most customers?
Design – how much flexibility and creativity is available to deliver effective messages via this channel?
Annoying – how invasive or irritating do customers perceive this channel to be?
In the ecosystem described in this article, the-ever-changing-mind-blowing era, marketers are required to closely analyze customer behavior, loyalty and retention. They need to communicate with their customers wisely, in all sorts of ways and tools according to his or her particular preferences.
Using different, less traditional channels, and more of them combined, is really the right path to success. Examining how people are more receptive to different sorts of communications, not only guarantees leaving no customer behind, but also gives tremendous added value to the customer, with content, offers, and messages delivered to where they're at.
November 14, 2017
Mobile Customer Recognition – the Third Estate of Customer Identification
By Matthew Kelleher, RedEye
In preparation for a recent webinar on the growth of the Customer Data Platform I was struggling to fully articulate the value of one key part of it. Although it’s only recently begun to create a conversation in the market place, we’ve been discussing its merits for the last seven years. At RedEye, we call it Cross Device Identification. Simply put, it is the use of cutting edge customer identification solutions to allow organisations to recognise mobile browsers through their devices, personalise messages to them and retain that valuable behavioural data within a true Single Customer View database.
Now, every decade or so I have a singular thought, one that surprises me and sticks in my mind for some time. This decade’s thought waited to appear until mid-way through the seventh year of the 2010’s. “It’s the third Estate” was my thought, but although this originated in my own brain, it took some deciphering to work out what I meant.
The point I wanted to make in the webinar was that, in building a true single customer view, many people consider that there are only, when you boil it down, two types of data – online and offline. I think this is, if not wrong, then significantly misleading. If you read the database marketing press (and they should know, right?) you see them talking about online and offline data. The point I wanted to make is that this is far too simplistic, that there is a third ‘type’ of data that many organisations who build customer and marketing databases and claim to build single customer views do not accommodate – mobile data.
I do accept at this stage that we could boil data down to dozens of sources that would differ for every organisation out there, but that is not the point I want to make. I also accept that many of you will consider mobile data to still be online data, but hear me out… Because of the challenge in identifying mobile browsers, because of the specific requirement to ‘stitch’ together the various elements of the online customer profile through the use of a multitude of different identifiers, because of the unique challenge and opportunity this presents, then we need to think about it as a third area of data.
But before I expand my point I should probably explain the analogy, and I have to start with an admission… I really could not remember what the third Estate was and so I resorted to Google to remind me what I had learnt at university! Of course, as most of us know (and have forgotten) in pre-revolutionary France there were two ‘Estates’, also known as ‘Estates of the Realm’, the forces that governed society. These were the Clergy and Nobility. Then a chap called Sieyès argued that real power actually lies in the third estate – the people.
The analogy that my mind was unwittingly stumbling towards was that traditional database marketing was built on an edifice that one can liken to the original Two Estates. The clergy, in my analogy, was the offline data, built around a household de-duplication structure that bizarrely decrees that without a domestic address you don’t exist as a prospect or a customer… In the world of online marketing this is a seriously flawed perspective. Bolted uncomfortably on to the clergy in the governance of post-medieval French society was the aristocracy (for examples of the uncomfortable relationship look no further than Henry VIII and the dissolution of the monasteries – but I am in danger of digressing too far). Here my analogy is that traditional database marketing ‘de-dupe’ or customer identification solutions have tried to work by bolting an email address to a household record – another uncomfortable and ultimately failing alliance!
Well, we all know what happened to the concept of the governance of the Two Estates in France and subsequently around the world… (a few clues, Robespierre, the guillotine, the Declaration of the Rights of Man). And this is where I must bring my rambling analogy back to the point.
The current multi-channel marketing automation landscape cannot be optimised by organisations who continue to rely on this dangerously outmoded form of customer identification at the core of their SCV or marketing database. The concept that a customer’s (or indeed prospect’s) household address should be the primary key (with email bolted onto it) is no longer relevant. This type of approach simply cannot deal with the wealth and variety of online, mobile and digital data that makes up the real potential asset at your fingertips – your customer data.
I want to expand this argument to cover three areas that are important to me. Firstly, the core inaccuracy of traditional database marketing customer identification (‘de-dupe’) techniques, secondly that building marketing solutions in 2017 focusing on historical data only is no longer fit for purpose, and thirdly that the real value in data for the future lies in that ‘Third Estate’.
Taking the first point, the inherent inaccuracy of a household based de-duplication. Systems based on such a limited number of identifiers could never efficiently differentiate between Joanne Smith, Jo Smith, J Smith and that remains the case today. Twenty years ago, marketers didn’t really care, you could just send the catalogue to that address and hope someone called Smith opened it. The solution, these organisations then went on to claim, was to bolt an email address to the household address. But multiple email addresses piled on top of inaccurate household data only confuses the situation further and whilst email is a key for a lot of online activity it is often poorly leveraged and does not open the door to mobile recognition, for instance.
The second point I would like to make in support of up-to-date customer identification solutions, is that the latest analytics are based on current data, i.e. how customers are engaging with your brand today, not what they bought two years ago. Historical data has its place… For instance, to calculate lifetime value one obviously needs historical transactional information. But current trends, for instance, towards predictive and machine learning, are based on the application of current behavioural patterns allied to historical data (putting it simply…). If your single customer view cannot track your customers mobile browsing activity (or indeed any browsing and engagement information from mobile to app) then you will never be able to leverage predictive analytics. I come back to the example of lifetime value. Traditional marketing databases that manage historical transactional information at a household level can give you a lifetime value calculation, probably down at segment level. But what they cannot do is forecast future lifetime value, at a business, segment or customer level, or drive insight in terms of the current behaviours that could influence an increase in that LTV.
And finally, I have to ask the salient question. If customers are increasingly active online (ComScore, for instance, say that 68% of all browsing is done on a mobile device), and if you agree that behavioural and engagement data is critical to customer understanding and up-to-date analytics as I do, then single customer views that do not incorporate this ‘Third Estate’ of data are increasingly anachronistic… just as the clergy and aristocracy were in the governance of France in the late 18th Century! It’s time for the guillotine…
You can get in contact with RedEye if you have any queries.
November 10, 2017
The Good Ol’ Campaign: Birthdays Still Matter
By Omer Liss, Optimove
Birthday emails are one of the most-common tricks up a marketer’s sleeve. New Optimove research probes just how well they still perform.
Blow out the candles, cut the cake, and go straight to your inbox to open your email. The birthday email is one of the best-performing personalized marketing campaigns, and has been for years. On that special day, this gesture strikes us all at our core — though few willingly admit — all we want is just to be remembered.
But is the birthday email still relevant? A recent research done by Optimove gathered new data that showed the birthday email performs better than ever. Direct emails tied to birthdays get the highest response rates, compared to many other customer-related communications. The data was received from over 1 million birthday campaigns sent by businesses since mid-2016, and whether consumers received coupons, free delivery, a gift or else, they all revealed their effectiveness. the new data was gathered from over 6 million customers in the gaming industry and more than 1.2 million from the e-commerce field.
Across the Sectors
The first thing worth mentioning is that birthday campaigns are statistically significant, and when compared to a control group (people who did not receive a birthday campaign/discount), the campaign significantly affected those who received a shout out.
The chart below shows the response rate for test and control groups (the test receives the campaign with a discount and the control doesn’t), and the percentage in which the test group is greater than the control.
First, we separated gaming and ee-commerce. Although there was a higher response rate in gaming, we can see clearly how well birthday campaigns perform in the e-commerce sector, as well.
After recognizing that birthday campaigns have a powerful impact on customer behavior, we wanted to examine how different groups react to these campaigns. When we implemented gender segmentation, we found that in both gaming and e-comm, men are more active on their birthday than women. The numbers in the chart show the exact percentage of men who were more active (new word) on their birthdays in contrast to women from those groups.
We can see from the chart that in gaming, men are 57% more active than women, and in e-commerce, they’re 72% more active (new word) than the women.
Men of a Certain Age
Our next segmentation is according to age. Here, gaming and e-commerce perform differently. As seen in the charts, there is a clear trend in gaming; the older the participant, the more activity on their birthday (to put it simply, people in higher age groups are more active than their younger counterparts). In e-commerce, however, that is not the case. The most active groups are the youngest, and then activity drops to the lowest point and raises slightly as age increases.
In gaming, the trend is consistent. Men order more than women in all age groups, and the trend stays the same (more active as age goes up). In e-commerce, the trend is similar, but we see that in some age groups, women order more than men.
Attempting to explain these numbers is more of a behavioral thing. Young people tend to be more involved on their birthday, and therefore, are less probably to play online casino games, but will take the time to treat themselves and place an order.
To sum it up: putting age, gender and sector aside, overall, people respond well to birthday campaigns. In this ever-changing world of marketing where marketers must be agile and adapt quickly, it’s important to tailor these campaigns to customers’ expectations, all while utilizing technology to reach specific targets.
November 7, 2017
CDP Institute Marks Its First Anniversary
By Davidi Raab, CDP Institute
Last week marked the first anniversary of the CDP Institute, which officially launched on October 31, 2016. Here are a few key statistics:
- Web traffic: 58,000 page views by 29,000 unique visitors
- Library papers downloaded: 3,600 including 2,500 Sponsor papers
- Members receiving daily newsletter: 1,600
It’s been quite fascinating to see which Library papers, newsletter entries, and blog posts get the most readership. Among Library papers, there has been a clear preference for introductory topics including industry overviews, advice on choosing a CDP, and the ever-popular CDP vs DMP distinction. There's less interest in case studies (a bit surprising) and vendor profiles. The best read blog posts have also been about CDP basics and differentiators.
Newsletter readers are perhaps more likely to be industry insiders: they’ve been most likely to click on items about CDP vendors, including funding, new entrants, and product updates. They’re also interested in surveys about personalization and other marketing practices. They’re less interested in news about data sources, privacy, and the Facebook/Google duopoly. But we're convinced those are important so we'll continue to cover them anyway.
None of this would be possible without our Sponsors, who have generously funded the Institute and offered much valuable behind-the-scenes advice. They’ve also been remarkably tolerant of published opinions that don’t align with their individual business needs. So here’s a big thanks to them.
Still, the real constituents of the Institute are you, the marketers and martech professionals who work every day to build better experiences for your customers. The Institute’s mission is to help you succeed by providing information about CDPs and customer data management in general. We’re constantly asking ourselves what more we can do to be useful.
But that's really a question we should be asking to you. So please take a moment and comment below or send your thoughts on what you like and don’t like about the Institute and what else we should be doing. We look forward to hearing from you!
November 6, 2017
CDP & GDPR – A Match Made in Scrabble Heaven
By Steve McGrath, RedEye
There are already a number of different pieces of UK legislation that cover data protection and data security. The predominant one currently is the Data Protection Act 1998 (DPA). We also have the Privacy & Electronic Communications Regulations (PECR… no sniggering at the back!), as well as others that cover individuals' privacy and data protection. And unless you have been living under a rock or are perhaps, a contestant on Love Island, you will also know that we have the General Data Protection Regulations (GDPR) coming into force in May 2018.
Now if you add all these acronyms up, not only can you get a pretty decent Scrabble score (my best was 6 letters – read to the end for my word), but you also have rather a lot to think about when it comes to data security.
A quick re-cap if you don’t have your GDPR notes in front of you; from May 2018 individuals about whom you hold personal data will have the following rights:
- Rectification (… you’ve got to fix it if it’s wrong!)
- Erasure (no, not the right to listen to 80’s pop)
- To restrict processing
- And the right to restrict profiling
All of which needs clarification by the powers that be over the next 11 months but one thing remains certain… your data platforms need to be able to achieve all of the above.
Wouldn’t it be great if you had somewhere you could put all your customer data that would address many of the regulations, existing and future, allowing you more time to get on with using that data for some brilliant marketing?
Drum roll please….
It’s called a Customer Data Platform (or CDP).
Now, you may be saying to yourself ‘Enough with the acronyms! I am still trying to work out what 6-letter word you had earlier in this article’! But hear me out, and I’ll explain how a CDP can help you, particularly in regards to some of the upcoming regulations in the GDPR.
I am not going to explain the benefit of a CDP from a marketing point of view. This is done far better in other articles on this site. What I will say is that a CDP is an evolution of a traditional customer database that combines data from multiple platforms to give a single view of the customer. It utilises profile, transactional and past behavioural data to predict future customer behaviour, all the while being marketer controlled and easy for external systems to push data in and pull data out from. It is intelligence driven marketing at its very best.
How does a CDP help with GDPR?
On top of this, there are some added benefits from a compliance point of view, particularly with GDPR in mind.
Subject Access Requests
Currently the DPA legislates for Subject Access Requests (the ability for a person to request all data from you that you hold on them). However, the GDPR will remove the ability to charge for this access and limit the time you are allowed to provide this data (a maximum of 1 month).
Having all your customer data in one place makes it more efficient to extract. You won’t need to engage your tech team to trawl though multiple data silos and you won’t need to spend hours collating all that information and putting it in a presentable format. If even 0.5% of your current customers asked for all the data that you hold on them, how much time and resource do you think that would currently take you to comply?
One of the main tenets of GPDR is more informed consent, but not only does it deal with whether you have gathered permission, it is also concerned with how you have gathered it. Not only that but the onus is on you to store and record how consent has been gathered. A CDP will store all this information and tie it all to the individual so it can be easily evidenced.
Linking back to the storing of consent, a good CDP will also store a complete audit trail of marketing permission, so a single customer state of consent can be shown at any given moment in their life cycle.
Using a CDP will mean all your subscription status’ and suppressions will be consistent and up to date across all of your customers.
In addition, the CDP will maintain a ‘single source of truth’ of your marketing consent and will make it easy for your other systems to access this ‘truth’.
Another facet of a good CDP is controlling access to stored customer data, particularly PII – Personally Identifiable Information. A platform like RedEye’s CDP can be accessed by external systems but only with the correct security privileges for access or secure methods for transfer such as SFTP. This is especially useful for areas of legislation that cover data protection and data security, meaning you will be mitigating your risk of a data breach.
Under the GDPR, breaches of some of the provisions could lead to increased fines of up to €20m or 4% of global annual turnover, so it is essential that you are doing all you can to mitigate your risk. At the same time, you are building trust with your customers by collecting, processing, storing and using their data in a transparent, respectful and legal way. A good CDP will help with many of these issues.
Finally, the 6-letter word was ‘carped’. Can you do better?
PS – we know there’s a potential 7-letter word, but we’re concerned we’d offend your delicate sensibilities with that one!
Questions or concerns over GDPR? Contact us now and we will try to answer your questions.
November 2, 2017
Getting Closer to Classifying CDP Systems
By David Raab, CDP Institute
It’s nearly two months since my last post on building a classification scheme for CDP vendors. Work has continued behind the scenes. I still don’t have a final solution but have made enough progress to warrant an update.
Where we currently stand is several of the CDP vendors have provided lists of technical features they see as important differentiators. I’ve reviewed these and classified them based on the core CDP functions they support. These functions fall into two categories:
- building the unified customer database itself (ingestion, transformation and identity unification, storage, and sharing with external systems). All CDPs do this.
- applications that use the database (analytics, campaign management, and personalization). Many CDPs offer one or more of these but it's not a core requirement.
Looking at the specific differentiators, it turns out that about half of them apply to nearly all customers who might want a CDP. I've labeled those "base". The rest relate to specific channels or uses. I’ve identified these as:
- semi-structured and unstructured data
- real time processing
- Web marketing
- mobile marketing
- B2B marketing
- offline marketing
I’m perfectly aware that these categories overlap: B2B marketers use the Web, Web market involves unstructured data, and so on. But each category is something that some marketers might use and others might not. Since the ultimate goal of this exercise is to help marketers find CDPs that meet their particular needs, breaking the features into categories related to those needs seems reasonable.
Naturally, two sets of dimensions (functions and uses) lends itself to a matrix. In practice, though, such a matrix would have many blank cells because so many items fall into the “base” use case. So to make the presentation a bit more manageable, I’ve presented two tables below: one that lists the base items only and one that has each of the other uses in a column of its own. In both cases, the rows are grouped by function. (To be clear, there’s no particular relationship among items on the same row in different columns. That said, it might make sense to have one row list the connectors appropriate for each use: B2B systems should have connectors to Salesforce.com; mobile systems should have connectors to SMS gateways, advertising systems should have connectors to DMPs, etc. )
If you read the entries carefully, you’ll see that I’ve marked several with questions, mostly about whether they’re too vague. Ideally the items would be specific enough that we could easily determine whether or not a particular vendor offers that item or not. So things like “has API” are too general, since APIs differ greatly. Tightening up these definitions, and adding more items as needed, is the next step in this process.
As always, I eagerly await your comments and suggestions.
(Note: tables converted from Excel using Tabelizer. Worth knowing about!)
|ingest||API load (specify features)|
|batch file load|
|client and server-side APIs for real time & batch connections;|
|data exchange via webhook, data layer, pixes, firehose, CSV|
|marketer can set up data collection without writing code|
|prebuilt connectors (specify system, categories)|
|transform/ unify||deterministic matching/stitching|
|extract input deltas (adds, changes, deletes)|
|identify best value per element ('golden record')|
|system-assigned customer ID (too vague?)|
|store||data stores supported (specify)|
|ingest and match 3rd party data (too vague? Not needed? Specify connectors?)|
|modify data structure without technical skills (too vague?)|
|open APIs to build custom features (need to specify API capabilities?)|
|scalable tech (need specifics e.g. virtual servers? Dynamic load balancing?)|
|share/ access||API access to data (specify features?)|
|prebuilt connectors to access data (specify channel? Functions?)|
|SQL access to data (specify details e.g. speed, scale, all tables, SQL-like e.g. HQL?)|
|analytics||Automated data normalization for predictive models|
|Automated feature extraction for predictive models|
|automated model building & deployment tools wihthin system (specify types? Features?)|
|Automated model validation|
|content analytics (specify details?)|
|GUI interface for analysis, profiling, mapping, segmentation|
|high throughput for batch recommendations (specify speed?)|
|ideal customer profile|
|Interpretable models (specify details)|
|manual model building & deployment tools within system (specify skill level needed)|
|marketer can define segments without writing code|
|nature language processing (too vague?)|
|product recommendations work with arbitrary catalog schema (too vague?)|
|real time segmentation (specify speed, capabilities, GUI)|
|system-generated incremental attribution models|
|user-selected fractional attribution models|
|campaigns||automated segment delivery (specify features? GUI?)|
|campaigns decision tree built with GUI interface (specify minimum features e.g. branches?)|
|campaigns with multiple channels in same campaign|
|campaigns with multiple waves in same campaign|
|predictive model-based message selection|
|real time trigger campaigns|
|rule-based message selection|
|personalize||personalize content (specify how e.g. variable substitution, rule-based dynamic content; specify channels)|
|Function||un- and semi-structured||real time||web||mobile||advertising||B2B||offline|
|ingest||JSON load||push to API to ingest continuous real-time data||capture non-click behaviors (time spent, scorring, page tags, product categories, etc.)||SDK for mobile load|
|ingest nested JSON objects||streaming load||extract UTM parameters||SDK: automatic collection of standard device attributes & location|
||convert unstructured to structured (feature extraction, etc.)||unknown to known conversion (keep history)||recognize devices and attach to customer ID||lead to account match||find name/address/company matches based on similarity|
|postal address standardization & cleaning|
||graph database (specify products?)||account/contact data structure|
|NoSQL database (specify?)|
|real time access (specify speed, capabiliteis)||SDK: push messages and in-app content to mobile||continuously recompute audience assignments|
|cookie synch w/DMP, DSP, etc.|
|DMP functions (specify)|
|ID synch with Google, Facebook, Liveramp|
|analytics||templates let marketer build & deploy web experiences without writing HTML, CSS, JS||include/exclude user segments for ad campaigns|
|manage display, social audiences|
|personalize||return recommendations in real time (specify speed?)||personalize on anonymous users (based on device, campaign, referrer, location, weather, history, etc.)||SDK for personalization (specify features)|
October 24, 2017
Tealium Talks Customer Data Platforms with CDP Institute Founder David Raab
By Julie Graham, Tealium
Tealium recently sponsored a whitepaper authored by David Raab on “Customer Data Platforms: How They Work, What They Solve & Why Everyone Needs One.” I had the pleasure of sitting down with David Raab, Founder of the Customer Data Platform Institute (CDPI), to get an in-depth look at the Institute, his inspiration and what excites him most about where CDPs are headed in the future.
Tell us a little bit about the CDPI. Why was it founded and what solutions is it providing for martech professionals?
The CDPI grew out of discussions with several CDP vendors who were finding that CDPs met many clients' needs, but few companies knew that CDPs were an option. We decided to set up the Institute to increase awareness and understanding of CDPs as a category. Our theory was that this would help the individual vendors more than doing vendor-specific promotions. To that end, the Institute has focused on educating martech professionals about customer data management requirements in general, use cases for unified customer data, what CDPs are, and how CDPs differ from other types of customer data systems.
Why do you think it’s taken so long for CDPs to emerge into the space when the need for them has always been so large?
Historically, the only data available about most customers was their name and what they bought. It wasn’t until the Internet that marketers could capture detailed customer information outside of purchase transactions. The value of that data in predicting behavior is huge and it’s what triggered marketers to start thinking seriously about building comprehensive customer databases.
CDPs emerged to meet that need after marketers first learned how to gather data from source systems (think: web analytics) and then created custom systems to unify it (think: data warehouses). Only after they had built those custom systems could they then build standard software products to do the same thing, which is a CDP. The emergence of “big data” technologies and cloud-based systems helped too.
That’s the technology side. From the customer side, demand for better treatment emerged as consumers got used to personalization from leaders like Amazon. Once they realized that was possible, they felt every brand should do it, which pressured marketers to deliver.
Digital transformation is a business initiative that most brands are now striving for – how do CDPs help in driving that highly sought after digital transformation?
Digital transformation is an interesting use case for a CDP because it extends beyond marketing to the entire organization. Like personalized marketing, many digital transformation initiatives rely on a complete view of each customer. A CDP can deliver that and is designed from the start to make the data available to any system.
What excites you about where CDPs are headed in the future and the capabilities they’ll have? How do you see CDPs continuing to evolve and innovate in providing even more exceptional experiences for customers?
It’s a very dynamic category. We see vendors expanding their capabilities, sometimes to add features available in other systems and sometimes headed into new territory. Some of the newer frontiers on the horizon for CDPs are:
- Very fast real-time processing, which could let a CDP support real-time personalization and even replace a DMP for ad bidding (which needs response time under 30 milliseconds vs 1 second response for conventional real time interactions).
- Integrated machine learning and predictive analytics that will help guide customer treatments and other decisions.
- New technologies, including artificial intelligence, to process unstructured data with minimum human effort. Ex: Adding new data feeds becomes easier.
- Special purpose CDPs, such as systems for particular industries or small businesses. Specialization lets companies provide features that are especially suited to a particular set of users. This further speeds deployment, reduces cost and adds value.
Beyond new features, I’m excited to see more vendors entering the space, greater understanding of CDPs by marketers and technologists, and more recognition by other marketing technology vendors that CDPs can increase the value their own products provide to clients.
The General Data Protection Regulation (GDPR) goes into effect in just 9 months and businesses are starting to scramble to ensure their compliance. How can a CDP help a brand with data governance, privacy and security?
GDPR has specific requirements for privacy by design, transparency, presenting data to consumers, propagating data changes throughout an organization, documenting consent for specific uses of specific data elements, and tracing how data is used. All of those require centralized systems that can scour the organization for customer data, bring together all of the data related to each individual, and track data changes over time. And that’s exactly what CDPs are designed to do. Beyond all of that, CDP vendors are experts on customer data, so they should become critical resources as the GDPR deadline comes closer.
Thank you David!
Read the entire whitepaper here.
Wanting to learn more about how CDPs work, their key benefits and how they integrate with other solutions within a martech stack? Learn more here.
October 19, 2017
How to Prospect for Gold in The Wild West of Customer Data
By Laura Patterson, VisionEdge Marketing, Inc.
“Why go to California? In that ridge lies more gold than man ever dreamt of. There’s millions in it.” At least that’s what Dr. M. F. Stephenson said in a futile attempt to convince the miners to remain in Georgia rather than to flock to California to chase adventure and what might be an impossible dream. And we’re here to tell you there’s gold in your own backyard, within your current customer information.
Dr. Peter Fader, author of Customer Centricity, defines customer-centric marketing as looking at a customer’s lifetime value and focusing your marketing efforts on the high-value customer segment in order to drive profits. Customer-centric marketing requires placing the customer at the center of your marketing strategy in order to create and extract customer value. It is the essence of enabling marketing to serve as a value creator.
Being able to achieve this capability takes true prospecting tools that can uncover customer gold. With so much data available today and more being created every minute, being able to tell real gold from fool’s gold takes more than a discerning eye. We can take a page out of the gold prospector’s handbook in order to do this well. Successful prospectors test several streams or veins, make notes about the various types of minerals they catch in their sifters, and then evaluate which locations produce the best results. If nothing turns up or if the pieces are too small then they change locations or practices. If they find a good amount they hope it indicates the presence of a “motherlode.”
The practice of prospecting for gold applies to marketing as well. Imagine the power of being able to separate your worst customers from the gold nugget customers! This entails evaluating and understanding the value of both new and existing customers in terms of what it takes to attract, acquire, keep and grow their value. And in time, you would be able to determine which “vein” has the potential to yield the most gold. As a result, you can be more efficient in your prospecting process and more effective in hitting the “motherlode.”
Defining the Solid Gold Customer
Before you begin mining your data, you first you need to know what a solid gold customer looks like. This means you need to know who your ideal customer is. This process takes answering questions such as: What businesses are they in? What problem do they need to solve? What process do they take to identify solutions? What channels do they use?
If you don’t know the answers to these questions, you may need to do some research. You want to be able to create an accurate picture of your solid gold customer, their spending habits, what they care about, and what they need from you.
How to Pan for The Solid Gold Customer
Once you can characterize your ideal customer it’s time to start mining data so you can answer questions such as:
- Are there enough of these customer to pursue?
- Where are they and how do they find us?
- What are their roles, profiles, personas?
- When, how and why did they purchase from us?
- What touchpoints and content do they use?
Always Prospect with A Treasure Map
So you think you’ve found the motherlode! Now you need a plan to uncover your treasure map. . This means you will need to craft a plan that will help you
- Demonstrate you understand and solve their problem.
- Produce a coherent, tailored, relevant, and compelling content across all channels and touchpoints they use.
- Sync the content, channels, and touch points with their buying process.
- Ensure all customer facing parts of your company are on the same page.
Turning your raw data of information into actionable customer insights and mapping the customer buying journey takes both process and analytics. But it’s the only way to prospect for gold. Learn more about transforming data into insights and treasure mapping the buying journey. The gold rush is back and knowing where to starting panning will make all the difference.
October 17, 2017
Maximizing Data Investments With Identity Resolution Strategies
By Julie Graham, Tealium
Customer data onboarding is a practice that makes a crucial connection between offline and known data on a customer so it can be leveraged as online audience segments within a marketers strategy. Having a unified view of a consumer, across all devices, can help marketers in their pursuit of personalized and relevant omnichannel campaigns.
Onboarding with the ultimate goal of customer identity resolution isn’t a one-size-fits all effort though, so marketers truly need to familiarize themselves with the critical steps of the process to develop specifications to best match their own engagement strategies and applications. In thinking about the specific steps one first must understand the types of onboarding solutions available to a marketer.
Tealium recently licensed a July 2017 Forrester report, “Making the Most of Customer Data Onboarding” that breaks down these 2 primary types of customer data onboarding options:
1. Direct onboarding – brands can do this through any of the major media and social networks like Facebook, Google, etc. Direct onboarding works by taking the uploaded data and matching it to the audience within that specific type of record. The resulting audience segment is then used in targeted advertising and measurement within the platforms’ respective properties and networks.
For example with Facebook a brand would upload customer data where the matched records are added to Facebook Custom Audiences for deployment into Facebook, Instagram and the Facebook Audience Network.
2. Third-party onboarding – brands can do this through the services offered by third-party vendors they’re working with such as LiveRamp, Neustar and Throtle. Third party onboarding works in that these vendor suppliers collect audience data from a network of data providers, typically hundreds of publishers who have registered users associated with cookies and devices. The resulting audience segments are then distributed for activation as targeted ads.
Maximize the impact of onboarding investments and download the entire July 2017 Forrester report, “Making the Most of Customer Data Onboarding” today!
October 13, 2017
How to Use a Customer Data Platform to Increase Your Facebook Engagement
By Samuel Carter, Fospha
As of July 2017, Facebook had 1.32 billion users log on daily – arguably making it the most powerful social media platform for marketers today.
Marketers are aware they should be using Facebook, and taking advantage of this super platform, but not everyone is sure how best to use it to generate results, conversions and revenue. Indeed, simply having a large audience on a particular day is not enough for marketing success. If marketers aren’t showing potential customers what they want to see, there is little chance of these potentials becoming actual, revenue-generating customers.
So how can marketers ensure they are pushing the right message out to the right people? The key lies in harnessing the power of their data, and for that, a Customer Data Platform is key.
Here are Fospha’s top reasons why a Customer Data Platform should be used to supercharge your Facebook marketing.
1. Enriched data
Advertising through Facebook typically relies on individuals giving away pieces of information about themselves. However, the social media platform tends to only collect generic demographic details – such as gender, age and location. Whilst this can be used for basic clustering and targeting – say to find females between the age of 18 and 24 – successful Facebook targeting is contingent on delivering an interesting message to your target audience. And, unfortunately, in this example it’s highly unlikely all females aged 18-24 will be interested in the same advert.
So how can you improve this?
A Customer Data Platform gathers, integrates and centralises customer data from various sources to create a rich, single customer view. This provides granular detail of where customers are in their journey, as well as what channels, devices and content they have interacted with. When this data is integrated with Facebook, marketers are provided with a wealth of information about their customers – so you can further segment customers based on interests, engagement or purchasing intent.
2. Clustering customers
As touched upon, clustering in Facebook is normally contingent on very basic data. However, with the richer data that a Customer Data Platform provides, marketers are able to cluster potential customers on the basis of highly specific behaviours. For instance, "has only ever read blogs on Facebook". Marketers can additionally segment customers even further, by combining behavioural and demographic information – to create clusters such as "females ages 18-24, who engaged with 3 Facebook articles on your website, got to check out but dropped out, two weeks later they clicked on a retargeting banner but dropped out again". This way of clustering enables you to targeting segments of individuals based on both their engagement and intent, meaning you can personalise to every aspect of their journey.
A Customer Data Platform strengthens Facebook clusters in this manner, with clusters being created either through simple drag-and-drop, or through complex machine learning and AI technologies. In both instances, marketers can be sure they are targeting highly specific groups of individuals who are much more likely to respond to their ad and purchase something from their website.
3. Finding customers
Another important feature of Facebook is its ability to deliver adverts to ‘look-a-like’ audiences – that is to potential customers who share the same traits as existing, high value customers. Considering the fact that the Customer Data Platform enriches the clustering of existing customers, it is a logical extension that it will enrich the building of look-a-like audiences. Indeed, rather than the granular customer data being used to target existing customers, this data is fed into Facebook so that audiences are built from this richer context to target new potential high value customers.
4. Increase customer engagement
When you are targeting the right people, with the right content, at the right time, you increase the probability that they will engage and ultimately convert. As a result of the rich data it provides, a Customer Data Platform is therefore instrumental in increasing customer engagement with your Facebook adverts. For instance, for one of our clients, we saw a 25% increase in click through rates when re-targeting old customers who had not engaged with the brand in 6 months, through Facebook. Click here to learn more!
5. Reduce costs but increase revenue
With standard Facebook clustering and targeting, you may get potential customers clicking your ads – but that does not necessarily mean they will want to purchase your product and convert – a reflection of the fact that you are targeting a much wider, generic audience. However, when you take advantage of a Customer Data Platform to supercharge your clustering and audience building capabilities, you will begin to find that – even though you are serving to a much smaller, defined audience – the customers who are clicking are more likely to convert; a study by Marketo (2016) found that over 78% of consumers will only engage offers if they have been personalised to their previous engagements with a brand. So, although your overall clicks may be reduced, overall revenue will increase.
Clearly, Facebook is a marketing channel that is here to stay. And marketers should definitely be taking full advantage of the opportunities it offers. However, a Customer Data Platform is becoming key to making the most of this platform. If you would like more information on how Fospha integrate their Customer Data Platform with Facebook, click here.
October 11, 2017
A Primer To Channel Orchestration
By Subra Krishnan, Vizury
Channel orchestration is a technique by which marketers can intelligently sequence different marketing channels for a given segment of users to maximize both positive user experience and also user engagement.
Most marketing clouds/technologies offer features like “Journeys” etc. These allow the marketer to create a sequenced multi-channel campaign. E.g. you can start off by shooting an email to a segment, wait for 48 hours, look at respondents. And then to the remaining users, send an SMS. And so on.
This approach has one major limitation. It does not account for an individual user’s channel responses and preferences. And therefore pushes message at every level of the journey to a large number of users.
Let’s take the example of two bank customers.
Sam is active on the bank website. He visits the bank website multiple times every week to do various NetBanking transactions. Also, the bank has sent him at least 10 promotional emails in the last three months, none of which he has opened. Also, Sam is frequently present on Facebook, but has never clicked on a NewsFeed ad though the bank has shown multiple ads.
If the bank wants to send a message to Sam, the ideal channel sequence is: Try the bank Website. Then target him on Facebook. Email is the least preferred channel.
Anna, on the other hand, does not visit the bank website more than once a month. However, she has opened three of the 10 emails sent to her in the last three months. She has also signed in for the bank’s browser push notifications and has clicked each and every one of the three push notifications she has received. She is rarely on Facebook.
For Anna, the ideal channel sequence is Browser Push, then Email, then Website. Facebook is probably not a good idea and should be the last resort for Anna.
With user level orchestration, marketers can ensure that both Sam and Anna experience a unique journey most consistent with their channel responses. This has two major benefits.
- The most obvious benefit is in the form of increased engagement. By ensuring users get targeted across channels where they are most likely to engage in, marketers are getting the best engagement in the most efficient way possible. In fact, Vizury’s work with a Top 5 global bank showed that there is a 2X efficiency that can be achieved.
- As you dig deeper into orchestrated experiences vs. standard journeys, there are major cost benefits too. Traditionally adtech (paid media) and martech (owned media) have operated in silos. The same set of users could be getting targeted across both these channels for the same message. This creates a pretty large cost inefficiency. In the example above, neither Sam nor Anna actually need to be targeted on Facebook, which is a paid channel, till owned media channels are all used up. Orchestration allows you to intelligently sequence your paid media channels after free media channels.
Increasingly, the fragmentation that used to exist in the digital world of your users is vanishing. And as they do, channels become “dumb” end points. User data, as well as channel selection, becomes centralized. Algorithms overlaid on this central data platform can take intelligent decisions on orchestration. This is the next big frontier in marketing that you should be aware of and tap into.
And the best part? In the next blog post, I will delve into how you can organize your best of breed marketing software stack to achieve orchestration.
To know more about Channel Orchestration and its use cases relevant to your business, please feel free to reach out to me at email@example.com
October 5, 2017
Consumer Views on Data Privacy: What Brands Need To Know
By Julie Graham, Tealium
The European Union’s General Data Protection Regulation (GDPR) enforcement begins in May 2018 and organizations involved in the control and processing of personal data about EU citizens will need to review their strategy, policies and procedures to ensure compliance before that time. And GDPR isn’t just affecting businesses – EU consumers are becoming more aware of this new set of rights which they have been granted by GDPR.
DataIQ, in association with Tealium, recently surveyed consumers to better understand their perspective on data collection, consent, context and control to create an extensive report of the findings in the publication “General Data Protection Regulation 2017: Identifying the Impact on Marketers and the Consumer’s Moment of Truth.” We’ve compiled the key distinctions from the report to show brands how aware consumers are of the way data is collected from their digital footprint as well as their perspective and general attitude around it.
Findings on Sharing Personal Information
Consumer attitudes towards sharing their personal information have become significantly more positive:
- For every 1 person who says they prefer not to share (36%), there are 2 who are either happy to if the need is explained (42%) or are happy to share if they trust the company (21%)
- By contrast, 6 out of 10 are only vaguely aware (24%) or not aware at all (38%)
Key Findings on App-Tracking
- 4 out of 10 consumers (39.4%) say they would prefer not to be tracked online and by apps and avoid opting-in (as is their right under the ePrivacy Directive)
- Nearly 1 in 6 (17.2%) say they avoid sites and apps which they know are tracking them
Key Findings on Personalization
Consumers notice when their devices and the services they are accessing seem to reflect who they are:
- For nearly 2/3 (64.4%), it is awareness of their location which is most evident
- Consumers rate personalized services most highly which are based around convenience:
- Autofill (3 out of 5)
- Personalized offers (2.93)
- Personalized content (2.92)
- Interest-based content (2.91)
- Being recognized by the brand (2.91)
- Location-aware services scored lowest with a score of just 2.59
- Half of consumers (48.7%) adopt a rational attitude that personalization is ok if they have a choice
- However, negative feelings are expressed by a significant minority of 4 in 10 consumers, with 1 in 10 (10.2%) saying personalization feels creepy if taken too far, 15.5% feeling worried, but unable to do anything about it, and 17% maintaining that they dislike personalization.
Key Findings on Ad Blocking Software
- 1/3 (36%) of consumers already make use of ad blocking software and more than half (55%) are considering it
- However, while using a private browser window may have the same effect as ad blocking software, only one quarter of consumers (25%) currently make use of this option.
These key findings and the entire report show that consumers are more aware than ever about how their digital activity is leveraged to obtain data and how it is used to build tailored experiences from the brands they’re engaging with. Though a willingness to share data is on the rise (nearly ⅔ of all consumers are happy to share personal information), this willingness is dependent on a clear understanding of the purposes for which that data is being requested, the choices available to the consumer and/or their trust in the brand.
Take advantage of these consumer views and insights on data privacy and download the entire report “General Data Protection Regulation 2017: Identifying the Impact on Marketers and the Consumer’s Moment of Truth" today!
September 29, 2017
First Ever Customer Data Platform Summit at Shop.org 2017
By Karen Wood, AgilOne
This week, AgilOne hosted our Customer Data Platform Summit – the first conference of its kind focused entirely on Customer Data Platforms (CDPs) and how enterprise B2C brands can benefit from them. The action-packed half-day event was held at the Los Angeles Convention Center in partnership with Shop.org. Executives and practitioners from over 80 brands registered and attended the CDP Summit.
We were fortunate to have some of today’s hottest trendsetters and industry leaders presenting at the event. They shared inspiring success stories, innovative ideas, and tales from the trenches about managing customer data and leveraging it across systems of engagement. Our speaker roster included:
• Steve Miller, VP Marketing and Business Development, JOANN Stores
• Michelle Kelly, CEO, Lilly Pulitzer
• Carla Rummo, VP of Direct Marketing and Ecommerce, Serena & Lily
• Jeremy Muras, Digital Director, Lion Capital
• Julian Chu, Digital Marketing Executive, Castanea Partners
Highlights from the Sessions
The Customer Data Platform Summit featured four deep-dive presentations and a panel from a mix of speakers. Here’s an overview of each of these sessions.
The Rise of Customer Data Platforms for Enterprise Retailers, presented by David Raab, David shared his insights into the rapidly growing Customer Data Platform market, including how he sees the CDP market meeting omni-channel retailer needs, and where the market will evolve over the next year and beyond.
How to Implement a Customer Data Platform for Immediate and Long-Term Success, presented by Steve Miller Steve talked about the steps JOANN took toward choosing a CDP approach, selecting a CDP, and implementing a CDP that would deliver quick wins and future-proof JOANN for long-term success in stores and across digital channels.
Using Analytical Insights to Reveal Who Your Customers Really Are, presented by Carla Rummo Carla discussed how Serena & Lily used reporting to gain a comprehensive understanding of customers and to inform segmentation strategy.
Data-Driven Decisions and Organizational Mindset in an Omni-Channel World, presented by Michelle Kelly Michelle explored how data can be a change agent across the entire organization, and shared Lilly Pulitzer’s experience with leveraging insights from a Customer Data Platform to transform decision-making in many key areas of the business.
Interactive Panel: Customer-Centric Personalization for Retail Brands Michelle Kelly, Carla Rummo, Jeremy Muras, and Julian Chu discussed pressing issues pinpointed by the Customer Data Platform Institute, including: What is the best approach for managing customer data? How can marketing insights feed into the rest of the business? What is the best way to leverage data from stores?
AgilOne’s “Unsummit” – Roundtable Discussions for All Participants
One of the goals of the event was to facilitate meaningful conversations among the marketing executives and practitioners in attendance. The middle part of the day featured two back-to-back “Unsummit” roundtables. Here’s how it worked: Attendees were instructed to choose two topics of their choice from a list of five. Then, they were assigned to the roundtable matching their preferred topic, where a discussion leader had background information prepared and 5-10 discussion questions.
The room came alive with lively dialogue across all the tables – impassioned statements, pressing questions, productive conversations. Here were the five topics attendees discussed:
How First Party Customer Data Can Help with Acquisition/Adtech Use Cases Adtech systems are quickly becoming customer addressable, such as with Google Customer match, Facebook Custom Audiences, Criteo and DMPs, plus onboarding companies making first party data available. This allows marketers to targeting media using actual customers rather than broad demographics. Google claims Customer Match will be their biggest focus for 2017. For example, we found that using Facebook custom audiences reduces acquisition cost by 50%, using Criteo matching reduces media costs by 20%, and increases conversion rates by 2x. These are individual examples, but we'd love to learn from your experience or thinking.
Real-Time Use Cases Driven by Customer Data Platforms Real-Time is a buzzword in customer experience discussions. Depending on the context, some may be referring to near-real-time experiences also by this term. Let's discuss the real-time use cases that are driven by the Customer Data Platforms. What are the realistic use cases driven by your CDP, which are already implemented by you? Which use cases do you expect to be realistic in the next 6-12 months? Which use cases will be realistic in a longer-term time horizon? And how do you measure the effectiveness of these use cases?
Getting the Most from Store Promotions One of the best ways to build awareness of your brand, engage your existing customers and attract new business is through event marketing. But figuring out what kinds of events to host and how to organize them can be daunting. The challenge to planning and pulling off successful marketing events is to make sure they grow organically out of your business. You don't want to tack an event onto your brand without giving any consideration to what your brand means, who your customers are and why you're hosting the event in the first place.
Solving for Issues Today and Tomorrow 66% of retailers agree that results will continue to erode unless they find a way to incorporate technology as part of their store experience (Forrester). Close to 50% of sales start with mobile search/information gathering. 80% of Internet users have smartphones, 40% read their email on mobile. Mobile provides location data crucial to store/geo-based engagement marketing. How are you planning to solve for trends such as the Internet of Things and other future issues and use cases?
Creating Urgency Within Your Organization to Set up a Customer Data Platform One of the challenges in setting up a Customer Data Platform is getting your organization behind the initiative. A person seeing the value of a customer data platform needs to get support from members of your marketing team, executives of the company and IT staff. Once everyone is on board, a person needs to create urgency around a CDP project that will benefit the business. But customer data lives in many more places than just marketing. Companies struggle as a whole with aligning around customer data, centralizing data, and leveraging it for use cases that go beyond what marketers need.
Stay Tuned for More
Now that we have the first Customer Data Platform Summit under our belt, get ready for more! We plan to replicate this template in more cities to come.
September 19, 2017
Achieving True Cross-Channel Personalization: “Live” & “Push” Channels
By Vijay Chittoor, Blueshift
Customer Data Platforms are the key to achieving Cross-Channel Personalization and Orchestration. One of the key roles of CDPs is to help unify customer data from multiple silos, and create a living breathing customer profile. An equally important role is to enable marketers to “activate” that data, in the form of personalized campaigns across all channels.
As we think harder about this opportunity for true cross-channel personalization, we must keep in mind that marketing channels come in two flavors:
- “Push” Channels: This includes channels like email, direct mail, SMS and app-notifications. The interaction is asynchronous, where the customer may open or view the message at a different time than when it’s “served” or sent. The number of “sends” per customer is typically limited to a handful or less per day across all the messaging channels. Most Push Channels involve known customers, albeit with different identifiers (email addresses, device tokens, phone numbers or mailing addresses). The customer identifier remains largely static over time.
- “Live” Channels”: This includes owned channels like your website, in-app content, as well as paid channels like Display Advertising or Facebook. These channels are synchronous in their interaction, and the content is consumed as it’s being served. The volume of impressions on Live channels can be huge for highly interactive sites: customers might consume tens of page views in a single session. On owned Live channels, customers may go back and forth between between being known (logged in) and anonymous. The anonymous behavior could extend across multiple browsers, and cookies could change over time. On Paid Live Channels, identifiers have to be synced with 3rd parties who own the media inventory, or with middlemen in the ecosystem.
Traditional Barriers to Cross-Channel Personalization across Push & Live
There are some key differences in how these channels operate, creating barriers to true Cross-Channel personalization:
- Organizational: Typically, ownership of these channels is split between multiple teams. The Push Channels are the responsibility of a “CRM” team or equivalent. This team almost always belongs in the marketing organization. The Paid Live Channels are typically the responsibility of a Media Buying team, also within the marketing organization. However, the Owned Live Channels (websites, apps) are typically the responsibility of a Product team with some input from Marketing. ►Because of these organizational differences, teams typically end up using technologies with different views of customer data.
- Technology: Given the synchronous versus asynchronous nature of the channels, the different identifiers, as well as the different latency and throughput requirements, the technology required for these channels is often different. ►Because of these technology differences, legacy applications have typically served only one type of channels (either Live or Push), not both, further accentuating the silos.
The holy grail for cross-channel personalization is to place the customer at the center of the experience. In order to enable this, CDPs must not only unify customer data and customer identity, but also break the mold of legacy applications in being able to serve both Live and Push use cases.
A New Architecture for Cross-Channel Personalization
Unlike legacy applications, the key to achieving this holy grail is a micro-services based technology architecture that can both consume fast streams of data, as well as enable high velocity personalization. Each micro-service could then be optimized for its needs (e.g. low latency, high throughput) using a custom data store that best fits the needs. The micro-services would interact with each other to ensure that every element of customer experience is personalized using the full 360-degree view of customer data.
- The core cross-channel pieces of personalization, including “segmentation”, “dynamic content”, “content recommendations”, “measurement and attribution” each deserve their own micro-services optimized for the separate needs.
- The campaign execution framework would consist of a set of separate micro-services, optimized for Push and Live channels.
That is why we are excited to announce today that Blueshift’s CDP is adding support for Live Personalization in addition to Push Channels, enabled by a modern architecture. Blueshift customer Tradera, a division of Paypal, has seen significant advantages in using unified customer data & AI on websites & apps in addition to using it on Push marketing applications (email, mobile app notifications and SMS). “Blueshift has really changed how our marketing team as well as our entire company as a whole work - now we are able to create real value for our customers and business rapidly”, said Ferraud Norberg, head of marketing at Tradera.
Additionally, Tradera has seen substantial lift in revenue and conversion rates with Live Personalization, according to Norberg. Compared to previously deployed versions of manually recommended content, clicks per session was up 80%. Out of all sessions ending in a click on the recommended content, gross sales (GMV) per session was up by 125%.
Cross-channel personalization is no longer a pipe dream. Modern architectures and AI-Powered personalization make it a reality.
September 13, 2017
More on Classifying CDP Systems
By David Raab, CDP Institute
Last week’s post on classifying CDP systems yielded many insightful comments, both public and private. A few of the themes worth noting were:
- purpose of the exercise. Let’s start at the beginning: the goal of this exercise is to find a way to reduce confusion in the market about the many different types of CDPs. Both buyers and sellers suffer when marketers can’t easily figure out what different systems are good for, and thus which they should explore in detail as solutions to their particular needs. We're not trying to gather all the information a marketer needs to make a choice, but rather to help marketers quickly narrow the field of products they consider. If we don’t do that, the marketers will soon decide the CDP label doesn’t mean anything useful and the category as a whole will suffer. That would be a loss since CDPs do provide capabilities that marketers might not otherwise be able to find, either because CDPs would be lumped into still broader categories (e.g. “data management”) or be ignored in favor of less suitable solutions that are more clearly defined (“marketing clouds”).
- type of information. Because the purpose is to help marketers understand roughly which systems are good for which purposes, we need to keep the whole scheme relatively simple. Yes, it’s possible to rate each vendor against a 400-point checklist (in fact, the Evaluation Guides in the Institute Library contain just that). But such reports are not useful to marketers just starting their selection process. Instead, they need a handful of items that help them to quickly understand the vendor landscape before they dive into the details.
- technology vs applications. Should a classification scheme be based on system technology or on applications? There’s no question that marketers buy applications. Most neither know nor care what goes on under the hood. But suitability for use cases is an extremely subjective measure. Many systems can be stretched to support use cases they’re not really designed for. If one of our goals is to find a classification system that vendors are willing to apply (or let the Institute apply), we need something that much less open to debate. Specific technical features are objectively present or not, although there are still plenty of nuances. Technical features also tend to reflect a system’s original design goals, which are themselves usually applications. So there’s a broad correlation between technology and applications, which should be enough to guide buyers in the right directions. Moreover, a few critical technical features will correlate with many capabilities. So if we pick wisely, we should be able to keep the amount of information at an easily digestible level.
- vendors in multiple categories. There was some confusion among readers but my intent in the original post was that one vendor could belong to several categories. It still is. This could be true whether the attributes relate to technology or to applications: some technologies are compatible without being mutually required; similarly, some applications are independent of each other. More concretely, a system that’s good at resolving postal addresses might or might not also be good at cross-device matching. Each task uses technologies that are different but don’t conflict. Marketers who need one, the other, or both types of matching could look at systems with the right technologies to quickly find the ones that might meet their needs (or, more to the point, to exclude systems that don’t meet their needs). It’s true that vendors will want to be in as many categories as possible, even if they just marginally qualify. That’s where using relatively objective technical criteria makes things so much easier. So I think it’s a manageable problem.
So far so good, but this is just warming up to face the real question: what should the categories be? I proposed one set last week but didn’t consider it final. The feedback from that post included a good number of recommendations for other categories and ways of organizing the categories. I started from scratch by listing the items suggested and seeing what clusters felt natural. Three dimensions emerged these were:
- activity supported. Some items related to building the unified customer database itself, some to sharing the assembled data, some to analyzing it, and some to selecting customer treatments (a.k.a. campaign management, decisions, or orchestration). Those felt like distinct enough categories to be treated separately, even though there’s some overlap, especially between analysis and treatment selection. There were yet other items that related to vendors, such as pricing and services provided. Rather than lose them, I gave them a category of their own.
- data type. There were some items that clearly related to online data, such as capturing Web or mobile transactions. There were others that related to “big data”, such as use of NoSQL technology. A couple more related specifically to B2B or offline data. The rest seemed to apply to pretty much everyone so I not-very-creatively labeled them “general”.
- specificity. Remember that my strategy is to find specific items that can easily and objectively be judged as present or not. About half the items fell into this category, either inherently or because we could add specifics such as having prebuilt connectors to certain key systems.
A table with the results is at the end of this post. I’ve highlighted the different categories to make things a little easier to see. A few observations:
- nearly half the items relate to building the database. That’s good. After all, we're talking about Customer Data Platforms, so the differences in how they handle data is our main concern.
- Most of the variations in data type also fall into the data category. Again, that’s good news since it means we have a good chance of finding markers to identify the different types of systems.
- Nearly all the data build items are specific. That's great. The sharing category is also in pretty good shape. Analyze and treat are mostly general, but those are categories where not every CDP competes, so it should be easier to make broad judgements about who’s in. Vendor categories are really outside the scope of this exercise. Buyers can screen on them after they’ve figured out which systems meet their technical / functional needs.
- Excluding the vendor group, there are ten unique combinations of categories between the first two columns (build/B2B, build/big data, build/online, etc.). Those could become ten clusters. No cluster has more than eight line items and most have fewer, which is quite manageable. The total of 48 rows is a few more than I wanted but we could probably get rid of some. All very promising.
These clusters don’t match the ones I offered last week. That's perfectly fine. Nor do I think this is the optimal set. But I think it’s moving the discussion forward. The next step is to get more feedback from the CDP community (that’s you) and to start tightening up the definitions of the individual items so we can make the data collection and presentation as straightforward as possible.
I look forward to hearing your thoughts.
September 6, 2017
How to Classify CDP Vendors
by David Raab, CDP Institute
Gartner’s latest Hype Cycle (August 2017) showed Customer Data Platforms moving from early innovation to near the top, a.k.a. “peak of inflated expectations.” That’s good news in some ways, although it also implies the next step is a slide down the “trough of disillusionment”. Sadly, that is a reasonable expectation: CDP has now attracted enough attention that we can expect to hear about bad experiences from companies who bought a CDP without understanding what to expect or preparing properly for deployment. As the hype cycle makes clear by its very existence, this is a normal though unpleasant stage in a category’s development.
The Gartner report also noted the confusion created by the variety of vendors using the CDP label. This is something we worry about a lot at the CDP Institute. The context is often a suggestion that we tighten our definition of CDP to exclude vendors without particular capabilities.
The idea has its attractions: in particular, it would give CDP buyers a clearer idea of what to expect. But I’ve so far chosen to take a “big tent” approach of including any vendor who meets the basic CDP requirements (marketer-driven, unified persistent customer database, shareable data) and has CDP as its primary product. (That last constraint excludes agencies and multi-product software vendors.) Arguments in favor of the more inclusive policy include:
- a wide variety of CDP configurations lets CDPs meet a wide variety of user needs. This means it’s more likely that marketers can find a CDP that is right for them. It’s better for everyone if marketers enter the CDP department store and find the right booth, than if they search randomly through the entire shopping district.
- CDP vendors came from many backgrounds but are becoming more similar over time. This is a normal development in any software category, as companies learn from each other and from customer needs. It’s true there’s a limit to how much most products can change without being fundamentally reengineered (which rarely happens). But many of the features cited as missing from some CDP products can be added without a full rebuild.
- vendors will use whatever terms they please. If companies find the CDP label attracts a productive stream of prospects, they’ll call themselves CDPs whether or not the Institute agrees. Maybe we could create a “seal of approval” that would carry some weight (more on that in a minute) but I’ve no illusions about our ultimate market power.
- more Institute sponsors means more Institute resources. It would be foolish to deny that growing the Institute has some appeal on its own. But more resources also lets us do more research, speak at more events, publish more papers, and do other things that educate buyers and promote the category. We’ve purposely kept the sponsorship fees low so we could attract many sponsors rather than be dependent on a few. That means expansion depends in good part on adding sponsors.
Nevertheless, it’s still clear that the variety of CDP vendors does create confusion about the category. The Institute has spent much of the past year working to clarify how CDPs different from other types of software that manage customer data. We’ve summarized that in the following chart and Venn diagram, used in many presentations:
I think our next step is to work on establishing a similar understanding of differences between different types of CDPs. I’ve recently taken a tentative first step by defining clusters of features that belong to different subsets of the CDP universe. All CDPs support the first cluster, which is the core CDP definition itself. Many support at least one additional cluster and some support several. That means the clusters are more like building blocks than mutually exclusive categories. (In plainer terms: one CDP could fit several categories.)
You can also think of the clusters as checklist items – so once buyers know which vendors fit which clusters, they can match the clusters against their needs and have a relatively simple way to find the subset of CDPs that are worth exploring in detail. To continue our department store analogy, the clusters would be the signs pointing shoppers to the different departments. It’s still up to the buyers to look through the wares in each department to make a final choice.
It should still be obvious that vendors who qualify for a given cluster will still have major differences. And, of course, vendors would have every reason to claim they should be listed in a cluster as many clusters as possible. But it’s just possible that the CDP Institute carries enough authority that vendors would be willing to let us classify them – based on objective criteria, rigorously applied, of course. If we made agreeing to this a condition of Institute sponsorship, we might lose a few sponsors but I think the industry as a whole – including buyers – would be well served.
What do you think?
August 28, 2017
How a Customer Data Platform Can Unlock +30% ROI in Paid Search
By Sam Carter, Fospha
The customer journey is becoming increasingly complex – with customers using multiple devices, channels and sessions in their path to purchase. Often, however, information about a customers’ journey is siloed – meaning marketers can only see each of these interactions in isolation, and lack visibility on cross-channel cost and revenue. As a result, they lack the single customer view that is necessary to attribute revenue (and cost) to each step in the customer journey, and struggle to optimise the cost of customer acquisition, retention and lifetime value.
We often find that this problem in greatest in Google – which is rapidly becoming the most powerful digital marketing channel, seeing over 2.3 million searches per day. With all these interactions, marketers are now paying a premium to get their brand noticed, reflected in the fact that pay-per-click advertising has nearly doubled over the past three years.
Customer Data Platforms are the foundation from which marketers can solve the challenges of gaining visibility on their paid search activities, and overcoming the rising costs of customer acquisition and retention in this channel.
1. Data Integration
Many businesses continue to struggle with optimising their marketing campaigns. The simple fact is that they do not have a grasp on their data - and lack the single customer view that is necessary to effective marketing channel attribution.
A Customer Data Platform alleviates this challenge by gathering, integrating and centralising customer data from various sources to provide a single customer view. Data sources are uniquely tagged in order to ensure that customer data is being captured at every possible stage of the customer journey. These sources are then integrated in order to provide a cohesive view of a single customer’s journey to purchase. For instance, website visits, email opens, and app interactions can be integrated with things like paid search costs and revenue.
The result is a stitched customer journey and single customer view that accurately displays a customer’s path to purchase, across a myriad of marketing channels, sessions and devices.
2. Data-driven Attribution
This data forms the basis for data-driven attribution modelling. On a basic level, this model identifies a set of unique user events that contribute to in some manner to a conversion, and assigns an actual, accurate value and cost to each of these events. This means you are able to truly understand the revenue generated from individual channels, and helps marketers understand whether they are acting in a cost effective manner.
Our attribution modelling works within our Customer Data Platform, and calls on the Markov model, which provides the most accurate value for each event through algorithmic probability, and which continuously learns from customers – making it more powerful every day.
With paid search, marketers are often unable to tell which keywords play a role in each step of the customer journey, and which don’t. However, data-driven attribution provides visibility on high and low performing keywords and campaigns. With this knowledge, marketers can cut or reinvest spending from keywords that are not bringing in conversions, and are therefore not showing high ROI. It solves the age old problem of knowing that half of your keywords don’t bring in revenue, but not knowing which. Without the rich, granular data that a Customer Data Platform provides, marketers are making these decisions based on emotion – and therefore risk not making the most optimal choices. Evincing this, we have seen a client make a 30% increase in their ROI by cutting wasteful keywords in this manner.
3. Optimise at Scale
Whilst valuable insights can be gained from using a Customer Data Platform and data-driven, multi-touch attribution, the level of granularity that these systems go into can result in several thousand rows of data being outputted. Whilst all of these contain valuable information about marketing activities, the scale of data can be difficult to manually optimise in real time – that it, it is difficult for a single marketer to employ all of the necessary, ROI boosting changes. Integration with a bid management platform is therefore key to scaling your paid search optimisation. Combining the power of the Customer Data Platform to discover high and low performing keywords, with the automation of bid management platforms, enables spend on poorly performing keywords to be quickly reallocated – automatically optimising your paid search. Alongside this, the need for manual keyword inputs and segmenting is eliminated with this integrated – with the Customer Data Platform acting in real-time in response to customers’ interactions with your keywords.
Once you have taken these steps to optimise your paid search channels, you are left with a Customer Data Platform that is the foundation for broader, cross-channel marketing optimisation. This means that you can begin to tackle other priority channels – to reduce costs and boost ROI – simply by integrating that data source into your Customer Data Platform and applying the same data-driven attribution modelling.
August 21, 2017
What is the Business Value of Connected Data?
By Carol Wolicki, RedPoint Global
The concept of “connected data” is a simple one. It means that all data across the enterprise is stitched together, cleansed, matched, and made available through a central location that has context and interrelationships pre-determined. Connecting data through a central location is the first step toward constructing a “golden record” for a customer, product, or service.
The perils in connecting customer data are particularly striking for two reasons: customers generate extensive amounts of data, with varying structures at different cadences, and this data is created through multiple online and offline engagement touchpoints. Competing in a digitally connected world calls for a central point of data control, which is where Customer Data Platforms (CDPs) fit in.
Why Connected Data Matters
Connected data is a foundational concept for highly personalized and contextually relevant engagement in today’s digital world. Without connecting data across systems, brands can’t hope to understand their customers well enough to provide the right message. A Customer Data Platform is the foundation for ingesting the data, connecting it, and making it accessible throughout the enterprise.
This single point of data control creates the right conditions to construct, continually update, and provide the unified customer profile that is so crucial to fostering omnichannel customer engagement. Whether the data comes from the CRM, email service provider, web analytics, social media, or the Internet of Things (IoT), all interaction data can be stitched together and made visible through a unified view. The unified view of the customer allows the enterprise to understand customer behaviors, preferences, and trends across touchpoints throughout the entire customer lifecycle. This comprehensive single customer view can also drive advanced analytics and real-time engagement flows, personalizing interactions with consumers in contextually relevant ways to close the engagement gap.
Connect Data with a Customer Data Platform
Customer Data Platforms are a dramatic reversal of the way enterprise data has historically been managed. Businesses for centuries found it easier to divide their activities and the resulting data among specialists. Those functions split into channels such as call centers, stores, internet, email, etc. Unfortunately, because the modern consumer has so many options for when and where to engage, those siloed channels create impediments to engagement.
As a result of this channel balkanization, the customer experience has taken a beating. In a Customer Experience Impact Survey, 86 percent of customers said they would willingly pay more money for a better experience. Yet only one percent of customers felt brands met their expectations. Brands have failed to close this customer engagement “gap” in large part because most companies still leverage siloed data generated from a fragmented technology stack. Connected data counteracts this siloed approach to technology investment, arming brands with the right information, at the right time, to close the customer engagement gap and forge powerful customer experiences.
Connected Data is the Future
The volume of data every enterprise must contend with has exploded. Recent research from Northeastern University found that the amount of data in the world will grow to 44 zettabytes by 2020, with 2.5 exabytes of new data generated every single day. The sheer scale of data created across the enterprise is enormous, and most brands have it all locked down tight in functional silos … inaccessible to everyone who doesn’t work in that silo.
With the rise of digital engagement, including IoT and all the device data it entails, it’s becoming less and less viable to operate with siloed data. Customers increasingly leave brands that are unwilling or unable to provide relevant experiences or act in a customer-centric manner. Connected data allows marketers and other stakeholders to know their customers more closely and more completely than ever before. There is tremendous potential value in creating a connected data environment, and the brands that recognize that will be the ones who remain competitive in the age of the empowered customer.
August 8, 2017
But, Do I Need Both? The CDP vs. DMP Discussion Continues: A Choose Your Own Adventure Story
By Cory Munchbach, BlueConic
Without question, our best-read content is about the differences between CDPs and DMPs. More than a year since we published the original blog post on the topic and coming up on a year since we consolidated it all into an ebook, it’s time to advance the discussion further. Yes, we still get asked all the time, “What’s the difference between a CDP and a DMP?” but now, more often than not, folks have a firmer grasp on the differences and want to know:
Okay, but do I need both?
And though it might be everyone’s least favorite answer…
In an effort to provide a more satisfactory response, though, we’ve put together some general guidelines for what your life looks like with a CDP, with DMP, and with both. (Note that we’re using BlueConic as the basis for the CDP capability and painting with a bit broader brush on the DMP side of things. Since not all DMPs are created equal, if you have specific questions about a particular DMP, we can go deeper based on our clients’ experiences.) To begin, here are the critical points of comparison:
Anonymous data management
Anonymous data storage; in other words, data that is collected from someone who may eventually become identified or known but isn’t right now/yet.
Anonymized data is data that has an identifier associated with it, but that PII has been specifically hashed or otherwise de-identified
Data sources (more on this here)
First party orientation: collecting data from all of the brand’s touchpoints and data sources to tie them to actual behaviors exhibited by a unique user, whether anonymous or known
Third party orientation: the data-set is based on millions of cookies from across the web and matched with non-PII attributes such as demographics and multi-site behaviors
Data expiration and storage
The profile created for each user is stored persistently in a database (Apache Cassandra in our case), so the data doesn’t expire and your profiles have no limit to the amount of data you can store in them, giving you a progressively richer view as the person continues to engage
Using the cookie for data collection means that a DMP “profile” typically only lasts for 90 days before it expires (sometimes shorter, and sometimes longer as some DMP vendors offer additional storage for an additional expense)
Identity matching (more on this here)
Deterministic: BlueConic only matches and merges profiles based on a unique identifier (email address, subscriber number, etc.)
Probabilistic: Because DMPs were built to expand advertising reach, the segments and matching are typically based on algorithmically derived guesses about links between people for look-alike modeling and high-level personalization
Real time, no minimum size:
When the marketer defines a segment in BlueConic, it is immediately active, so the profiles that match the segment’s criteria associate and disassociate in real time (24 milliseconds) as their attributes change. There also does not need to be a minimum number of people in a segment for it to be a viable definition.
24 hour processing, minimum number of people: Once a segment is created in Krux, it takes 24 hours before it becomes usable for ad targeting, etc. Each segment needs to have at least 20 people in it because the algorithms require at least that number to still have a target cohort once you assume 40-60% matching.
So do you need both? Potentially, yes, but perhaps not. As a marketer, a CDP without question gives you a better base of data to work with: it’s a dataset of your actual audience that can be activated for use cases across the entire life cycle: acquisition, engagement, conversion, and retention. There aren’t restrictions on the types of data you can collect, the segmentation is real-time, and it’s channel agnostic to ensure the seamless flow of data. If you want to:
- Collect first party data for anonymous and known individuals;
- Assemble segments in a few clicks that can be exchanged with any other system;
- Deliver personalization across channels to that individual based on their unique needs and context…
Then you don’t need a DMP. And in fact, will be underwhelmed by the DMP’s approach to doing this kind of thing where such functionality appears to exist.
However! If you have an extensive digital advertising and modeling program that relies on the DMP to:
- Find look-alike models elsewhere on the web, particularly on other companies’ pages;
- Gather data about target audiences’ interests based on their behavior on 3rd party sites;
- Align campaign messaging with specific audiences as they move throughout the internet…
Then you have good reason to keep using a DMP because you’re benefiting from what these solutions are built to do: get more of the right people interested in your brand.
Taken together, a CDP + DMP can unlock some pretty compelling use cases, including those that we’re currently running with many of our customers who:
- Pass the third party segmentation in the DMP to BlueConic for on-site personalization and distribution to other systems readily
- Use BlueConic segments as the basis for the DMP’s look-alike modeling so that the brand’s actual audience’s traits provide the starting point of a look-alike audience
- Make BlueConic the conduit for data into the DMP from a CRM, ESP, or POS (hashing the identifiable data to comply with the DMP’s privacy, of course) so that the DMP has access to some of the data in those systems
(Additionally, server-to-server integrations between BlueConic and DMPs 1) provide data about users who haven't even been to the website, 2) provide data that was layered in after the fact, and 3) assign segmentation based on rules that weren't there when a user was online, or that changed after they left.)
The scenarios above play out differently based on your industry-specific use cases as well, but we’ll save that for another time. Unless you can’t wait, in which case shoot us a note to chat.
August 2, 2017
Five Things To Look For In A Customer Data Platform (CDP)
By Julia Farina, Lytics
Background: What Is A CDP?
The 2017 edition of Scott Brinker’s iconic Martech Landscape Supergraphic revealed that there are more than 5,000 marketing technology tools on the market today. While the customer data platform (CDP) is a relatively new category to the scene, the need for a neutral hub in the middle of marketers’ tech tools has taken off. In fact, according to a recent MarTech Advisor whitepaper, 28 percent of marketers ranked their multiple, disconnected systems as the biggest problem they face.
Validated by analysts such as Forrester and Gartner, Customer Data Platforms (CDPs) solve the fragmented customer data problem and improve traditional marketing workflows. The vendor-neutral Customer Data Platform Institute defines CDPs as a “marketer-managed system that creates a persistent, unified customer database that is accessible to other systems.”
Having a defined foundation is a great start. However, customer data platforms can and should provide so much more: intelligence that helps you access meaningful insights about your first-party data — and of course — integrations with your marketing tools to help you achieve a quick return on investment.
Here are the Five Things You Should Expect:
1. Predictive Insights Using Data Science/Machine Learning:
As a centralized customer data hub in the middle of your marketing-tech stack, customer data platforms are better-positioned to offer customer behavioral trends (e.g., “Highly engaged across marketing tools”) and predictive insights (e.g., “Likely to Convert”) than individual marketing point tools (e.g., email service providers, website personalization tools, etc.), which only have access to marketing channel-specific data.
Having access to powerful data science—specifically machine learning algorithms—that can be used to evaluate many data sources make the marketer’s life much easier. The system constantly learns without the marketer having to pre-define and re-define arbitrary rules (e.g., “if someone clicks X and does Y, that must mean that they are likely to Z”). These rules tend to be the default for most marketers; however, us humans just can’t compete with how fast machines can learn.
Many CDPs on the market today either weren’t originally built with self-learning machine learning algorithms or they simply started as another type of tool entirely, such as a CRM that has since been retrofitted. A more robust customer data platform should offer behavioral insights (e.g., identifying a tire-kicker versus a binge user), predictive insights (“likely to engage” or “at risk of churning”), and other machine learning-based segmentation as a key feature — not an afterthought.
2. User-Specific Content Affinity Using Machine Learning:
Content marketing is a critical part of marketing strategy but to see any results marketers need to match relevant content to individual users. This sounds so obvious and basic, but it can actually be really difficult to parse out who out of your 100,000 visitors is interested in “data-driven marketing technology” over “Southeastern Asia politics” let alone track how these interests might evolve and change over time.
Customer data platforms should help out with this using machine learning because any other approach is either too simplistic or not scaleable by a human. Look for a content affinity engine that helps you categorize your website into nuanced topics (not just marketer-defined keywords), track topic-based affinity levels for each individual and quickly build affinity-based audiences that deliver marketing results.
3. Data-Processing Speed:
With so much information flowing into CDPs, the method and speed with which it gets processed is an important factor. There are a couple of approaches to consider. Relational databases process data at the time of the request (i.e., when you go to build a segment) which involves scanning and grouping users within each data source separately — scanning potentially tens of millions of data rows — making the process of audience segment-building quite time-consuming.
Event-processing databases, on the other hand, process all data events at the time they occur so that they only have to scan the user database once as you build your audience segments. For example, if an event comes in from email (e.g., user clicks on email and goes to the website), the CDP updates the user database immediately, the user becomes immediately placed in or out of any relevant segments on the backend and this information gets sent to all of your marketing tools (e.g., Facebook, Google, Email Service Provider, etc.). From a marketing perspective, this means that you can almost immediately trigger a personalized email or display a specific piece of content to the user on your website, which is a pretty awesome thing.
4. Ease of Use — and Flexibility:
Customer data platforms are extremely efficient because they allow marketers to create one audience segment and sync it ac