They expect one-to-one communication, requiring you to know what they did in the past, what they are currently doing and what they will most likely do in the future (no pressure!)
At the same time, companies need to account for new channels, technologies and data sources. For you as a Marketer, it is challenging to keep pace with the rapidly changing ecosystem and potential knowledge gaps. How can you cope with all this? For many companies, the answer is spelled CDP.
What is CDP?
Let’s start by defining the concept Customer Data in Customer Data Platform.
Customer data is the information your customers provide while interacting with your business via your website, mobile applications, product, surveys, social media, marketing campaigns or any other online or offline channel.
All this data acts as a backbone to a successful business strategy. Data-driven companies realize the importance of this and take action to ensure that they collect the necessary customer data points that would enable them to improve the customer experience and fine-tune their business strategy over time.
More often than not, this data is stored in silos, which means that it’s spread over different systems, teams and channels. This leads to a difficulty in interpreting the data and getting a single source of truth. Thus it becomes cumbersome to act on the data insights in real-time and provide a unified customer experience across all channels.
So what exactly is a Customer Data Platform?
The CDP Institute defines a Customer Data Platform as “a packaged software that creates a persistent, unified customer database that is accessible to other systems.”
It’s a system that centralizes customer data from all different sources – such as the web, email, customer support, apps, social media – and creates a 360° customer view. This data is then made available to other systems, such as a marketing automation system.
This in turn means that the data becomes actionable and can be used for marketing campaigns, customer service or to enhance the customer experience.
A CDP should be able to manage personalization, campaigns across different channels and at the same time follow the GDPR guidelines. It enables marketers to group data into profiles, thus creating a better and more personalized customer experience.
Do I need a Customer Data Platform?
If you’re a medium or large size company, the most likely answer is this: yes! To help you figure out if this is the case for your business, I have gathered the following list of 10 questions that you can ask yourself to find out if a CDP is a right fit for you:
Are you able to ingest the right volume of data in various formats (structured/unstructured/semi-structured/relational/binary) to develop a clear understanding of each individual customer?
Do you have multiple engagement channels? If so, do you have an existing approach to combining data (including offline and online) from these systems?
Is third-party anonymous data included in your data strategy?
Can you easily clean, transform and standardize your data?
Are you able to resolve identities and ensure privacy/compliance to deliver a great customer experience?
Can you deliver a seamless experience to customers regardless of the channels through which they choose to interact?
Are you able to segment and analyze customers in real-time to enable personalization and improve relevance in customer experience?
Do you generate universal customer profiles and make these profiles accessible to the analytics solutions that you may need, in the required timeframe and format?
Are you able to create look-alike audiences and identify new, additional customers likely to purchase products or identifying customers that are going to cross/up sell or even churn?
Can you act instantly, based on the insights you get, by making the unified data directly available to other systems such as a CRM, a Marketing Automation system or an advertising platform?
If the answer is negative to at least one of the questions above, then you probably want to consider a CDP solution, which helps to solve all of the above.
Before you start typing an email to me requesting a demo, I want to give you some inspiration on what you can accomplish with a CDP. Why are use cases important? Well, a software can do as much as you have planned for; nobody wants to purchase an SUV car just to drive to the grocery store and back. Hence here is some inspiration on what you can achieve with a CDP.
3 use cases to get started with your CDP journey
#1 Optimize marketing spend
Stop advertising to existing users and target only new potential customers (cut marketing spend).
You can target look-alike audiences and avoid spending budget on acquiring users that will not convert or that have a customer lifetime value that is not big enough.
#2 Unify online and offline data
Merge data from social media, your ecommerce platform, CRM, ERP, POS into one place. Unify customer data from online and offline to deliver a holistic customer experience with personalized offers.
Through deterministic and probabilistic matching, we can create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
What does this mean? That we can create a unique customer profile, if we connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
#3 Run win-back campaigns (and avoid churn)
Choose criteria relevant for your business. For example, a customer:
Files more than 1 complaint/month
Visits the ‘how to return your order’ page more than 2 times/month
Hasn’t purchased anything in the last 3 months (online or offline)
Have an alert be created from your CDP solution (sms, email) to win-back this customer, either by calling him/her or via email/sms.
Remember: “It can cost five times more to attract a new customer, than it does to retain an existing one.”
That was it! Hopefully by now you can answer the question ‘do I need a CDP’ with confidence and you are equipped with a few use cases to start your CDP journey.
If you are still unsure about buying a CDP or you want to discuss your use cases further, feel free to reach out to me :)
October 15, 2020
3 steps of marketing measurement: Design, collect and measure
To measure average speed of a car halfway through a given distance it is critical to design activities that account for this measurement objective. We want to take actions and measure their impact – scientifically.
More often we are interested in measuring the impact of our actions – and doing this scientifically requires some planning. To measure average speed (of a car) halfway through a given distance, we should mark the midpoint and should have a clock. To measure conversion through a print catalogue and to compare that against the same from a digital catalogue, the design of the catalogues should allow you to identify the source of conversion and should be able to differentiate between print and digital. While these measurement activities can be done in numerous ways, what is critical is to design activities that account for those measurement objectives.
Here is a three-prong construct to help designing marketing activities in order to effectively be measured.
Design to collect
Let’s take an example of measuring impact of a catalogue (print / digital) on customer conversion (buying). In some cases, a customer may see the print catalogue first and then jump to digital channels (e.g., uses an app to buy online). Ideally, these activities are to make the conversion (buying) easier for a customer. But that would also create ambiguity in terms of which activities to be attributed for conversion (in this example, print or digital catalogue). This introduces the concept of activities sequenced to drive a customer behavior in certain direction, also known to us as “customer journey.”
The role of analytics in designing these activities is critical. The design should include what is intended to be measured. This is where the third prong of the construct (measure) drives the first (design).
We have seen vanity URLs and unique 800 numbers used to handle this challenge. But those involve other issues – for example, cost of registering different 800 numbers, possible confusion to customers with different 800 numbers/URLs, losing catchiness and simplicity, etc.
With modern digital innovations, collecting parameters at digital channels for direct marketing is no longer a challenge. Many technological supports (software/platforms) do collect measurement parameters as out-of-the-box capabilities. There are other considerations here, but let’s hold those thoughts until we reach the third prong.
Collect to measure
An effective first prong of this construct would make this step (collect) simple and purely operational. This would then be nothing but populating a table with data for which structure was created in the first step. Alas, life is never so straightforward. Often we fail to design accurately, mostly due to ever-changing priorities and market dynamics, which leads to some retrofitting-type marketing activities. Since those activities could be tactical and opportunistic, we won’t further discuss them here, but we should consider that granularity of collected information and data is critical in bringing adoptability against the ex post changes in the measurement requirements.
However, a broader question would be, can we “time to market” the process of designing and deploying (collecting)? This can be substantially achieved by squeezing the time needed to design and closing the gap between design and deploy. Ideally, we should be able to design quickly based on the business objectives and deploy immediately once designed. (https://www.sas.com/sv_se/customers/ica-banken.html)
Measure to improve
Here is the piece of the puzzle that has gone through tremendous research and has significantly evolved in recent years. AI and machine learning based algorithms are also being used for attribution analysis. Storage capacity and processing speed are no longer constraints. Traditional methods like first touch or last touch attribution are not lucrative enough to modern marketers; they are now looking for algorithmic approaches. And why shouldn’t they – customers’ expectations are changing faster than ever, and businesses are competing for their attention. Any additional insight to recalibrate the customer engagement process would add significant value. My colleague Suneel Grover has blogged about some recent advancements in attribution measurement.
Quest of sophistication in this part creates newer challenges in designing the (sophisticatedly) measurable activities. Data scientists have developed attribution algorithms that go beyond the traditional rule-based and typical out-of-the-box methods. Someone willing to use more sophisticated algorithms to measure attribution needs to incorporate elements to collect in the design of the activities.
The whole cycle of our three-prong construct also requires us to follow the principle of agility and should support time to market. I would tend to call this something like “EMAP” – effective marketing attribution principles, which, in conjunction with the three-prong construct mentioned above, would be described as below:
Flexibility to modify activities and metrics.
Flexibility to post facto extend measurement and analysis.
Total cycle time should be quick enough to be able to react based on analysis outcome.
Test and learn is another aspect that requires significant swiftness in the design and execution of marketing measurement but may not truly be categorized as attribution analysis. It was traditionally assumed that only basic measurement methodologies could be adopted in these cases, because added sophistication would delay the results. While this was true until few years ago, advancement in technology has allowed marketers to be detailed, analytically sophisticated and quick, all at the same time. Ball is now in marketer’s court, rather than technology department’s. Those technologies must be in place to enable automated measurement (and attribution) using analytical methods. Furthermore, based on the results, actions should also be automated, which may be picking the most effective variant of content or message.
Attribution analysis in the context of multichannel customer journey
This topic can be considered as an extension of this blog. While the above is not necessarily limited to single-channel marketing activity only, customer journeys across multiple channels and activities bring a lot of complexity into measuring effective attribution.
October 13, 2020
Twilio has acquired Segment. Why? And was it a good idea?
Last Friday, Twilio (TWLO) announced that it had acquired Segment in an all-stock deal worth north of $3.2B. The move has significant ramifications for the entire marketing and data technology ecosystem. Major players like Salesforce (CRM), Adobe (ADBE), and Oracle (ORCL) have the beginnings of a new, developer-focused marketing cloud competitor on their hands. Other martech vendors have new existential questions around the partnership and competitive landscape. Investors want to know if it’s a good deal. And brands still just want to message customers more effectively.
Is new-look Twilio the answer? Is it a threat? Is it even a good idea? All of the above?
Let’s look at the deal and see what’s going on.
Who is Segment? And how do they fit Twilio’s market thesis?
Founded in 2012, Segment began life as a segmentation tool (hence the name). Quickly, however, it pivoted into a new sort of tag manager that leveraged its late-mover advantage to differentiate based on its orientation to streaming data. Segment has since become the gold standard for web and mobile tracking, as seen by players like MetaRouter and RudderStack leveraging their code in an open source capacity. Over the last eight years, it has built an impressive roster of reportedly over 20,000 customers and $150m+ ARR.
Easily the largest non-marketing cloud player in the CDP space, Segment, like many other “CDPs,” has had an uneasy relationship with the category. Last year, it branded itself “Customer Data Infrastructure,” emphasizing its developer-first orientation and seeking to separate from the muddled mass of “CDPs” with varying functionality and maturity - and with very different buyers and end users. In the press release announcing the acquisition, however, Twilio’s CEO Jeff Lawson embraces the term, writing:
As the leading CDP, Segment enables developers to unify customer data from every customer touchpoint, and empowers marketing, sales and customer service leaders with the insights they need to design and build relevant, data-driven customer engagement.
The quote provides good context on both Twilio’s orientation to Segment and to the market at-large. Twilio is a developer-first shop. Their fundamental market hypothesis is that developers will drive innovation in martech and customer communications through access to superior infrastructure. They believe that by owning the “pipes” that drive all customer communications, they at once control the emerging martech ecosystem (who build platforms on their pipes) and emerging brand ecosystem (whose developers build internal tools to send through them).
Twilio’s 2018 acquisition of SendGrid affirmed this strategic orientation. Many, if not most, next-gen marketing clouds (e.g. Braze, Iterable) used SendGrid to send email - often exclusively and at tremendous volume. Many, if not most, emerging brands (e.g. Airbnb, Uber) used SendGrid to send both product and marketing emails - often exclusively and at tremendous volume. Twilio’s acquisition meant that developers from both ecosystems could now work with one progressive, high-scale delivery provider.
Culturally and structurally, Segment feels almost identical. Segment sells mostly to developers. Segment views itself very similarly to SendGrid - Peter Reinhardt, Segment’s CEO, has spoken publicly about being the data infrastructure for the next generation of marketing tech. Developers at progressive, high growth brands have used them to manage their data flows, and - in both cases - once deployed, they’re deeply entrenched.
What does the acquisition mean for Twilio?
With the acquisition, Twilio can step past simply being a one-stop shop for marketing infrastructure. Twilio’s press release conspicuously stresses concepts like “customer experience” and “empowering marketers.” Coupled with a reference to Twilio Flex, this provides important clues into their forthcoming strategic orientation.
The full formula for managing customer experience is simple: you need data solutions to help to understand customers, orchestration solutions to decide what messages they receive, pipes to send those messages. Until now, Twilio has concerned itself exclusively with pipes while driving itself to a >$45b market cap. Turns out, controlling the pipes is pretty profitable.
By acquiring Segment, Twilio is betting that significant additional opportunity lies in controlling the data flowing through those pipes. Snowflake’s IPO, along with the early spike in Twilio’s share price on Monday, certainly confirmed that public markets share this view. But Twilio’s press release doesn’t stop at controlling data and pipes. Specifically, there’s a telling reference to Twilio Flex, intended to be a developer-first orchestration solution:
Over time, the addition of Segment will allow Twilio to integrate data intelligence into Twilio Flex and every one of our offerings to provide highly personalized customer touchpoints.
The writing is on the wall here: Twilio likely intends to build a developer-first experience cloud. Flex will be their orchestration offering, and they’ll be taking on Adobe, Salesforce, and Oracle through a developer-first product suite that is designed for a more modern organization.
Will Twilio’s strategy be successful?
Twilio has shown a tremendous ability to evolve and change. Their longer-term success ultimately hinges on five important factors:
Continued Martech Ecosystem Success - As noted above, a major pillar of Twilio’s strategic advantage is the extent to which marketing technology providers (e.g. Braze, Iterable, Attentive) use them as pipes for their own solutions. Segment’s partnerships with similar entities have provided important tailwinds to all involved. With a Flex + Segment-powered experience cloud, Twilio now faces Google Pixel-like competitive dynamics with their own customer base. Skillful navigation of this dynamic will be critical.
Continued Strong M&A Performance - Twilio’s acquisition of SendGrid was smart and timely; the acquisition has been relatively smooth, owing largely to the cultural and product similarities, along with a relatively low tech-integration burden. The Segment acquisition can be a similar boon, but execution will be critical. Moving forward, the M&A teams at Twilio will need to close gaps in other areas. Will Flex actually be sufficient, or will an orchestration-focused acquisition be necessary? How will Twilio address other channels, e.g. web?
Supportive Product Development - With heavy bets seemingly placed on Flex, the product and engineering teams at Twilio have their work cut out for them. Flex is far away from being anything approaching a sufficient orchestration solution today. Orchestration is a complex space with a long tail of features that are necessary for competitive enterprise parity with incumbents like Salesforce, Oracle, and Adobe. While Twilio’s developer focus can stave off some of this pressure in the short term, parity will eventually be required, and they may be years away.
Beyond simply orchestration, as David Raab at the CDP Institute notes, absent a meaningful customer acquisition story, Twilio will have to address additional channels and other critical functionality like reporting and content. The enterprise wants to integrate CX across product, marketing, and engineering - a far cry from Flex’s footprint today - or anywhere in the near future. Additionally, integrating Segment brings its own challenges for enterprise companies which can be traced to its tag manager roots; ongoing product development to drive differentiation and integration enablement will be critical.
Brand + Ecosystem Development - Twilio has a very different brand profile than Salesforce or Adobe, two companies with tremendous name recognition among enterprise marketers and technologists. Dreamforce is essentially a gigantic martech networking festival; Salesforce Trailblazers and MVPs provide critical mechanisms for celebrating marketing and technology specialists. Adobe has similar constructs with its Summit and other programs. Both have huge service and support networks surrounding their product suites. No one gets fired for buying these providers, and there are significant career upsides for investment in these ecosystems. To compete, Twilio will need to find ways to cultivate deeper relationships with non-technical buyers, which remains largely uncharted territory for them.
Enterprise Sales Development - Neither Twilio nor Segment have meaningful enterprise sales pedigrees, particularly when it comes to targeting marketers. It is easy to underestimate the power that folks like Salesforce and Adobe wield via their massive sales engines; Twilio will need to develop real competencies around better penetrating enterprise accounts, building consensus, and driving decisions and upsell where they already have a presence.
The final point here is particularly nontrivial. Selling and implementing solutions like Segment in the enterprise is incredibly challenging, and may have had something to do with their desire for a deal. Twilio doesn’t have deep experience, and the headwinds are going to be significant. A CTO of a publicly-traded company shared this thought with me over the weekend along those lines,
“So many of today’s digital enterprise platforms have legacy systems and/or technical debt that makes deployment of systems like Segment incredibly difficult. The migrations and upgrades I led at [Company] made these sorts of deployments easier, but it was a long road that many shops won’t want to go down”
“Beyond simply the technology challenges, accounting for measurement anomalies is a huge burden in the enterprise. Traffic numbers will change following installation and we had to deal with their auditors to account for the differences. When we first launched, traffic was off by .5M per month and we did an insane dive to figure out all the differences and account for it. We required a disclosure when releasing their EOQ numbers which was exquisitely painful.”
Why did Segment sell rather than IPO?
Twilio’s strategic trajectory and long-term ambitions are only one side of the story. Segment chose to do this deal, and to do it against the backdrop of Snowflake’s valuation more than doubling following a last-minute repricing and significant private market valuation increases in the period leading up to IPO. Why? Public markets want data infrastructure companies. Data is the future. Outside looking in, doesn’t Segment stack up well against these criteria?
Ostensibly yes. But now their strong alignment is Twilio’s to benefit. Why did they do the deal? Three theories:
The Deal Was Just That Good: This is presumably what Segment would want you to believe, but was it? We aren’t privy to the full mechanics of the deal, but on the outside it looks like a valuation around 20x. At $3.2B, that’s just over double Segment’s last private market valuation. In a vacuum, that’s an excellent deal for all investors and shareholders. For comparison, though, businesses like Twilio are trading north of 25x - and Segment is still relatively early. Maybe Segment’s revenue has been overreported, and we’re looking at closer to a 25x multiple. Maybe there are other factors at play.
Segment Struggled To Expand Outside Of Their Initial Market: Obviously, Segment’s growth has been tremendous - but were they hitting their own projections? And were they set up to scale at a pace that would be consistent with the expectations of public markets? It’s possible that their developer-focused, mid-market/SMB TAM was getting too saturated to sustain growth rates, and new products weren’t delivering sufficient incrementality. There have been consistent rumblings that their Personas product was underperforming relative to internal expectations, and they’ve switched sales leadership repeatedly in an effort to unlock an effective sales motion to marketing and enterprise teams. It’s entirely possible that, despite good growth, the board and leadership team felt it was time to cash out - and not risk future down rounds, particularly given the murky macroeconomic waters ahead.
They’re True Believers: It’s reportedly an all-stock deal, which is somewhat unusual. Yet given the initial spike in Twilio’s share price, it’s perhaps a savvy move. It’s entirely possible that the Segment board looked at the sum of both companies and thought the upside was significant enough to offset the opportunity cost of not IPOing. Time will tell if this is the case, regardless of the cause. But, as noted above, there’s some strong synergy here if both companies execute well.
Ultimately, it may well be a combination of the above causes - and the truth of the matter tends to leak out after the dust settles on deals like these.
Final Thoughts
At the end of the day, the success of this acquisition will be dictated almost entirely by two factors - Twilio’s share price and Segment’s ability to catalyze their long-term strategy. Jeff Lawson has strong ambition - and a matching checkbook - to take on large marketing cloud incumbents. Their developer-led, land-and-expand framework pairs perfectly with their current product ecosystem, and Segment fits well into this landscape. If Twilio can develop a more powerful marketer-led enterprise GTM motion/brand, continue to make and integrate smart acquisitions, and navigate delicate ecosystem dynamics, they should be set up well.
For the rest of the marketing technology landscape, there is both incremental uncertainty and opportunity. A CDP, your preferred definition notwithstanding, has been acquired for multiple billions. There will almost certainly be a scarcity-driven increase in CDP acquisition by relevant folks. From the perspective of other marketing tech providers, Twilio and Segment no longer seem like innocuous supporting pipes, but are now a daunting entity with a potentially nefarious agenda. The partnership landscape will undoubtedly shift. Some, like Sparkpost, will benefit, while others (e.g. pick your Twilio-powered ESP) may find it difficult to adjust.
Brands, for their part, continue to struggle. Customer experience expectations are more intense than ever. Channels continue to proliferate. Data privacy policy is evolving in near real-time. The underlying challenges of true experience management remain daunting and expensive problems for almost every company. Expensive problems tend to command expensive solutions, so budget will be allocated. But who will get it? Time will tell.
October 12, 2020
Build vs. Buy: How to Know When You Should Buy a CDP Over Building an In-House Solution
This is probably the big question that comes to mind when IT and Marketing join forces to evaluate new technology that would like to add in their software arsenal. Choosing how to proceed with a Customer Data Platform is not an exception.
You want to deliver a world-class customer experience, based on your data. After thorough research, you decided that a Customer Data Platform (CDP) will enable you to reach your goal (congrats!) How will you go about it?
You’ve got two choices: either you build your own solution in-house, with all the management and upkeep that this entails, or you buy a solution from a vendor. It can be a difficult decision, with a number of benefits and drawbacks to either approach.
You might think that I am biased (transparency: of course I am!): I’d rather you buy our CDP Data Talks PRO and keep you as a loyal customer until eternity. But I will take a different approach being fully objective (I promise!) so that you are much better equipped to make the right decision for your company’s growth future.
Ready? Let’s dive right in!
Imagine you enter the meeting room where Marketing & IT stakeholders discuss the way forward. You can cluster their opinions into two main groups:
Group A: We have a large development team with a wide range of IT competencies (data engineers, backend and frontend developers, security developers, data scientists, analysts and so on) with substantial budget. We can afford to wait 1-2 years until we roll out the solution. In that way, we can fully customize our solution to our needs instead of compromising with a generic software.
Group B: We have a strong development team but our core business should not be interrupted from building and maintaining a Customer Data Platform. Instead, we would like to run pilots (POCs) quickly before we go all-in, test that a CDP is the right solution for us and leverage the knowledge that a vendor has in the field to fuel our growth. We might even find a vendor who is specialized in our line of business.
Which one is right?
Well, as always, it depends.
Group A refers to enterprises that operate within software business and have already a rather big dedicated team that they can allocate in various IT projects. What they might miss is the people investment required to deliver and maintain a custom CDP which is considerably higher than the buy option. In addition, scope creeps, failure to accurately define specifications (& use cases), cost overruns can transform the project to a large-scale IT project lost in translation. However, done right, they can build a solution which fits their needs ~ 100% and reap the benefits once it is launched.
Group B refers to medium/large companies that either operate within software business but don’t want to lose sight from their core business or companies with a not-software-related core business (i.e sports, entertainment, utilities, ecommerce, banking). They are up for buying a pre-packaged CDP (SaaS) which results in having a much lower upfront cost and quick onboarding that takes away risks, while providing a go-to market solution today. What they might miss is cooperating with a vendor who does not provide a solution crafted for their specific industry.
So instead of focusing who is right, you should investigate what suits your business at this specific time. In every build, there’s something that you’re buying – be it a cloud platform, a data warehouse, a workflow engine or an SMS provider. And whenever you are buying, there’s also a build component, like industry specific integrations or some logic within your application platform.
Common pitfalls & how to think around them
Below are some of the common pitfalls that I have noticed that Marketing leaders fall for when discussing a CDP solution. Let’s look into them:
#1 Our business is special
“We have unique requirements that makes it impossible for a third-party vendor to efficiently meet”.
All businesses were not created (& shouldn’t be) equal; this does not mean that you cannot learn from how others have tackled similar challenges. You might be impressed with how a moving company thinks and apply the same logic to a sports club.
#2 We already have a CDP
“We own the data, we have integrated all systems with each other (more or less) and we also have some analytics platform. So maybe we already have a CDP in place”.
There is a modern way of connecting the data for optimal performance and there is an old school way of doing that. If you don’t leverage your data today to its full potential, it is time to re-think the process.
#3 Built it as an IT led effort
Focus heavily on solving for the technology layer, even before solving for core CDP capabilities.
We will discuss below what the core CDP capabilities are but you should keep in mind that a CDP is a tool for Marketers and they should be involved in the process as much as the IT department. This will ensure that, not only the infrastructure is robust, but also that the Marketer gets the insights that (s)he is looking for.
Requirements that your CDP solution should fulfil
Whatever your business needs are, and no matter if you choose to build or buy a Customer Data Platform, there are a set of general requirements that have to be met for it to be successful. These are the 4 core CDP capabilities that we mentioned above.
Data Ingestion and Integration
Customer Profile Management
Real-Time Segmentation
Expose customer data to other systems
Data Ingestion and Integration
The CDP should be able to digest any data such as event-level behavioral data (e.g. websites, apps, mobile browsers), demographic & firmographic data, transactional data, offline & modeled data (e.g. RFM models, propensity scores, next best action).
Customer Profile Management
It should be able to connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
Through deterministic and probabilistic matching, it should be able to create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
Real-Time Segmentation
The CDP should make it easy for you to define and manage rule-based segments on the fly.
Expose customer data to other systems
As a Marketer, you want to be flexible when it comes to which external channels that can consume your valuable customer data. Hence the CDP should be able to integrate out-of-the-box with any software system through connectors and ready-made APIs, allowing access to data for deeper analytics while boosting customer engagements.
Last but not least, you need a marketer-friendly UI and UX. You should be able to create, deploy and evaluate campaigns without the help of the IT department.
The speed and lack of friction in that process is a critical component and a critical goal of the CDP.
Framework to choose the right option
I attach below a helpful framework that will enable you to make the right decision - build or buy. Of course, I advise you to treat it as a rule of thumb but I am pretty sure that it will yield the right result. Let me know otherwise :)
Build - develop Inhouse
Buy (SaaS)
Do you normally build IT solutions internally?
Yes, we have a dedicated team internally.
No, we normally subscribe via a SaaS business model.
Uniqueness of the problem
If the problem is completely unique and has never been addressed before, you might have to build.
If it has been solved in a way where you can pay for part of it, yet get the full value, why not getting it ready?
Initial investment
• People investments
• Platform investments
• Opportunity cost
• Relevant stakeholders’ time
Time from decision to using the solution
Considering 6,5 weeks for each integration, you can expect from anything between 32 - 52 weeks.
3-4 weeks, including onboarding
Running cost
High, depending on how many people needed to maintain the solution and staying updated with the software changes.
Medium, since the right vendor provides the support and knowledge needed.
Internal tech resources
High (explained above)
Low
Internal tech competencies
• IT
• Development
• Maintenance
• IT
Solution lock-in
If you build it, it’s hard to change your mind and adapt when the business requirement changes. You will have to continue building, or accept a large sunk cost.
A SaaS model gives you an easy way out if the solution doesn’t work for you and your organization, and most SaaS providers will hear your requests and discuss future developments with you.
Security
Build a secure solution to avoid a costly data leak.
Buy a secure solution from a trusted vendor.
Bringing it all together, CDPs have proven to be one of the most effective technology platforms to empower data-driven marketers in an era of complex, multi-channel, personalization-led experiences. No matter what your final decision is, build or buy a CDP, I hope that you have all the information needed to make the right decision for your business.
In case you want to discuss the above further, feel free to reach out to me :)
Customer Data Platforms (CDPs) are here to stay; the CDP Institute estimates that the industry revenue will reach $1.3 billion in 2020, a 30% increase over 2019. Although the term was coined in 2013 to describe several types of marketing systems that shared the ability to build a unified customer database, there are still some misconceptions of what a Customer Data Platform is.
But worry not, I will clarify these below. Brace yourselves, it’s a long post but very enlightening.
Ready? Let’s dive in!
# 1 A Customer Data Platform is the same as a CRM
A Customer Data Platform (CDP) and a Customer Relationship Management (CRM) software share some similarities. However, their primary purpose and function have many differences.
A CRM stores data of customers who had some interaction with your business. It could be data about your business prospects and customers, their product needs and purchasing history. Hence a CRM is critical for Sales and customer-facing roles to manage customer data.
On the other hand, a CDP is a database which consolidates useful customer data including personal identifiers, website visits, purchase orders, email responses, social media comments, audio recordings, customer service interaction, mobile app touch-points and any other data related to the customer. The CDP pulls this data from different sources and then cleans and combines it to create a single and unified customer view. Thus CDPs are essential if you plan to execute scalable, personalized and omnichannel campaigns.
It’s not about choosing between a CDP and a CRM. Rather, Marketers should know the difference between CRM and CDP in order to take the necessary action to the respective software for each use case.
#2 Customer Data Platforms are the same as Data Warehouses (or Data Lakes)
To store data you need a data storage system. Many companies today use a Data Warehouse to store data, while more and more are starting to use a Data Lake. Many companies need both. (If you don’t know what a Data Warehouse or a Data Lake is, no worries, just keep on reading to find out).
A data warehouse and a data lake both serve the purpose of storing data, but in very different ways.
Data warehouse: A Data Warehouse is a system that pulls together data from different sources for reporting and analysis purposes. The reports are often used to make business decisions. A Data Warehouse stores processed and refined data according to the business logic. This means that you need to prepare the data by cleaning, transforming and aggregating it before using it for analytical purposes.
Data lake: A Data Lake is a system that stores data in its raw format. Basically, you can store your data as-is, without having to first structure it. Data stored in the Data Lake can be in structured, semi-structured or unstructured format. You can import this data to your Data Warehouse for adding more business value to it, or you can use that data in dashboards and visualizations directly from the Data Lake. Beware that the direct visualization of data from the Data Lake can be risky, as data is not cleaned and it might contain corrupt or duplicate records for example, that might affect the final figures quite a lot. By using a Data Lake you are building a strong data foundation for better decisions and a single source of truth.
A Data Warehouse focuses mainly on reporting, and the data modelling and format is very strict, which limits the data you can store. A Data Lake on the other hand, is more flexible and can handle more sources of data with any kind of format. It will also give you patterns about your customers, instead of pure facts, which will help you to create an engaging and relevant customer experience.
What kind of data can you store with a data lake?
Structured data
Relational data (rows and columns)
Semi-structured data (logs, XML, JSON and CSV)
Unstructured data (emails, PDFs and documents)
Binary data (audio, images and video)
If you’re looking to act on your data – a Data Lake is what you need. It’s the foundation for any data-driven company. And as you might have guessed, it is included and an essential part of a CDP!
Thus a CDP includes a Data Lake (and/or a Data Warehouse) but it does not stop there. The CDP is responsible for the orchestration of the omnichannel and personalized campaigns that you will run, as well as the analysis, predictions and reporting of your data.
A CDP can be connected to your existing Data Lake or Data Warehouse, fueling your existing business data with insights from the Marketing activities.
#3 A CDP is the same as a DMP
Let’s define the various data categories first before we clarify the difference between a Customer Data Platform (CDP) and a Data Management Platform (DMP).
What kind of data can you collect?
Zero-party data: Any data that a customer intentionally and proactively shares with a brand is called ‘Zero-party data’. It can include preference centre data, purchase intentions, personal context, and how the individual wants the brand to recognize them. This differs from first-party data since while brands own first-party data, they do not own zero-party data. Instead, consumers grant a brand the right to use their zero-party data for the purpose of a particular intent or value exchange.
First-party data: This is the best type of data because first party data is the information you yourself have collected about your audience.
Second-party data: This is the next best thing. Second-party data is someone else’s data (usually a trusted partner who’s willing and has the consent to share their customer data with you).
Third-party data: This helps to complement the current data. Third-party data is usually provided by companies, also known as data aggregators, that sell user data. You should be very careful when using this type of data. Make sure that you can trust the source before you commit to a long-term contract.
A DMP is used when you want to build marketing campaigns for audiences that are unfamiliar with you. DMPs are best for this because of their use of third-party data. They can give you access to audiences that you don’t know. You can then use that new data to build a targeted marketing campaign.
CDPs are built for processing zero, first and second party data. If you plan to create highly personalized marketing campaigns based on your own data, use a CDP. Your CDP can gather website data and send it to any number of different tools, depending on your needs.
There is a high chance that you need both platforms in your marketing arsenal. Your CDP can handle the segmentation and creation of look-alike audiences, while you might use a DMP to target these audiences in your preferred advertising platform.
#4 A CDP is a Marketer-managed and Marketer-only system
A CDP is defined as a Marketer-managed system designed to collect customer data from all sources, normalize it and build unique, unified profiles of each individual customer. That should not be interpreted as that only Marketers should manage and work with it.
Sure, Marketers should drive the implementation since they are the ones who have defined the use cases and will be responsible for proving the ROI. However, the IT should be involved as well, at least during the partner selection and the initial implementation of the solution. That will ensure that major technical hiccups will be avoided or spotted early in the process. In addition, keep in mind that the CDP will be ideally connected to the existing Data Warehouse (or Data Lake) hence IT involvement is needed for a smooth project.
Once it is implemented, your Data Scientists, Database Developers or Analysts might want access to either fetch some data or implement a machine learning algorithm, supporting your Marketing activities. Last but not least, the Finance department might want to double check how much you have spent for a specific campaign in a specific channel since the invoice from the advertising vendor seems too high this month.
#5 A CDP’s main capability is Identity Resolution
It is correct that your CDP should be able to connect many different identifiers from multiple platforms and devices in real-time to enable people-based targeting, personalization and measurement.
Through deterministic and probabilistic matching, it should be able to create universal and persistent consumer profiles by solving the identity of customers and visitors across different states (known & unknown).
However, keep in mind that the Customer Profile Management described above is just one of the 4 core CDP capabilities, explained in more detail here.
#6 CDPs are known for handling only personally identifiable first-party data
A CDP can handle zero-, first-, second- and third-party data! Sky is the limit on how you can leverage your CDP. However, most of the companies use a CDP to handle zero, first and second party data while third party data is being processed by their DMP.
#7 Implementing a CDP involves replacement of our Marketing Automation or visualization software
That is a tricky one and depends on the vendor that you will choose to partner with. It might be the case that the vendor has already a full suite of products, hence yes, you should stop using your preferred Marketing Automation (MA) software or visualization software. However, there are vendors who are MA or visualization software agnostic.
Why is being that flexible a huge benefit?
Let’s say that you implement a CDP solution today and after 6 months, for whatever reason, you decide that the Marketing automation software (or your visualization software) does not meet your business expectations. Instead of throwing all of your CDP-implementation out of the window, you can easily switch to your selected MA solution and the CDP partner will do the rest for you. Pretty cool, right?
#8 The implementation of a CDP takes years
This is a reasonable concern. You read all this information online about the CDPs, you speak with a few vendors and you understand the complexity. On average, for an enterprise, it takes 6 weeks to build one integration. Given that you have 5-6 data sources, one Marketing Automation software and a visualization tool to connect, that is about… well, no need to do the math. It seems like a never-ending project.
So it is not hard to assume that the technical implementation will take years which you cannot afford; you need to show the ROI internally in a few months after signing the contract.
Well, I have some good news for you!
Basically the integrations and the various connections are pre-built normally from the vendor which means that you will save a lot of time and money. Not to mention that you will not have to maintain these integrations once it is live.
Given that you select the right partner and that your stakeholders are committed to the project, the technical implementation does not last more than one month. Thus in a couple of months you can have your first use cases up and running, demonstrating the return-on-investment.
#9 CDPs are all the same
As you might have guessed, this is not true either. The CDP Institute groups CDP vendors into four categories based on the functions provided by their systems. Each category includes functions provided by the previous categories. There are great variations among vendors within each category.
Bringing it all together, clarifying these 9 misconceptions above is one step further on uncovering a CDP’s core value and the impact it can have on the bottom line. Therefore, it is important that you understand the nuances of the Marketing technology stack and especially the CDP features and capabilities. The more accurate the understanding, the better it will serve in selecting the right Customer Data Platform for your business.
Have I missed anything? So interested to hear from you if you have any questions or input!
Getting their martech stack right is the key that unlocks the door to untold opportunity and transformational customer experiences for data-driven marketers. In an ideal martech environment, where all platforms are tied together by a unified data framework, the entire business can benefit and take action based on real-time, informed insight.
Yet for many marketers, piecing together the martech puzzle remains a challenge. There are many questions to consider:
What martech platforms do I need?
How can I ensure platforms are correctly implemented, integrated and optimized?
What data strategy and frameworks do I need to support a non-siloed, unified environment?
How can I make the most of existing and new platform investments to drive real value and better ROI?
And those are just the high-level questions.
Including a customer data platform (CDP) in their martech stack is (one of many) attractive options for marketers looking to limit data fragmentation and achieve a unified, omnichannel approach. So, what is a CDP? What benefits can CDPs unlock – and how can you tell if a CDP, or another data solution, is the best for your organization’s tech stack?
Should Marketers Look to Invest in CDPs?
Although CDPs work to centralize data across multiple sources for a unified database, because there’s no one single formula all CDPs follow (and as there are multiple diverse, ever-evolving and maturing data solutions on the market today), it can be complex to understand what each one does, and which is the best fit.
Indeed, confusion about CDPs is a common challenge Acxiom helps customers address; the CDP Institute itself recognizes more than 100 CDP solutions, and Acxiom is also tracking the landscape to make informed solution recommendations to clients based on their requirements. Whether marketers are considering a CDP investment, or looking to evolve and optimize their martech stack, they face common questions: “Is a CDP the right solution to unify my data, and do I need one in my martech stack?”
The answer here typically depends on the organization’s unique position, existing frameworks and technology. And with vendors evolving their positioning and technology, the scope of CDP capability, standard features and support can vary greatly.
What are the Benefits of a CDP?
Despite the variety of CDP solutions, support and features on the market, most CDP vendors say their solutions address a number of common challenges – to support and solve:
Marketing Fragmentation – Working to create a single customer view across marketing channels.
Digital and Offline Integration – Most CDP solutions support standard connections for source systems such as marketing applications and CRMs – a requirement to collect and integrate customer data from digital and offline sources, and support data integration.
Identity Management – CDP solutions should be able to create universal and persistent consumer views, using identity resolution to create accurate profiles of individual customers or prospects across touchpoints.
Segmentation – CDP solutions should work to create segments for real-time marketing applications, and enable marketers to make segment data immediately (often instantly) available for use in other marketing applications.
Data Silos – CDPs help expose data to other systems, including customer analytics, customer engagement platforms and more.
Governance and Legislation – Governance and privacy legislation compliance (such as with GDPR and the CCPA) are crucial for any data solution, and help marketers maintain a transparent, responsible and secure approach to data.
Considering this, investing in a CDP to achieve a centralized, omnichannel solution may seem an obvious choice – and for some organizations they are! Yet it’s important to keep in mind that as capability can vary between vendors, a CDP may still require support from other technologies in the tech stack, and may need advice and consultation from a data and technology expert to be truly optimized for real value.
For this reason, and because different marketers and organizations have differing requirements, data ecosystems and existing martech, it often helps to consult a data partner to identify:
Which platforms or tools can help you meet your goals.
If a CDP (or another solution such as a unified data framework, or a combination or tools) is the best option for your organization.
How you can optimize your existing CDP and martech stack.
And, how to consider your martech and data strategies together for a unified, effective approach.
How Can You Optimize to Ensure Value from Your CDP?
If you do have a CDP solution, or are looking for one, what can you do to optimize it to ensure it delivers best value, performance and ROI?
As a substantial enterprise solution, it’s important to make sure your CDP is the right martech solution for your business goals – so key considerations may be:
Can the solution provide a truly unified foundation for a closed loop, omnichannel marketing and advertising ecosystem?
Does it help to align data across digital and offline channels?
Can it resolve identity across known and anonymous?
Does it support optimal marketing stack evolution, and integration needed for scale? (Some CDPs conversely take a “rip and replace” approach.).
Will it support closed-loop measurement and analytics?
Is the platform (and your team) well supported – through integration, implementation, campaign strategy, analytics and reporting and day-to-day management?
As implementing such a substantial solution across an enterprise can be a complex challenge, with many considerations to ensure optimal ROI, many organizations choose an expert data partner to advise on the best path to drive success.
With many standpoints, most organizations likely are either:
Looking for solution options, and considering if a CDP is the right fit for their business
Looking to purchase a CDP solution, and considering how to make their implementation successful
Reviewing how to optimize and integrate an existing CDP solution with their martech ecosystem – and investigating how to educate the wider team on how to use it to drive value.
Or, may have invested in a best-of-breed martech stack, and are considering replacing it with a CDP – or investigating alternatives.
Whatever your standpoint on a CDP, Acxiom can help advise, support and enhance your current martech stack and platforms. With a services framework to help you navigate through the CDP/martech landscape, we support clients at all stages, from initial platform decisions (mapped to business requirements etc), to ongoing support services.
Do you know what a Customer Data Platform (CDP) is?
Don’t worry, not many marketers actually know what it is.
Right now, there is a shift happening in many companies. Marketing is moving closer to IT and technology. Maybe a little too close for some. This opens up for new challenges and unexplored territory for many marketers.
The shift includes new technologies and a more digitalized approach to communication and marketing. It enables companies to communicate about the right product, at the right time, to the right person in the right channel – the equation for increased customer loyalty and growth.
The fact that many companies have a feeling of “lagging behind” when it comes to digital maturity may not be so strange. Especially when most of us may have a tendency to compare our own company’s digital transformations to the giants at the forefront such as Spotify, Netflix and Apple.
As much as we have to stop comparing ourselves privately in social media, we must also stop comparing our companies’ digital development to those that are digitally mature. Because the fact remains: consumers and buyers will not lower their expectations of a more personal and relevant communication and a really good customer experience.
Either we pursue a digital development, no matter where we are today, or we fall behind. It’s as simple as that.
The pursuit of customer loyalty drives the growth of CDP
There are many concepts and technologies today for marketers to understand, such as CSM, CRM, DSP, DMP - and one of the latest is CDP. A Customer Data Platform, that is. In 2013, the term was established when David Raab founded the CDP Institute - even though CDPs existed before then.
It’s no longer about offering the best product at the best price. Customers today are generally not loyal and most people have no problem switching brands. Therefore, the deciding factor is not the product or the price, but the customer experience. Thus, communicating the right product, to the right person, at the right time, in the right channel has never been more important.
What is a Customer Data Platform (CDP)?
Before we go into why you as a marketer should use a CDP, we must define exactly what it is. We start with the concept of Customer Data in the Customer Data Platform.
Companies today have more data than ever before. There is everything from demographic data, transaction data, product usage data to behavioral data, profile data and attitude data.
Most often the data is stored in silos, which means that it is spread over different systems, teams and channels. This makes it difficult to make sense out of the data. What becomes even more difficult is to act on the data and create a uniform customer experience across all channels.
Data is a huge opportunity for companies, but many companies today do not know how to gather their data or extract business benefits from it. And it is in the customer data that the keys to loyalty, commitment and growth lie.
So, what is a Customer Data Platform?
The CDP Institute defines a Customer Data Platform as “a packaged software that creates a persistent, consistent customer database that is accessible to other systems.”
It is thus a system that centralizes customer data from all different sources - such as email, the web, customer center, apps, social media - and creates a 360° customer view. This data is then made available to other systems. This in turn means that the data can be used for marketing campaigns, customer service or to enhance the customer experience.
>A CDP should be able to manage personalization, campaigns across different channels and at the same time follow the GDPR guidelines. It enables marketers to group data into profiles, thus creating a better and more personalized customer experience.
Does that make sense?
So it is all about collecting, analyzing and acting on the data.
5 benefits of a CDP for marketers
A CDP is primarily built for marketers, but it helps provide insights and information for the entire company as it reflects your customers and their behavior. For you as a marketer, you have five specific benefits below with a CDP:
Get a 360° customer view - A CDP is designed to combine data from different sources and create a uniform customer image. This way you as a marketer can better understand your target group and their behavior.
Get transparency in your marketing efforts - Many times it is difficult to know the actual costs and returns of marketing efforts. With a CDP you can clearly see what you are spending and how each channel and campaign is performing. This way, you get transparency in your digital marketing efforts.
Gain insights that help you make better decisions - By gathering, analyzing and acting on your customer data you can make better and reality-based decisions. Your company can respond to changes faster, both in regards to the market and with customers.
Focus on business benefits - Today, many marketers and analysts spend a lot of time collecting and understanding data. By automating this and getting it delivered in real time, you have time left over to create profitability and a better customer experience.
Create a better customer experience - By having a 360° customer view you can create a unified customer experience. Today, we use more channels and devices than before, and we expect the experience to be the same. This becomes possible, thanks to a CDP.
September 24, 2020
Shifting Customer Journeys with Customer Data Enrichment: A Marketer’s Guide
Marketing leaders are experiencing a glut of customer data—with estimates from the IDC that the global datasphere will grow from 33 zettabytes in 2018 to 175 zettabytes in 2025. (One zettabyte is a trillion gigabytes or a billion terabytes). At the same time, customer journeys have been shifting rapidly as customers’ needs, technology, and new generations have radically altered the buying experience.
To put that data to use in building and nurturing an ongoing relationship with your customer, your data needs to be highly accurate, detailed, and up-to-date. In other words, getting just your customer’s name and email address won’t cut it anymore. Today’s marketers want to know, for example, if their best customers have recently moved or had a baby. They want to know what brand of tires they buy and what time of day target buyers typically do their grocery shopping. And they need to pull together a host of online activities and behaviors, and resolve them into one accurate profile per customer—especially since so much of each customer’s experience happens online, not in physical stores.
De-duping, Resolving, and Unifying Customer Data
Data enrichment is a requirement for today’s marketing organizations, especially if you rely on customers themselves as your primary source of data. In that case, it’s easy to end up with multiple phone numbers or fake names and email addresses. Or—if you’re running a big advertising campaign—you could end up with too small an audience segment.
By gathering and unifying second-party data and third-party data with your own data, you can remove that duplicate information, fill in missing or inaccurate attributes, and update the record with the most current information. That’s the power of data enrichment. And for best results, you’ll want to go through this process on a continuous basis.
Here are some common types of data to include in your data enrichment process:
Social Media Profile
Purchase and Transaction History
Behavioral Web
Mobile Data
Loyalty Program Histories
Geolocation Data
CRM and Customer Support Data
Using data enrichment, you can create a detailed composite of your customer by gathering additional data and combining it with your own proprietary data, like email addresses, phone numbers, and mailing addresses. Once all the data is stored in one place—like Arm Treasure Data Customer Data Platform (CDP)—you can analyze it to gain insights, inform your business strategy, and adapt as customers change the way they shop over time.
How do you gather the right second-party and third-party data? Many data providers specialize in certain types of information. Acxiom and Nielsen provide household demographic data. Experian and TransUnion provide credit score information. Bombora and Dun & Bradstreet provide business-related data. These data providers often collect and analyze data using a range of public and proprietary sources including census data, property records, warranty information, customer surveys, and store purchase panel data.
The real value of data enrichment, ultimately, lies in what you do with your customer data. Data enrichment helps with key business operations, such as prospect profiling, ad targeting, identifying look-alike prospects, and message personalization. Using powerful machine learning, you can build new customer segments and create advanced predictive models to analyze those segments. You can pinpoint key target groups of prospects and improve efficiencies across your campaigns.
For example, data enrichment helped Subaru identify the most valuable segments of prospective customers and tailor its marketing campaigns accordingly, boosting the “highly likely to buy” estimate from 26 percent to 73 percent.
How Data Enrichment Works
CDPs combine data sets—such as customer demographics and thousands of behavioral data points—into a single environment. Once that data is ingested and unified into a complete customer profile, it can be easily visualized or exported for modeling purposes. Marketers can also build segments with shared attributes and immediately activate them into campaigns designed to increase sales, boost engagement, and reduce churn.
Key considerations in developing data enrichment methods
How to employ new processes for filling in the blanks and adding new data
The advantages of a repeatable, continuous process for data enrichment
Lead generation is complicated enough without having to fight with your CRM and martech stack to correct your data. Data enrichment combined with a CDP can make all the difference.
September 17, 2020
New Study Finds Data Key to Unlocking Superior Customer Experience
I often see the words “customer experience” or “customer-centric” used in marketing messages as companies look to meet increasingly high consumer expectations. That’s not a surprise as today’s technologies and data-driven approaches have enabled brands to provide more personalized experiences across the customer journey—and consumers have taken notice. According to a new Arm Treasure Data and Forbes Insights study, “Proving the Value of CX,” nearly three in four consumers (74 percent) are either somewhat or very likely to buy from a company based solely on their experience, regardless of product or price. This shows the incredible importance placed on the customer experience (CX), but what does CX really mean and how can companies better deliver it to their consumers?
Defining the Customer Experience
While every company wants to be more customer-centric, the term CX can mean many different things to many people. The study found that 45 percent of CX leaders define CX as “the customer’s aggregate perception of your company based on all their interactions with your brand, product or service.” Essentially, that speaks to the customer journey from initial interest to purchase to customer service and everything in between—a critical process to building loyal and repeat buyers. In fact, 65 percent of consumers say a consistently positive experience through their entire interaction would make them a long-term customer of the brand.
Placing CX at the Forefront
However, it’s not easy building an effective CX strategy into an existing business. According to the report, one out of four CX executives say not having the right people involved is a constraint that prevents their team from implementing a streamlined CX approach. To be successful, businesses should look at CX as the whole business, not just part of it. Reorganizing around CX requires reimagining a company’s processes and taking a data-driven approach that breaks down silos across the organization. In fact, the companies that are excelling in personalization and individual customer preferences are those that can understand and draw insights from their individual-level customer data.
Harnessing Data for Better CX
The CX and customer journey are heavily reliant on data from start to finish. It starts with obtaining a holistic view of all the customers’ interactions with the brand across both digital and in-store. While this can often be challenging as touchpoints are spread out across multiple systems—such as POS, CRM, social, customer service, and more—leveraging a customer data platform (CDP) can help securely unify all of the data to provide a single source of truth. Data can then help to define which features will encourage said customers to stay loyal. Finally, data is essential to assessing the potential lifetime value of a customer. Predictive modeling that compares usage patterns of new customers with longer tenured ones can help project which are the most valuable.
Creating a cycle of data-driven improvement is what differentiates the most successful companies from their peers. To create that cycle, having the right tech stack is a minimum investment that directly enables the talent, skill sets, and customer-centric mindset. This is well worth the investment as 83 percent of executives surveyed said they faced moderate to severe revenue and market share risks due to unimproved CX. Not to mention that 56 percent of companies now look to data that captures the interactions of the most engaged customers to evaluate which customer segments to nurture. Ultimately, the urgency to implement such plans to drive better CX cannot be ignored and taking control of your data should be at the forefront of your approach.
To download a copy of the complete report, visit here.
Survey Methodology
The Forbes Insights and Arm Treasure Data report is the result of two surveys.
The CX executives survey includes the views of more than 200 global CX executives. Executives held marketing, sales, CX, product, and IT titles, and represented a variety of industries. All executives came from organizations with over $150 million USD in annual revenue, with almost half from large enterprises (revenue over $1 billion USD).
The consumer survey includes more than 1,000 consumers globally, spread across all age groups (18+ years old) and income brackets. In addition, they represent a variety of purchasing categories including automotive, appliances, consumer technology, media and entertainment, retail and e-commerce goods, and financial services.
September 14, 2020
CDPs come to the rescue of marketers to deal with CCPA
#Ownyourdata campaign created ripples across the world. It led the movement towards providing the right to data privacy for consumers. Every nation responded to this ask from the consumers. National legislations reacted to it with national data privacy laws that protects citizen consumers within the country and in an international scenario.
Since the United States of America is led by a Federal system. California state was one among the few that passed a legislation called California Consumers Protection Act, 2018 (CCPA).
The California Consumer Privacy Act is a state statute intended to enhance privacy rights and consumer protection for residents of California, United States.
Understanding CCPA
Understanding CCPA in brief helps determine the onus that is laid on the businesses by the law. Customer Data Platforms like FirstHive are designed and updated to help companies remain compliant with CCPA.
Is CCPA applicable to your business?
CCPA is not applicable for all businesses and does not replace any existing laws. It is relevant and applicable to businesses that buy, receive, or sell the personal information of 50,000 or more consumers, households, or devices. Businesses that derive 50 percent or more of their annual revenue from selling consumers’ personal information; or those that have gross annual revenues greater than $25 million. For-profit companies do not necessarily have to be based in California to be subject to the statute.
How does the Subject data request process work as per CCPA?
CCPA provisions for subject access requests. Organizations must be prepared to intake and effectuate consumer access and delete requests as they come in.
Businesses that fail to comply with these requirements or tend to release personal information to the harm of the consumer would face litigation, as well as other regulatory enforcement actions.
Is Data Mapping covered under CCPA?
Data Mapping of personal information is a recommended best practice on data protection which aids subject data requests. According to CCPA, organizations should know the types of personal information that have been collected in at least the past twelve (12) months, the purposes for which it was collected, and who (including the types) of entities such data was shared with, all tracked on an ongoing basis.
To comply with this clause, CDP ensures the same and keeps the marketing team informed about how personal information is mapped across the organization.
How do online privacy and cookies notices affect your organization under CCPA?
Your organization has to explicitly state about why, how, when and for what is the consumer data collected, stored, used and distributed. This means your website should have a clear and updated cookie and privacy policy in compliance with the CCPA guidelines. The legislation requires you to cover both online and offline data means. It also requires you to describe the internal policy that is under implementation. The internal policy should be targeted towards how you ensure that the data hacks or breach does not occur from someone or a process or entity associated with your organization.
Who is associated with you to help with this?
Yes, the law also requires you to announce your association with any third-party vendors that are assigned with the duties of managing your customer data. It needs to be a transparent affair when it comes to who, when and where is the customer data made available and for what purpose.
How does FirstHive as a CDP facilitate CCPA compliance for your business?
CCPA promotes consumer data protection which empowers consumers with more rights than apparent by the law itself. This is where a CDP ensures that businesses adhere to what is expected of them in the realm of data protection.
Data updates and preferences
CCPA allows consumers to request organizations to update and change their data preferences any time. Even though the data provider is not involved with the business in a monetary transaction, the right still applies to the consumer. CDPs automate this process and update customer and lead records in real time.
First-party data for Identity Data resolution
Businesses still depend on third-party data for some basic analytics and tracking a customer. However, that is not considered authentic by CCPA. It requires a business to use only first-party data to build customer profiles that resolve identity issues that arise due to staggered data coming in from fragmented sources of interaction and information.
Right to be Forgotten
Consumers can demand any time for the right to be forgotten. CDP allows seamless implementation of opt-out and deletion requests from customers. This marketing technology is programmed to provide a single source of truth and hence can update the entire database to adhere to such requests.
Cross-Channel Preferences
Consumers interact with your business across different channels. Each consumer comes with a unique set of information and action preferences from each touchpoint. These can be asynchronously updated in the database and fields managed by a CDP.
Data Scrutiny
If a customer demands disclosure, which is allowed by CCPA, a CDP provides a transparent interface of how the customer’s data is being collected, stored, used, and updated from time to time.
Rest of the world is responding to the need of consumer data protection. Each nation is releasing new laws specific to address the data needs of consumers. There are constant updates to the existing legislation. CDP brings the ability to execute marketing campaigns in adherence to the evolving data environment across the world.
For further queries, please drop in a note to marketing@firsthive.com
September 10, 2020
B2B Marketers, Here’s How Customer Data Platforms Make Personalization and Selling Easier
Do you market B2B products or services? Ever feel like your B2C colleagues have it easy?
As a B2B company, we know that feeling. Your customers are entire companies, not individual consumers. So instead of millions of prospects, you probably have hundreds or thousands. You also often have to deal with lengthy, intricate buying processes, involving multiple people and departments. What’s more, all of these individuals likely have different preferences, interact with different channels, prefer different kinds of information, and respond to different marketing messages.
Somehow, you have to find and reach these decision makers with content and messaging tailored to them, while mounting a coherent campaign across the entire organization. The task gets even harder when your data on these customers is incomplete, inaccurate, or scattered across multiple internal systems.
An enterprise Customer Data Platform (CDP) solves these problems for B2B marketers.
A CDP provides what marketers need to deliver personalized interactions across all marketing channels: a single, unified view of who each buyer is and how they’ve interacted with your business. And it enables you to reach prospects and customers with a consistent, targeted message every time they interact with your company, online or offline.
What a CDP offers, above all, is the ability to cut through the complexity of the corporate buying process through account-based marketing. A true enterprise CDP can associate multiple individuals with an account and help you orchestrate hyper-personalized, omnichannel campaigns. The end result: more targeted, efficient, and effective B2B marketing.
How a CDP Empowers Marketers with Data
No doubt your marketing team already has systems for collecting, storing, and working with customer or user data. These may include customer relationship management (CRM) software, web and mobile analytics solutions, email automation software, call center databases, a data management platform (DMP) for your online ads, and IoT databases for connected devices.
An enterprise CDP, such as Arm Treasure Data, doesn’t replace these other systems. Instead, a CDP pulls in data from all of them to generate a single, comprehensive view of every customer. In addition, it provides actionable insights and tools to help you manage all your interactions with the customer, at every step on a buyer’s path to purchase.
An enterprise CDP assembles all your first-party data into a unified profile of every buyer, enriched with second- and third-party data. That includes behavioral data gathered in real time from any source: web, mobile, CRM, email, call centers, and so on. A CDP constantly updates each profile as new data comes in—and the platform can store unlimited amounts of persistent data, so you always have a complete view of every customer’s history.
A CDP also provides data management and modeling at scale, along with tools to guide your marketing campaigns. Such tools help you model buyers’ behavior, create fine-grained segmentation, automate workflows, and activate your other marketing applications. In addition, an advanced enterprise CDP will allow you to work directly with large data sets and build your own machine learning models and analytics.
In short, an enterprise CDP isn’t just one more specialized tool for managing a chunk of your internal customer data, or one small slice of your marketing activities. Rather, it’s a marketer-controlled system that unifies all of your company’s data about customers and helps you direct every aspect of targeted, omnichannel campaigns.
Why a CDP is the Ultimate Tool for Account-Based B2B Marketing
Account-based marketing treats each company as its very own market, with content and messaging tailored to match. Through such targeting, an account-based approach can increase conversion rates and reduce the costs of landing corporate customers.
Nevertheless, an account-based approach is hardly simple to pull off. Most companies make purchasing decisions by committee, with various individuals and divisions contributing to the outcome. That inevitably makes for a longer, more complicated, more uncertain buying process. And it means you have to sway multiple decision makers within your target account, each with different interests, intents, and patterns of data consumption.
Here’s where an enterprise CDP really shines. A CDP enables you to target the individual buyers within an organization with personalized, omnichannel campaigns, while also managing your campaign at an account level.
With a CDP, you can associate individuals with a corporate account, and then build profiles and map out identities and personas for all the individuals involved in decision making. An enterprise CDP can create profiles for both the account and individual buyers, so you can view and target corporate customers at both levels.
Armed with this information, you can then use CDP analytics and segmentation tools to understand the behavior of the different individuals in a corporate account, then create campaigns tailored to each person and their data consumption preferences. Such a hyper-targeted approach speeds up purchasing decisions and helps you make the most of your marketing budget.
Depending on the CDP, you may have access to powerful predictive tools that provide intelligent guidance for decision making. Arm Treasure Data’s predictive customer scoring engine, for example, applies machine learning to determine how intensely buyers are engaged with your company and how likely existing customers are to churn.
Meanwhile, an enterprise CDP’s unlimited storage of persistent data helps B2B marketers tackle a corporate buying process that can take months or even years. No matter how long a company’s purchasing cycle, your campaigns are based on a complete record of your interactions with both individuals and accounts, going back to your first contact.
How Account-Based Marketing with a CDP Looks in Action
The first step in account-based marketing is to identify the right corporate account to target. Then you can identify likely decision makers within the account, and launch campaigns to reach each of those people with targeted campaigns. Let’s see how that process works with Arm Treasure Data CDP.
Step 1: Identify the account
Let’s say an anonymous user is browsing your company’s website. Using a third-party vendor, the CDP identifies the IP address through account mapping and extracts the account information.
Step 2: Identify decision makers within the account
Working with another partner, Treasure Data then uses account mapping to determine who is part of the buying journey for your product or service and helps create demand units. Eventually, you’ve identified people from five different departments involved in buying decisions. Each of these committee members will have different viewpoints based on their personal data consumption.
Step 3: Build out profiles of each individual
Beginning with just a few pieces of information, the CDP builds out a profile of each person from your first-party data, while enriching it with data from outside sources.
The system can then extract useful information about each buyer’s intent from their behavioral and other data—such as their web browsing activity, mobile app usage, email correspondence, interactions with your digital ads, or communications with call centers.
Step 4: Create segmentation and targeted campaigns
Using the information and tools provided by the CDP, your company then targets each buyer within the account with a campaign tailored to that specific individual, not just a hypothetical persona. This may entail delivering personalized content, automating workflows and setting up event-based activation. When a user interacts with a particular piece of content, for instance, that behavior could trigger a targeted Google ad campaign.
Step 5: Use incoming data to optimize campaigns
Throughout this process, the different channels you are using send new signals back to your CDP, which it uses to generate new insights and update the customer’s profile. Marketers can then use that information to decide what steps to take next.
For example, you could evaluate whether a buyer took the action you wanted after viewing a piece of content—and if not, you could then mount a retargeting campaign aimed at that individual. The CDP can enrich each profile with third-party data, giving you a fuller picture of your buyers and helping you make better decisions over time.
So What Do B2B Marketers Need in a CDP?
For account-based marketing, it’s essential for a CDP to provide segmentation on multiple levels—by account, by individual, and by industry. That’s not a given with every solution on the market. In addition, here are some important factors to consider when shopping for an enterprise CDP.
Scalability: B2B companies have to work with relatively low volumes of data, since they have fewer customers than B2C companies. Nonetheless, scaling is vital for any type of sophisticated data analysis or processing.
Flexibility: A flexible CDP can easily connect with a wide range of outside systems and handle any type of data, from any source. Some vendors may offer CDPs that work well with their suite of products, but have difficulty integrating data from other systems without an add-on or workaround.
Extensibility: This means you can deploy useful new functionality quickly, rather than having to build components from scratch. For example, Arm Treasure Data has a library of prebuilt extensions for B2B analytics, churn prediction, next-best-action recommendations, sentiment analysis, and many other use cases.
Security: Any CDP should ensure the security and privacy of your customers’ data. For example, you may want to verify whether it complies with SOC 2 and ISO 2701 standards, encrypts all data and complies with the European Union’s General Data Protection Regulation (GDPR).
Financial stability: Once you’ve invested in a system, the last thing you need is to find yourself scrambling for a new solution because the vendor has suddenly closed up shop. That’s a risk with some CDP startups that have only limited funding.
To sum up, a CDP could give you the competitive edge you need to land that next big corporate customer. But enterprise CDPs are far from interchangeable, and some of them may be better suited to B2C than to B2B marketing—especially if you’re pursuing an account-based marketing strategy. So it pays to take your time, consider all your options, and see which one is truly right for your business.
Two years after GDPR and with CCPA now in full affect – how is it still possible that there are still so many unanswered questions about how consumer data is used? Nearly 80% of Americans are still concerned about their personal data and only 6% know what is being done with the data collected. Does the future hold a proliferation of CCPA-like regulations in every state? Or is there an opportunity for business leaders to rebuild consumer trust?
This is where the notion of responsible data use comes into play. A simple example? Consider the needs of traffic planners. Historically, planners were limited to costly and time-intensive surveys and physical observation. Today, we can gather much richer and more accurate traffic patterns based mobile phone data. Analyzing home locations to understand popular routes and destinations or even population demographics enables new levels of sophistication in transportation optimization. Most importantly, these use cases only require aggregate information about the people traveling (i.e., time of day preferred by morning commuters). They do not require knowing which specific individuals are on the roadway.
Responsible use means that the there is a narrow and well-crafted goal for the using the data (i.e., road construction), the application of that data achieves that goal (routes taken by demographic segments), and only that goal, and the application does not rely on identifying any specific individual. The standards of responsible use strike the balance of delivering innovation – bringing the power of data science to improving consumer experience and discovering new market opportunities – while keeping trust and transparency paramount.
The Current Approach to Personal Information
Before we get into how we make responsible data use the norm, we need to understand what is currently considered personal information and deserving protection.
Historically, data privacy centered on personally identifiable information (PII) — data that could identify a person or be used to commit fraud (i.e., name, Social Security number, driver’s license number). In recent years, the focus has broadened to Personal Information which includes persistent identifiers such as Advertising ID, IP Address, and cookies as this pseudonymous data reflects an individual’s behavior and may be used to indirectly identify a consumer if combined with more information.
With the rise of 5G and IoT, data interactions will continue to explode and, as a result, we will see continued evolution in consumer preferences for what is considered private (and more “grey areas” for regulatory policies). It thus falls on the companies creating and using this data to enforce standards for the “responsible use of data” and address the question “should we do this?” rather than “can we do this?”
4 Questions That Define Responsible Data Use
Responsible data use is based on the answers to 4 simple questions. It starts with starts ensuring that the problem statement being addressed only requires aggregated and de-identified data.
Is the need for aggregated or individual insight? The platform should require a minimum threshold of records for inclusion (aggregated data). It should also never need to identify any individual specifically. Data that does not meet this standard should be discarded so as not to keep isolated cases.
How has consumer consent been validated? It is not enough to confirm consumer consent was collected by the first-party data source. Organizations need to ensure that the data that they are using was obtained with consumers’ rights and benefits in mind, that consumers know why the data is being collected and they can change their participation status at any time.
Are data inputs privacy-aware? Once validated, all personal or user-level identifiers should be discarded by either the first-party provider or the platform. Privacy-aware refers to using the intelligent outputs of de-identification or aggregation, rather than the original data which can identify a unique individual directly or indirectly. For example, privacy-aware outputs could be an obscured device ID so what the platform sees is a random string – not identifiable to any individual. Platforms that only consume privacy-aware data eliminate human error or maleficent actions.
How transparent is the data policy?Recent research shows the importance of open and friendly communications in conveying how the data provided by consumers clearly matches the purpose for which it is needed. It is similar to changes implemented after the Credit Card Act of 2009 – where simpler language and presentation can make data approach and data lifecycle (data source, types of data interactions, and data retained in the platform) much more accessible.
The Virtuous Cycle of Innovation and Trust
There will still be questions on the nature of de-identification and aggregation (and extreme examples of “extrapolating” individual identities). However, without user-level identifiers, it is nearly impossible. This ongoing consternation reflects the extent that trust has eroded in technology.
Businesses do not need policymakers to define evolving privacy needs. This is the opportunity for companies to rebuild consumer faith. The standards for responsible use of data provide a framework for utilizing the power of data, without compromising fundamental rights of privacy. By pro-actively adopting responsible use standards, we can start to differentiate applications that respect consumer privacy and create a virtuous cycle where consumers gain confidence in the benefits of these applications.
Marketing Privacy Glossary
Term
Acronym
Definition
A
Access Control List
ACL
A list of objects, and who is allowed to access each object.
Act on Protection of Personal Information
APPI - Japan
Japan law, applying to businesses that hold personal information of more than 5,000 people. It requires companies to specify the purpose for which personal information is utilized. Data subjects can request disclosure of information that is held about them.
Active Data Collection
Collection of data provided directly by the subject.
Ads/Marketing Compliance Manager
System to manage data regulations related to advertising and marketing activities, such as gathering consent.
Affective computing
branch of artificial intelligence dealing with measurement or simulation of human emotions
Algorithmic Trust
psychological phenomenon that people perceive algorithms as more trustworthy than humans
Anonymization
The process of removing personal identifiers from a data set, so that identity cannot be derived from the remaining data. Anonymization is irreversible. Compare pseudonymization.
Appropriation
Using another person's identity without their approval. Also called identity theft.
Authentication
The process of ensuring a person (or other entity) possesses a piece of information they have previously provided. Compare with verification, which ensures a person is who they claim to be. For example: a password proves a user is authorized to access a social media account (authentication) but additional proof is needed to show the account was opened by the person whose name is on it (verification).
Automated Policy Inheritance
Ability to govern data by the rulies under which it was originally captured, regardless of where the data is subsequently used.
B
Biometric Information Privacy Act
BIPA - US, Illinois
Illinois law dating to 2008 that restricts collection of biometric data and gives private individuals the right to sue for damages after a violation.
Bodily Privacy
Privacy related to a person's body, such as physical searches or drug tests.
Breach Disclosure
Practices related to informing authorities or subjects if their data is exposed.
Bring Your Own Device
BYOD
Practice of allowing workers to access company systems through their personal devices.
Bring Your Own Identity
BYOID
practice of enabling Web site visitors authenticate themselves by connecting to identities they have etablished in other systems such as Facebook, LinkedIn, Google, Amazon, etc.
Browser Fingerprinting
Practice of identifying a device over time by storing and comparing a combination of technical attributes associated with the Web browser used on that device, typically without explicit permission; provides an alternative to other identification methods; may violate privacy rules.
C
California Consumer Privacy Act
CCPA - US, California
Privacy law implemented in State of California in 2020; includes extensive personal rights regarding data use including opt-out from sale of personal information and data portability.
California Privacy Rights and Enforcement Act of 2020 (also known as Proposition 24)
CPRA- US, California
California law, appearing as Proposition 24 in November, 2020 election, that expands privacy rights provided within California Consumer Privacy Act (CCPA).
Children's Online Privacy Protection Act
COPPA - US
U.S. federal law governing how Web sites treat data for people under age 13.
C-I-A Triad
Information security principles: confidentiality, integrity, availability.
Commission nationale de l'informatique et des libertés
CNIL - France
The national data proection authority for France.
Consent
Permission granted by a data owner to use their information for specified purposes; may be implicit or explicit.
Consent Mgmt Platform
CMP
System that collects consent in compliance with legal requirements.
Content Data
The actual text, images, and other information contained within a communication, or information derived from this; contrasts with metadata, which is limited to routing, etc.
Contextual Advertising
Advertising based on the content of a Web site or search query where the ad appears; does not require information about the individual receiving the advertisement.
Cookie
Small file installed on a Web browser to capture user information and share it with the cookie owner.
Cookie Consent Manager
System that collects consent to use Web browser cookies to store information about a user.
Cookie Directive
Ammendment to the European Union ePrivacy Directive, adopted in 2009, that requires user consent to installation of cookies and other online tracking technologies. The ePrivacy Regulation, based on this directive, is still under negotiation.
Corporate Owned, Personally Enabled
COPE
Corporately Owned, Personally Enabled: the business practice of providing employees with computing devices for personal use.
Cross Border Data Transfers
Movement of data from one legal jurisdiction to another. May be forbidden or governed by rules to ensure protections granted in the original jurisdiction are maintained.
Customer Access
Ability for a consumer to review and manage data collected about them; see Data Subject Access Requests.
Customer Identity and Access Management
CIAM
Technology that manages, authenticates, and verifies customer identity and profile data
Cybersquatting
creation of a Web domain name similar to a another, popular domain, done to divert traffic or force a purchase by the rightful owner.
D
Dark Patterns
Methods used to trick users into taking unintended actions, including purchases or revealing personal data.
Data Aggregation
Analytical method that creates summary measures (sum, average, median, etc.) of similar data items, often to obscure information about single individuals.
Data Anonymization
See Anonymization.
Data Breach
Any unauthorized access to data collected by an organization.
Data Breach Notification (EU)
The process of informing authorities and data subjects whose data has been exposed by a breech. Many privacy regulations impose specific requirements for when, how, and how quickly notifications must be made.
Data Classification
Process of determining the type of data stored in a particular object or field, so the data can be handled as required for that data type.
Data Controller
Under GDPR, an organization that determines the purposes and means by which personal data is processed. Compare Data Processor.
Data De-Identification
The process of removing personal identifiers from a data set, so that identity cannot be derived from the remaining data. May be reversible (pseudonymization) or irreversible (anonymization).
Data Lifecycle Management
DLM
Systematic approach to managing data from acquisition through use to disposal.
Data Mapping
Process of tracking where each type of data is stored in company systems, to facilitate data management processes.
Data Masking
Process of hiding actual values of data elements without changing the format.
Data Minimization
The practice of collecting and using the minimum amount of personal data needed for a particular purpose.
Data Pipeline
Technology to automate the process of ingesting, preparing, and exposing data for analytics and operations.
Data Portability
The ability to move personal data from one system to another, despite format or structural differences. Goal is to avoid lock-in by the original system.
Data Portability
ability to easily move personal data between systems, to avoid lock-in by original system
Data Privacy
Ideas and practices relating to control of personal data, especially by the subject.
Data Processor
Under GDPR, an organization that processes personal data on behalf of a Data Controller.
Data Protection Authority
DPA
National body responsible for enforcing data protection regulations under the 1995 Personal Data Directive of the European union. Now called Supervisory Authorities under GDPR.
Data Protection by Default
Requirement under GDPR to collect the minimum required amount of personal data and to use it for only the specified purposes.
Data Protection by Design
Requirement under GDPR to design systems to implement data protection principles and safeguards.
Data Protection Impact Assessment
DPIA
Analysis that assesses the impact on fundamental rights created by a proposed data collection process or project and identifies steps to control the risks. Required in advance of data collection under GDPR.
Data Redaction
Techique to preserve privacy by removing a portion of data, such as names from a document or digits from an ID number.
Data Removal
Practices related to deleting data from company systems, either on request or on retention schedule. May require removing or masking data in connected systems, back-ups, etc. Includes keeping records to prove requested removals have taken place.
Data Retention
Practices related to storing and processing data for specified time periods and deleting it after the period ends, as defined with contracts.
Data Schema
Structure used to organize stored data.
Data Subject Access Rights, or Data Subject Access Request
DSAR
Processes related to accepting and executing requests by a data subject to an organization to review, change, and delete data about the subject held by the organization.
Data Subject Rights Management
Processes related to giving subjects control over data an organization has collected about them.
Data Watermarks
Technique for tagging data with its origin in ways that cannot be removed and are hidden from unauthorized users.
DataOps
Methodology to improve data quality and currency in support of analytics and data processing.
Deletion Validation
Technique for confirming that data has been erased.
Denial of Service
DoS
cyber attack that disrupts a system by creating an unmanageable volume of interactions
DIFC Data Protection Law No.5 of 2020
DIFC - UAE
UAE privacy law effective October 2020. Designed to match EU privacy standards.
Differential Privacy
technique to share data while preserving privacy by exposing only pools of individual records that cannot be used to detect the presence of a specific individual
Digital Fingerprinting
Practice of identifying a device over time by storing and comparing a combination of technical attributes associated with the device, typically without explicit permission. See browser fingerprinting.
Digital Rights Management
DRM
Techniques to track ownership and use of digital assets. Typically applied to content such as writing, music, or video but may apply to any type of data.
Disassociability
Technique of removing information from a data set that can be used to identify an individual while still allowing a system to meet its purpose.
DNS spoofing
attack method that corrupts the Domain Name System by altering entries that direct traffic to the proper IP address, sending traffic somewhere else
Do Not Track
DNT
Request by a data subject that a system not capture or share information about the subject's behavior. Usually applied to tracking Web site behavior for marketing purposes.
Draft Decree on Personal Data Protection
DPDP - Vietnam
Vietnam proposed law governing collection and use of personal data
E
Encryption
Technique of transforming data so it cannot be understood, but can be transformed back into its original format with an algorithm and/or key. In this sense, encyption is reversible. Compare to masking or anonymization, which are not reversible.
European Union Agency for Fundamental Rights
FRA - EU
The independent center of reference and excellence for promoting and protecting human rights in the EU.
F
Family Education Rights and Privacy Act
FERPA - US
U.S. federal law governing access to student data held by educational institutions.
Federal Information Security Management Act
FISMA - US
U.S. law establishing information security framework for federal agencies
Federated Learning of CoHorts
FLoC
Method for grouping consumers based on browser behaviors without revealing personal identities. Used for privacy-safe ad targeting.
First-Party Data
Data about a person collected by a company as part of a direct relationship, such as during a visit to the company's Web site or when making a purchase from the company. Compare second- and third-party data
Fuzzing
software testing or attack method that submits large amounts of random data to a system
G
General Data Protection Regulation
GDPR - EU
European Union regulation governing treatment of data collected about EU residents, adopted in 2016 and taking effect in May 2018. Defines rights of individuals and imposes requirements on organizations collecting personal data.
Google Privacy Sandbox
Technology being developed by Google to enable ad targeting without use of cookies.
Gramm-Leach-Bliley Act
GLBA - US
U.S. law establishing data sharing notification and security rules for financial institutions
Granular consent options
Practice of defining consent options related to specific uses, or purpose, data types, or data users.
H
Health Information Technology for Economic and Clinical Health Act
HITECH - US
U.S. law establishing breach reporting and notification rules for health information
Health Insurance Portability and Accountability Act
HIPAA - US
United State law that regulates health insurance. It includes data privacy, security, and confidentiality. Healthcare providers and others covered by HIPPA have permission to use patient data if it relates to treatment, payment, or other healthcare needs. Any use of patient personal health information (PHI) for marketing or sales would require specific consent.
I
Identifiability
Degree to which data can be connected to a specific individual, in terms of precision (determining which individual), confidence (connecting to the right individual), and security (avoiding misrepresentation).
Identifier for Advertisers
IDFA
ID for Apple devices made available for advertising tracking. Apple rules added in 2020 require user consent for sharing with advertisers, limiting coverage.
Identity Verification
The process of ensuring that a person is who they claim to be. Compare with authentication, which confirms that a person possesses a piece of information they have previously provided. For example: a password proves a user is authorized to access a social media account (authentication) but additional proof is needed to show the account was opened by the person whose name is on it (verification).
Incognito mode
Browser feature that enables users to browse without allowing access to identifiers or tracking devices. Any similar technology in other systems.
Information Commissioner's Office
ICO
Independent body that upholds information rights within the UK.
Information Governance
Process of managing data from through use to disposal, including privacy compliance.
Information Lifecycle
Systematic approach to managing data from acquisition through use to disposal.
J
K
L
Lawfulness of Processing
The GDPR principle that any processing of personal data must have a legal justification. There are six justifications: consent, contract, compliance, public interest, vital interest, and legititmate interest.
Legal Basis for Processing
see Lawfulness of Processing
Lei Geral de Proteção de Dados
LGPD - Brazil
Brazil privacy law to be in effect by 2021. Includes extensive personal rights regarding data use. It applies to almost all sectors of economy, public and private; has extraterritorial scope, has a broad definition of what personal data is - and virtually any data can be considered personal and subject to law.
Ley Federal de Protección de Datos Personales en Posesión de los Particulares
FDPL - Mexico
Mexico law which went into effect in 2012. Closely follows APEC Privacy Framework. It protects any data that could lead to identifying a person and data controllers may only collect data relevant to their commercial purposes. Personal data must be deleted when the controller no longer needs or uses it.
M
Mandatory Access Control
MAC
Data access control built into an operating system.
Metadata (also see DRM)
Data that describes the data elements stored in a system.
Mobile Device Forensic Tools
MDFT
Technology to recover digital evidence from mobile devices, including mobile phones and other devices with communication abilities.
Mobile Device Management
MDM
Technology that allows remote control over mobile devices, typically by a corporate owner providing the device to its workers.
Multi-Factor Identification
Authentication process requiring two or more pieces of information, such as a password plus fingerprint.
N
Noise Addition
Injection of false information into a data set to make subject identification more difficult.
Notifiable Data Breaches Act
NDB - Australia
Australia law establishing breach reporting and notification rules
O
Onward Transfer
Transfer of data made by someone who did not receive it directly from the original data collector (controller). Example: a subcontractor for a data processor.
P
Payment Card IndustryData Security Standards
PCI DSS
global security standard for data related to credit and debit card processing
Perimeter Controls
Technology that protects entry into a network from the outside.
Persistent Storage
Storage of data in a medium that retains it indefinitely, such as tape or a hard drive.
Personal Data
Term defined in GDPR article 4 (1) as "‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person;". Personal data can be used to identify a specific individual, either by itself or in combination with other information.
Personal Data Protection Act
PDPA - Singapore
Singapore law governing collection and use of personal data
Personal Data Protection Bill
PDP - India
India proposed law governing collection and use of personal data
Personal Data Protection Draft
PDP - Indonesia
Indonesia proposed law governing collection and use of personal data
Personal Information
Term defined in CCPA Section 1798.140(o)(1) as “information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly, with a particular consumer or household”. Personal information can be associated with a specific individual but may not identify them.
Personal Information Protection and Electronic Documents Act
PIPEDA - Canada
Canada law that applies to private sector organizations across Canada that collect, use, or disclose personal information. Individuals have the right to access data held by organizations and have a right to challenge accuracy. PII can only be used for purposes for which collected and must be protected.
Personally Identifiable Information
PII
Term used primarily in the US to describe data that uniquely identifies a specific individual, such as a Social Security Number or email address. Sometimes also includes data that can identify a specific individual when used in combination with other data, such as gender, Zip code, and date of birth.
Policy Definition
Technology to define rules that govern use of personal data, often based on the data type, subject location, consent status, and other conditions.
Policy Enforcement
Ttechnology to enforce data privacy policies that are defined within a system.
Principal Agent Problem
problem that an agent may act in her own best interests, to the detriment of the principal she is representing
Privacy Act
US
U.S. law governing collection of data about individuals by federal agencies
Privacy Assessment
Review of processes and technologies that an organization applies to privacy compliance. Compare with Privacy Impact Assessment, which applies only to a specific project.
Privacy by Design
PbD
Systems engineering approach that includes data privacy as a fundamental consideration.
Privacy Impact Assessment
PIA
Process or project analysis that defines what personal information is involved, how it is handled to comply with privacy regulations, and how risks are mitigated.
Privacy Impact Assessment Triggers
Events that require a privacy impact assessment to be conducted, such as merging data sets containing personal data.
Privacy-Enhancing Technologies
PET
Technology that protects or perserves personal privacy, often by enabling subjects to expose the minimum amount of personal data needed to achieve a task.
Private Facts
personal information that is not publicly known, is not a legitimate public concern, and the subject prefers to remain private
Profiling
Techniques to classify or predict individual behavior based on personal data.
Programmatic Buying
Any form of automated advertising media buying, especially forms based on evaluating personal data of ad recipients.
Programmatic Digital Out-of-Home (pDOOH)
pDOOH
Out of home digital advertising, such as electronic billboards and in-store signs, that is sold via automated bidding.
Proportionality
Concept of balancing the amount of personal data collected for a process against the value and risks of that process.
Protection of Personal Information Act
POPIA - South Africa
South Africa privacy act protects personal information processed in South Africa and applies to any organization processing information in South Africa.
Provisioning
assignment of resources to a personal or system, typically when setting up a new account
Pseudonymisation
The process of removing personal identifiers from a data set, so that identity can subsequently be derived given additional data. Pseudonmyization is irreversible. Compare anonymization.
public safety data sets
data sets containing information related to public safety, such as crime statistics, traffic accident locations, flood plains, vehicle recalls, and epidemiological records.
Purpose Limitation (Principle of Finality)
Principle that the purpose for which data will be used should be specified when it is collected and subsequent use must be compatible with those purposes or justified by other legal bases.
Q
R
Record of Processing Action
ROPA
Requirement under GDPR for data controllers to keep a records of personal data processing and to put protections in place.
Rectification
The right for a subject to require corrections to inaccurate data an organization holds about the subject.
Right of Access
The right for a subject to view data that an organization holds about the subject.
Right to Be Forgotten
RTBF
See Right to Erasure
Right to Erasure
The right for a subject to require an organization delete data it holds about the subject.
Right to Restriction
The right for a subject to restrict, under specified conditions, how an organization uses data it holds about that subject.
Risk Analysis & Alert
Analysis of the risks posed by personal data held by an organization or used in a particular project or process.
Role-Based Access Controls
Data access controls based on assigning specific rights to specific user roles, with the intent of only granting to data when it is needed for a specific purpose.
S
Sarbanes-Oxley Act:
SOX - US
U.S. law establishing governance and auditing requirements for public companies
Secondary Use
Using personal information for purposes not specified when it was collected.
Second-Party Data
Data about a person collected by a company as part of a direct relationship and then shared with another company. Compare first- and third-party data.
Sensitivity Label
Label assigned by data subjects to indicate how important they feel it is to keep the data private.
Service Level Agreement
SLA
Set of performance measures that a vendor agrees to meet.
Single Factor Identification
Authentication process requiring a single piece of information, such as a password.
Social Engineering
Security attack method that relies on tricking authorized users into unintentionally enabling a breach, such as revealing a password or installing malicious software.
spoofing
see DNS spoofing
Standard Contract Clauses
SCC
Standard contract terms that specify how personal data will be used to ensure compliance with GDPR rules when the data is transferred outside of European Economic Area (EEA) to a location where data protection has not been assured through an adequacy decision.
Statistical Noise
Random variations in data values. May be introduced purposely as part of a differential privacy process to obscure actual values that can be used to reidentify individuals whose personal data is included in a data set.
Subject Erasure Handling
Process of removing subject data from an organization's systems in response to a subject access request.
Sugging
Selling under the guide of research.
Supervisory Authority
National body responsible for enforcing data protection regulations under GDPR. Formerly known as Data Protection Authority.
Surveillance-as-a-Service
business model that is based on customers paying to be surveilled.
T
Telematics
Technology to collect and distribute data generated by vehicles or related devices.
Territorial Privacy
Privacy related to a person's location, such as one's home or vehicle.
Third Party Consent
Consent for a property search granted by someone with legal access to the property who is not the subject of the search, such as a co-tenant.
Third Party Data Sharing
Data shared with someone who lacks a direct relationship to the subject.
Third-Party Data
Data about a person purchased from a company that lacks a direct relationship to that person, such as a data compiler. The original source may have been a company with a direct relationship or a collection technique that works without a direct relationship. Compare first- and second-party data.
Time-Stamping
Process of recording when an activity took place, used in audits and verification.
Tokenization
Process of replacing a specific data element with another element, often generic (e.g. replacing person's name with 'Name').
Transient Storage
Data storage that lasts only a brief period, such as during an interaction.
Transparency Consent Framework
TCF
Technical standards, policies, and provider registries developed by IAB Europe to help publishers comply with GDPR consent regulations.
U
UK GDPR
The post-Brexit transposition of the GDPR into UK law.
Unambiguous Consent
Consent that meets GDPR standard for being a clear, voluntary indication of user intent.
User-based Access Controls
Access based on rights granted to a specific user.
V
W
Workflow Management
Technology to follow a structured process to execute a task.
X
Y
Z
Zero Knowledge Proofs
ZKP
Data verification method that works without sharing the data being verified.
Zero-Day Vulnerability
0-day
Security flaw that a software developer knows about but has not developed a patch to fix
Zero-Party Data
Data that a person intentionally provides to a company within a direct relationship, such as a survey response. Compare first-party data.
August 17, 2020
Getting Your House in Order: Privacy Tips for You at Home and at Work
Pull up a sofa. Now that home is where the majority of us are working, individuals and companies are becoming acutely aware that ensuring data privacy is a shared responsibility. This goes well beyond what was necessary prior to COVID-19 when people took laptops home from the office where presumably appropriate protocols were in place to guard against data misuse and outside intruders gaining access to company assets. For marketers who work with customer data, it’s crucial to handle that data with care to avoid it getting viewed, shared or accessed inappropriately.
The first step is to look at basic home information security. These are the things we all know we should do, like making sure our WIFI network is secure; not connecting with unsecured networks; using a VPN; not using personal computers if we have business-assigned computers we are supposed to use; not letting others access our business data or computers; using strong passwords and changing passwords periodically; and being very selective about emails you open and links you click on.
These articles from Data Privacy Manager and Forbes cover this in more detail, but a good rule of thumb is that if you don’t recognize something, check it first – whether it’s an article source, the sender of an email, or the validity of a link.
Managing customer data appropriately has its own specifics and challenges, and it’s important to work closely with your company’s data security experts to make sure you and your team understand what access you should have to data and how you can use it. Familiarize yourself with your company’s privacy policy. Keep in mind, that customer information has to be handled with great care and that the company has legal obligations to do so. This applies to data records and also to discussing, copying or displaying customer information whether via email, IM, or via Zoom or other meeting platforms. If your company has not provided your department with specific guidelines for this, you should ask for information and check periodically for updates both on what you should be doing and how you can ensure nothing’s been breached.
In terms of list management do and don’ts: do remove personal identifiers whenever possible, delete customer data as soon as you’re done using it, and encrypt personal data that you do store. Don’t download client lists or import or merge outside data. This is also a good time to take an audit of your data to see what you’re storing and why.
Discuss how you can recognize or check for a data breach and put procedures in place with company experts and your own team to be prepared to immediately address a breach, should one occur. Educate and involve your employees at the outset, so they understand what needs to be done to protect data, why it is critical to do so, whether there is any individual liability for that data, and how everyone can work together to comply.
These precautions and protocols are good proactive measures that are easy to implement and far better to do ahead than to deal with the consequences of a problem afterwards. It’s also important to recognize that however long it takes for the current pandemic to be tamed enough to allow a return to the workplace, this has caused a clear rethinking of the work-home paradigm that will likely extend well into the future.
August 13, 2020
What’s in store for retail with the customer data platform
According to industry analysts, 2020 will bring exciting innovations that promise to reshape the retail industry. Let’s take a look at the opportunities and explore the data challenges resulting from these trends.
Augmented reality (AR) takes the stage
Technology is catching up with expectations, with second generation augmented reality tools that make the technology more immersive than ever before for retail customers. Customers can try on outfits or makeup or even see how furniture will look in their own living room. By enabling try-before-you-buy experiences, augmented reality not only boosts sales, but also reduces returns from online shoppers. With tangible benefits to both the top and bottom line, more retailers will eye opportunities presented by augmented reality.
Digital and physical are converging
It’s no longer either or when it comes to online and offline. Savvy consumers want the best of both worlds, so in the near future, e-commerce gets physical with augmented reality while stores go digital. The death of the store has been greatly exaggerated. Instead, there will be a transformation of retail space. Customers still like the experience of in-person shopping, which combines entertainment and utility. A new breed of stores without walls will go beyond the confines of the physical space to create a blended experience where customers at the store can research products they see in person, order items that are out of stock in the store, or get location-targeted texts about items they have browsed online that are available in store.
Shopping goes social
Thanks to the expanded e-commerce capabilities of social platforms, social shopping is gaining wide-spread traction. Social shopping is the merging of social media and commerce, and it’s yet another channel for retailers, big or small, to build brand awareness, generate leads, and engage customers, as well as transact. From buy buttons to shoppable posts and stories, it’s time for retailers to get in on the action. Social networks give brands access to highly engaged audiences with high purchasing intent.
The rise of chatbots
As technology improves with natural language processing and AI, customers are becoming more comfortable interacting with chatbots for customer service and purchases. Chatbots can assist customers and give them the sense that they’re interacting with a knowledgeable retail associate. Chatbots deliver a high level of personalization with automation, allowing brands to deliver consistent, high-quality customer service at a lower cost. Answering customer questions immediately keeps customers on the site and lowers the chance that they will leave for a competitor or defer the purchase while a smooth hand-off to a live agent will strike the right balance between digital and human touch.
Customer data platforms (CDP) fuel exceptional retail experiences
These four innovations present retailers with the opportunity to take the lead in customer experiences. But they also create new challenges with even more customer touch points and data silos. For retailers who are already grappling with data overload, it’s hard to move forward. That’s where a customer data platform (CDP) comes in. It gives retailers the freedom to innovate without the complexity and cost of managing customer data and extracting value from the data. A customer data platform gives the line of business the tools to deepen its understanding of the customer and enhance the customer journey. The platform unifies an ever-increasing volume of customer data from all sources, including not only traditional sources like campaigns, transactions, and calls, but also emerging sources like social shopping, augmented reality, and chatbots to create a single view of the customer which is essential for powering personalized experiences at scale.
Take for example augmented reality and social shopping. In the morning, your customer browses your website, trying on products with the help of augmented reality. In the evening, they scroll through social media. In an ideal world, they see highly relevant shoppable social posts featuring the products that they’ve not only tried on virtually but also are most likely to buy based on previous purchases. This seamless experience is powered by a customer data platform that is able to bring together disparate data across web, augmented reality, social, and past transactions to create a single view that is at the core of personalized engagement.
Microsoft Dynamics 365 Customer Insights
Microsoft Dynamics 365 Customer Insights is a preassembled and flexible customer data platform with built-in artificial intelligence (AI) to unify customer data across all sources and generate actionable insights that power personalized experiences. Using prebuilt connectors, the solution brings together customer data from any source. The solution automatically resolves customer identities using AI and creates a persistent, unified customer profile. Customer Insights proactively identifies segments and generates predictive insights such as churn rates, lifetime value, and recommended products. Real-time integration with business applications and business processes ensure that marketing, sales, and service efforts are tailored for each customer. Brands see results faster with Customer Insights, an intuitive and ready-to-go customer data platform that requires minimal training and IT assistance.
Microsoft’s customer data platform powers personalized experiences for retail customers while maintaining the strictest compliance and security standards so that all customer data is securely managed and adheres to the General Data Protection Regulations (GDPR). Built on a hyper-scale Microsoft Azure platform, Customer Insights allows organizations to tap into powerful analytical and full customization capabilities using Microsoft AI, Azure Machine Learning, and Power Platform.
Learn more about Customer Insights and see how global brands are transforming the retail experience with a single view of the customer.
August 6, 2020
Our vision for the Microsoft customer data platform
Empowering organizations everywhere to gain insight from all their data sources and deliver personalized customer engagement
There is a fundamental change occurring across industries and organizations: Data now comes from everywhere and everything. As customers have access to more content, purchasing channels, and brand options than before, the touchpoints become exponential — every website visit, use of a product, and interaction with a customer service representative creates an observation or generates data. But this data is often siloed across multiple systems and organizational departments, making it difficult to gain a single source of truth.
With such an overload of information and choices available, organizations must demonstrate they both understand and value their customers. To this end, we’ve been working to bring customer experiences to the forefront of the business conversation with a new set of intelligent applications with our modern, unified, intelligent, and adaptable business applications. Today, I’d like to dig into our vision and strategy for Microsoft’s customer data platform — a critically important investment from Microsoft. Specifically, how it is helping organizations overcome data silos and leverage artificial intelligence to guide decisions and empower organizations to take meaningful actions for their business.
Dynamics 365 Customer Insights: Microsoft’s customer data platform
Historically, the customer interaction with a brand ended the moment they completed the purchase and walked out the door — limiting an organization’s understanding of why or how its customers are using its products and services. Dynamics 365 Customer Insights enables organizations to gain the most comprehensive view of their customers by unifying data across diverse sources — be it transactional, behavioral, or observational data — as well as uniquely enriching profiles with market insights and real-time product usage.
From data analysts to marketing, sales, and service professionals, every employee in an organization can leverage AI-driven insights. These include churn risk, customer LTV, and recommended next best action, to power business processes across the customer journey that help boost personalization and build richer relationships.
The Microsoft CDP enables breakthrough experiences for customers while maintaining the strictest compliance and security standards so that all customer data is securely managed and adheres to GDPR regulations. Built on a hyper-scale Microsoft Azure platform, the application allows organizations to run powerful analytical capabilities using Microsoft AI and Azure-based machine learning models. Customer Insights can easily extend and customize with the Microsoft Power Platform for even richer data processing and customizations. Customers can benefit from the Microsoft partner ecosystem for development of custom applications and solutions to fit specific industry or business needs.
Let’s look at a few organizations using our CDP to deliver business outcomes and rich customer experiences:
Empowering organizations worldwide
The United Nations Children’s Fund (UNICEF) works tirelessly in over 190 countries and territories to save the lives of millions of children. As private donors and volunteers are increasingly hard to find and retain, UNICEF Netherlands found that personally engaging supporters increased their overall commitment to the organization. Key details on donors, such as their contact information, philanthropic interests, and donation history, were housed in disparate data silos, making it difficult to gain a unified view of the donor base and personalize interactions at scale. To solve this problem, the team adopted Dynamics 365 Customer Insights to quickly and easily combine data from multiple sources, analyze the data to derive insights, and activate the insights via marketing and communication channels. Customer Insights’ out-of-the-box interoperability with Dynamics 365 Marketing helps the team create and optimize marketing campaigns on-the-fly, enabling UNICEF to better develop a customer lifetime value model, which will help it identify and optimize engagement with high-impact donors.
American Electric Power Energy (AEP Energy) — a competitive retail energy solutions company serving more than 400,000 residential, small-business, and commercial customers nationwide — is using Dynamics 365 Customer Insights to deliver efficient and sustainable energy solutions to customers. Previously, AEP Energy faced cumbersome manual processes that required lots of analysis and input to piece together various customer information for a holistic view of its customer, which was both timely and costly. With a cloud-first approach, using Dynamics 365 Customer Insights, AEP Energy can now easily migrate large data sets across various systems of record into a single, unified customer profile. As a result, AEP Energy can better understand its customer needs, identify gaps in product offerings, and ensure both front and back-end operations are focused and efficient to deliver quality, tailored experiences to its customers.
What’s next?
Thus far, we’ve seen great momentum and impact resulting from the customer data platform and how businesses are evolving customer engagement and experiences. Looking ahead, we will continue to innovate on the platform and plan to deliver more opportunities and features in the coming months.
August 3, 2020
The Fifth P: Privacy Lands Squarely in the Marketing Mix
Data privacy, once seen primarily as a legal and IT concern, is now integral to marketing, and marketers are increasingly central to ensuring compliance with privacy regulations. To do this, marketers need to understand how privacy compliance will change their marketing and how those changes relate to the company as a whole. This is a complex and rapidly evolving area that’s crucial to moving forward with a successful data strategy.
Marketers widely recognize that getting ahead of privacy regulations is good for business. In one recent report, 94% said they saw advantages in implementing stricter privacy standards before they became mandatory, citing benefits including better brand perception, higher market valuation, industry leadership, and long-term cost savings. And they’re acting on that belief: 71% expected (pre-COVID) their company increase privacy investments in 2020. Yet more than half cited complexity, cost, and time as barriers to implementation.
Clearly, marketers are looking for help. They recognize that people and process are part of the solution but also that technology is essential for moving ahead. Privacy-related systems perform four main tasks:
Data Management. This is knowing what data you acquire and hold. It requires examining your existing systems to identify any customer information they capture and store and processing this information so it can be used appropriately.
Regulation Tracking. This is knowing what information you’re legally allowed to have and use. It requires tracking regulations for different data types and jurisdictions and mapping these to your customer data stores.
Policy Implementation. This is governing access to customer data. It ensures that your company operations comply with privacy regulations and that you keep the records to prove it.
Customer Interface. This is capturing consent and responding to customer requests to view and change their data. It’s how you ensure that your customers trust the relationship they have with you, so they will remain loyal to you and your brand.
Each of these tasks requires different software functions. Some vendors have built products to support just one task; some have combined several tasks in the same product; and some have embedded one or more tasks within broader software such as CRM or CDP systems. The best approach for your company will depend on the size and location of your business, systems in place, and budget. But, however you assemble the components, it’s important to understand what to look for in each category. Let’s dive into those details with special attention to the vocabulary you’re likely to encounter.
Data Management. This encompasses data collection, reading, comparison, mapping and sharing. Key functions include:
Data Discovery and Data Classification examine existing systems to determine what data they hold and, in particular, to identify data that is subject to privacy regulation. Such data may include:
Personally Identifiable Information (PII) which can be used to directly identify an individual. Examples include name, email address, and passport number.
Personal Data which (as defined under the General Data Protection Regulation) can be used to directly or indirectly identify an individual. In addition to PII, it includes unique-but-anonymous identifiers such as a browser cookie or IP address, which could be tied to an individual with additional information, and non-unique information, such as first name, birthdate, and postal code, which might be combined to identify an individual.
Personal Information which (as defined under the California Consumer Privacy Act) is any information that can be linked to an individual, whether or not it could be used to identify them. In addition to PII and Personal Data, it could include Web behaviors, purchases, and demographics.
Particular features may include natural language processing to automatically identify data types; real time monitoring of data as it moves between systems to identify unexpected contents; and targeted searches for particular items or types.
Data Mapping records the results of data discovery and defines relationships between fields in different databases. It is needed to orchestrate data migration, integration and other aspects of data management. Some systems can automatically generate data maps based on their discovery and classification processes.
Data Retention defines how long your company holds onto various kinds of data. This is often governed in part by regulatory requirements.
Encryption transforms data so it cannot be read without additional information, typically a key that is used to decrypt the data back into its original form. This prevents unauthorized use of the data even if someone gains access. Encryption may cover data at rest (i.e., in storage such as sitting on a disk drive), in transit (which is it being moved from one system to another), or in-memory (when it is being processed).
Data De-identification uses processes such as pseudonymization (replacing personal identifiers with anonymous IDs that can later be reconnected with a specific individual), anonymization (replacing personal identifiers with IDs that cannot be reconnected with an individual), or other means to make data useable without identity. It’s important to realize that even when data is encrypted or anonymized, there is a risk it could be re-identified through matching, AI, or other hidden clues.
Data Security controls which people, systems, and processes within a company can access its data. It includes data access rules that govern what is allowed and data protection measures, such as encryption and de-identification, that prevent unauthorized use. Privacy-specific security features should be integrated with the company’s primary security systems.
Data Sharing controls data sent outside the company. It includes sharing with vendors (data processors, in GDPR language) who work for the company and with third parties who license the data for their own purposes. Both types of sharing are generally governed by contracts with the company that owns the data, which remains responsible for ensuring it is used appropriately. Cross-border transfers often require special treatment to ensure the data remains properly controlled when it is outside of its original location. European Union rules require that such transfers be covered by Standard Contractual Clauses which are standard terms that comply with GDPR rules.
Data Subject Request Fulfillment describes execution of requests made by customers to exercise privacy rights including review of data the company has collected about them, correction of errors, and removal of stored data. Manual execution of DSRs can be very expensive, so automation is important.
Data Deletion is the function of removing customer data from company systems. Some privacy regulations give customers the right to require complete deletion of their data. This can be difficult because it requires changing backups and historical information.
Data Breach Management helps a company respond if a data breach occurs. Breach identification would be handled separately by security systems, but breach management includes standard processes to secure the breach, access the impact, and inform the affected individuals and authorities.
Data Protection Impact Assessments are required by GDPR at the start of new data projects. Some systems provide assistance such as templates and access to data maps that isolate the risks associated with whatever data elements are used in a planned project.
Regulation Tracking. This is determining what laws apply to you, both geographically and in your business category and operations. It requires working with legal experts, either on staff or outside the company, to understand current laws and develop policies that embody them. Key functions include:
Compliance Benchmarking to identify which local, national and international laws, such as GDPR (EU) and CCPA (US-California), apply to which data and what those laws require. Jurisdiction may be determined by where the person lives (GDPR), where the company is based (CCPA), where the data is processed, or other factors. Some systems provide a knowledgebase of compliance regulations to help in this process. Some vendors also have expert staff available to help their clients.
Privacy Analysis to link the data elements uncovered in the Data Discovery, Classification and Mapping projects with the laws that govern them.
Privacy Law Obligation Comparison to identify the specific obligations created by each law for each data element it governs.
Risk Analysis to assess the exposure created by existing processes and to help prioritize remediations.
Privacy Law Alerts to stay abreast of changes in compliance laws and the cost of non-compliance.
Policy Implementation. This is the cluster of functions that ensure your company publishes accurate and current privacy policies. Functions include:
Privacy Policy Authorship to manage the writing and legal vetting of the privacy policy and related documents defining how your company will act. Some systems provide automated privacy policy generators.
Policy Monitoring and Change Detection to keep abreast of legislative changes and determine when they require a change to your policies.
Policy Management to ensure necessary changes are made and that corporate and management are kept current.
Privacy Policy Enforcement to specify who is responsible for making sure policies are adhered to company-wide and how they will do this. This is the critical link between published policies and the execution of those policies within the Data Security and Data Sharing components of Data Management.
Employee Management and Incentivization to educate and motivate key stakeholders and staff to enable them to help execute and internally monitor compliance.
Customer Interface. These functions control the direct interactions between the company and its customers. They may be the most critical for marketers because they create the trusted relationship needed for people to willingly share their data. Usability is a key issue, since users need to be offered a large and complicated set of choices in ways that are understandable and easy to execute. Components include:
Advertising and Marketing Compliance involves how you treat potential customers before they identify themselves. This covers where their data comes from, how it is captured, which elements are retained, who has access to the data, and what they can do with it. Functions to manage include:
Cookie Consent for use of Web browser cookies
Mobile App Consent to collect and share data through mobile apps
Website Monitoring to manage data captured by site tags and other technologies that do not involve cookies
Data Rights Management gives identified customers control over data they have provided. Functions include:
Identity Verification enables customers to prove they are who they claim, before they can change permissions or view or change their data.
Consent Management outlines what permissions customers can provide, including what data will be collected, how long it can be retained, what it can be used for, and specify how long the data can be retained and what it can be used for. It also includes tools for customers to change their permissions over time.
Preference Tracking under GDPR, which requires giving contacts the right to opt out of your tracking their Web behavior including opens, clicks, forwards, shares and browsing behavior.
Data Subject Access Requests (DSARs) let consumers review and correct data you have collected about them. Privacy regulations including GDPR and CCPA include specific requirements for DSAR accessibility, response times, and documentation. Request fulfillment is a Data Management process, although automated fulfillment requires direct integration between the interface that collects the requests and the systems that execute them and return the results.
Subject Erasure Handling, also called the Right to Be Forgotten, lets customers request that you delete all of the data you have collected about them. Like Data Subject Access Requests, these must be fed to Data Management processes for execution.
Data Deletion Validation confirms that data removal requests have been successfully completed.
July 30, 2020
Customer data platform: A key to personalized experiences
In today’s digital economy, customers are continuing to set the bar higher and higher in terms of what they expect from the brands they interact with. Power that was once held by the providers of goods and services has now shifted to the customer, whose demand for a seamless and highly relevant experience at every interaction is driving a shift in the way businesses must operate.
Customers today have access to more content, buying channels, and brand options than ever before. With an overload of information and choices available, businesses can no longer survive by simply providing the bare minimum necessary to keep customers from leaving; instead they must deliver exceptional customer experiences and outcomes. Personalized interactions allow businesses to cut through the white noise of irrelevant engagement and offers to not only drive sales, but establish deeper, lasting customer relationships. According to a recent report by Accenture, 91 percent of consumers report they are more likely to shop with brands that recognize them and provide relevant offers or recommendations – making it vital for businesses to implement a personalization strategy in order to remain competitive.
It’s all about the data
Personalized engagement is clearly no longer an option, it’s a necessity. Yet the reality is that many companies are still falling short of providing the individualized experiences that customers expect. According to the 2018 State of Global Customer Service report, more than 61 percent of customers report they’ve stopped doing business with a brand due to poor customer service. In a market where customers are free agents and where adoption and abandonment occur at the blink of an eye, companies must demonstrate that they both understand and value their customers.
This is where data comes in. Customers generate enormous volumes of data as they engage across various channels, and organizations that can successfully leverage this data for personalization will set themselves apart, while those that do not will lose business to their data-savvy competitors. On average, companies that leverage advanced analytics gain rapid insights derived from their customer data, and achieve revenue gains of 5 to 10 percent according to a customer experience report by McKinsey.
Historically, it has been a struggle to successfully capture, integrate, interpret, and act on all this data effectively, and for many companies, still is. Adding to the challenge are disparate systems across organizational silos, making it difficult or impossible to unify data in order to generate the holistic customer profiles and insights needed to positively impact the customer experience.
The customer data platform
The ability to effectively personalize at scale requires a complete, unified view of customers from both transactional and behavioral data, that when brought together enables intelligent, actionable insights – and that’s where the customer data platform (CDP) is helping to evolve the personalization landscape. With the ability to consume and unify customer data from multiple sources, CDPs provide holistic customer profiles and management, support real-time customer segmentation, and integrate customer data with other systems.
Many vendors have offerings that are primarily analytics or engagement based, but lack the rich data unification and synthesis that defines a true CDP. With increasing pressure on businesses to provide deep personalization, the need for richer data platforms is mounting and vendors are scrambling to get on the bandwagon whether they have the necessary capabilities or not.
The biggest standout capability of a true CDP is the ability to combine customer data from all sources into a single customer profile, derive valuable insights, and share those insights out to other systems and resources to enable action. The sheer speed and volume at which CDPs can ingest, process, and output the data enables businesses to react at the fast pace necessary to be effective. Without this, insights derived from customer data are often incomplete, or have already become irrelevant by the time they’re surfaced.
Benefits that only a customer data platform can deliver
CDPs offer organizations the opportunity to deliver more meaningful, individualized engagements to customers, as well as streamlining the capabilities of marketing teams to make their efforts more effective. It currently takes marketers far too long to analyze and draw conclusions about the impact of a campaign or tweaks made to the customer experience. According to a report by Forbes, 47 percent say it takes them more than a week to analyze the results, which is an eternity in today’s fast-paced buying market. Of organizations already utilizing a CDP, 53 percent seek to be able to react more quickly to market changes and customer preferences, or improve and streamline internal support operations. CDPs give marketing teams unified, on-demand access to customer data pulled together from a range of initiatives and interactions like online engagement, advertising campaigns, or purchasing histories, providing greater visibility into their customers’ needs. With this enhanced, 360-degree view of customer behaviors and preferences across all touchpoints, organizations can provide not only the highly personalized experiences that customers demand, but can cultivate deeper relationships and in-turn drive increased customer loyalty and retention.
Until now, the data leveraged for customer experience efforts was primarily drawn from straightforward customer actions like website clicks or purchase histories. Yet with enormous volumes of data now being generated from customers engaging across numerous devices and channels, as well as the addition of AI-driven interactions, organizations need more power than ever in order to manage, interpret, and act on all this data at scale – and the customer data platform provides the solution.
Dynamics 365 Customer Insights
Among the authentic customer data platforms, Dynamics 365 Customer Insights stands out with an even greater depth of capabilities. It not only enables complete data synthesis for holistic customer profiles, but also integrates directly with the rest of the Dynamics 365 engagement, AI, and analytics tools, as well as Azure-based machine learning and power apps, providing both rich customer insights and the means to act on them.
With an enhanced customer data platform like Dynamics 365 Customer Insights, companies can unify their customer data across all of their sources to gain a truly 360-degree view of their customers, empowering every employee to provide personalized, authentic engagement at every touchpoint.
Customer Insights is a self-service CDP solution, enabling faster time to initial value with zero to minimal consulting engagement. A wide range of pre-built connectors make it easy to bring data in from any source, automating standardization and merging of data records with built-in AI to create comprehensive, unified customer profiles. Microsoft’s proprietary audience intelligence also adds to data enrichment, delivering more complete customer insights. With Dynamics 365 Customer Insights, organizations are able to maintain complete control of their data as it resides in their own tenant, and can innovate freely to create custom solutions with full extensibility and compliance.
In today’s content and product-saturated marketplace, personalization is the key to standing apart from the competition. According to a recent study by Frost & Sullivan, by 2020 customer experience is expected to overtake price and product as the key brand differentiator, and companies globally lose over $300 billion each year due to poor customer experience. Given those numbers, failure to personalize customer engagement will negatively impact an organization’s conversion and retention rates, making it difficult or impossible to remain competitive.
Personalization across all lines of business
With the addition of a customer data platform (CDP) like Dynamics 365 Customer Insights, organizations can unify data from every channel and source, deriving insights that extend directly to other business applications to enable intelligent action across the entire organization from marketing, to sales, and customer service. This not only powers omnichannel, 1:1 content, and engagement at every touchpoint, but allows organizations to know, segment, and target customers with unprecedented accuracy, leveraging every customer interaction with the business.
Having a 360-degree view of customers helps organizations determine the best action possible for each individual customer in any context or stage of the journey, whether it be acquisition, conversion, or retention, to provide the right engagement, to the right individual, at precisely the right time. Moreover, it enables all departments to share the same view and history of a customer to improve service and customer satisfaction at any point of engagement.
Marketing
The goal of marketing today is no longer to simply convert a customer in a single transaction – the bigger picture is to focus on higher-value prospects that will not only be more likely to make that initial purchase, but generate ongoing business in the future. Forward-thinking marketers can leverage CDPs to unify and enhance customer data, develop rich segments, and target customers more accurately and personally. By increasing the relevancy of content and engagements and streamlining cross-channel campaigns, they can raise the likelihood of conversion, increase the ROI of marketing efforts, and gain competitive advantage with measurable results.
Marketing use cases powered by Customer Insights
Enrich customer profiles and identify the best prospects to drive acquisition
Enable affinity marketing to achieve greater wallet share
Personalize offers and website experiences based on historical customer data and micro-targeted segments to increase conversion rates
Monitor campaign effectiveness and key performance indicators to improve the ROI of marketing efforts
Sales
With an abundance of available options for every product and service, sellers need to understand their customers on a deeper level in order to be successful in selling to today’s highly selective consumer. It’s about providing the right offer to the right audience at the right time, and to do that requires not only complete customer data compiled from all sources of previous interactions, but the means to create detailed segments that enable highly targeted sales engagement. Organizations like Marston’s, a large pub chain based in the UK, are leveraging Customer Insights to collect and interpret customer data in order to provide personal engagement to every patron, from customized email offers and reservation preferences, to personalized drink recommendations from staff members, driving greater sales and repeat business.
Gain insights to identify opportunities, predict customer intent, and foster stronger relationships
Foster relationships with a complete view of a customer’s interactions to better understand the health of the relationship
Increase customer lifetime value with data-driven next best action suggestions and intelligent cross-sell and upsell recommendations
Deliver consistent cross-channel experiences enabling an online-offline feedback loop
Service
The future of service lies in delivering frictionless, convenient, and personalized service through any channel that a customer chooses. By democratizing data and empowering every call center or customer service representative with 360-degree customer profiles, organizations can provide proactive, omnichannel support that leaves customers feeling valued and understood, ultimately strengthening loyalty, trust, and retention. Tivoli Gardens, one of the world’s oldest and largest amusement parks, is leveraging Customer Insights to track customer behaviors and preferences to provide personal service that strengthens retention, from sending a loyalty offer for an upcoming event based on a customer’s interests, to greeting customers by name with personal activity recommendations when they visit the park.
Service use cases powered by Customer Insights
Leverage holistic customer profiles to accelerate issue resolution and improve customer satisfaction
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Deliver personalized loyalty offers to increase customer lifetime values and reduce churn
During the height of the crisis the UK Office of National Statistics announced that just under 50% of employed adults in the UK were working from home – This shift is something that the CX community has been championing for call centre staff for years but has now swiftly become a reality.
The time has come for customer service and experience professionals to move from talking about change to actioning it. The Covid-19 crisis has enshrined in most CEO’s minds that customer support isn’t just a cost-centre, it’s the face of the business and a crucial driver for retention and engagement. From AI and data-driven innovations through to maximising your agent’s effectiveness, the customers’ expectations are increasing rapidly, and you need to ensure you’re keeping up.
Restrictions on movement and businesses have highlighted the importance of digital channels for many areas such as retail which traditionally may have relied on a mix of physical and virtual. Whilst there has been huge turbulence on CS operations with interactions moving completely to online channels or remote agents, the transition has been remarkable. With entire industries such as retail, travel, learning and many more transitioning to online models and performing well, many CS leaders will be asking whether the bulk of their operations can remain remote.
In a recent podcast interview, Sarah Metcalfe the Head of CX for Sure PetCare commented “Look at what we have achieved, look at the we could never do that’s which have become possible. One question I would have for organisations is, your employees are working from home, if that is working for everybody do, they have to actually be in the office?”. Whilst this change has been thrust upon us, it’ll be fascinating to see whether the shift to remote working is one which implements a long lasting change.
According to a recent Zendesk report, Whatsapp has seen an incredible surge in usage with customer queries through the channel up 148% since late February. What this suggests is that customers trust in digital channels is increasing and for non-urgent queries they are more willing to submit a request and receive an answer direct to their messages. With many interactions able to be automated, companies can now focus their agents on the more complex cases where a higher quality, empathetic response is needed.
As part of their CX and Marketing series of B2B virtual conferences and content pieces, Reuters Events have announced they will be bringing together global CX and CS leaders next month. Following on from the Covid crisis, the trends we were seeing in the space beforehand have rapidly accelerated with automation and remote working quickly becoming the mainstream. With over 30 sessions over the course of both days, conversation will centre around the tectonic shifts in call centre’s way of working as well as the huge leaps made in automated technology for customer interactions.
Registration for the Reuters Events Customer Service and Experience can be accessed here: https://bit.ly/3gTpwCR
Glossary of CDP-Related Terms
Please comment to suggest changes or additions!
a/b test
a testing method that compares results from two or more test groups whose members are similar except for being given different test treatments
adtech
any system used to support advertising activities; in particular, systems that work with digital media
algorithmic attribution
a type of multi-touch attribution method that allocates fractions of total revenue to different marketing contacts based on statistical analysis of historical data that estimates the impact of each contact
analytics CDP
a CDP whose primary functions include assembling, sharing and analyzing unified customer profiles, typically including predictive analytics
anonymization
the process of removing all personal identifiers from a data set, making it impossible to connect personal data in the set with the individual who generated it
anonymous individual
an individual that is not connected to any personal identifiers which can be linked to a specific person in the real world
Application Program Interface (API)
a method for communicating between systems (or between components of the same system) that makes requests ("calls") for the other system to send data or take an action (cf Webhook)
arbitration
the process of selecting which message to send to an individual who is eligible to receive messages from several separate campaigns. Users must specify the selection criteria (highest immediate value, highest renewal rate, etc.). The decision is usually based on a combination of decision rules and predictive models.
artificial intelligence
computer processes that mimic human thought processes
attribute data
data describing individual characteristics, such as birthdate, address, or education; one person typically has a single value for each attribute and the attributes change infrequently or never
attribution
the process of estimating the revenue (or other measure) caused by a particular marketing contact (or other interaction with a customer)
banner ad
a type of Web display ad that appears in a box at the top, bottom or side of a page.
batch processing
processing a set of data that is accumulated over time and fed into the system at once, such as a file containing all transactions during the previous day. This precludes immediate response to events reflected in the data, such as someone visiting a Web site.
behavioral data
data describing individual actions, such as purchases, Web page views, and customer service calls; one person be associated with many behaviors of the same type
big data
technology to capture, store, access, and analyze very large data volumes in general, and semi-structured or unstructured data in particular.
buying stage
the current relationship of a customer to a specific purchase, where relationships are described as a sequence of states (awareness, interest, selection, purchase, use, replacement). Buying stages mark progress in the buyer journey.
California Consumer Protection Act
a Californai reguliation that restricts how personal data is collected and used; it gives individuals rights to reject commercial use of their data
campaign CDP
a CDP whose primary functions include assembling, sharing and analyzing unified customer profiles, and selecting personalized messages for individuals
case study
a description of how an actual user completed a business task, typically including results. Used to illustrate the capabilities of a system and the value the system helps to create.
CDP inside
a system that provides CDP functionality but whose primary functions include delivery and operational processes
channel preference
the likelihood that a customer will engage with messages in a particular channel. Generally based on past behavior. Used to select the most effective channel for each individual. May vary by message type.
churn
the process of ending a customer relationship; a measure of how many customers stop being customers
citizen developer
a person who creates software without having acquired conventional programming skills; typically a business user rather than IT professional
cloud-based
a system deployed on remote servers accessed through the internet and maintained by an external vendor.
cluster analysis
any statistical analysis that classifies cases into groups whose members are in some way similar to each other
collaborative filtering
a type of predictive modeling that identifies products an individual is likely to purchase, based on past purchases by that individual and by other individuals who have similar purchase histories
consent management
the process of collecting, classifying, retaining, accessing, and updating individual consent for data use under privacy regulations.
consent management system
software that manages the consent management process. May be a stand-alone system or part of a larger product such as a CDP.
content management system (CMS)
software that manages and deploys formatted information, such as Web pages and documents.
contextual advertising
advertising targeted on the basis of context, such as the type of editorial content the advertising accompanies. Requires no information about the individual viewing the ad.
control group
a group that is held out from testing to provide a baseline for estimating results that would have occurred without any test
cross device match
a match that links two devices to the same individual, based either on deterministic or probabilistic matches
cross-channel marketing
a marketing program where the same campaign includes messages in different channels
customer data platform (CDP)
packaged software that builds a unified, persistent customer profiles accessible to other systems, including primarily first party data and known individuals
customer experience
all interactions between a customer and a company, across all stages of the customer relationship. Includes both prepurchase events (marketing and sales) and post-purchase events (product use, customer service).
customer journey analysis
the process of tracking customer interactions leading up to a specified event, such as a purchase, or interactions across their entire relationship with a company. Typically includes identifying more common sequences and differnces between the sequences leading to different outcomes or taken by different customer segments.
all data associated with a person, collected and organized for easy access
customer relationship management software (CRM)
software that stores details of direct interactions between a company's customers and its sales and service personnel
data activation
making use of data; specifically, sharing customer data with systems that will use it for analytics, personalization, or marketing campaigns
data CDP
a CDP whose primary functions are limited to assembling and sharing unified customer profiles
data cleansing
the process of making data more usable through error correction, standardization, transformations, and other processes. Exact steps will depend on the intended purpose.
data enrichment
the process of adding new information to customer data, most often by importing third party data and appending it to existing customer profiles
data governance
the process of controlling how data is collected and used in a system, with particular focus on ensuring data quality
data lake
a collection of data copied from company systems, stored in its original forms and accessible for analysis and further processing
data management platform (DMP)
software that stores anonymous customer profiles, primarily to support Web display advertising
data quality
the degree to which data is fit for its intended purpose(s); more broadly, how accurately data reflects the real-world entities it represents
data standardization
the process of placing data in a consistent format so that all instances of the same item are the same. May be done by applying rules (e.g. 'all phone numbers are divided into country code and domestic number, with no separators') or reference data (e.g., list of formal first names and variations, all changed to the formal first name; all postal addresses changed to match postal agency standards). Important for accurate matching and reporting.
data transformation
the process of converting data from one format to another. Enables disparate data to be combined.
data warehouse
a collection of data copied from company systems, reorganized and often summarized for analysis
delivery CDP
a CDP whose primary functions include assembling, sharing and analyzing unified customer profiles, and selecting and delivering personalized messages for individuals
demand side platform (DSP)
a system used by ad buyers to purchase digital media, typically through automated bidding
derived variables
data that is based on other data, usually through calculations such as summary of purchases over time. Predictive model scores are a sophisticated type of derived variable.
descriptive analytics
statistical methods that find patterns and relationships within existing data sets, such as identifying customer segments
deterministic match
a match that links two personal identifiers to the same individual, based either on information provided by the individual or by the individual's actions (e.g., logging into a customer account on a specific device; see 'identity stitching').
device ID
an identifier linked to a device such as a computer, mobile phone, or smart TV. These may be a permanent attribute of the device itself, such as a serial number, or impermanent because they related to software running on the device, such as a Web browser or operating system
digital asset management (DAM)
software that manages and deploys any type of digital content, including documents, videos, sound files, etc.
display advertising
Web advertising that appears on Web site or social media pages and is purchased by contract or by bidding on impressions. May be targeted by Web site or by individual.
dynamic content
digital content that changes depending on the recipient and other variables, typically achieved by creating a content template that includes rules which select different elements based on data about the recipient and situation (time of day, local weather, product inventory, etc.)
dynamic list
a customer list that is automatically updated as customers become qualified or disqualifed for the list's selection criteria. Membership may be adjusted continuously (as new data is received) or updated each time the list is used.
earned media
marketing messages that are delivered by unpaid third parties, such as the press. These are often considered to be news rather than advertising.
event triggered campaign
a marketing program that is started when a specified event occurs. Typically the program is targeted at individuals and the trigger event initiates the program for a single individual (e.g., an onboarding program triggered when someone becomes a new customer).
feature extraction
the process of identifying attributes within unstructured data so these can be treated as structured data. Typical examples include finding company names within a press release or products within a video. Extracted features are usually applied as tags to the original item.
fingerprinting
a technique that uses device attributes such as operating system and build date to identify specific devices, even without a specific device ID. Generally done without user consent and potentially a privacy violation.
first party cookie
a Web browser cookie set by the domain of the Web site that sets the cookie
first party data
personal data that an organization has acquired directly from an individual
first touch attribution
an attribution method that allocates all revenue to the first marketing contact with a customer
fractional attribution
a type of multi-touch attribution method that allocates specified fractions of total revenue to different marketing contacts based on when they occurred relative to a purchase (first, middle, last)
fuzzy match
a match that links two sets of personal identifiers to the same individual, based on identifiers that are similar but not identical (e.g., two similar postal addresses)
General Data Protection Regulation
a European Union regulation that restricts how personal data is collected, used, and protected; it gives individuals rights to consent, review, and demand deletion of personal data
geofencing
targeting of marketing and advertising messages based on the recipient's passage into or out of a specific physical location, such as entry to a retail store. Sometimes used in combination with data known about an individual.
geotargeting
targeting of marketing and advertising messages based on the recipient's location, often in combination with other data known about the individual
golden record
a record containing the version of each item that is considered the most appropriate for use; this is usually the version judged most accurate and complete. Typically shared with other company systems.
ideal customer profile
the set of personal data associated with a company's best customers. Used to define targets for sales and marketing efforts.
identity graph
a set of relations among personal identifiers, indicating how each has been linked to the others and which are linked to the same individual.
identity resolution
the process of linking personal identifiers to individual identities, whether known or anonymous
identity stitching
the process of connecting a personal identifier to an individual through an intermediary personal identifier (e.g., new device linked to an email address provided by a customer; the device is associated with the customer even though the customer has not herself reported the connection).
incremental attribution
a type of multi-touch attribution that estimates the increase in total revenue resulting from a particular type of marketing contact.
individual
a distinct person; more formally, an entity linked to at least one personal identifier that can distinguish it from other entities. Identity management systems assign a unique, permanet "master ID" to each individual and then connect all personal identifiers to that master ID.
ingestion
the process of gathering data from one system and loading it into another
in-memory data
data which is stored in system memory for immediate access. In-memory data is typically discarded after use, although it may be copied to persistent storage first. Some systems keep all data in-memory, to enable high-speed access. This becomes more affordable as memory costs drop, although it is still typically used for relatively small data volumes.
integration platform
software that moves data between systems to support processes that span multiple systems, but does not store the data internally
intent data
data that indicates how likely a person is to purchase a particular product. Generally based on behaviors such as store visits, social media commentes, and consumption of related Web content.
journey orchestration
coordinating customer treatments over time and across channels, either to achieve a specific purpose (e.g. a marketing campaign with a defined goal) or throughout a company's relationship with a customer
key performance indicator (KPI)
a measure that correlates with achievement of specific business goals. Separate KPIs are often defined each business project or objective.
known individual
an individual connected to at least one personal identifier that can be linked to a specific person in the real world
last touch attribution
an attribution method that allocates all revenue to the last marketing contact with a customer
lead to account match
the process of connecting individual records to business units associated with those individuals. Applies to business-to-business data and relates specifically to the data structure of Salesforce.com Sales cloud CRM, which stores people as either "leads" (individuals not connected with an account within a business) or "contacts" (individuals associated with an account). Most B2B marketing programs expect all individuals to be associated with a business.
life stage
the current relationship of a customer to a business, where relationships are described as a sequence of states (prospect, new customer, existing customer, at-risk customer, lapsed customer). Life stages mark progress in the customer journey.
lifetime value
the total value generated by a customer throughout their relationship with a company. Often expressed in revenue although profit is more meaningful. May be measured in terms of future value only (e.g. for a new customer), past value only (e.g. to identify most important customers), or total value. Future values are typically discounted and may be limited to a specific time frame e.g. next five years.
location data
data that reports the physical location of an entity over time. Based on latitute and longitude but may also include derived data such as political jurisdiction or aisle within a retail store. Typically collected by mobile devices and used to target advertising and other marketing messages.
look alike modeling
a type of predictive modeling that identifies individuals similar to a company's current customers, used to select advertising audiences.
machine learning
automated processes that build predictive models with little human assistance
marketing automation system
software that maintains customer and prospect lists and runs campaigns against them. Primarily used for outbound campaigns (e.g. email) but some systems also support real time interactions (e.g. Web site messages). Largely limited to data generated within the system itself and to imports from CRM systems.
martech (marketing technology)
any system used to support marketing activities; in particular, systems that work with customer-level data
master data management (MDM) software
software that reconciles different versions of information about an entity (person, product, location, etc.), selects the version to be used as a standard across company systems, and shares this version (called a "golden record") with those systems. MDM systems may perform identity matching as part of their function.
multi-channel marketing
a marketing program where separate campaigns run in different channels (email, Web, etc.)
multistep campaign
a marketing program including multiple messages over time, typically including the ability to adjust later messages based on each individual's response to earlier messages
multi-touch attribution
an attribution method that allocates fractions of total revenue to different marketing contacts; multiple allocation methods are possible
multivariate analysis
any statistical analysis that uses multiple variables as inputs
multivariate testing
a testing method that estimates the impact of different combinations of variables on results; can estimate results from combinations that have not actually been tested
natural language processing
a branch of artificial intelligence that works with human language, typically to extract features (e.g. people mentioned) or meaning (events described, intent, sentiment, etc.)
next best action
the treatment that a business believes will produce the most desirable result for an individual customer; typically based on a combination of rules and predictive analytics; requires specification of the measure that is desired
no-code software
software that can be built or configured without using conventional programming skills
NoSQL data store
a data store organized not organized into tables, rows, and columns. There are many types, optimized for different purposes. Generally more flexible than SQL databases because columns are not predefined. Used for structured, semi-structured, and unstructured data.
offline data
data collected by physical interaction such as retail purchases, local events, shipments, etc.
omni-channel marketing
a marketing program where the same campaign lets customers interact in whichever channels they choose
onboarding
broadly, the process of adding people to a system; narrowly, attaching personal identifiers to individual profiles so each customer can be identified and contacted across multiple channels. In particular, it refers to sending offline identifiers (name, postal address, phone number) to third party vendors who match these with online identifiers (email address, device IDs, cookies, etc.)
online data
data collected by digital systems include Web, mobile apps, smart TVs, etc.
on-premises system
a system deployed on servers controlled by a company. May include "private cloud" deployments as well as deployments in a company's own data center.
operational CDP
a CDP whose primary functions include assembling, sharing and analyzing unified customer profiles; selecting and delivering personalized messages; and operational activities such as order processing or customer support
out of the box data model
predefined set of data objects and relationships provided with a system. Typically designed to meet the needs of a specific industry or company type. Purpose is to save design effort compared with building a custom data model.
owned media
marketing messages delivered through a company's own channels, such as email or Web site
paid media
marketing messages that are purchased, such as paid advertising
persistent data
data which is stored in a stable format until the user decides to discard it. Actual retention period may be limited by legal requirements.
persistent ID
a personal identifier that does not change over time and thus can be used as a permanent "master" ID. It is linked to other personal identifiers which may change (e.g. postal address)
personal data
data that is linked to an individual, including attributes and behaviors
personal identifier
information that can be used to identify a specific individual, either by itself or in combination with other information
personalization
creating communications that are tailored to a specific individual based on data about that individual
personally identifiable information (PII)
information that can be used to identify a specific individual; same as personal identifier
predictive analytics/model
statistical methods that use data to predict outcomes such as response to promotion or membership in a group
prescriptive analytics
statistical methods that use data to recommend decisions such as customer segments to contact or offers to develop
privacy by design
a design approach that builds privacy requirements into system planning; this often includes collecting and exposing the least personal data needed to complete a business task
probabilistic match
a match that links two personal identifiers to the same individual, based on behavior patterns that suggest but do not prove a relationship (e.g., two devices frequently used in the same places at the same times)
programmatic advertising
a type of ad buying based on automated bidding for each impression, typically in real time. Originally developed for Web display advertising and now applied to other digital media.
prospecting
the process of searching for new customers
pseudonymization
the process of masking personal identifiers in a data set, so that someone with the right information (such as an encryption key or reference list) could reconnect personal data in the set with the individual who generated it
reactivation campaign
a marketing program aimed at convincing a former customer to renew their relationship
real time
responding to event so quickly that there is no perceptible delay. Required time depends on the situation: for human interactions it is typically considered one to two seconds. For computer-to-computer interactions such as programmatic ad bidding, it may be less than 1/10th of a second.
real time access
receiving a data request from an external system and returning the data to that system in real time
real time decision
receiving a decision request from an external system and returning the decision in real time; often includes real time data access, calculations, and rule execution
real time ingestion
loading data into a system, completing whatever processing is needed, and making the data available for use in real time
real time interaction
exchanging data with a system or person in real time, such that each action takes into account all previous actions including the most recent
RealCDP
the CDP Institute's criteria used to certify that a system provides CDP functionality. Criteiria include: load all data types; store all original detal; retain data as long as the user desires; assemble unified customer profiles; make profiles available to other systems.
recommendation engine
a system that suggests which product to offer an individual. Selection criteria may differ (highest likelihood to purchase, highest expected value, highest future purchased, etc.). Selection method is usually a combination of business rules and predictive models. Selections are usually based on a combination of individual data (purchase history, behaviors, etc.) and context (inventory, product demand, season, etc.)
regression model
a statistical method that finds estimated relationships between multiple inputs and a result and expresses these in a mathematical formula
retargeting campaign
a marketing program aimed at convincing a customer to purchase a product they had apparently considered buying but did not purchase
search engine optimization
the process of maintaining a Web site to achieve the highest possible ranking in Web search engines and thus attract as much organic traffic as possible.
search marketing/paid search
Web advertising that appears on search engine pages and is purchased by bidding on keywords.
second party data
personal data that an organization has acquired through a direct relationship with the organization that collected it as first party data
segmentation
any method that divides an audience into groups of individuals who are in some way similar to each other, typically so they can be treated similarly for marketing purposes
sell side platform (SSP)
a system used by ad sellers to offer digital media, typically through automated bidding
semi-structured data
data that is presented and stored in a format where the elements and contents are defined together, as in event logs or key:value pairs (e.g. , ,)
shopping cart
the area of an ecommerce Web site where buyers assemble the set of products they plan to order. Placing a product in a cart is a high indicator of purchase intent and is often the basis for retargeting an individual with offers for the same product if they do not complete the purchase.
site tag
Javascript code embedded in a Web site that collects specified information and sends it to an external destination, such as the site owner for analytics or an ad network to track visits and ad views. Site tags may also place cookies on a Web browser to track return visits.
software development kit (SDK)
instructions and tools for building software, and in particular for building connectors between two pieces of software. Often used to enable mobile apps to send data to a customer database.
SQL data store
a data store organized into tables with rows and columns, where each row represents a record and each column represents a predefined data element. Used for structured data, primarily to process transactions and store attributes.
static list
a customer list that is selected once and not updated or is only updated on demand.
stream test
a type of test where customers are divided into groups and each group receives different treatments over time. Used to measure results of fundamental differences, such as different price or service levels, which must be held constant over extended periods to show their results.
streaming data
data received in a continuous flow, such as Web site activity or location history
structured data
data that is presented and stored in a fixed format where each element is in a specified location, such as the columns of a relational database table or the fields of a data file
tag manager
software that manages Web site tags, typically replacing individual tags with a single tag that captures data required by multiple tags and distributes that data to the appropriate destinations.
third party cookie
a Web browser cookie set by a different domain other than the Web site that sets the cookie
third party data
personal data that an organization has acquired through a marketplace relationship with an organization that acquired it directly or indirectly
tracking pixel
an image link embedded in a Web site that calls an external server to return a single pixel. Used to track site visitors. Captures less information than a site tag.
tree analysis
a statistical method that classifies cases into groups with different expected results by repeatedly splitting groups of cases into subgroups, using a single variable for each split
unstructured data
data that is presented and stored in a format where the elements are not defined, such as a block of text, video, or audio files
use case
a description of the steps that an agent takes to complete a business task. Used to illustrate the capabilities a system needs to support a task and to illustrate the tasks a system may support.
Web content management (WCM)
software that manages and deploys Web pages and other Web site contents.
Webhook
a method for communicating between Web systems that sends data to other systems, typically after an event in the originating system (cf API)
Q. What are the principal elements of a CDP and how do you leverage these elements to Grow Your Business?
When people are introduced to a new technology, they typically ask, “What is this?” So, the answer they get is a definition. For example, a CDP (Customer Data Platform) is a marketing technology that can ingest customer data from any source, create unified customer profiles (Golden Records), model behavior, and share that data with any source that needs it.
But what they really mean to ask is, “What can this technology do for me?” The CDP Periodic Table answers that question by listing categorized examples (elements) of use. The elements are the titles of Use Cases that are often cited as requirements for a CDP.
The elements are categorized by general CDP objectives, and they are the foundation for asking the most important questions:
How do I grow my business with a CDP?
How do I make money with a CDP?
How do I beat the competition with a CDP?
The answers are in the elements, categorized by color:
Each element has a number in the upper left corner. This corresponds to the definition and benefits that have been summarized on a separate page.
To access the definitions and benefits, click anywhere on the CDP Periodic Table, or click on the definition’s icon.
To return to the Periodic Table from the definitions and benefits page, click on the icon that says, “Click to CDP Periodic Table”. See the example to the left.
Using the CDP Periodic Table when Interviewing Vendors
Step 1. Review the various elements, look up the definitions and benefits.
Step 2. Print the table and circle the desired features in your marketing system.
Step 3. Use your selected elements as examples of requirements when talking to CDP vendors.
While it is a good start to know your basic requirements, there are other considerations that should be documented before approaching vendors. These include a summary of your marketing channels, marketing priorities (e.g. awareness, acquisition, customer value, retention, expense reductions, etc.), as well as your budget and industry focus. You will also need to complete a review of your existing marketing technology (e.g. Gap analysis) so that you are not overlapping existing technology.
All these details will widen or narrow the list of candidates when interviewing your prospective vendors. Ultimately, the requirements will make their way into an RFP (Request for Proposal).
If this sounds like a lot of work, it can be. Aside from the requirements documentation, it can be an enormous task to research prospective vendors and weed through their bloated marketing collateral. Subsequent hours of phone calls, meetings, demonstrations will cull the group of vendors down to those that have, and can, address your specific needs.
Another Way
The CDP Institute has created a tool that will provide you a list of top matching vendors, as well as a draft of your requirements in an RFP. Literally, taking a 15-minute survey can save you weeks of work. [See our article, “CDP Journey- Finding a Vendor”.]
This service is currently offered for FREE by the CDP Institute and is accessible on Google Forms page at https://bit.ly/CDPInstitute. During the month of July 2020, the CDP Institute is offering 30 minutes of FREE, vendor-neutral advice from one of the Institute’s expert consultants.
The Easier Way
Call the CDP advisors at DataEM to discuss your specific goals. 954.906.2590
DataEM is a Customer Data Platform (CDP) consultancy. A CDP can ingest customer data from any source, create unified customer profiles (Golden Records), model behavior, and share that data with any source that needs it. Brent Dreyer is the Managing Partner at DataEM, and one of the expert CDP consultants assisting the CDP Institute with their vendor-neutral advisory services.
June 25, 2020
COVID-19: The Unexpected Catalyst Driving Digital Transformation in the Retail Sector
According to Michael Leboeuf, “A satisfied customer is the best business strategy of all”. In other words, it simply means put the customer first by understanding their needs and behavioral patterns and then map it backwards to your products.
Due to the increased adoption of smartphones, faster networks, and increasing consumer propensity to spend, the global ecommerce sales was expected to reach $6.5 trillion by 2023, with a penetration rate of 15%.
This was based on customer and market research in a pre-COVID world. But, the current COVID-19 pandemic has altered the market landscape and consumer behavior beyond recognition. The penetration rates are expected to increase to 25% by 2025.
According to the recent National Retail Federation (NRF) survey, these are some of the key consumer behavioral changes that are affecting retailers, like you:
9 in10 consumers have changed their traditional shopping habits due to COVID-19.
More than half of the consumers have ordered products online that they would normally purchase at the store.
Nearly 6 in 10 consumers say they are worried about going to the store due to the fear of being infected.
In a pre-COVID world, traditional enterprise retailers largely focused on driving growth, acquiring higher market share, increasing footfalls to their physical stores - instead of prioritizing the building of an online presence.
But, the current COVID-19 crisis has altered consumer behavior in 2 major ways:
The reluctance to mingle in crowded public places, due to governmental lockdowns or personal health precautions
The rapid migration to e-commerce websites and mobile apps
This is highlighted in an emarketer report that shows that:
85% of people in China continue to avoid crowded places.
On an average 68% of people in the US, between the age groups of 18-44 are likely to avoid visiting physical stores.
What does this mean for offline retailers like you?
You need to be agile in adapting to these changes – accepting and embracing the new normal is crucial. A missed inflection point has cost a lot for brands in the past like Kodak and Nokia.
You don’t want to and can’t afford to miss the bus right now!
In such a scenario, you can reshape your business strategy on 2 pillars built on the foundation of customer data:
1. Digital Transformation
What does this mean in the retail industry?
It simply means two things:
online presence
omnichannel experience
Let’s consider the Future Group-owned BigBazaar in India as an example.
With the increase in demand for staples and household supplies amidst this pandemic, the Future Group launched BigBazaar.com and stepped up their efforts to service customers online, building omnichannel models within ten days that have since then scaled to about 10K orders per day.
Strengthening your online presence and continuing to deliver customer value is well within your grasp!
Your digital transformation journey can be broken down into 2 steps:
Data Collection
Converting Data into Useful Insights
(i) Data Collection:
It is very important to inculcate a data-first mind set. It is not about one single metric that you need to pay attention to, at the same time it’s not hundreds either. And, the basis of these metrics stems from a Unified Customer View.
What is a Unified Customer view?
It is capturing diverse data-points about every single customer both online and offline, across all channels and devices.
As a retailer, why do you need it?
The ongoing digitalization has resulted in a new generation of customers who use different channels and different devices during their buying journey. Today’s consumers are ultra-connected, increasingly empowered to get in touch with brands however and whenever they want, and hyper-aware that these businesses are collecting information about their behavior and preferences every step of the way. They expect that the brands will use this information to provide seamless, personalized experiences across each interaction – regardless of whether the touch point is digital or live.
How do you tackle this?
Companies with a strong Omnichannel customer engagement are able to retain 89% of their customers, compared to 33% for companies with a weak omnichannel customer engagement.
A simple CRM or a Data Management Platform will not help you retain 89% of your customers; you need to have a Customer Data Platform (CDP) in place.
Why do you need a Customer Data Platform (CDP)?
CDP uses only first party data (data collected by your company):
First party data is the data that comes directly from your audience. It gives you an insight into how your customers and website visitors behave and what their preferences are. With first-party data you don’t have to guess what your customers like - you have the information directly from the source. You can capture demographic, geolocation, device-related, and behavioral data-points such as:
Date/time
Location
Device type/model
IP/Browser version
Items purchased online
Frequency of purchase
Preferred method of payment
Eyeball data: seen and not clicked
Using this first party data you can develop a rich individual customer profile. These unique profiles get enriched in real-time by capturing data with every customer interaction across your website, mobile app, marketing campaigns and in-store purchases.
CDP focuses on the entire lifecycle of customer’s actions:
When as a retailer you are looking into strengthening your online presence in order to deliver a seamless omnichannel experience, it is very important to import data from in-store purchases. By combining your offline sales data with your e-commerce first party data, you can get a holistic understanding of the purchase behaviors of your customers. This can give you insights into which products are most popular and what types of customers prefer which products.
Siloed data prevents you from creating a consistent and hyper - personalized omnichannel customer experience. With CDP this problem is solved, by gathering first-party data and by unifying customer data across all channels and devices, CDPs enable the most effective marketing decisions powered by accurate data.
This will empower retailers like you to deliver a seamless hyper - personalized experience across the customer’s journey with your brand thus ensuring that they will keep coming back to you.
(ii) Converting Data into Useful Insights:
Once you have collected relevant first party data and you have your CDP in place, making sense of data and using it to drive business strategy is the next important step you should focus on. You can now effortlessly create customer segments based on certain defined data-points.
For instance: If you are a fashion retail brand, you can build out laser-focused segments based on geolocation, past purchased products, products viewed but not clicked, products viewed and products added to cart, preferred categories, frequency of purchase, etc.
The more granular your customer segments, the more targeted and effective your multi-channel marketing strategy will be.
2. AI-led Hyper-Personalization
Merely equipping yourself with actionable insights and not identifying the right messaging, products, and offers to showcase your digital customers is a lose-lose proposition!
You need to then convert these insights into profitable actions.
You ask, why?
Let me take Colin Cherry, a cognitive psychologist’s help.
In the 1950’s he coined and explained a term called ‘The cocktail party effect’. At a cocktail party, you will talk to several people and engage in several conversations. You will tune in and out, remembering very little from most of your interactions. But then someone says something that actually interests you and is relevant to you, that conversation will receive your full attention.
Your mind will not only remember the details of the conversation, but you will also remember the person who spoke. This will compel you to come back to that person to have more such interactions.
A 360-degree view of your customers allows you to create ‘The cocktail party effect’ or what we call Hyper-Personalization for your customers.
What will this help you address?
(i) Onsite product discovery is no more a problem:
59% online shoppers believe that it is easier to find the products they like on personalized online retail stores. A few use cases to achieve the same:
Category page reordering: For any ecommerce platform that operates at scale, there is a large number of products in the product catalogues. If the customers can’t find what they are looking for easily or if they spend too much time, they will leave the website.
Our AI engine, Raman, predicts user interest, and you can reorder the sequence in which the products are displayed. Since the most relevant products to that particular user is shown at the top, it makes it easier for the customer to find the products they are interested in. This can lead to 60-80% increase in CTRs.
Create smart segments based on certain defined data points (in sync with your KPIs).For instance, if you are an ecommerce retail app/web - track the geo location, behavioral data, frequency of purchase, past purchase, etc. Using this data you can create a hyper-personalized end to end journey - both product recommendations and messaging for each customer.
(ii) Lower costs and higher customer engagement rates:
Our AI engine, Raman will help you analyze the most preferred channels where a customer is more likely to respond based on historical responses with your marketing campaigns.
For instance, if Customer-A is more likely to respond and convert via emails and Customer-B is more likely to respond and convert via app push notifications, spend money only on that - thus lowering your costs.
Orchestrate intelligent customer journeys to effectively engage with customer segments on preferred channels to achieve your conversion goals. Send personalized product recommendations and personalized content on the customer’s preferred channel. This means you can offer customers personalized customer engagement messages offline when the user is not active on your web or app, thus ensuring quality customer engagement rates at a minimal cost.
(iii) Higher retention rates leading to increased CLTV:
56% of online shoppers are more likely to return to a site that offers product recommendations. Go beyond clickstream data. Capture crucial eyeball data-seen and not clicked, other behavioral data like products added to cart, wish list, frequency of purchase offline purchase, etc. to offer predictive personalized product recommendations via product recommendation widget.
Create personalized virtual boutique for every unique user-consisting of product recommendations across categories based on data and Raman predictions. Doing this will help you achieve 120-150% higher CTRs.
The Ball is in Your Court!
We all knew that the world was turning digital, but due to COVID-19 the pace has suddenly increased exponentially.
Fortifying your business strategy in response to the new normal, built on the pillars of Digital Transformation and AI-led Personalization, is critical to survival and future success.
To learn how you can get started on your personalization journey and increase conversions by 8-13%, get in touch today!
June 22, 2020
Identity is A Constant During Continuing CDP Evolution: How First- and Third-Party Data Can Be a CDP’s Key Differentiator
“What should I be looking for in a CDP?” This is the first question from nearly every brand that’s in the market for a Customer Data Platform. As a leading data provider, we have conversations on the exact subject with brands and publishers every single day.
CDPs are an evolving technology and they can still seem nebulous to the brands that are considering them for their martech stack. But there is a universal way to make a CDP and the brands that leverage it more competitive: provide the best foundation of deterministically linked, updated, complete and enhanced customer identity data possible.
Brands need to know exactly who their customers are -- especially in rapidly shifting online and offline environments. And their marketers demand a blend of authoritative deterministic data as well as probabilistic data resources in order to be confident in their decision making. To keep pace with the evolution, a CDP has to help unlock the power of a brand’s first-party data and deliver the third-party data that fills in the gaps from the moment of ingestion all the way through deployment.
As Winterberry Group has said in its research, among CDPs’ biggest challenges are “wrangling customer data into a persistent, universal profile and making the data available for analysis and action.” Up-to-date first- and third-party data are critical to providing relevant engagements in all channels and improving the customer experience -- especially with the imminent demise of third-party cookies. Linked consumer data with life stage and preference insights is the glue that creates the “stickiness” that will make a CDP an essential part of any brand’s martech stack.
The most competitive CDPs drive identity resolution. They provide authoritative linking, and utilize a combination of client data, third-party deterministic consumer data, and powerful linking technologies. But they don’t do it alone. Here are the top identity-driven factors to look for in a CDP.
Powerful Data from the Start
Unifying customer data across platforms and marketing channels is the crucial first step for any CDP. It ensures accurate linking at the moment that data is ingested into the platform. But ingestion must also include rigorous processes to verify, clean, hydrate, normalize, complete, and score the data, ensuring that a brand’s first-party identity information is complete, accurate, and deployable.
Ongoing Data Optimization
Data can be messy. It must be cleaned and completed upon ingestion in order to build a foundation for success. But that’s only the beginning of data maintenance. CDPs must continuously optimize a brand’s customer data, linking multiple (and conflicting) data elements from disparate silos to ensure that identities are resolved to a single consumer, while keeping the data up-to-date. In maintaining linked, de-duplicated and cleansed consumer identity data, the CDP will empower brands to put an end to data silos and minimize costly data decay. In doing so, marketers are able to reduce waste and maintain their ability to reliably reach the right consumers at scale with relevant and compelling messaging as their lives evolve and change. Maximizing data hygiene now minimizes the mess that customer service agents must clean up after misidentifying a loyal customer later.
Paint the Full Picture
Accurate linkages within a CDP ensure a 360-degree view of consumers. But CDPs should continue to add value by providing a wealth of demographics that marketers -- no matter their industry -- can activate. CDPs that can offer a comprehensive view of each customer that consists of both online and offline indicators provide the greatest value to brands. Aside from identity markers such as name, address, phone and email, CDPs should also include rich attributes to enable personalized messaging and segmentation.
Personalization Is Not Optional
Customers expect personalization in nearly every interaction with a brand. CDPs that leverage the best third-party data available ensure marketers are in a position to create relevant and valuable 1-1 messaging. And a CDP’s lifestyle attribute data and intelligence should not only fuel personalization, but also track changing preferences and needs as life journeys unfold (e.g. marriage, presence of children, new homes) so brands’ messaging can evolve along with their customers. By filling these gaps in consumer records with third-party data, brands are empowered to drive smarter marketing that leads to better conversion rates, ROI, and lifetime value.
No matter the brand’s goal, a CDP’s handling of identity data is a competitive differentiator. CDPs that can verify, score, link, cleanse, normalize first-party data offer the strongest possible foundation for a 360-degree view of the consumer and can become an integral part of the martech stack. CDPs must empower brands to personalize interactions with relevant messaging no matter the customers’ life stage through identity completion and enrichment. Adding rich third-party demographic and lifestyle attributes will help CDPs stand out from the pack so that these CDPs will enjoy the same brand loyalty they enable for their own brand customers.
June 18, 2020
EdTech: How to Keep Learners Engaged Post Lockdown
We have all been tempted to pick up a new skill or two during the COVID-19 lockdown haven’t we? Whether it’s a result of peer pressure or the situation demanding us to upskill ourselves, we have definitely come to the happy realization that our journey of learning doesn’t stop despite the lockdown. For online learners like business professionals or students, this can be attributed to Education Technology (EdTech) platforms. EdTech and e-learning has opened up a whole new avenue for us to make use of our time productively during the lockdown. And yes, the five-minute food recipe videos on YouTube have been a great source of inspiration too.
The EdTech industry has been a huge beneficiary of the digital revolution in the education sector. EdTech in India alone is a $2 billion industry, while being home to over 4,450 EdTech startups. From basic school education to professional certification programs, there’s something for everybody. EdTech at large caters to learners across generations, and the need to engage them all is a great challenge.
With learners eagerly adopting EdTech platforms during the lockdown, EdTech has garnered a lot of interest. With learners being restricted indoors, their behaviour has also changed. Learners are regularly accessing EdTech platforms on their desktops/laptops for a more immersive experience rather than through hand-held mobile devices with smaller screens.
A report by SimilarWeb in early May this year analyzed the top 35 e-learning websites, to understand the pandemic’s overall impact on the EdTech industry in India. It found that this segment has seen a growth of 25.87% over the last year. Players like Udemy, BYJU’S (Think & Learn Pvt. Ltd.), Coursera, Toppr, Gradeup (Gradestack Learning Pvt Ltd), and Unacademy are seeing these times as an opportunity. Some of the takeaways from the analysis were:
In April 2019-Feb. 2020 the segment had average visits of 102.2M. However, in the last 28 days (prior to 5-5-2020), the segment has seen 128.8M visits already (4.6 M visits daily).
Bounce rate has improved by 8.5%, and visitors are spending 2 minutes 49 seconds more time on these platforms.
There is also a shift in device preference among the users, as 68.29% of users till February end were using mobile websites. However, desktop has overtaken mobile web traffic by 51.75 in the last 28 days.
A lot of these platforms have been offering deep discounts and free subscriptions as a part of their initiatives to enable learners to continue their learning journey at home. How can these platforms adapt to a post-COVID situation when learners go back to their regular lives? While remote learning is here to stay, how can they plan for future growth?
The way ahead for EdTech
Artificial Intelligence (AI) in 2020 is a powerful tool to enhance customer journeys with personalization. It can help address core business objectives like user experience for EdTech platforms. End-to-end user experience across multiple platforms like mobile, web and apps are like an infinity loop, they cannot be ignored post the lockdown or COVID-19 crisis. EdTech players should therefore work towards creating a wonderful experience for their users at every stage. This will go a long way in reducing user churn rate in the long term. Additionally, users with higher lifetime values should be identified and prioritized with value offerings.
So what can EdTech platforms do differently to retain their users post the lockdown? Well, they can surely take a leaf out of the playbook that OTT streaming platforms have been using for some time now. While EdTech platforms rely mostly on video-based content like OTTs, it is now the time for them to innovate with personalization technologies. They will need to optimize, personalize and humanize their offerings in the near future. As competition in the segment is already cut-throat, it will become critical for EdTech platforms to know the likes, dislikes and interests of its users by carefully analyzing their digital footprints across devices with the help of Marketing Technology (MarTech) tools.
Here are some ways EdTech can continue to holistically engage its users in the post-COVID world:
Use AI to gather better insights
EdTech platforms today can be accessed via both apps and internet browsers on a variety of devices. To ensure user engagement and delight throughout, AI led tools can be used to plot personalized customer journeys. Customized recommendation engines can be built which are geared towards optimized course suggestions and a variety of other applications for its users.
For example: if a segment of users on an EdTech platform have signed up for courses related to digital marketing, with the help of AI they could receive:
Personalized course recommendations and reading references
Peer based gamification elements like module completion
Reminders to complete modules through mobile push notifications
Updates on live webinars or test results over WhatsApp
Leverage AI to improve Customer Experience (CX) with chatbots
Customer service can be a make or break for competitive industries like EdTech. AI powered chatbots can be used to quickly address customer queries 24*7. AI powered chatbots in contrast to logic based chatbots are more efficient, not to mention more affordable these days. They can be intuitively used for a variety of use cases.
For example: chatbots can help users get a quick walk through of the courses that a platform offers in a certain domain and guide them to one that best suits their requirements through a series of self-assessment questionnaires.
Use gamification elements
Gamification is a great way to get users to spend more time on a platform and explore all its features before the trial period ends. EdTech platforms can engage users in a fun and interactive way with the use of dynamic elements like timers, spinning wheels, shareable course completion badges and learning contests.
Use app insights to hyper personalise user experience
With all the user data generated during the lockdown, it will become essential to learn about habits of users, interests, their likes, dislikes, wants and needs to stay relevant and competitive in the market. These insights can further be used to cross sell products or services to existing users and retain them.
For example: a learner who has completed a digital marketing course may be interested in a marketing analytics course in the future.
Use customized app push notifications
App push notifications may be annoying to some users, but if done right, they can yield great results. Highly personalized messages can be sent to nudge users in a relevant and timely way using AI. Notification reach can be amplified with send time optimized media notifications. User attention can be garnered with customized notifications by testing various parameters with the help of AI.
For example: users who have signed up for digital marketing courses can get app push notifications about course updates and new courses in digital marketing which may be of interest to them.
Use website notifications to your advantage
With the latest updates on popular internet browsers like Firefox and Google Chrome, website notifications have taken a hit. These changes favour quieter notifications on browsers for increased engagement. Quiet notifications are triggered discretely only after the user has completed certain actions on a webpage like read 75% of its contents. Customized and contextual notifications will result in higher intent traffic flowing into websites, better engagement, lower churn rate and a seamless experience for users who are likely to spend more time on a website.
Make use of the right email automation strategies
While the lockdown situation has come with its own set of challenges, it offers a great opportunity for marketers to break the clutter with exceptional email campaigns. Alert users about webinars, offers, new courses and industry updates of their preference with email automation. Customized email campaigns triggered by customer actions in real-time can result in an uptick of high-intent traffic on platforms. Check the efficacy of multiple versions of email campaigns with MarTech tools.
Leverage WhatsApp notifications
The lockdown has created a situation where messaging platforms like WhatsApp have witnessed up to 40% increase in usage. By using WhatsApp Business Solution, EdTech platforms can seamlessly interact with customers on WhatsApp. With features suited for a wide variety of use cases in the form of templates like customer queries, billing, feedback and alerts, instant user gratification is guaranteed with this service.
Leverage User Generated Content (UGC)
The power of user created content is relatively unexplored in e-learning. Make use of UGC on EdTech platforms (example - math tricks & shortcuts) to improve peer learning and engagement. Studies have shown that peer learning is powerful. But UGC should ideally be monitored using AI and help drive search relevance on EdTech platforms.
Empathize with users
It is important to keep your communication with users as humane as possible during these testing times. Keep users in the loop about all the latest happenings in the industry and help them choose courses most aligned to these needs. This is most relevant to users considering shifting career domains like an IT professional looking at the data sciences job market.
EdTech users may be required to reskill themselves post the lockdown too. Using the best MarTech tools, EdTech platforms can help deliver what their users need the most - the right kind of learning to keep themselves relevant in the post-COVID world.
The pandemics and crises in the past have shown that the insurance sector has been more than prepared to take the brunt. But then COVID-19 isn’t like any other crisis. The global slowdown is inevitable and with the world GDP dropping, every bulletin is not showing signs of a quick recovery.
But the show must go on, so does engagement between your brand and your customer. Here are a few ways the biggest brands in the insurance industry are engaging with their customer during the crisis.
Business As Usual?
What happens if I have a claim in such times? What if my insurance renewal is due? What if I want to buy a new policy?
So many what-ifs. The COVID19 crisis made us question everything. That includes the operative ability of critical businesses. The brands in the insurance sector had to combat these uncertainties rising in the customers’ minds on time.
And they did it well.
We Care for You
As COVID-19 came knocking the whole country went into a sudden lockdown. No one was prepared for it. No one could have. Neither the brands nor the customers. In moments like these, all you need is empathy and care. And that’s what the brands did right.
There’s a catch with the insurance business - no one wants to be in a situation where they have to use insurance. But then a pandemic is exactly one of those times. And the brands didn’t disappoint. They used all the channels at disposal including emails, SMS, social media, push notifications, and more to spread the right information and to guide customers in the right direction.
Here’s how the brands did it right:
We’re Here for You
With the rising uncertainty about almost everything, the customers needed to know that they can still rely on the brands they have trusted for exactly times like these. Brands responded by communicating the availability information of their staff at select branches.
SMS, the underdog of all communication channels, came to rescue. We saw brands choosing texts to be the most reliable channel for this vital communication.
When the Work From Home became mandatory for almost everyone around the country, the brands ensured uninterrupted support with the help of Chatbots on top of conventional support systems.
Digital is Default
With physical contact becoming highly risky, the importance of going digital became indispensable. All the preparation and investments that the brands have been making in their digital transformation efforts paid off big time.
We saw brands being creative about communicating this and we absolutely loved all of it. Here are a few that demand a mention.
Get Busy Living
The lockdown came with its own set of perks - a lot of family time. People finally had more time on their hands than they can spend. Brands took this as a chance to engage with them in a non-transactional way.
The notifications went buzzing with home exercises, mindfulness tips, kitchen hacks and so much more.
Content is King. Engagement is Noble.
“People will forget what you said, people will forget what you did, but people will never forget how you made them feel.” - Maya Angelou.
With the transactional communication reduced to a bare minimum, the brands had two options - be silent or be innovative. The majority chose the latter. They started engaging with their customer base with creative ideas and offers.
From free e-newspaper subscriptions to virtual fitness sessions, brands went up and beyond to be relevant and engaging. A lot to learn here.
What More?
Watching the biggest of brands executing top-notch engagement campaigns inspired us to don our own thinking caps. Here’s our inspired list of a few more campaigns that you can add to your COVID-19 marketing playbook:
Rise of the Bots
WhatsApp as a channel just opened up for business and brands are killing it with customer engagements at an unimaginable scale. We did something really cool that involved Big B, Big Billion Days, Flipkart, and WhatsApp.
However, those were Pre-COVID days. So what now? Especially for the insurance sector?
WhatsApp bots can be your foot soldiers to automate document collection, e-dispatch of policy, payment collection, and all other transactional customer communication and interactions.
#GetPersonal with Your Customers
Social distancing mixed with lockdown has restricted the human advantage that business leverage. How to ensure personalized experiences for each one of your customers?
Personalization.
Work on delivering an omnichannel hyper-personalized experience for experiences based on understanding their micro-engagements.
Make Your App COVID-19 Relevant
Times are not as usual. So will be the use cases for your app. Can you push quick updates on your app to make it relevant in these times?
PhonePe, one of India’s biggest UPI apps changed their app homepage overnight to show just the essentials allowed and necessary during the lockdown.
There’s a play there. Here are a few features the brands in the insurance sector can build:
a. social trackback
b. self-health check
c. locating health networks
What’s Next?
One thing that comes out in almost all our customer interviews is: We might never get back to the old normal. Post-COVID world will be a new normal world. The new normal will be empathy driven world. A more humane one.
That needs to translate into your messaging, engagement, and overall brand experience.
The brands that stayed relevant during the crisis were the ones who were agile to respond to the change and build their experiences around the tenets of empathy, creativity, and, personalized experiences.
June 4, 2020
Mobile App Personalization: 10 Ways to Convert and Retain App Users at Scale
We live in an inter(net)-connected world where smartphone penetration, ever-improving mobile network infrastructure, and enhanced access to cheaper data are driving the app economy.
With over 2.7 billion smartphone users and 1.35 billion tablet users globally, there are 2.8 million apps on the Google Play Store and over 2.2 million apps on the Apple App Store. In fact, there were over 205 billion app downloads in 2019 alone.
That’s staggering!
Sure there’s a massive opportunity to empower users, strengthen your online presence, generate top-line revenues, and counter competition - but, that’s easier said than done.
One fool-proof way to consistently power user engagement, conversions, and retention at scale is by integrating a strong personalization strategy into your mobile (and omnichannel) marketing machinery.
And, personalization - today - has gone above and beyond the obvious. Addressing your users by their first names over a push notification or sending them a discount coupon on their birthdays over an email campaign is great.
But, it’s not enough! And, especially so in the current COVID-19 environment where hyper-competition for users’ screen-share, mindshare, and wallet-share has only heightened.
Your users expect and demand an end-to-end, tailor-made customer experience, right from first-time app launch. This assumes even greater significance across industries such as e-commerce, OTT, and news and media.
At Smartech, we understand how important 1:1 personalization is across platforms, channels, and devices to your omnichannel marketing efforts. And, with that in mind, we’ve bolstered our mobile app personalization module.
Here’s how you can now deliver highly differentiated user experiences at scale on and through your mobile app:
1. Build a solid foundation of and on user data:
Effective personalization is dependent on you gathering the right user data across channels and platforms. Gathering the right demographic, geolocation, and device-type data-points is important. Start capturing these basic data-points at the registration or login stage of your user onboarding flow.
But, you also need to capture your users’ in-app behaviors, actions, inactions, responses and interactions to multi-channel marketing campaigns. This will help you create and constantly enrich a unified view of every user, in real-time.
For instance, if you’re an e-commerce app, you need to log actions and details such as products searched, product categories browsed, products added to cart or wishlist, products purchased, payment mode chosen, and most common paths towards conversion.
If you have a physical store, you also need to ensure your transactional data is funnelling back into your data backend so as to personalize subsequent shopping experiences across both your website and/or app.
Simultaneously, you need to track relevant metrics attached to these actions to gather granular insights - recency and frequency of app launches and purchases, average time spent per screen, ratio of products added to cart and finally purchased, actual conversion rates, etc.
Analyzing these diverse data-points will help you gain actionable insights and develop relevant user segments.
Our AI engine, Raman, can actually help you dive deeper. Now slice and dice behavioral data to arrive at a segment of one, with AI doing all the heavy-lifting for you! Our cutting-edge collaborative and content-filtering algorithms make it possible for us to ingest large amounts of user data-points and behavioral footprints.
Raman also constantly learns from both clickstream and customer eyeball data; i.e. from both live actions and inactions that can be attributed to a user.
That’s not it, though! We also help you map and leverage data-points such as device price and brand of device to further fortify device-related information.
Once you have your user data and analytics backbone in place, you can focus on adding muscle to your personalization strategy.
2. Personalize the app home screen on first-time launch:
Modern marketing has driven home the fact that one size doesn’t fit all. Depending upon your app category and the quality (and quantity) of demographic data-points that you’re able to gather during the first-time onboarding flow - you can immediately start delivering a personalized user experience on your app homepage.
For instance: If you are an OTT music streaming app; data-points like name, age, gender, preferred genres and languages, favorite artists, etc. can be used to curate a first-degree personalized list of content recommendations on your app home screen instantly.
You need to strike the right balance between deploying an onboarding flow that educates new users on functionality, key features, etc. and capturing relevant demographic data-points (without being explicitly intrusive) to start delivering 1:1 user experiences ASAP.
Notice how Hungama Music, one of India’s leading home-grown music streaming apps, does this during the user onboarding process to quickly begin personalizing content recommendations when the home screen launches for the first time.
You can subsequently highlight the most relevant recommendations on the app home screen on future app launches. This can increase your CTRs by 90-120% while uplifting content consumption by 5-7%!
3. Tailor-make the app navigation experience:
Apart from your users’ search, browsing, click behaviour and consumption history, you can personalize how your users navigate across your mobile app.
Other parameters such as gender, buyer personas, geolocation, time of day/timezone, seasons/weather, etc. can be harnessed to personalize the banner images, graphics, CTAs, trending products and offers, etc.
Also, leverage insights from your users’ most common paths towards conversion to further optimize their navigation journey. The more individualized the navigation journey, greater the probability of your users finding exactly what they want, faster!
Check out how AJIO, a leading e-commerce clothing retailer offers a gender-based app navigation experience for both men and women.
4. Personalize the search experience:
Any search made by a user on your app is a solid signal of intent. Intent to purchase a product. Intent to book a ticket. Intent to consume content. And, you need to value these search inputs.
Every search action undertaken by specific users tells you about their instant wants, needs, and preferences. And, your objective must be to direct your users to exactly what they want faster.
With the assistance of our AI engine, Raman, you can now instantly populate product or content recommendations based on the partial or full search terms inputted in the search tab. This would also take into account historical searches made.
These recommendations continue to get more accurate with each subsequent search action that a user takes, helping you to significantly reduce the path from product discovery to purchase.
For instance: As an e-commerce platform that specializes in the online retail of cosmetics, you can start giving the most relevant product recommendations for lipsticks when a user is searching for a particular brand of lipsticks.
Here’s how Amazon leverages search personalization to show me sub-product categories that are most relevant to me.
Falling user attention spans and quick access to alternate apps in the same category can fuel switching behavior. This is why you need to respect the time an individual user potentially invests when he/she launches your app. Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption.
Our AI engine, Raman, enables you to show the most relevant product or content recommendations across your Home Screen, Product Display Screen, and Product Listing Screen.
Much like our existing onsite dynamic personalization for websites, you can replicate the same for your mobile app. These live recommendations are optimized for mobile display and click, ensuring that you maximize the use of screen-space on every scroll.
Moreover, these recommendations get more refined with each shopping session that an individual user engages in.
Not only does our AI engine account for clickstream data, but it also accounts for customer eyeball data. This implies that negative signals for any product or content recommendations that are “seen-and-not clicked” and “not seen-and-not clicked” feed right back into the AI engine - all in real-time.
This helps you improve behavioral predictions by almost 20%.
6. Re-order product or content categories for greater context:
If you have an e-commerce, OTT, or news and media app; you are bound to have hundreds of products or content options within your product catalog or content library.
Our patented AI algorithms can re-order these categories in real-time for individual users so the most relevant product or content recommendations show up right at the top.
Remember the Golden Rule?
Near-instant product discovery and top-of-sight visibility become critical to conversions or consumption!
In the below example, our AI engine can actually re-order listed product categories to show the most relevant categories from top to bottom, to individual users in real-time.
7. Create an in-app personalized storefront or playlist:
Go one step further and allow Raman to specially curate a mobile boutique - composed of only those products or content recommendations that an individual user is most likely to view, buy, or consume on your app!
Delight your users with a unique online shopping or content consumption experience while increasing CTRs by 120-150%, every time they launch your app.
Neural networks bolster Raman’s real-time, reinforced learning capacity; as it refreshes the list of recommendations every time a user chooses to launch the personalized storefront, watch-list, playlist, or read-list.
Netflix curates a personalized watch-list for each subscribed user that is refined and refreshed after each active session on their app or website.
Spotify actually goes a step further to create multiple weekly playlists based on preferred genres, favorite artists, and music language.
8. Deploy live contextual recommendations via in-app messages:
You can optimize your in-app personalization strategy by triggering relevant product or content recommendations and appropriate offers at individual users through in-app messages.
These can be triggered when a user lands on certain pre-defined screens or when a user scrolls a finite percentage on certain pre-defined screens.
Our AI engine, Raman, intelligently triggers these in-app messages only when they truly make sense, without disrupting the ongoing user experience.
For instance: As an e-commerce app that has a health and hygiene product category, if a user has added hand sanitizers to his/her cart, you can trigger a relevant recommendation for facemasks and hand-washes, as a dynamic product bundle.
Such a strategy can also help you increase your average order value through effective cross-sell and upsell opportunities.
9. Personalize your recommendations across other channels:
While personalizing the entire in-app mobile experience is important, what do you do when your user is not in an active session? You can’t leave that to chance.
The essence of personalization lies in delivering a rich and unique 1:1 user experience across multiple digital touchpoints. This implies that you need to activate other critical mobile marketing channels such as emails and app push notifications to continue delivering these personalized recommendations.
With Smartech, you can now trigger laser-focused product recommendations across these channels to maintain top-of-mind recall, bring inactive users back to your app, and potentially nudge them towards a conversion event.
Here are the kind of contextual recommendations that you can deliver to pursue conversions beyond just your mobile app, especially in the e-commerce space:
“Suggested for You” Recommendations: These are the best possible recommendations tailored to individual users based on their general historical behaviour; i.e. product/product categories viewed, items added to cart, purchase, etc.
“Cart Abandonment” Recommendations: These are generated based on the products added to an individual user’s digital shopping carts where the user may have dropped-off or left a purchase incomplete.
Buying Pattern Recommendations: These are generated based on an individual user’s most recent product purchases. For instance: If you identify a user segment that has repeatedly purchased Washing Detergent and Fabric Softeners and these out of stock due to a demand surge - you can trigger personalized app push recommendations updating this segment when these products are back in stock.
Viewing Pattern Recommendations: These are triggered based on an individual user’s most recent products or product categories viewed.
Bestselling Recommendations: These are generated based on the highest selling products on your mobile app. These are products that are being purchased the most when compared to other products over a period of time. For instance: In the current scenario, health safety and hygiene products such as facemasks, disinfectants, and hand sanitizers have quickly become bestselling products - over the last 2 months - and will see a steady demand even when the lockdown period is relaxed.
Trending Recommendations: These are generated based on the most trending products on your mobile app. Essentially, these are products whose consumption has shown a percentage increase over a period of time. For instance: Building on the above example, health safety and hygiene products like facemasks rapidly became a trending product within its category and very soon emerged as a bestselling product, as the COVID-19 turned into a pandemic.
Recently Viewed Recommendations: These are generated based on the most common products that have been recently viewed by individual users on your mobile app.
“New Arrival” Recommendations: These are generated based on the new products that have been added to your product catalog. Our AI engine, Raman, maps these products to the ones that are most relevant to individual users and displays the ideal recommendations, capable of nudging him/her towards an eventual purchase.
Depending upon what channels of customer engagement are working best for which customer segments and buyer personas, you can optimize your multi-channel mix, as well as the send-times for these recommendation campaigns.
Raman evaluates the real-time performance of these triggered recommendations for relevant user segments to offer you deep-dive insights on the preferred channels and send-times that are likely to produce the best engagement and conversion rates.
10. Re-target existing users with personalized ads:
Regardless of your best efforts at encouraging your users to make a purchase or spend more time consuming content on your app, there will be drop-offs and users that straddle dormancy.
That’s the harsh reality of mobile marketing!
But, our AI engine, Raman, can actually target individual users with the most relevant product or content recommendations through optimized re-targeting ads across Google, Facebook, and Instagram - at the right time.
For instance: As an e-commerce app specializing in fashion apparel, you can re-target existing users with the most relevant t-shirt, shirt, or trouser related recommendations that are most contextual to individual male users. Depending upon where a user is most likely to see this ad, Raman will display a mobile-optimized ad, nudging the user to click and re-launch your app.
Personalization at Scale = Stickier 1:1 Experiences = User Retention
Mobile app personalization is not a set-it-and-forget-it project. While our AI engine, Raman, will help you deliver relevant recommendation-led 1:1 user experiences; human intelligence also plays an important role. When AI and human intelligence comes together, you can continue to optimize experiences for a segment of one!
Also, remember that mobile app personalization is another weapon in your larger omnichannel personalization arsenal. Wowing your users just on one platform is not going to cut it! They expect a consistent and frictionless experience across your website, mobile app, and marketing channels (across devices).
To learn how you can get started on your mobile app personalization journey - with AI as your sidekick - like the giants in the industry do; get in touch with our growth experts today!
[P.S. Hang on. We’ve got something special in store for you! We understand that it may not be business as usual for you currently.
To solve the problem of fragmented sources of data, marketers have traditionally used rule-based approaches, but today that is simply not enough. Rule-based approach only fits into those scenarios within the limited set of rules, falling way short of what is needed and keeping the marketer out of the other set of unknown possibilities.
These limitations brought us to introduce machine learning algorithms in our core CDP platform that can support the following use cases.
Cohort Auto-segmentation
This is one of the top preferred features among FirstHive users. Using data such as look-alike customers, response rate, demographics, and many other parameters, machine learning algorithms can predict customer segments with formulation cues. These are segments created using automation within the platform. This helps in building more mature segments that would hence be designed for optimization.
Advanced Customer Data Management
Predictive models are used to develop advanced identity resolution algorithms that come with the ability to support multiple logical data stores and apply different rules to them. They come with in-built connectors and capabilities for advanced data transformations. They also carry out complex data management and schema changes on an ongoing basis using a graphical interface.
Offline Aggregation to include Omnichannel Strategy
Only a few of the CDPs like FirstHive also cater to the function of offline data aggregation which most often occurs at PoS terminals, QR codes reading, connected smart devices, and other similar instances. This is critical for content recommendations that are in the offline universe of marketing channels. To estimate which of those offline channels are best for content delivery, predictive recommendations bring in true value.
Customer Support
Integrated systems such as email, ticket resolution, chat, and voice calling that build a customer support interface can be managed better for optimized resolution. Based on customer profile and persona tagging, each customer could be handled in a way that is most appreciated by her. Customer support associates will be equipped with information about how each customer is comfortable with a support channel and their response. If the customer is a first-time support user, then the associate will be informed about her preferences.
Algorithms provide proactive recommendations that the customer support executive can have handy while tackling customer queries.
Real-time Customer Data
Apart from use cases where algorithms are deployed to churn out recommendations and have them stacked in a dashboard, real-time data is also a predictive model capability. Machine learning algorithms are formulated to churn out recommendations using a combination of historic and real-time customer data.
Such data is most often used as a feedback mechanism for campaign activation across different channels.
More Use Cases
Some other use cases where predictive modelling can be actively deployed are outbound marketing campaign support, e-commerce recommendations and optimization, lead scoring and predictive scoring models. Within a CDP the most common models that are put to use are clustering models for customer segmentation, propensity models that determine probability and predictions, collaborative filtering for recommendations, and content-based models that are used at times when your systems lack historical data to build recommendations upon.
Should you have any questions about your specific use case, feel free to reach out to us at marketing@firsthive.com.
April 30, 2020
5 Ways Marketers Get Better Results with Customer Data Platforms
If you are a marketer, you may be wondering what’s behind all the interest in customer data platforms? At the top of the list is the ability for your team to transform the way it works, as well as the way customers interact with your brand. CDPs break down martech silos, unify online and offline customer experiences and data, make marketing more efficient, and drive better results. A CDPs capabilities range from collecting and storing comprehensive customer data, to seamlessly integrating with existing marketing technology, and delivering actionable, data-driven insights.
These are just a few of the features that make CDPs a wise investment for any data-driven marketing team – which should be every marketing team. While addressing marketing and IT’s key concerns, CDPs play a key role in improving marketing’s efficiency, data quality, and security.
What Is the Definition of a CDP?
David Raab, founder and head of the CDP Institute, defines the term CDP like this: “The official CDP Institute definition is ‘packaged software that creates a persistent, unified customer database that is accessible to other systems.’” Raab says the following points are critical in understanding what a CDP is:
A CDP is packaged software, rather than a custom-build project like a data warehouse or data lake, which makes it quicker, easier and cheaper to deploy.
A CDP builds an actual database rather than assembling information on the fly by querying external systems, which yields better performance and allows time-series analysis.
A CDP works with identified customers, not just anonymous cookies like a DMP.
A CDP lets other systems read its data rather than just holding it internally.
One critical thing to realize is the distinction between a customer database in general and a CDP in particular: a CDP is packaged software to build a customer database; you could also get a customer database by building it yourself or buying a larger product that included it.
Here are five ways you and your team can get better results with an enterprise CDP:
1. Finely Tuned Segmentation
The more data you have – and the more correctly unified it is – the better you’ll understand your customers, including their purchase behaviors, desires, and motivations. This, in turn, helps you create smart audience segments for more relevant customer personalization.
With clear, unified views of your customers, you’ll know exactly how to market to them and lead them further down the path to purchase – and a CDP can provide you with that view.
2. Real-time Engagement
What’s even better than detailed data about an individual’s history, preferences, and behavior online? Getting that information in real-time.
To deliver more targeted offers and better outcomes, a holistic understanding of customer engagement is crucial. It involves the real-time aggregation and analysis of data across marketing, sales, and customer service. A CDP can deliver this information and even use it to orchestrate campaigns, promotions, and tailored user experiences. As a result, you can more consistently provide personalized omnichannel brand experiences for your customers, no matter what channel they’re on.
A CDP also helps keep your customer data accurate, reliable, and secure by constantly cleaning, translating, and updating data over time. With round-the-clock access to this up-to-date data, you’ll quickly improve the quality of your customer interactions.
3. Omnichannel Customer Experience
With an enterprise CDP, you can unify all customer data securely and provide a consistent, positive experience that helps guide customers through their journey. A good CDP can process information from multiple applications – Marketo, Facebook, Salesforce – and combine them to get a full understanding of what your customers want, buy, read, visit, watch, and more.
Advanced CDPs will automatically analyze new data based on the unique rules you establish within the system. You can analyze and organize data for audience segmentation, customer personalization, campaign optimization, push and pull notifications, syndication, and more.
From there, you can use this newly categorized information to answer your most pressing marketing questions and begin improving results across channels by improving or changing your marketing strategies.
4. Cross-Sell and Upsell
By using your CDP to quickly consolidate information from all channels, you’ll know when your customers make a purchase and it’s time to shift to a cross-sell or upsell strategy. Once a customer decides to buy, you can improve retention with personalized, data-driven recommendations. This can extend your relationship far past the first purchase.
5. Improve Products and Services & Identify New Revenue Streams
Customers expect personalized content and better customer experiences – but not at any cost. Marketers need to get creative and rethink the formats of content used to reach customers across devices. This includes what promotions marketers send – and when – as well as what types of alerts and notifications are pushed when customers step into a physical store.
Customers want special attention and improved products and services that meet their specific and unique needs. A CDP helps you gain a more complete understanding of your customers’ preferences, so you can prioritize your product and service offerings to more closely align with customers’ wants. Or, you can create an entirely new stream of revenue by identifying and capitalizing on trends borne out of your customer data.
Delivering Customer Data and Better Results
Marketers can’t ignore customers’ increased demand for frequent, personalized marketing messages. It’s time to invest in a customer data platform to transform your marketing department and deliver exceptional customer experiences.
Learn how Arm Treasure Data enterprise CDP can help you take advantage of the big opportunities in omnichannel marketing. Request a demo to get started.
April 23, 2020
The Omniscience Option: Next-best-action Recommendations that Work
What marketer wouldn’t want to be omniscient, especially when it comes to understanding customers? Unfortunately, it’s not yet a martech option, but next-best-action prediction is as close to omniscience as marketers are likely to get in this lifetime. Its purpose is to use data-driven insights and analytics to predict the next action to take, whether the application is a customer service call or a marketing or social media campaign. Often called “next-best-action decisioning,” it’s been a marketing Holy Grail for a while.
But the difference lately is that predictive martech and analytics – fueled by advances in data lake technology, Customer Data Platforms (CDPs), and Big Data analytics – have gotten superbly good at it. So good, in fact, that “the omniscience option” is no longer a science fiction fantasy.
Next-best-action Helps You Market to Individual Customers
Next-best-action is uniquely suited to service, support, and marketing because it typically uses data from all of these functions. It focuses on specific customer preferences instead of positioning a product for larger nebulous groups of buyers. The idea is this: if you can accurately forecast what a buyer wants – exactly when they’re looking for it – and you can figure out how to reach them when they’re ready to engage, you can provide a great experience for them that improves sales and cements customer relationships. Plus, you also deliver all that at the lowest cost. It’s a win-win!
While easily explained in a blog, this vision is a bit harder to achieve in the field. It requires a lot of data about customer behavior, a real-time engagement feedback loop, and the power of prediction. These three capabilities alone are hard to solve, so you can imagine the challenge of putting them together into one seamless process, especially if you have to do all the technical work in your own organization.
While some marketers have prematurely written off next-best-action decisioning as an improbable application for customer analytics, other marketers are investing in the expertise and technologies they need to build next-best-action systems.
The Key Ingredients in a Next-best-action Recommendation System
The basics of next-best-action recommendations require marketers to understand what, where, and when buyer engagement happens. If this engagement is happening through digital channels – which is likely, given that 89 percent of buyers start their process of discovering products and services with a search engine – behavior becomes easily quantifiable. You get to understand what products visitors to your website are viewing and how many. You can determine their interest through numbers of clicks on a page or on an advertisement. As well, you can analyze patterns people display across channels (such as logins to mobile apps and number of previous in-store purchases), that signal they’re ready to purchase something.
With rich data sets that are unified around individual customers, the what, where, and when of engagement is ready for action. We’ll break them down for you in the next sections and dive into the technological capabilities to make “best actions” happen.
Campaign Activity Defines the What in Next-best-action Systems
For marketers, the “what” in next-best-action systems is defined by campaign activity. This is how marketers engage with buyers. So, when a marketer sends an email, delivers a message through an ad, or sends a promotion in the mail, they’re looking for a response. Maybe that response is a coupon redemption or click-to-download content.
Sometimes, a buyer’s response is to do nothing. This is important to know for next-best-action decisioning, so you need to ensure that your methods of aggregation can handle “null” values without a lot of translation and workarounds. No matter the reaction, what that buyer did (or didn’t do) to respond is going to determine what the next offer should be. Are you collecting what buyers do in response to your campaigns? And, where is all of that response data?
Most likely, that data lives in the systems you used for the individual campaign tactics – your email system, your social site, or your agency’s database. Some of it might be in your loyalty program database or point-of-sale systems. And, I’m going to bet that it’s not all in one place, unless you’re using a CDP, in which case you might already have a set of accurate, unified customer profiles.
The Where and When Requires Predictive Analytics
Once behaviors are analyzed and patterns are discovered, marketers can make some decisions about the next touchpoint. Do they want to retarget people who visited the site two or three times but never purchased anything? Do they want to send a follow-up email to people that stopped by their booth for samples? Maybe they want to do both, but when do they initiate the next touchpoint? Generally, marketers reach people by using a couple of different strategies:
Where those people were when they responded (such as online or in the store)
Where that marketer expects them to engage next based on a customer journey analysis
Both have their merits, but what’s really important is where and when a person is most likely to want to engage with your content, message, or offer. This is the context of “where” and “when” in the next-best-action recommendation system, and if the prediction is accurate, it can save you from unnecessary campaign activity and free up time for more meaningful projects.
Again, the details of your campaign data can be put to work here to help predict which channels matter the most to specific people and when they can be reached. Where do they respond and which channels do they ignore? Are they more likely to engage at night while they’re online? Through machine learning, daily or even hourly data can be analyzed to produce a channel propensity score. A high score represents a strong correlation to a particular channel (such as social site or email) and time. These scores can then be added to an individual buyer’s profile.
To use those scores, a marketer will need a way to discover individuals according to their behaviors (what) and their propensity to engage in a specific channel (where) and at what time (when). Just as they have for years with demographic and household data, marketers can use segmentation tools to discover people with similar behaviors and preferences. The process of audience segmentation is the same, save for one major adjustment; it requires the flow of large, fast moving data sets to place individuals into specific segments automatically.
Without the ability to dynamically segment people, using behavior as a basis for analysis would be very hard for marketers. Can you imagine the time you would need to discover when shoppers abandon their carts and sort them one by one into groups for your next email campaign?
Dynamic segmentation tools allow marketers to zero in on precise groups of people as they exhibit the behaviors to be followed up on, for example, all people who browse a site without purchasing. As well, marketers can use those tools to drill down further into where and when those same people are most likely to respond to another message, offer, or piece of content.
Putting the Next Best Action Pieces Together
Making next-best-action decisions requires the “what, when, and where of buyer engagement.” Marketers want to make smart choices to engage buyers, and they want to make those decisions quickly and effectively. There are important capabilities and knowledge needed to support the process of using behavioral data for next-best-action decisions. Here’s what you need to know to make the recommendation system work.
First, event-level data needs to be captured continuously and timestamped for understanding the sequences of those events. Visitor behavior on your website needs to be streamed in and organized alongside email and ad response data. This unification process isn’t trivial and requires data management expertise, but the outcome is an individual profile of every visitor, customer, and potential buyer. It reveals their behavior and when they exhibited good and bad responses (such as downloading content or abandoning a cart).
Second, machine learning algorithms can pick up on signals marketers wouldn’t be able to discover on their own. How likely a buyer is to engage in a given channel at a particular time can be more accurately determined by moving past frequency analysis to propensity modeling, which captures more data points based on how correlated they are to the behavior you want to predict. With greater accuracy, marketers make more confident decisions on where and when to engage a buyer.
And last, dynamic segmentation tools can automatically discover behaviors as soon as they’re exhibited and help marketers initiate communications, offers, and other touchpoints quickly. Propensity scores add key information about when and where to best reach people for the greatest impact. With scores and event-level details at their fingertips, marketers can get as granular as they’d like with their segmentation and target people with the best offers, in the right places, and at the optimal time. With greater precision, they can be more effective, fast.
These days, an ever-increasing number of customer interactions are taking place over digital channels and every single digital interaction offers an incredible source of customer intelligence for organizations to tap into.
With every visit, customers leave a valuable trail of digital breadcrumbs. These breadcrumbs give organizations the ability to follow each individual customer journey and each customer’s experience along the way. With every browse, click, like and share your customer creates their own digital footprint. And with their consent, brands can harness this rich source of data to anticipate and deliver on the needs of each individual customer, optimize each customer’s journey, and unlock new competitive value for the organization.
Of course, this data must be treated as personal data and companies should provide comprehensive cookie notices to educate users on how they plan to use their personal data, on an opt-in basis.
But despite many customers still opting-in to share this data, organizations are struggling to tap into this readily available digital intelligence in a meaningful and effective way.
The reason? These five recurring challenges create barriers to unlocking the true value of digital data:
Tagging is still the predominant method for digital analytics tools to capture data. Not only is there cost and time involved in creating, testing and deploying these tags, but they need to be constantly updated. Updates are required every time there’s a new area of interest or there are changes to the website. This invariably leads to delays in campaigns, and lost data and opportunities.
Many digital analytics solutions focus on visits, page views, clicks and campaign triggers. The data collected is rarely at an individual customer level. This makes it challenging to join digital data up with offline data from CRM or single customer view systems, where data needs to be held at individual customer level.
Too many organizations are focused only on behavioral data – what a customer clicked on and what they saw, rather than experiential data. Experiential data could include what a customer didn’t see, what price they were quoted or what products were not in stock. Collecting behavioral data without experiential data often leads to an incomplete or misleading picture of cause and effect.
The number of digital channels, technologies and techniques for measuring customer experience within those channels has exploded but the data and insight is held in siloes make it difficult to obtain a joined-up view of the customer experience.
By the time data is extracted and analyzed for insights, the customer has already completed their interaction. Organizations are still reporting on the past and unable to use data in real time to impact customer experience ‘in the moment.’
Organizations leading the field in digital intelligence are opting for a single view of individual customer-level behavioral and experiential data across digital channels that can be easily joined up to offline data to gain much deeper insight into the customer journey.
The ability to analyze data at this level of detail is helping these organizations go beyond the “what” and “how” of traditional digital analytics and answer the more valuable “who” and “why” questions. Who are my most and least valuable customers? Why do they behave as they do on my digital properties? What simple changes could I make to alter some of this behavior?
By capturing granular, time-stamped customer-level data from every digital interaction about everywhere your customer went, everything they did and did not do and everything they see and did not see, organizations can optimize their customer experience and create competitive advantage.
Digital advertising has reached new heights of complexity. Today’s omnichannel ad campaigns reach across many different platforms at once, from publisher websites and mobile apps to search engines and social media. Yet paradoxically, campaigns have become more and more personalized, using highly tailored, targeted ads to reach specific audiences.
All of this makes for an intricate process with many different participants, from advertisers and publishers to third-party vendors.
As you move further into ad tech, you’ll find yourself navigating a complicated and ever-changing ecosystem. Here are some of the most important types of technology you’re likely to encounter, including important differences in function and usage.
1. Demand-Side Platforms
As the name suggests, demand-side platforms (DSPs) serve people who want to purchase ads from publishers. If you’re in marketing, that means you.
A DSP is an automated system that vastly simplifies the buying process for advertisers, who can purchase targeted ad impressions from many different sources through a single interface. DSPs usually use real-time bidding – that is, automated auctions that take place in just milliseconds.
To make targeted ad buys, DSPs may use audience data from other sources such as publishers, advertisers, and external data providers. A DSP can use these datasets to guide targeted ad buys. The system can also track campaign performance and use this data to improve ad targeting and media buys.
So where do DSPs find ad impressions to buy? DSPs may buy ads directly from publishers, supply-side platforms, and ad exchanges.
In recent years, DSPs have come under pressure to provide more transparent pricing, better ad quality, and more reliable data about campaign performance. Ad fraud continues to be a major challenge – as it is for the entire industry – and leading DSPs have tried to differentiate themselves by providing buyers with better protection against phony publishers and malicious ads.
2. Supply-Side Platforms
While DSPs serve advertisers looking to buy ad inventory, supply-side platforms (SSPs) serve publishers looking to sell ad inventory to generate ad revenue from their websites or apps.
Publishers use SSPs to sell ad impressions through various external platforms. These may include DSPs, ad exchanges and ad networks. An SSP integrates with all of these technologies, so publishers can manage and fulfill all of their inventory sales through a single interface.
Ad impressions are only a means to an end, though. What publishers really offer is access to audiences. And that requires data.
Through an SSP, a publisher can share a wide range of data with different buyers, including advertisers, ad networks, and DSPs. For example, publishers can share information about the type of content being displayed, or concerning the demographics, location, and purchase behavior of website visitors. Such information helps advertisers segment their audiences and hone their targeting.
Like DSPs, SSPs have faced complaints about low-ad quality, high pricing, and fraudulent ads. These trends have pushed large providers to develop better auditing technologies, more transparent fees, and better industry standards – although these efforts are very much works in progress.
3. Ad Exchanges
An ad exchange is an online system that provides a marketplace for ad buyers and publishers. Such platforms operate a lot like online stock trading platforms. On an ad exchange, publishers auction off the inventory they haven’t already sold to ad networks. Buyers then compete to acquire these unsold ad impressions, often through real-time bidding.
All transactions are highly automated, since it’s extremely difficult for humans to perform the calculations required to trade so many targeted ad impressions in real time. While making their trades, buyers and sellers also exchange data that allows advertisers to segment and target their audience.
Some ad exchanges provide open marketplaces for buyers and sellers. But this model makes it hard for publishers to control what kind of ads their audiences see. That’s a drawback for companies that want to protect their brands.
As a result, publishers are seeking alternatives to open ad exchanges. In some cases, they are using private exchanges that limit entry to select buyers, so they can prescreen who places ads on their properties. Other publishers are turning to a “programmatic direct” model. This method uses automated systems to enable more efficient publisher-to-advertiser sales, without any auctions.
4. Data Management Platforms (DMPs)
Data management platforms (DMPs) enable their users to store, manage, and analyze data about ad campaigns and audiences. Unlike a DSP or SSP, a DMP does not help you buy or sell ad inventory. Instead, a DMP feeds useful data to these other platforms, enabling both marketers and publishers to make more effective decisions.
For marketers, a DMP and a DSP complement each other to make more effective ad purchases. The DMP provides information that helps the DSP manage and direct ad buys. The DSP, in return, sends back valuable campaign performance data. Some DSPs include DMP functionality, creating hybrids that fuse aspects of both systems.
Using a DMP, marketers can create temporary user profiles and target audiences based on demographics, behavior, or other characteristics. As advertising platforms, DMPs are restricted in their ability to use personally identifiable information. As a result, they mainly use anonymous, short-lived data acquired from third-party vendors to build their profiles.
Recent privacy laws have limited the usefulness of platforms that rely on third-party data, forcing DSPs to change how they operate. In particular, the European Union’s General Data Protection Regulation (GDPR) has made it harder to acquire and use certain kinds of data for targeting. Meanwhile, the most advanced DMPs continue to add features and develop new capabilities – for example, the ability to use richer sources of first- and second-party data.
5. Customer Data Platforms
Like a DMP, a customer data platform (CDP) ingests data from other systems and builds profiles that can be used to target audiences. The difference is that a DMP is primarily designed to improve ad targeting for new prospects, while a CDP provides insights into your existing customers’ journeys and informs every aspect of your marketing.
A CDP (such as Arm Treasure Data) consolidates all your customer data and presents a single, actionable view of every individual customer. That enables you to develop more targeted, effective, personalized experiences for audiences on all channels.
Unlike a DMP alone, a CDP helps you build complete profiles of known individuals based on personally identifiable information from any source. An enterprise-level CDP can collect and store unlimited amounts of data from every system that interacts with customers, both online and offline. In addition, it continually maintains and enriches its data, giving you a full history of every customer. The best ones are integrated with more than 100 off-the-shelf integrations with data coming from sources as diverse as CRM and ERP.
With its detailed customer insights, a CDP can enhance the performance of personalized ad campaigns. For example, a CDP can be used to identify the company’s most valuable customers and create a lookalike audience based on their behaviors and attributes. It can then push this information to a DMP to use in making targeted ad buys from SSPs, ad exchanges, and ad networks.
In short, a CDP doesn’t replace other ad tech platforms. Instead, a CDP can activate and orchestrate such systems, improving the reach of your ads and integrating your campaigns with the rest of your marketing. Some companies, such as Subaru, Wish.com, and Kirin, are already using CDPs this way.
As the preceding discussion makes clear, ad tech is growing increasingly diverse, and it is automating much of the drudgery of campaigns, often at a pace faster than humans can hope to operate. Some martech, such as CDPs, is evolving to help produce better control and higher-quality marketing insights. Find out more about your options for increasing efficiency, targeting and insights.
April 9, 2020
Customer Data and CDP Martech: What Does a Unified View of the Customer Really Mean?
Every marketer wants to know their customers – from online and offline and across multiple social channels and devices. But what does a “unified view” of your customers really look like?
We know consumers demand personalization, and their expectations continue to rise. Meaningful customer personalization requires data. And that data must be complete and up to date to be any good.
So how do you know when you are seeing the whole picture and not pieces of each customer? A unified customer view will uncover opportunities that used to be missed while also building deeper brand loyalty and increasing purchases and revenue.
Here’s what we believe a true unified view looks like – and how you can get there with your customer data platform.
Customer Data and the Unified View
There are already parts of the customer journey that marketers can see with great clarity: We can track social media traffic to a blog or product page, we can retarget ads to those who have visited the website, and more. Creating a complete, unified view is about filling in the gaps; it’s bringing together data from across the organization, sanitizing, consolidating, and making it useful.
Finding Customer Clones
What do these five people have in common?
The person who read your blog
The person who browsed your product pages
The person who visited your brick-and-mortar location
The person who chatted with your chatbot
The person who made a purchase online
In many organizations, these five people appear in different databases in different departments. Marketers might only concern themselves with the first two. The sales department may only be aware of the middle two. Customer service might only know about the last guy.
When you start consolidating data, including offline and siloed data, you can see the truth: these five people are all the same person. Each point of contact was a stop in the customer journey.
But that’s still not the whole view. Let’s add in three more people:
The person who purchases
The person who subscribes to your blog
The person who calls customer service with a question
It’s easy to imagine how much more persuasive, relevant, and robust your contact with this customer could be. Your next email drip could include helpful tips for the product they called customer service about. It could suggest accessories for their purchase and fresh content that’s relevant to their interests.
Making Marketing Smarter
Using this data wisely can give your marketing some memory. Think of it this way: You know that feeling when you run into acquaintances and they blank on your name? And you have to say, “It’s Grace… we went to the same middle school… I drove you home after Amber’s birthday party… we have worked in the same building for three years…” And eventually they pretend to remember?
That experience doesn’t make you feel special or valued. But that’s often how marketing treats consumers. We send an email that says, “Hello, VALUED CUSTOMER, would you like some SHOES? Here’s an article on BUYING SHOES.” And they’re thinking, “It’s me, Grace… I bought a pair of shoes last week.”
How much better would it be if your next email said, “Hi, Grace, I hope those new stilettos are working out for you! Here’s an article on how to rainproof the velvet, and a link to a handbag that matches perfectly.”
Instead of an awkward blank stare and a sales pitch, now you’re being helpful, and maybe even initiating a conversation or deepening the relationship.
Upgrading Relationships
Consolidating marketing, customer service, and sales data makes it easier to solve problems, inspire loyalty, and drive referrals and repeat business. A complete view of customer data levels up relationships at every stage of the journey:
A casual browser nurtured with hyper-relevant content becomes a repeat visitor.
A repeat visitor nurtured with smart retargeting and contextual offline reinforcement becomes a customer.
A customer who receives personalized follow-up becomes a repeat customer.
A repeat customer with continued smart nurturing becomes a raving fan.
A Single ‘Golden Profile’ for Every Customer
It starts with seeing the customer as a single, complete, multifaceted person, not a series of unrelated brand contacts. That means not just pulling data from multiple sources, but also combining, sanitizing, and consolidating that data, intelligently using it to fill in your blind spots, so that you arrive at a single “golden profile” of each and every customer. Each profile can then be updated with each purchase, phone call, loyalty program awards, mobile app data, and more.
Outstanding data management is the foundation of a truly unified customer view, and enterprise Customer Data Platforms (CDPs) are designed to help businesses unify, analyze, and activate all of their data.
Want to see what unified customer data looks like in action? Learn how retailer Muji used customer data and an online app to increase in-store revenue by 46 percent.
“Customer is indeed the king, but running a kingdom is no child’s play
Knight, minister, jester, advisor, are you there for your king in every way?”
A marketer’s job isn’t easy. With each passing day, your customers’ expectations are rising, the amount of customer data available to you is increasing, and the challenge to acquire, engage, and retain customers is becoming more and more difficult.
Well, just like every challenge comes with a solution, so does this one. And the core solution lies in knowing your customers well. The more you understand them, the better positioned you are to win their trust and loyalty.
There is no dearth of customer data today. It depends on brands how efficiently they collect the information and leverage it. And with a proliferation of data collection methods, sky is the limit when it comes to uncovering customer needs and decoding what exactly they want.
As per a recent article by McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result.
However, leveraging customer data for marketing can sometimes prove to be a double-edged sword – marketers across industries often find themselves struggling to make sense of data when there’s too much of it.
While customer relationship management (CRM) platforms definitely made it easier to manage data, extracting meaningful and readily-usable insights from customer journeys is still a challenge. Now, Customer Data Platforms (CDPs) have emerged that offer promise to fill the gap, not as a replacement for other tools, but to augment their functioning.
What is a Customer Data Platform?
A CDP is a database software that organizes customer data across numerous touch points and interactions to create a unified view of the customer, accessible to other systems and software. A CDP builds a 360-degree picture of customers on an individual level by collecting real-time data from a multitude of sources – CRM, DMP, social media, web forms, email, website, et al.
This single customer view can then be accessed by third-party tools such as marketing automation tools to execute strategies and measure performance. A CDP is primarily meant for marketers like you, who can use the tool with little technical support.
The CDP Edge
Customer Data Management tools have been around for a long time now. When the CRM software was launched in the 90s, organizations found they could manage interactions with current and potential customers with ease, apart from being able to perform data analysis to drive retention. However, its major limitation was that it managed data for registered clients only, using predefined first-party data. The next frontier was the launch of Data Management Platforms (DMPs) in the 2000s, aimed at planning and executing media campaigns. DMPs could segment anonymous data and work with second and third-party data.
The industry soon realized the need for a more sophisticated tool for delivering an improved customer experience through omni-channel strategies. CRM and DMP platforms created data silos that presented a challenge to marketers. CDPs solved this problem by creating a unified view of the customer on a single, comprehensive platform.
What Kind of Customer Data Does a CDP Collect?
The sheer volume of digital data overwhelms traditional database software. A CDP is built to manage customer data from various channels and touch points. Following are the primary kinds of customer data that CDPs collect and organize:
Identity Data: Identity data lies at the heart of an individual customer profile in a CDP, allowing an organization to prevent costly duplication. This typically includes information like name, demographics, contact, location, social media handles, company-specific user IDs, etc.
Descriptive Data: This data presents a more comprehensive picture of a customer. This will include information on their lifestyle, career, hobbies, family, etc.
Quantitative Data: This data allows companies to understand how a customer has engaged with it through actions and transactions. This includes information on transactions, email communication and interaction, website visits, product views, and customer service interactions.
Qualitative Data: This offers a clearer context for customer profiles, focusing on motivations, opinions, attitudes expressed.
As is evident, CDPs collect a wealth of customer data, most of which depends on a company’s business and industry.
Difference Between CDP & Other Data Management Platforms
While there is some overlap between a CDP, CRM, and DMP, there are clear differences.
Both CDP and CRM collect customer data for sales and marketing activities. However, CRMs focus on intentional customer data and interactions; for instance, a customer’s telephonic interaction with a salesperson. A CRM collects general customer data, not huge data sets from multiple touch points. On the other hand, a CDP focuses on the lifecycle of a customer’s actions. While a CDP collects offline data, a CRM cannot retrieve offline data unless manually entered.
A DMP sorts and analyzes customer and ad data from multiple sources, with the aim to aid you in learning about customer demographics and buying triggers. DMPs focus on anonymous data (devices, cookies, IP addresses), looking at general behaviours rather than customer-specific ones. While CDPs are built for marketing, DMPs are meant for advertising. While DMP data typically emerges from third-parties, CDP data is collected via a company’s internal user base. As a CDP collects more data, it gets more powerful. On the other hand, DMPs store data only for a short period since ad targeting changes quickly and data soon becomes outdated.
How to Use a CDP – Use Cases
Deploying a CDP can help you achieve both high-level goals as well as lower-level ones. Here are some of its most important use cases:
Online to Offline: Merging online and offline activities to create a unified customer profile for easy customer identification.
Customer Segmentation: Segmenting customers based on behaviour to deliver a personalized and omni-channel experience.
Predictive Scoring: Enhancing customer profiles with predictive data
Behavioural Retargeting: Running acquisition and retention campaigns through integration with online ad channels.
Product Recommendations: Building recommendation models and delivering a personalized shopping experience to drive engagement, up-sell, and cross-sell.
Omni-channel Automation: Personalized messages across channels to enrich customer lifecycle and enhance acquisition and retention.
Using CDP to Improve Customer Lifetime Value
Fostering customer loyalty rests on delivering a quality, consistent, and personalized experience. CDPs make this possible at scale, allowing for nurturing loyalty by solving the problem of siloed data. When data is siloed, creating a consistent and omnichannel customer experience is impossible. By unifying customer data, CDPs make it accessible to everyone in an organization, at all times. By gathering first-party data i.e. information directly from customers, CDPs enable the most effective marketing decisions informed by accurate data. In essence, CDPs equip marketers like you with a powerful tool to manage customer relationships accurately and effectively.
Customers today have high expectations of businesses – personalized services, consistent experiences across channels, and tailored recommendations. To be able to deliver the experience a customer is looking for, and keep them coming back to you, investing in an excellent CDP that offer deep insights, is now a necessity, not a good-to-have thing. Are you CDP-ready already?
March 23, 2020
Change Management Strategies for a Successful CDP Initiative
According to research from McKinsey, almost 50% of digital transformation initiatives fail to achieve their expected value, with only 10% exceeding expectations. If you’re planning to leverage a CDP to help transform your company into a customer-centric organization, then your initiative is no exception to this somewhat daunting statistic.
People and Process are Critical to CDP Success
Fortunately, there’s an almost surefire way to beat these odds. At ActionIQ, we developed and deliver one of the industry’s leading CDPs. But curiously enough, the solution to this challenge doesn’t lie in technology. Rather, it’s dependent on people and process.
Based on our experience in helping deliver CDP-centric transformations at some of the largest and most respected brands in the world (like Michael Kors, The New York Times, Pandora, Saks Fifth Ave, Verizon and more), we’ve learned that the most successful CDP initiatives always lay out a strong change management plan at the outset.
“In fact,” says James Meyers of ActionIQ, who previously served as an analyst and CDP advisor at a leading international research firm, “the vast majority of inquiries I received over the years were from executives who were far more concerned about change than they were about technology.”
Why is a change management plan so important? Put simply: changing people’s behavior is difficult. You can run a flawless, buttoned up project to swap out old martech and bring in new systems, but if users don’t adopt the new systems, your project is a failure. And, getting people to adopt new CDP technology is about much more than simply training them to use it.
Here’s why. Your CDP puts your customer at the center of everything you do. It takes today’s multi-step, multi-department processes (like determining which audiences merit prioritization for various campaigns) and enables marketers to operate with100% self-service. It breaks data silos. But for companies and teams that are still organizationally siloed and focused only on traditional metrics – say, aligned around a channel and measured purely by its P&L – customer-centricity is a foreign concept. They don’t have the right org structures, goals, incentives and skill sets in place to realign around the customer. So they fail to maximize the benefit of using the CDP.
“Reflecting on my 20+ years in the field of customer analytics, I’ve come to the realization that one of my biggest challenges has always been getting my business counterparts in the organization to look at a customer holistically,” says Tamara Gruzbarg, head of ActionIQ’s change management practice. “Very few are ready to accept the fact that the customer couldn’t care less who owns the P&L, or whether she is considered a ‘store customer’ or an ‘online customer.’ She wants an experience that makes sense for her. And if it doesn’t, she will leave.”
Without a well-constructed change management plan – backed by the full support of your company’s top executives – the inertia of the status quo will win. Users will stick to the old systems and processes, and your customer-centric vision will remain unrealized.
So what goes into a well-constructed change management plan? It starts with the realization that people and processes require equal weight to technology when designing your transformation.
“In the early days of ActionIQ, our most forward-thinking clients began to ask us for help with change management,” says ActionIQ’s Ryan Greene. “Back then, we assisted in more of an organic ‘roll up your sleeves and get it done’ kind of way. But over the years we’ve developed repeatable best practices that are now a standard part of ensuring the success of every client implementation.”
Here are three foundational change management elements that will get your CDP initiative started off on the right foot:
Executive sponsorship – Identify the goals and use cases most important to your company, and then get key executives on board as sponsors.
Cross-functional alignment – Gather input on challenges and requirements from all stakeholders from all relevant departments, and co-opt them as partners in achieving your goals.
Governance and accountability – Set up the task force that will drive your transformation; establish accountability; document the jobs to be done as well as the governance and reporting frameworks.
Lean on Experts for CDP Change Management Guidance
Of course, this is a very high-level framework. Mapping these practices to your specific organization takes thoughtful planning, diligent execution and a lot of hard work. Just like you lean on partners to provide and implement technology, you can also lean on partners to help with the people and process aspects of your CDP initiative.
When looking for a partner, you should only trust people who have deep experience as marketing, analytics and data practitioners, who have a track record of implementing organizational change, and who can help you mitigate risks before you encounter them.
Digging Deeper
If you’re interested in more detailed change management frameworks, including:
How to align your project to goals and desired outcomes
Identifying where you are in the customer centric org maturity model
Defining tasks and roles for before, during and after your implementation
Modern brands share a relationship with their customers vastly different from the ones that traditional organizations did. If one were to outline each interaction (both physical and digital) between a brand and its customers, we’d find an extensive map with multiple touch points. With these numerous contact points comes the dilemma of delivering a seamless transition between different channels and a consistent, unified messaging throughout. Though brands have been relying on Data Management Platforms (DMPs) for achieving this goal, the emergence of Customer Data Platforms (CDPs) has presented a compelling alternative to marketers. By the looks of it, the two may seem similar but they hold major differences. However, one cannot necessarily replace the other.
This guide will help you understand each of these platforms and the value they deliver.
Say, you have been given a 500-piece puzzle which you easily solve. To your surprise, you’re told that it is simply part of a bigger puzzle and you’re given 200 pieces more as a result. You carefully finish that as well though it is relatively time-consuming. But, before you could take a deep sigh of relief, you’re asked to expand the puzzle further with 500 more pieces!
Well, let’s see how a Customer Data Platform (CDP) fits into this puzzle analogy. Consider each piece of the puzzle to be a data point about an individual customer and your “understanding” of this person keeps expanding in varying degrees (with every new puzzle piece). Now, the final puzzle will represent the unified profile for that customer and the board/area on which you’re building the puzzle will depict the CDP database.
In technical terms, a Customer Data Platform or CDP is a marketing system or software that creates a unified and steady customer database accessible to other systems. The purpose of a CDP is to gather all customer data and to stitch them in order to create unified customer profiles that can be used for marketing campaigns and customer service initiatives.
What are Data Management Platforms?
The Data Management Platform (DMP) market size is expected to have a net worth of whopping $3 billion by the year 2023 with a Compound Annual Growth Rate (CAGR) of 15% between the years 2017-2023. Let’s take a slightly different approach to understand the platform with such impressive prospects. Consider the example of a dealer or merchant whose employees wear blindfolds and earplugs full-time. As a result, they are unable to see or hear their customers. All they do throughout the day is punctually stock the shelves with products and take care of the register. There’s no contact whatsoever with the customers. In fact, they don’t even know who they are! Hence, naturally they are clueless regarding their customers’ preferences and likes/dislikes. And after a “good” day’s work, when they sit down to tally the register, all they can figure out is the number of goods sold and at what cost. They have no clue of who bought what and why (was it for self-use or maybe as a gift?). And the worst part is not oblivion but indifference – the dealer doesn’t know and he couldn’t care less. This is exactly what a marketer or business professional that does not use a Data Management Platform (DMP) looks like.
A DMP is more or less like a data warehouse – it is a system or software that gathers, analyzes, stores, and delivers useful customer information to marketers, web publishers, and businesses. The data managed by DMPs is used to generate audience segments which are in turn used to target specific users in online marketing campaigns.
Aren’t they the same?
Well, they do sound the same and a superficial understanding can even give one the impression that they indeed are the same but nothing could be further from the truth. CDPs and DMPs differ on several grounds:
Personally identifiable Information (PII)
Perhaps, the greatest difference between CDPs and DMPs can be established in terms of their use of customer identities or Personally Identifiable Information (PII). In marketing terms, a PII is a combination of data points used to identify a specific customer. Since CDPs function in a manner that the more data you collect about a specific customer, the more relevant experience you can provide to them; it relies on PII to operate. Whereas, DMPs process data that is anonymous and this makes it more difficult to determine whether or not the data was sourced ethically.
Data Sources
Another drastic difference between the two platforms can be found in terms of the data used by each. CDPs primarily use first-party data (data collected by your company) and a little of second-party data (first-party data collected and sold to you by a non-competitive partner company). DMPs, on the other hand primarily make use of third-party data (first-party data collected by a data collection company and made available for purchase) and a little of second-party data as well.
Use Cases
CDPs and DMPs also differ in terms of their use cases. Where, on one hand, Customer Data Platforms are used in gathering customer data in its organic form, powering different marketing systems, cross-device data coordination, and leveraging Artificial Intelligence (AI) for channel marketing. The use cases for DMPs include optimizing acquisition, prospecting and modelling, audience suppression, cross-channel segmentation, and remarketing.
Data Retention
Another important difference between the two platforms lies in how long each stores customer data. CDPs store customer data for long time periods. Smart CDPs even allow you to set the time limit for which the data must be retained. This is especially useful in cases where, say, a furniture dealer wants to give a discount of 20%to his most valuable customers (CDP database for the previous year can help determine the high-value customers). DMPs are completely the opposite. They function best when data is retained for shorter periods (like 90 days). This is especially useful in cases where an advertiser is seeking to target travel aficionados (there’s no point in targeting people who were interested in travelling five years ago but aren’t right now).
Scope of Reach
Since CDPs mainly have access to the data of your existing customer base, they cannot help you to create lookalike audience segments to target potential customers in external databases. DMPs, on the other hand, can help you in extending your target group by creating lookalike audience segments.
Do they have anything in Common?
Well, yes! Now that we’ve looked into the differences between CDPs and DMPs, let’s take a look at the several similarities:
Both CDPs and DMPs function by leveraging existing customer data.
Creation of a Single Customer View (SCV) or a 360-degree view of the customer is the aim of both CDPs and DMPs. By creating a single view of the customer, the platforms aim at helping businesses understand their customers better.
The data gathered via both platforms is used for Audience Activation. Meaning, the data is used for powerfully engaging with high-value audience segments and for delivering personalized and relevant user experiences.
Both platforms render the following functionalities – reporting, analysis, and optimization.
CDP or DMP: Which one should you choose?
Since CDPs help in engaging and improving relations with existing customers, marketing departments of individual companies will find them the most profitable to manage customer data and to generate personalized messages via different marketing channels.
DMPs are your best bet if you need to manage and process large sets of audience data and wish to extend your target group to external databases. DMPs prove to be the most profitable to web publishers, marketing agencies, media houses, etc.
Furthermore, as mentioned earlier, CDPs and DMPs do not necessarily replace one another but are complementary in nature. Meaning, data collected by CDPs can be enriched for better segmentation using DMPs and CDP data can help in creating better lookalike audience segments when used within DMPs. Hence, depending upon your marketing needs, choose either one or both of these valuable platforms.
March 16, 2020
Data Privacy Has a Day – And Companies Better Pay Attention
Data Privacy Day, occurring every January, is an international effort to raise awareness and promote data privacy and protection best practices. It originated in Europe in 2007 and was adopted by the US several years later. While searching for quotes on data privacy to honor the day, I came upon an eye-opener from 2009 by former Google CEO Eric Schmidt:
“If you have something that you don’t want anyone to know, maybe you shouldn’t be doing it in the first place.”
To be fair, the quote was in response to a conversation about how tech companies share information with authorities, but the context was that the amount of information said companies really know about consumers would “shock” and “confuse” them. We really have come a long way on data privacy - or maybe not.
The largest fine levied under the GDPR so far, $57 million, came shortly before last year’s Data Privacy Day, and was given to Google for not properly disclosing to users how data is collected across its services — including Google Search, Google Maps and YouTube. The regulators claimed that Google did not meet the requirement of obtaining clear consent and that consumers are largely unaware of the data collected and shared by Google. Note that Google disputes the claims.
Unfortunately, I think I know what the regulators mean. Early last year, a Google screen popped up on my phone asking me to rate places and businesses including a law firm, a retail store and a national park. These were all places I had visited recently. It turns out that every location I had physically been to in the last several months with my cell phone in tow - which is almost everywhere I went - had been tracked, stored and visible to me and who knows who else. I certainly never knowingly gave explicit permission for them to track my physical location. Even worse, rescinding this permission was an arduous and non-intuitive process that involved navigation across six different screens.
This is the antithesis of clear and unambiguous consent. I don’t mean to pick on Google here – because in our data-driven world this type of tracking is the rule rather than the exception. We must change our thinking on this. Both consumers and legislators are demanding it.
Consumer Expectations are Significant
While my informal poll of non-tech working consumers indicates that most are not aware of International Data Privacy Day, they do have definite expectations around data privacy.
A recent survey of global consumers, CX2030, illustrates how focused consumers are on the issue. CX2030 did not focus on privacy specifically, but instead was designed to predict what customer experience would look like into the future. Privacy, specifically trust, came up as one of the pillars that companies will be increasingly compelled to deal with. Unfortunately, the overriding sentiment from consumers was one of concern:
76 percent of consumers are concerned with the amount of data brands gather when they search for or purchase a product.
71 percent feel companies should not be able to share their data.
78 percent want to see what data has been collected and want control over changing, updating or deleting this information.
73 percent are concerned with how brands are using their personal data to the point where they feel it is out of control.
61 percent feel they have no control over the level of privacy they need for themselves, their family, or their children.
50 percent believe brands are hiding “bad things” they’ve done with user data and privacy.
When consumers use phrases like “out of control” and “hiding bad things”, companies had better sit up and take notice.
Legislators are Paying Attention As Well
Consumers are not the only ones paying attention.
In another European privacy enforcement action last year, German antitrust regulators ordered Facebook to seek users’ explicit consent to combine non-Facebook data from Instagram, WhatsApp and various 3rd party websites into a comprehensive social media profile. Facebook must submit compliance proposals or face significant fines of up to $5 billion. Facebook plans to appeal, however, the top antitrust regulator for the EU has indicated that it is watching this case.
Facebook is also facing numerous lawsuits over data misuse and ad targeting including one brought by Washington, D.C., Attorney General Karl Racine, accusing the social media giant of wide-ranging privacy violations. They are also under investigation by the FTC to determine if they violated a 2011 FTC consent decree requiring them to give consumers clear and prominent notice of how information is collected and used and to obtain consumers’ express consent before sharing information beyond established privacy settings.
Both Google and Facebook have been sued multiple times for violating the Children’s Online Privacy Protection Act which imposes requirements on companies on collecting data on children under 13 years of age. Moreover, the City of Los Angeles has sued the IBM subsidiary, The Weather Channel, for “covertly mining the private data of users and selling the information to third parties, including advertisers.”
A battle is also brewing in the US over state and federal privacy laws. Several states have passed laws aimed at data privacy and ethical use. The most prominent and restrictive of these is the California Consumer Privacy Act of 2018 - taking effect now and billed to be the toughest data privacy law in the country (incorporating many GDPR-like restrictions). Silicon Valley has lobbied hard against this and other state bills, pushing for less restrictive measures and asking that a uniform federal law supersede all state legislation. To this end, both the US Chamber of Commerce and the Internet Association, which represents companies like Amazon, Facebook, Google, and Twitter, have released their own recommendations for a federal bill. The Data Care Act introduced by a group of US senators, a competing congressional bill, The Information Transparency and Personal Data Control Act, and the White House Administration’s recommendations round out the plethora of proposals.
Regardless of where we end up in terms of data privacy regulations – several things are clear. The privacy mandate is expanding. Consumers expectations are increasing. And there will be regulation here in the US as well as in Europe. If you don’t keep up, there will be consequences.
March 12, 2020
Augmented Analytics To Transform Big Data Into Smart Data
It was in 2005 that Roger Mougalas coined the term Big Data. Ever since, it has captured the imagination of industries across the spectrum, across the globe. After more than decade, the world is now staring at the next frontier in data – Augmented Analytics.
It was back in 2017 Gartner predicted Augmented Analytics to be the future of data, and in 2019, it already is the number 1 trend in data analytics. As per a research published by Allied Market Research this year, the global market for Augmented Analytics will reach USD 29.86 billion by 2025.
Data On Its Own Holds No Value
Data-driven insights are no longer just good-to-have, they are crucial for staying ahead of the curve. Most future-ready organizations today have embraced data analytics to deepen their understanding of customers and drive bottom-line growth. The problem is, their ability to leverage the power of data is severely limited, leading to a failure of data analytics projects. According to Gartner estimates, an incredible 60% of big data projects fail!
Data, by itself, holds no value for a business.
Say, a company’s data reveals that sales figures are dropping by 5% every month. But what does that really mean? This decline could be attributed to a failure of advertising methods, industry trends, or something entirely different. There is no way to figure the cause out unless you take a deep dive into the issue to uncover the real reason behind the decline in sales. For instance, you may come to the realization that your paid ads are less effective and need a different approach. Now you have an actionable insight that tells you exactly what to do.
The lesson – you need actionable insights, not simply informative data.
A data analytics project involves a number of processes – data aggregation, extraction, cleansing, pattern analysis, insights generation, to name a few. While the process itself isn’t so complicated, the tricky part is generating the right insights. Because data scientists are in short supply, other than being an expensive resource, companies need an advanced yet affordable tool for analysis at scale.
By harnessing the power of AI and ML, Augmented Analytics offers freedom from the tedious process of processing, aggregating, and visualizing data. Naturally, it is the next big disruptor in the world of business intelligence.
What is Augmented Analytics?
Augmented Analytics (AA) presents a novel solution to businesses to make sense of swathes of chaotic data. Augmented Analytics combines Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to automate the process of insight extraction from data.
The AA-driven tools organize, manage, filter, and analyze datasets to produce actionable insights, speeding the process of turning data into digestible information. Owing to the reduced manual involvement and dependence, businesses can rapidly analyze data at scale, and easily obtain patterns and trends.
Augmented Analytics reduces an organization’s dependence on data scientists and other manual processes by automating this crucial process with little to no supervision from a technical expert. Essentially, it cuts down on the human intervention part, weeding out less relevant insights automatically. Thus, the risk of missing important insights or making errors is vastly reduced, resulting in a streamlined and reliable data analytics process.
Augmented Analytics is set to create a new standard for business growth as organizations consume and generate massive streams of data from multiple sources but face challenges in making the data readily usable. Let’s see how.
How Does Augmented Analytics Work?
A business’ engine needs data to fuel growth. By automating a crucial part of the insight generation process, Augmented Analytics fuels this engine at an accelerated rate. When repetitive data cleaning and organization tasks are automated, data scientists will have more time on their hands for strategic analysis and decision-making. Additionally, this shrinks the scope for human error.
Smart data, fueled by Augmented Analytics, brings together the whole picture. When an organization’s data is siloed i.e. distributed across several different platforms, it presents a hurdle in smart decision making. To solve problems and identify areas for improvement, the decision makers must be able to view how the engine works on a whole, not how different parts work separately. By integrating data points into a unified system, decision makers and CMOs can track the entire picture on one platform.
In a paper published this year, Gartner outlines the different facets of augmented analytics:
Data preparation and discovery
Currently, most existing augmented analytics technologies lie in this stage. Here, the algorithm’s primary job is to automate data preparation tasks such as cleaning, labeling, collection, etc.
At this stage, business stakeholders can use machine learning to automatically detect, visualize, and narrate relevant findings without having to build complicated models or algorithms.
Signal detection
At this stage, the analytics algorithm is able to detect true signals in data with a good measure of reliability. But, it cannot connect the discoveries with business actions or situations.
So it still needs assistance from data scientists to transform such discoveries into concrete business insights. The upside is that the time they must spend on each insight is reduced drastically.
Insight generation
This is where the augmented analytics engine can directly interface with executives with almost no input from a data scientist. The algorithm will leverage its knowledge of past business cases to connect trends in the data with the larger business context.
Then, it can go further and offer concrete action steps based on its insights. In fact, the engine can also track the implementation of such actions and offer additional insights to the business for optimizing its operational effectiveness
Benefits of Augmented Analytics
Augmented analytics takes the benefits of business intelligence to the next level, with unprecedented efficiency and accuracy:
Improved accuracy
When data scientists manually work on datasets and prepare them for analysis, there is room for error owing to the human element. Statistically speaking, the larger the volume of data, the greater the possibility of an error. By leveraging machine learning, the room for such mistakes shrinks greatly.
Increased speed
With standard business intelligence tools, the time required to manually prepare data and the wait time for related parties to respond to requests delay the completion of projects. Augmented analytics speeds this by immediately beginning request processing and leveraging AI to cull appropriate data – at the speed of a machine, not a human.
Reduced bias
When data scientists work with analytics tools, there is scope for blind spots and biases to creep in, leading to missed insights. Machines, on the other hand, take a more thorough and infallible approach, with no inherent bias. By quickly analyzing exhaustive data combinations, augmented analytics can identify the right insights.
Greater resources
Instead of reducing the need for human data scientists, augmented analytics can increase their value by freeing them from manual labor, allowing them to focus on more high-value tasks i.e. creating richer, deeper insights.
The automated insights generated by augmented analytics can thus be leveraged to assess business performance, identify growth pockets, and understand how a brand compares to the marketplace, thus contributing to a solid business strategy.
Ultimately, this results in cutting-edge insights driven by algorithms that would otherwise demand a huge investment of time and energy. This means data is democratized so that data scientists aren’t the only people in an organization that can make sense of the results.
Democratizing Data Analytics for all Stakeholders
Augmented analytics is fast becoming a popular data analytics tool, one that doesn’t need the involvement of data scientists, effectively collapsing the wall between asking questions and getting the right answers. One of the biggest advantages of embracing augmented analytics is the democratization of data.
Data scientists and analysts enjoy freedom from repetitive and low-value tasks like running routine reports. Instead, they can focus on solving complex queries and data science projects, offering critical business insights to the relevant stakeholders.
For small companies that don’t have the resources to build a team of expensive data scientists, augmented analytics will infuse accessibility and affordability into data-driven insights.
A bigger advantage lies for marketers like you. Augmented analytics is set to change how you make sense of customer data on a daily basis. Unlike earlier you don’t have to rely on an analytics team for in-depth research and reporting, a dependency that made your work time consuming and inefficient. With augmented analytics tools, you can regain control and track the entire customer journey, right from acquisition metrics to retention insights.
With augmented analytics, everyone in the organisation will hold the power to make informed and data-driven decisions, without having to depend on data scientists to furnish the required information. Naturally, this opens the doors for businesses to accelerate their growth at an exponential rate.
How can Marketers Benefit from Augmented Analytics?
Augmented analytics reduces the gap that existed between data scientists and other business users. The benefits of advanced data analytics are now available to every employee in every department, including marketing.
Lower costs
Because of a dependence on data analytics professionals, marketers faced a shortage and strain on resources. There is a high demand for the rare expertise of data scientists. However, hiring them is a lengthy and expensive process.
By opening the doors of insights to everyone, augmented analytics empowers marketers to take charge of data and thus allocate resources in an efficient way.
Moreover, Augmented Analytics also solves the problem of a waste of resources. Because traditional business intelligence tools demand grunt work (cleaning, unifying, entering, sorting data) out of data scientists before they actually get to the real job, they waste both time and talent.
By automating the processes involved in data preparation and handling, Augmented Analytics tools allow marketers to reduce workload and extract the maximum value out of the data scientists they recruit.
Cutting-edge insight
For effective decision-making, marketers need granular insight at speed. Given that customers today expect personalized communications across channels, marketers need a real-time view of their individual needs and behaviors to offer what they’re looking for. Augmented Analytics tools allow marketers to do this without wasting any time.
They quickly organize data and run analysis using a combination of ML and NLP. Interacting with huge quantities of data, they uncover patterns, trends, and anomalies – information that is gold to marketers.
For instance, augmented analytics may identify the channels a specific audience segment engages with the most, and the types of ads that receive maximum response. With this insight, marketers know exactly where and how to reach customers to elicit a desired action. They can also minimize wastage by allocating resources to the channels with the highest probability of capturing audience interest.
Additionally, augmented analytics can help with customer retention too. For instance, the data uncovered by Augmented Analytics tools can tell marketers not just where customers are spending their time but also how to appeal to them better. When marketers tailor campaigns to suit customer preferences and deliver relevant information, retention will increase over time.
Effective performance measurement
Augmented Analytics tools have the capability to measure the outcomes they drive. Marketers can, thus, evaluate progress and measure performance through an analysis of channels and audiences.
This has powerful implications – marketers always stay in the know of what’s working and what’s not so they can adjust campaigns and reallocate resources.
Actionable insights
Once patterns have been identified, the results must be communicated to executives. The traditional ways of sharing data through reports and presentations is additional work for executives who are hard-pressed for have time to interpret data.
Augmented Analytics tools can read a chart or report and translate the findings in a simple manner.
Marketers can leverage the visualization abilities of augmented analytics to understand and share findings in a simple format with the C-suite. With actionable insights, the organizations can participate in data-driven decision making.
Businesses of all sizes have something to gain
Traditional data analytics platforms come with a major disadvantage. Before one can even get down to uncovering patterns, a great amount of manual labor is involved. While a business may onboard a data scientist to uncover insights, it may soon find that they spend most of their time cleaning and harmonizing data, not extracting insights from it.
Augmented analytics is on its way to transform the way businesses analyze data. Marketers, in particular, have much to gain. They can finally regain control of massive sets of data and meet customer expectations with personalized communication and experiences.
Ultimately, this can have a significant impact on the business’ bottom-line. By automating large-scale analysis and allowing marketers to generate insights, Augmented Analytics is paving the way for a more productive business landscape.
It is important that modern businesses understand the benefits of augmented analytics – speed, democratization, and insights. Armed with these, businesses are better equipped to anticipate what customers want, improve business processes, and lay the groundwork for success.
The Road Ahead For Augmented Analytics
In the present landscape, businesses are producing such a large volume of data that it has become impossible for data scientists to explore it on their own. Manual data exploration always runs the risk of missing key insights.
With augmented analytics, organizations have a tool to explore all possible hypotheses from the collected data and automate a great deal of data science tasks. When data scientists and augmented analytics work together, data insights will become democratized i.e. become available to a wide pool of business users.
There is no doubt that augmented analytics is here to set a new standard for business growth. The quicker you leverage this technology, the faster you will reap its benefits and be able to exploit growth opportunities.
March 9, 2020
How to Pick the Right Customer Data Platform (Learn from CDP Experts)
You’ve decided a Customer Data Platform is the right tool for your company. It’s a good choice: the market has matured and there are many vendors to choose from.
But that also means…there are many vendors to choose from. And of course you want to choose the right CDP for your company. It’s a tough decision, and the wrong choice can yield poor results and create an impression that CDPs are all hype.
But the right choice will create new possibilities for your company, a better experience for your customers, and improved cross-departmental collaboration.
To help us navigate this topic we talked to some of the top CDP experts and picked their brains to help you with the processes of choosing and implementing a CDP.
They’ll discuss two important things:
how to purchase the right CDP for your company
strategies to make sure that CDP is a success
Let’s introduce our expert panel. It’s a group with decades of combined experience in making CDP projects successful.
Hugh leads a global sales team and has over 18 years of experience in the digital marketing industry. He understands how a company can make new technologies successful.
As the Head of Solutions at a major CDP company, Bruno leads a team that helps bridge the gap between project initialization and implementation. His experience allows him to speak with confidence about what makes a project work.
Dale uses his years of experience in SaaS sales to ensure that companies have a successful CDP implementation, and he’s sharing that knowledge with us here.
Step One: the Purchase
The first step in the process may seem overwhelming: choosing the right vendor. How should you proceed? What should you look for?
Below, the experts share what your first steps should be after deciding to go with a CDP.
Question #1: How would you proceed if you were the one (CEO, CMO, etc.) in charge of getting a new CDP?
Dale Farrey, Senior Sales Manager
First I’d ask my team to provide insights as to why they believe they need a CDP. I’d then turn those insights into a brief with 5-10 key deliverables for the CDP, which would form the basis of the review criteria I’d use during the selection process.
Bruno Gorgulho, Head of Solutions
I would first validate the key value proposition of a CDP and make sure it aligns with the business problem I’m solving. If I truly require a system that can centralize customer data for a B2C business, provide analytics-driven insights, and then send that intelligence to other systems to trigger campaigns, then I need a CDP.
Then I would map existing internal capabilities - which systems I have and why. Then I could see which systems I could eliminate entirely, which could be replaced, and which are critical for my business. I would have to build a system analysis independent of my existing teams, since new technology is likely to disrupt the way my teams are organized.
Once the business needs, teams, and systems have been mapped I could start talking to vendors who could also help shape the requirements.
Takeaway:
Be crystal clear about which issues you want to address with a CDP
Understand that new technology can mean a paradigm shift
Map internal capabilities (people and systems) – what should you eliminate, keep, and replace
Understand your needs before looking at vendors
Now that you know exactly how you’ll use a CDP, it’s time to find the right one. With a larger and larger pool of vendors, how do you narrow down your choices? We asked the experts to weigh in.
Question #2: How would you evaluate different CDP vendors? What are some of the indicators that tell you a vendor is right for you?
Bruno Gorgulho, Head of Solutions
I’d want an answer for the following:
Is the platform real-time, for both reading data and executing campaigns?
Can it handle data from multiple sources? Be sure you know those sources before you start your vendor search, so you can get informed answers from vendors.
Can it merge customer profiles in a dynamic way?
Does it integrate with the systems I use in a simple and reliable way, and is the data it stores easily extractable?
Does it have capabilities like customer segmentation, AI, and native campaign execution, or will I need another provider for those?
Hugh Kimber, Global VP Sales
I’d want to know about their customer base — do they work with companies like mine? What kind of experience do they have? I’d want to see evidence of businesses growing thanks to their technology (and not just 10% increase in conversions, but how have they helped a business over a period of 12+ months).
What’s the cost of change: how easy is the implementation? Will it make my company more efficient?
Daniel Viglas, Solutions Manager
I need to know if the company can create a single customer view. Then I need to look at my short- and long-term goals, and compare them to what the vendor offers. These are the areas you might consider when thinking about CDP relevancy:
Security, Stability & Scalability
Insights and intelligence (including AI)
Data-based campaign orchestration
Takeaway:
Make sure the vendors you’re looking at meet all of your requirements
Narrow your list of vendors down to those that have proven track records and are familiar with your industry
Customer data platforms are still new, and how we define a CDP continues to shift. This leads to some misunderstandings about CDPs and just what they can do for you.
What are some of these misunderstandings? Our panel outlines several below to help you with your selection process.
Question #3: What are the most common misunderstandings about CDPs out there?
Dale Farrey, Senior Sales Manager
Within the early stages of the sales process, the CDP is commonly brushed off as having the same functionality as a marketing automation platform or marketing cloud, but it’s actually much more. The customer is typically fully educated by the end of the second meeting.
Bruno Gorgulho, Head of Solutions
A common mistake is when people expect the CDP to provide some of its own data (which is usually the scope of a DMP or similar platform – a CDP works with a company’s data). Also, some companies expect the CDP to be able to resolve customer identity, but their existing processes to contact the customer are completely unstructured - remember that the CDP will only read the real-life events between your company and your customer.
Takeaway:
A CDP can provide value for your company, but don’t expect a DMP
Question #4: What would your advice be to anyone buying a new CDP?
Daniel Viglas, Solutions Manager
Think about what you want to achieve with a CDP, short-term and long-term. Maybe in the short-term I want to improve my emailing with behavioral data, and that may give me an answer about which CDP can satisfy that. Maybe my long-term play is something else, and that narrows down my options even more.
Hugh Kimber, Global VP Sales
Make sure the CDP you are choosing is future-proofed. A single customer view is an immediate requirement, but you need a CDP that can act on the data or integrate with existing suppliers you wish to keep.
Takeaway:
Remember to weigh your long-term goals. This will help you find the right CDP.
Question #5: What else can help companies choose the right CDP?
Daniel Viglas, Solutions Manager
I would ask myself how a CDP could benefit my company from these perspectives:
use cases (what can I do practically?)
time (will it improve efficiency?)
security & clarity (will I avoid risk?)
strategy (will it activate future goals?)
Dale Farrey, Senior Sales Manager
Take into account the history of the provider. Some companies positioning themselves as a CDP are built on a flat file database because 95% of their customers use them for email only...they were built that way historically. Consider a =more modern CDP approach built in the era of the CDP SCV (Single Customer View).
Hugh Kimber, Global VP Sales
Clearer differences between CDPs; some push “AI everything!”, some push execution. Look for a deeper explanation of different CDPs and what they are good for. Who is a ‘jack of all trades, master of none’ and who is a specialist and why?
Takeaway:
Most CDPs on the market were originally something else. Understand their history and you’ll know whether they can help you achieve your goals.
Step two: After the Purchase
The post-purchase phase is crucial to making a CDP work successfully for your company. While every company is different, most successful CDP implementations follow a similar pattern. We spoke with two more experts to find out what that pattern is.
Expert 1: Adam Lebeda – Global Head of Partnerships
Adam currently works to help find valuable opportunities for businesses looking for advanced marketing platforms. Previously, Adam was the Senior Manager of Digital Services at T-Mobile Czech Republic, where he successfully implemented a modern CDP.
According to Adam, there are three key points that lead to a successful implementation of a customer data platform:
Start small
Create a habit
Use a tool people love to use
Start small
It’s unrealistic to think you can buy a platform and immediately change all the processes of your business, especially with a larger company or stakeholders involved. An effective strategy is to start small with quicktime-to-value use cases. This proves the value of the tool to the people who work with it and leads to buy-in throughout the company.
Create a habit
When the people who work with it become internal advocates for the platform, their enthusiasm catches on and helps to make the new CDP a habit. Then it’s much easier to onboard the rest of the organization.
Use a tool people love to use
For all of this to fall into place, the CDP must be a tool marketers really enjoy using. A good CDP gives marketers the ability to execute their own ideas, without needing to overly rely on the assistance of more technical employees. This is what led to a successful implementation at T-Mobile, for example.
Peter leads a team of CDP Client Success Managers. His experience implementing CDPs at multiple companies has given him great insights into what leads to the success of a project. He tells us what he believes are the most crucial aspects of the implementation process:
First steps
Mindset and attitude
A good vendor
First steps
Know who the key stakeholders of the project will be, both internally and on the side of the vendor. Implementing a CDP requires buy-in from multiple roles: IT people, data analysts, CRM owners, digital and campaign teams, project managers, and more.
All these internal stakeholders need to have a clear counterpart on the vendor’s side. For the project to succeed, alignment across all of these teams is a must.
Mindset and attitude
Implementing a CDP will require your team members to embrace changes. Processes will look different and routines will be upended; don’t expect things to stay static. This will be a time to discover new capabilities that weren’t possible with your previous solution. This might require a mindset and attitude shift in your organization.
A good vendor
A good vendor will not blindly agree to everything you request. They should be willing to challenge you in a beneficial way, out of respect and a desire for your growth. They will be more a partner, focused on reaching mutually set goals and delivering value.
Finally, be sure that your vendor is future proof. Study their product roadmap and ask, will they accommodate my needs in the future? You don’t want to get locked in to a contract with an outdated solution.
If you can establish good starting logistics, have the right mindset, and work with a good vendor, your odds of success are significantly higher.
In Summary
The processes of choosing and implementing a CDP can both be daunting, but there are things you can do to make them easier and give yourself a higher chance of success. Interviews with the above experts pointed to several similar steps:
Be clear about what you expect from a CDP, before you look at vendors
Think about the short-term and long-term to find a future-proof solution
Understand your options and look into vendor history (do they have the modern CDP capabilities you need?)
Success can hinge on the right mindset and attitude
Taking on a CDP project is big. But with the right preparation, success is much more likely. Set yourself up for a positive experience from the very beginning, and take advantage of all the capabilities a modern CDP has to offer.
March 2, 2020
How will enterprises drive effective engagement during these times of cookie extinction?
Webkit browser engine which powers Apple Safari and all IOS browser has released its latest update to Intelligent Tracking Prevention (ITR) feature – ITP 2.3. It follows in the footsteps of previous releases beginning from 2017 which aims to provide the users with a higher degree of privacy by restricting how Ad tech companies track users online and the duration of time data can be held for a user, and is in line with the general direction of a cookie-less future in the industry.
A quick look at changes brought by ITP so far:
Cookie and data storage
Third-party cookie has been completely blocked
First-party cookie set to expire in 24 hours if used for tracking
Local storage set to expire in 7 days
Cross-site request referral header to be stripped off from links
Mandatory first-party relationship with the user for any data from the linked website
Now, let’s look at how these changes are disrupting today’s marketing ecosystem. If your organization is leveraging any of the below systems or technology, you will have to take a relook at your current marketing operations.
DMP’s: Perhaps the biggest casualty of these changes will be the Data Management Platform (DMP’s). DMP’s are heavily dependent on third-party cookies and with ITP the business model is facing an existential crisis today.
Web analytical platforms: Platforms such as Google Analytics and Adobe Analytics use cookies to capture the information on web traffic and customer interactions. They will have to take a relook to deliver the same capabilities within the ITP framework.
Campaign attribution: With a significant lesser attribution window, marketers will misattribute credits to campaigns. This results in marketers not identifying what worked and what didn’t and crafting ineffective campaigns
Testing Period: A/B testing window has to be within 7 days else it may result in showing the different variation to the same test group users if he comes after 7 days rendering the results inaccurate./li>
One open question that is asked in the marketing universe is where to slot CDP in midst of these changing privacy landscape, is it a beneficiary of the changes or is it one of the systems that is bound to be negatively affected by it.
In this context let’s look at how a CDP works; put simply CDP builds unified customers views based on their interactions across multitudes of source systems and interfaces deployed by the organization and makes it available for analysis and action. CDP breaks down data silos within the organization so that every system connected to it starts seeing the customer, their preferences and behavior consistently; helping to craft consistent experience across each touch point.
Organizations are now re-evaluating their entire marketing stack to identify the systems impacted most by these changes and evaluating ways to ensure they are still able to market effectively to their customer base. Organizations are coming to be increasingly dependent on CDPs and its capabilities to archive the same.
CDP is driven by first-party data: For opted in customers; a marketer can still target them either through third parties (DSP’s, Facebook, Google, etc) directly or through segment/ audience push capability of a CDP; the real impact will be felt in attribution. While third party attribution window has been cut short to 1 day leading the same customer to be counted twice or more; a CDP will be able to attribute it to the same single customer since it identifies the customer and merges his multiple visit and activities to the same profile.
Unified View of customer: The biggest advantage of CDP is that it creates a persistent unified customer profile based on all first-party data available both offline and online. This provides marketer clear advantage in using CDPs to create their target segment rather than using third parties such as (DSPs, Facebook. Google, etc) in isolation as they depend exclusively on online data which is now severely curtailed by ITP.
Personalization: The ability of CDP to personalize marketing communication based on customer context will not be severely impacted by ITP changes as CDPs are able to consume real-time data from all kinds of channels such as Email, SMS, Notification, etc and is not just dependent on cookies. For example, a Customer has clicked on email link with Product A, he will be identified when he visits a website and can be retargeted with product A. While the post-login personalization capabilities remain intact; CDPs will face few limitations on pre-login personalization. Additionally with GDPR and CCPA being considered norm going forward; a CDP is increasingly becoming a critical system that owns the entire customer data. Without a CDP, the organization will struggle to comply with provisions of the regulations as the data will be siloed across multiple systems with different identities for the same customer. Organizations cannot dependent on third parties to manage consent with a limited view they have into a customer with the latest changes to ITP.
The upshot is that even though CDP systems started off with the objective of solving the marketer’s problem with even increasing touchpoints, it has evolved into a system of intelligence for the whole organization. In this light, the recent changes to ITP will act as a constant force of evolution on CDPs but will not take away from it what it really does well and this is why the CDP segment continues to grow and is now over a US$ 10 billion market.
We are always interest in comments from the industry, so please let us know what you think here, or drop us a note at marketing@firsthive.com
February 25, 2020
Why you should take a leaf out of HSBC’s book and remember your email system is ‘not an island’
There’s a lot of hype these days about omnichannel marketing and the importance of crafting a consistent customer experience across every platform. The fact is, though, that all channels are NOT equal when it comes to customer engagement. According to industry analysts Gartner, an Adobe 2018 Consumer Email Survey found that 50% of consumers named email marketing as their preferred brand communications platform, compared with a maximum of 20% who said they preferred any other channel.
So, to maximize sales it is important to get your email targeting right – especially since Gartner’s own 2018 State of Personalization report found that, while 86% of consumers surveyed were fans of personalized communications, 48% said that they would unsubscribe from irrelevant or annoying emails. Meanwhile, the same survey found that brands saw a 20% increase in commercial benefits when customers perceived their communications to be helpful.
In other words, just addressing your marketing email to ‘Mary Smith’ won’t cut it anymore. These days, Mary will expect you to send her personalized and relevant content, carefully tailored to match her interests and speed up her path along your brand journey.
The challenges of personalization
However, that’s not as easy as Mary might think. Why? Because Email Marketing Systems are, by nature, siloed. Everything they know about the customer comes from the one platform, and remains within that one platform. This means that marketers are able to determine which emails a customer has opened, what time they tend to read their communications and which links they have interacted with, but won’t be able to share that information with their other platforms to help enrich the engagement with that customer across the journey.
Nor will they be able to tell that the customer has just bought the advertised product, which might suggest that further email communications should be either suppressed, to stop the customer receiving ads or offers for the item they have just bought, or changed to promote different products.
As well, a lot of information might be held about the customer on other platforms, such as their last purchase, what they’ve looked at on the website, products they’ve liked or commented on in your social media, and what they’ve swiped their loyalty card for in store. However, if you’re to use that information to help you select an audience for an email campaign or personalize your email creatives, you’ll need to leave the email platform, collect together the necessary insights, and then manually insert them into your emails on re-entering the platform. If interesting results come in from your email campaign, you’ll have to perform the same process in reverse to share them with other platforms.
What you need is an email marketing channel powered by a Customer Data Platform (CDP) that integrates your email marketing with your other channels, and is therefore able to utilize all the customer data, transactions and interactions from across the organization - not just the data residing in the email platform itself.
The benefits of CDP-powered email marketing
A shared databank
Being able to see everything your company knows about the customer – their demographics, location, preferences and past transactions/interaction history – in a CDP will make it much easier to understand what your customer actually wants from your brand and how you can give it to them. Having every byte of data about your customers available for the segmentation and personalization of an email ensures that your communications will stand a better chance of reaching the right person with the right information, whilst also bypassing the arduous or expensive process of data wrangling to move data between systems.
Actioning your insights
With CDP-powered Email Marketing you’ll be able to better segment your customers into suitable audiences to receive bespoke campaigns. Emails designed to engage women aged 30-50, who are residents of Portland, Oregon and have a propensity to respond to discount offers by email, for example, will be sent only to people within that niche bracket, resulting in incredible open and conversion rates. Better still, that same email can be highly personalized en masse, using dynamic content driven by all the data variables from across your business, to ensure that each recipient gets content relevant to them or will be sent their promotion via an alternative channel if they have repeatedly ignored your past emails.
It’s not just about how you sell the right product, it’s about when you sell it too. If a customer is on your website looking at perfumes, then providing them with a website pop-up or nudge to use an exclusive discount code is proven to be a great real-time personalization tactic. However, if they ignore the pop-up, or perhaps don’t see if for whatever reason, now might be the time to send them a discount code by email. After all, the margin on such products is huge and a bottle can last for several months, perhaps even a year, so it pays to strike before they have a chance to browse for competing products and retailers. And, if someone has just bought a new bike in your store, they might genuinely appreciate an email containing advice on how to maintain the bike, along with recommendations of associated products they can purchase to help them do so.
Similarly, if an online customer abandons the items in their cart, knowing that immediately will give you a chance to trigger an email to jog their memory, or send them a small survey to determine what their concerns might be (such as price, slow checkout or a lack of product information), so that you can attempt to allay these or identify issues with your website.
Continually improving your omnichannel approach
With CDP-powered email marketing, data can be utilized from any system and shared with any system too. That means you’re not only able to glean information from your CMS, SMS or social media channels to enrich your emails, but have the freedom to feed back the insights gained through your email marketing campaigns. As a result, it won’t just be your emails that will hit the spot with your target audience more consistently, all your marketing efforts will become more refined.
Together, your marketing efforts will thrive
Just as HSBC argues that Britain’s international character makes it stronger, so achieving a two-way flow of information between all of your Martech systems and platforms will enrich every campaign you run.
A CDP-powered email marketing channel will overcome all of the challenges of personalization by ensuring that your email campaigns are powered by data insights gleaned from a 360 degree Single Customer View, so that customers will only receive emails that are relevant to them. It will enable an offline purchase to trigger an online action, it will mean a complaint or call center interaction can suppress the request for a product review, and it will ensure that the 80% of your audience who don’t interact with your email are automatically selected be marketed to via another channel next time, to maximize the impact of a multi-channel campaign.
To find out more about how better data insights will help you to get more opens, clicks and conversions than ever before, read about what our new email module has to offer now.
Discover how a CDP could be your route to real-time, cross-channel marketing with our live demonstration.
Over the past few years, the industry buzz has been about the rise of Customer Data Platforms (CDPs) and how they’ve extended the value of first-party data, while third-party data took another bad rap.
In short, we aim to prove that while first-party data certainly has its benefits, relying on it alone will only get you so far.
Think about how often your data goes bad? Every month about 3% of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, or pass away. Whatever the reason, this fact is why marketers are always in need of updated and accurate customer data.
Let’s go through the facts on why we need third-party data more than ever.
1) Fraud lives in first-party data
You may think that your own data is the cleanest, safest data you can have, but the truth is your first-party data will show its true colors.
Ask yourself how often an individual misrepresents their identity or gives you false information when filling out forms, or surveys when applying for that discount, free sample, or any other special offers. You receive that data from the consumer, but that doesn’t necessarily mean it’s correct.
Even when it comes to a simple email address, there are many ways for fraudulent data to end up in your email marketing lists such as:
Disposable Domains: These are temporary emails that WILL deliver to the inbox. An example can be a shared public email account with no passwords for different people to use for a multitude of reasons. These emails are used for marketing purposes and are a waste of resources if you send to them.
Bots: Programs designed to locate signup forms on the web and submit fake email addresses and other information. They are purely malicious and mess up your metrics, and they provide no real usable information. As such they are not only pointless to market to but can also be dangerous.
Moles: Are fake emails that report campaign stats to real-time blacklists.
Seeded Trackers: Are addresses used in marketing campaigns to track delivery rates. They are there for a simple reason of someone tracking your activity, so if you send to them, they won’t help your open or click-through rates, because they aren’t real.
2) You Need More Data
Automotive dealerships are receiving leads every day, but they aren’t exactly sure which leads are qualified or not when measuring with their own metrics. The good news is they do have a good understanding of the target audience, but when a lead comes in there are two potential outcomes, is the consumer browsing or are they a potential buyer?
Another industry that has obstacles with first-party data is the Consumer-Packaged Goods (CPG) Industry. If you’re familiar with the industry most CPGs don’t have the rights to the consumer data, the retailers do.
We live in a world where data is what is keeping personalized shopping experiences moving, and CPGs will need to find ways to stay on top of this issue and not get left behind. Ultimately, they need more consumer insights to move forward.
If these industries want to know their customers, they need to create a wider view of their customers and not just rely on their own data.
How do they do this?
Third-Party Data Wins Thanks to CDPs
CDPs’ appeal to marketers is their ability to collect and connect first-party data from multiple sources across the organization.
What they often lack is keeping your data up-to-date, and this is where certain CDPs and organizations considering a CDP purchase partner with third-party data providers to help clean and add additional insights to their customers’ data such as purchase history, demographics, interests and much more.
There isn’t anything bad about third-party data providers, the data simply is just someone else’s area of the customer picture. You’re always going to have those low-quality data providers who tarnish the industry reputation that you must watch out for.
Everyone wants to work with trusted, compliant data quality providers. We understand the fear, but you’re never going to run successful campaigns if you’re not working together to capture the full picture of your customers and prospects.
Third-party isn’t dying, it’s only getting better. We forecast there will be more CDPs and CDP customers in the future willing to work with third-party data providers to help enhance data.
Unified Communications refers to the integration of various real-time communication channels such as voice, video, text and data into one solution, which aims to boost communication and collaboration within a business. Unified Comms come in many forms, including the integration of a company’s customer communication channels (i.e. email, social media, web and chat) or simply the pairing of various internal channels such video conferencing and instant messaging (i.e. Skype for business).
Improving productivity is a challenge all businesses face, and Unified Communications proposes various ways it can address this challenge. A study found that businesses adopting UC experienced on average a 52% improvement in workplace productivity as well as a subsequent 25% increase in profit. Sounds promising, but how does UC help achieve this?
Improved Speed
Firstly, Unified Comms can significantly add speed, and therefore productivity to business processes. For example, in contact centres, UC can help by improving customer response times. With all of an organisation’s communication channels merged onto one platform, (i.e. social, email, web, chat, telephone, SMS) it becomes much easier to pick up these queries/conversations as well as respond to them. For sales teams, unified comms can be vital as it means each team is alerted of queries much quicker and so have a better chance of converting each ticket coming in. With the help of UC, staff across various departments now address more queries during their working hours, thus boosting productivity.
Internal Collaboration
Secondly, UC can have a positive impact on workplace collaboration and communication. Presence apps let you know in real-time if your colleagues are available for meetings, calls/call transfers, or collaboration on projects. Having this information can be very practical, as often you cannot physically see your colleagues and therefore determine whether they are available to speak or not. Presence apps can eliminate unnecessary call transfers and can also ensure that when a colleague needs to assemble a team or communicate or reach various people, they know when and how they can do so, which improves internal communication and productivity.
In addition, video, visual and messaging capabilities integrated together (onto one platform) enable employees to remotely facilitate virtual meetings with each other, eliminating the need to move between offices/ locations and ensure everyone is present at the same time. This helps speed up internal communication and can significantly improve productivity.
Mobility
Lastly, implementing UC can enable employees to work remotely, accessing their data and work files even when they are not in the office. An article written by the Telegraph found that many employees who commute to work also like to get work done whilst making their journey in. More than a fifth (22%) said working on their commute allowed them to get more work done, and more than a quarter (29%) said that they were able to do so because of the access they had to technology. This goes to show how UC can create tangible benefits for productivity, by giving workers access to their work station even when they aren’t at the office.
Similarly, a nine-month study by HBR found that productivity in call centres increased when employees were given the ability to work remotely from home. The study found that those who worked from home completed 13.5% more calls than their counterparts in the office, and the business itself saved around $1900 per employee on furniture and space over the nine-month period. Working from home couldn’t happen without enablement from UC technology, and so its implementation can facilitate home working which can boost productivity.
To sum, Unified Comms can boost business productivity by improving speed in the contact centres, which can lead to quicker response times and more conversions. In addition, UC can boost internal communication and collaboration, making teams more efficient and effective which can boost productivity. Lastly, UC enables remote working which means employees can work even when they are not in the office (ie commuting or at home) which has been shown to increase productivity.
Stefan Docherty is Marketing Executive at Call & Contact Centre Expo which runs in London, March 18-19, 2020). Information is at www.callandcontactcentreexpo.co.uk/
To remain competitive in today’s data-driven world, sales success is highly dependent on a superior omnichannel customer experience. That means your customers have a consistently positive and seamless experience across your brand’s many touch points as they journey from awareness through evaluation to finally making their purchase decisions.
Unfortunately, many companies still struggle with integrating and analyzing data from the disparate technology platforms and apps that make up the omnichannel experience. In fact, a new Arm Treasure Data report, “2019 State of the Customer Journey,” found that nearly half (47 percent) of respondents struggle to gain insights from their marketing data due to silos.
Making sense of the data
The marketing technology landscape has exploded in recent years, with more than 7,000 vendors offering martech solutions. These companies provide tools for independently managing everything from mobile advertising to interactive content to influencer marketing activities (plus dozens of other sales and marketing functions). Due to the rapid growth of tools and data, it is no surprise that businesses are struggling to access and properly analyze the data.
The report also found that 54 percent of companies say they don’t have a full picture of their data and thus their customer journey. These blindspots put achieving an exceptional customer experience at risk. It’s like trying to finish a puzzle with only half of the pieces. Without a complete view of each customer journey, the odds of sending the wrong marketing message at the wrong time increase considerably.
Other key findings from the report:
● Customer journeys are complicated. Most (61 percent) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32 percent) reporting six or more touchpoints.
● Many don’t know what works. Nearly half (48 percent) say they are not using a formal attribution strategy, making it difficult to determine which of their efforts produced a sale.
● It’s a marathon, not a sprint. Long buying cycles make it critical to keep track of customer journeys. About 40 percent report the timeline from first engagement to purchase is four months or longer. Nearly a quarter (23 percent) of respondents don’t have any idea how long their customer journey takes from first interaction to purchase.
● Unreliable data sources lead to marketer confusion. The lack of a clear data picture means companies have potentially misplaced confidence in the effectiveness of their own marketing channels. Respondents cite salespeople and the company website as two of their three most influential marketing channels, while simultaneously acknowledging those aren’t the primary channels they turn to when making their own purchase decisions.
Why is it so hard to get the customer journey right?
Siloed data, combined with gaps in data analysis skills and lack of resources in marketing, technology, and data science, makes it tough for many organizations to develop accurate pictures of their customer journeys. Companies with many different types of marketing technology often suffer the most from the silo effect because data in one system is difficult to use with software and data in other systems. For example, an email marketing solution might not be able to easily share data with an advertising platform, decreasing the usability of the data.
To help break down silos it’s important to integrate the data you already collect. Useful marketing technology, such as customer data platforms (CDPs), combine data from many sources, online and off, to create a full picture of the different customer journeys your buyers and prospects take. With more unified data you can find out what compels customers to buy and why.
Silos Might Be a Big Point of Failure in Marketing Programs
Some of the questions CDPs and unified profiles can help answer are everyday problems. Ask yourself: Is something off about the responses you get to your marketing campaigns? Do you sometimes get odd or conflicting results?
You’re definitely not alone if you do. Our recent research on the state of the customer journey found companies are still struggling with data integration. More than half of those surveyed (54%) say their biggest barrier to leveraging data is fragmented or siloed data, which makes it difficult to get an accurate, integrated view of the customer journey.
And it’s no wonder — customer journeys are often long and complex. Most of our survey respondents (61%) report having three or more pre-purchase customer touchpoints, with about a third of all respondents (32%) reporting six or more touchpoints. And these touchpoints frequently happen over a course of several months.
With complicated buyer’s journeys becoming the new normal, you’d expect to see an increase in the use of multi-touch attribution strategies to ensure companies understood the path to purchase for their customers. (A multi-touch strategy divides up credit for sales or conversions among lots of touchpoints, rather than just using the last customer touchpoint as “the cause” of the conversion.) Yet nearly half (48%) say they are not using a formal attribution strategy at all, let alone one that can track multiple omnichannel interactions with the customer. This makes it increasingly difficult to determine which sales and marketing efforts produced a sale.
Relying on the Unreliable
Unfortunately, in the absence of a reliable source of integrated customer data, people tend to rely on unreliable sources — even though they know better. That’s why we see marketers reporting on easy-to-access vanity metrics or only tracking the final touch before a sale, and guessing at what came before it. That easy-to-come-by customer data can lead to some costly but ultimately avoidable customer data marketing missteps:
● Email platforms make it easy to see which emails garnered the highest open and click-through rates. But you often have to dig deeper to understand what is being clicked on. That means you may not realize that your high CTR email, the one that shows up in the top-line reporting, is driving people to unsubscribe or to view the email as a web page due to poor formatting or image sizes.
● Social media platforms make it easy to identify the customers who engage the most with content you post and your most engaged followers. But without a layer of sentiment analysis — and looking at what the engagement actually entails — you can end up boosting content that your ideal customer has actually been annoyed with, or showcase content that’s trending, but as an example of what not to do.
● Website analytics can show you how a customer that was ready to make a purchase found you. But, if you aren’t using advanced tracking, and integrating other channel data, you may decide to double down on your website or search engine advertising and not realize those customers were hearing about the product in a podcast or through influencer word-of-mouth and searching for your brand name specifically as a result of that initial engagement.
When we use easy customer data to make decisions, it makes us feel better than just going with our gut. But it may actually cause us to just make bad decisions with more confidence.
Gartner Estimate Says Poor Data Costs $15 Million
Gartner research found organizations believe poor data quality to be responsible for an average of $15 million per year in losses. Marketers at Shutterfly ran into this when they made some big assumptions presumably based upon browsing data. They sent emails congratulating new parents on the addition to their family — only, many of the recipients definitely hadn’t just had children. While some of the people on the receiving end were amused by it, and took to social media to post at the brand’s expense, that email also likely ended up in the mailbox of customers who weren’t able to conceive, had miscarried, or had lost a child.
Timing Is also Everything
Also, not all bad personalization is off the mark solely due to its message. Sometimes timing is an issue. Like the pair of shoes that follows you all over the web...starting the day after you purchased them from that retailer online. This sort of customer journey mismatch is caused by a lack of real-time data integration.
Between the time that customer viewed the shoes on the web, had them sitting in their shopping cart, and ultimately bought them, that customer’s data had to make its way through your internal processes and systems to eventually fuel a retargeting campaign.
If your data had been up-to-the-minute, you could instead be pitching that same customer on buying the shoes in another color, or on purchasing a completely different pair that your customer data shows is popular with purchasers of the initial pair of shoes.
Subaru, for example, used a customer data platform to unify all of its marketing and sales efforts. Not only was the company able to easily distinguish those who were ready to buy from those who weren’t, but the buying process was streamlined and accelerated, and when the sale was finally closed, Subaru didn’t have to waste resources on someone who had already bought a car. Rather, the company could begin automated marketing efforts steering customers back into dealerships for service, upgrades, and after-market products.
“Blasting Emails to Everyone” Doesn’t Work, But CDPs Do
Good, complete customer data, on the other hand, helps you tease out subtle shifts in customer attitudes and behavior, something Shiseido learned when it began using its own customer data platform (CDP) to unify its loyalty, browsing, and ad campaign data.
"Our new customer data platform built on Treasure Data is fundamentally changing how we communicate with our customers,” says Kenji Yoshimoto, Chief Analyst for Direct Marketing, Shiseido. “Blasting emails to everyone who tried samples or bought a particular product won’t lead to customer delight. Detecting a mood swing in each customer and changing the tone of push notifications does.”
And of course, when you rely on bad data to make significant business decisions, you not only miss out on that opportunity to delight the customer, you may even permanently turn them off to your brand. You’re not only missing today’s opportunity; you’re risking an unsubscribe or losing to a competing offer that forecloses on tomorrow’s opportunities.
Consumers want to buy from brands who provide them with an omnichannel “know-you” experience. But to deliver on that expectation, brands must invest in both data integration and tracking customer activities throughout the purchase process to ensure accurate attribution to use for future marketing decision-making.
February 6, 2020
Experience 2030: The Future of Customer Experience is... NOW!
Modern technology has upended the way brands and consumers engage. New products, services, consumers, and competitors have arrived and keep evolving. Consumer behavior, likes and dislikes continue to change. What will the customer experience look like in 2030? And how will brands evolve to meet the expectations of future consumers? These are some of the questions addressed in “Experience 2030: The Future of Customer Experience” by Futurum Research and sponsored by SAS, the leader in analytics – free to download now.
The research found that technology will be the major driver behind the reimagined customer experience (CX), and that brands must rethink their customer ecosystems to keep pace with empowered consumers and evolving consumer technologies.
According to the research, consumers expect to embrace new technologies by 2030:
80% say they expect to accept delivery of a product by drone or autonomous vehicle.
81% say they expect to engage with chatbots.
78% expect to use an augmented, virtual, or mixed reality (AR/VR/MR) app to see how a product will look – such as how a piece of clothing might look on a shopper or how a piece of furniture might look in a home.
56% expect to be “visiting” remote locations or experiencing vacation and entertainment events through mixed reality devices by 2025.
Eight out of 10 expect to use a Smart Assistant (such as Google Home, Amazon Alexa, etc.) to make an online purchase or control a Smart Home.
78% say they expect they’ll be controlling other devices with their wearables.
Additionally, by 2030, 67% of engagement between a brand and consumer using digital devices (online, mobile, etc.) will be completed by smart machines rather than humans, according to the research. And by 2030, 69% of decisions made during customer engagement will be completed by smart machines. This agility and extreme automation will drive the customer experience.
For more findings and insights, click above to download the global report, which highlights the findings from a global survey of more than 4,000 panelists, spanning three dozen countries across a range of consumer, industry and government sectors.
February 3, 2020
CDPs Could Revolutionize the Way We Drive — And Improve Dozens of Other Everyday Products Too
Self-driving cars could prove to be a wonderful technological advancement, but what is there to help drivers in the meantime, before the technology becomes generally available and accepted everywhere? The answer might just lie in the same AI, analytics, and data technology that customer data platforms (CDPs) use to help marketers understand their customers. An interesting example is a new Advanced Driver Assistance System (ADAS) that electronics giant Pioneer recently introduced.
The Pioneer ADAS system uses real-time data streams from a variety of sources — weather data to determine driving conditions from moment to moment, car instrument data on the driver’s abilities and habits, road data, IoT data, and more. With the help of AI and machine learning, the Pioneer system comes up with a constantly updated display that shows how likely the driver is to have an accident at any given moment. Drivers can influence the “score” by slowing down, driving less aggressively, and by generally being more cautious.
How Pioneer’s ADAS Uses Predictive Modeling to Help Drivers with Real-world Hazards
But how would that look in our everyday lives? Picture this scenario.
You’re driving to work, the same route you take every day. But this time, a storm passes over: you’re suddenly faced with heavy rain and reduced visibility. All of a sudden, the “accident score” meter on your car’s dashboard moves into the red. You ease off the gas, move out of the passing lane and your score drops down to amber — you can’t get it into the green due to the adverse weather conditions — and it stays yellow as you finally pull safely into the parking lot — and breathe a sigh of relief.
Such real-time monitoring requires leveraging many different technologies, including data management of sensors powering the Internet of Things (IoT), AI, and machine learning. Arm Treasure Data enterprise Customer Data Platform (CDP) integrates all of these technologies to provide predictive customer scoring, which Pioneer is leveraging as part of its Intelligent Pilot offering for the Asia-Pacific region.
What’s at the Heart of Real-time Predictive Modeling?
Advances in predictive modeling and predictive scoring — in retail customer personalization, fraud detection, finance, and insurance — are stimulating heightened interest in using AI in accident mitigation. Accidents are also an important emotional issue, both for their impact on victims and for the many citizens that feel more needs to be done to reduce the problem. And some of the causes of accidents will only get worse over time, as cities become increasingly traffic-bound and rural roads don’t always receive the timely upgrades and maintenance, making the driving environment even more challenging.
Why Do We Crash? Customer Data Helps Calculate Individual Scores
Professor Kazuya Takeda, a well-known expert on analyzing accident data and part of the team that developed Pioneer’s Intelligent Pilot, an AI-driven ADAS, has found that factors highly correlated with accidents include a variety of behavioral data specific to each driver — such as reaction time, age, overall approach to driving, and other individual influences.
Environmental factors such as weather changes can dramatically up the odds of a collision. Rain, snow, or obscurants such as smoke or fog can block views, and water or ice increases stopping times. In addition, limitations in the streets and driving surfaces themselves can create a risk; some roads are better designed and maintained than others. The amount of traffic can also dramatically change the risk of having an accident. And even the most skilled drivers can be a little “off” in their reactions if they’re distracted, angry, or just having a bad day.
Capturing all of these variables to make decisions that positively affect the outcome of a journey — whether it’s a physical journey or what marketers call a customer journey — is no easy feat. Yet Pioneer, using Arm technology, is looking at how it can be achieved — and in real time.
Using this technology, Pioneer has developed “YOUR SCORING,” an in-car display feature that shows drivers a real-time, constantly changing estimate of their accident risk. The score is based on external map and road-condition data as well as the behavior of the driver. By combining and analyzing these data sources to create an overall picture of a journey in real time, YOUR SCORING is able to help drivers reduce their risks.
The AI and data management capabilities help Pioneer to quickly gather and analyze numerous data feeds (such as street layout, traffic signals, telematics, and third-party data) and apply machine learning (ML) techniques to deliver a single score through the system. At any given moment, the system could be processing reaction-time data from the driver, factoring in previous driving history and combining this information with terrain and current weather data from the nearest IoT sensors.
Personalized Data-driven Displays Could Be Next
Arriving at an accurate accident-risk score is one thing. But delivering this real-time, potentially life-saving information in a well received, actionable way is another important technical feat as well. Data plays a major role in this respect as well, and displays could be the next frontier for personalization.
Research has shown that people vary quite a bit in how they react to different types of displays and instructions — just as they react differently to various types of sales initiatives and targeted marketing efforts. A simple thing like the design of the risk display can affect some people’s willingness to take instructions.
These displays and user interfaces can now be personalized to people’s preferences and refined by the AI’s monitoring of a driver’s compliance as the display is varied. The next generation of driving technology might be individually tailored in many dimensions, to meet such individual customer behavior and preferences, just as retailers use the same technology for personalized customer journeys and customer experiences. Full-featured CDPs with the ability to integrate many different types of data, are obviously a critical technology for enabling such on-the-fly data unification and real-time analytical decision-making.
So, if you’re hearing a lot from the backseat drivers in your life, stay patient and calm. Someday, they might relinquish their roles to a smarter — and potentially much less annoying — AI. And unlike your current backseat chorus, the AI won’t ask “Are we there yet?” — because it will already know.
January 30, 2020
If You Want Customer-Centricity, Dismantle Your Data Silos
It is not surprising that CDPs have rocketed to the top of many marketers’ wish lists. Marketers have a very real need to corral customer data currently residing in disconnected silos both inside and outside the organization. In an HBR survey on using real-time analytics to improve customer experience the top challenges marketers faced were legacy systems, data silos and multichannel complexities. In a Forbes Insights study on the rise of CDPs, only 1 in 5 executives surveyed considered their companies to be leaders in customer data management and only 13% believe they fully utilize customer data. These difficulties persist despite the fact that we have been trying to uniquely identify customers and consolidate first-party customer information since the late 1980s.
Marketers are not the only ones facing these challenges. Data silos and misaligned technology also make every list of issues impeding companies as they try to move toward customer-centricity across all their business lines. The sheer number of technologies in today’s martech stack, many of which create or store their own copies of customer information, is astounding. Add to that the customer-oriented data warehouses and data lakes facilitating analytics and master data management applications facilitating operational activities that we still see, and the magnitude of the problem is clear.
Even with the adoption of CDP, if we are not careful, the technology alignment problem will simply continue to grow.
Enabling customer-centricity
While there is no silver bullet for this problem, there are steps that companies can take to ensure their technology enables customer-centricity rather than disabling it:
Develop a customer technology strategy. A strategy for customer tech is critical. Customer experience (CX) leaders should work with their IT partners to answer questions such as the following:
What capabilities exist?
Are they being fully leveraged?
Where are the integration gaps?
How many areas have their own customer data?
How will these align or integrate with the CDP?
Once the existing data silos are understood (this includes marketing and CX technology, sales and service automation applications, web and mobile applications, and analytics), the assessment should expand to include gaps in technology, disintegrated sources of data, incomplete information, and independent applications that are not or cannot be integrated. The CX team can then use customer journey maps as prioritization tools to fill the gaps and realign the technology.
Get tough on acquisition. The vast set of shiny new tools available to the CX team can be very tempting, but if the goal is to dismantle data silos, tough decisions will have to be made. The technology strategy can help. Every potential purchase should come with a clear integration plan and budget. If the money or resources for integration are not available, the purchase should be reconsidered. Vendor solutions, including CDPs, should be closely examined for their ability to integrate with other applications. Closed applications or black box solutions should be considered only as a last resort.
Understand that there is no virtual view. Of customers, that is. The shortcut that no CX leader can afford to take is skipping the single customer view. Identity management is the top requested capability for CDPs for a good reason. It is difficult to achieve, and digital channels complicate the situation significantly. The temptation to implement multiple customer profiles is quite strong today because many large technology applications come with their own customer database. Attempting to match across these applications on the fly is difficult at best and fraught with peril at worst.
Picking a single customer master application like a CDP and doing the block and tackle integration work as applications with customer databases are added is a critical step to tearing down customer data silos. Multiple disintegrated customer databases are never the right answer, and looking to them to solve a CX problem will add to customer-centricity challenges rather than helping to resolve them.
Becoming the de facto integrator between siloed business groups will be increasingly critical for the CX leader of the future. Start planning today for what your organization will need tomorrow.
January 27, 2020
MobilityWare Uses CDP and Predictive Scoring to Learn What Makes Some Games So Irresistible, Play After Play?
What makes a game stay appealing, play after play after play? That’s what mobile games purveyor MobilityWare wants to know — and it’s using a Customer Data Platform (CDP) and predictive analytics to help figure that out.
If you’ve ever played games on your phone, chances are you’ve played a MobilityWare game — either its flagship Solitaire or any of the many other games the company offers, such as BlackJack, Spider, or Jigsaw Puzzle. Founded in the 1990s, the company’s continued success and platform transitions are due in part to its deep understanding of its players and their customer journeys. And lately, MobilityWare’s creative use of customer insights is getting an assist from Arm Treasure Data enterprise CDP.
How to Improve Customer Lifetime Value? Keep Players Playing
One of the toughest things for a gaming company to do is keep its best customers playing, but that’s pivotal in increasing customer lifetime value (CLV). And the answer is usually highly dependent on the game, the demographics of the players, and even individual customer journeys and player histories. Teasing out player incentives or other customer retention strategies requires careful customer data analysis, the kind that CDPs can quickly and automatically perform.
The other problem games sellers face is how to monetize their games, and a major concern with any contemplated change is not cannibalizing revenues from current play. MobilityWare makes money from in-app purchases and ads, plus rewarded video.
Predictive Scoring & Modeling Help Dissect the Churn Problem
The challenge that the MobilityWare team faced was this: How do you figure out when a customer is likely to quit playing? And which player incentives could delay or stop customer churn?
Chris Densmore, director of analytics for MobilityWare, set out to get answers. He focused primarily on “dedicated players,” who had installed the game more than two weeks ago, but who had not played in the previous two weeks.
“Dedicated players can be incentivized, they’re more valuable, and we have a larger behavioral dataset because they’re more likely to have played a lot in the past,” Densmore says.
“Also, we can do more to influence the outcome, which is important,” he adds.
Densmore used MobilityWare data and analytic features from the company’s Arm Treasure Data CDP. Then he chose a logistic regression model, because it can still produce valid results even if some variables are correlated — as they usually are in real, live users — and because logistical regression makes it possible to interpret contributing factors as well. Another bonus: The coefficients that come out of regression can be easily placed in SQL scripts to produce predictions.
Customer Data Insight #1: Coins Don’t Work Like Boosters
Densmore’s work got some surprising results. Rewarding people with a coin that can be redeemed within the game wasn’t the clear winner. It did reduce churn, but had a negative impact on monetization. People tended to hoard them for unspecified “later use,” rather than play more and use them in the game. And who wants to take steps that hurt revenue if they don’t have to?
Customer Data Insight #2: Gamers Want Boosters, Not Coins, and They Pay More If You Help Them
The real surprise was that giving someone a “booster,” or a small assist or tool they can use to win, was almost as effective as coins in combating churn, PLUS it increased ARPU (average revenue per user) by more than 450 percent for those with a probability of churning between 60 and 80 percent.
A little bit of help — but not so much that it devalued the accomplishment of winners — was the key to reducing churn and renewing player interest in the game. And without the CDP data, it would have been tough to get the customer insights that pointed the way to the right customer experience. With it, hypothesis testing and predictive modeling pointed the way to higher profits.
Just think how many other product “churn” problems could be informed by similar analysis. Many other areas where fashion or trends are short-lived come to mind. For example, Shiseido uses a CDP to analyze loyalty data and combine it with other data feeds to predict when people might be open to new beauty products — or even a major makeover. Telecommunications companies, which live or die by the difference between new customer acquisition rate and churn, could also use CDPs plus predictive analytics to gain insight about what makes someone switch to a new plan or provider. The insights that CDPs can provide are only just beginning to be harnessed for better marketing and even improved product design. Predictive analytics are clearly more than a fad, and with the help of CDPs, they are fast becoming an easier-to-use tool in every marketer’s workshop.
January 23, 2020
Customer anonymity can’t be guaranteed, but differential privacy can help change the game
The relationship between companies and consumers is increasingly complex, with concern about privacy growing among consumers and pressure increasing on marketers at companies to use customer data more to engage with and find new customers. The key question is how to achieve a workable balance between the two.
With consumer experience projected to become more important than price or product in ensuring customer loyalty, the more trust you engender the more likely you are to grow your customer base. Customers want to understand how their data is being used and to know how to participate in the process of protecting their privacy.
The first step on the part of the marketer is to recognize that data is at risk whenever it is shared. You’ve heard about studies that have shown that even anonymizing data doesn’t always work. Back in 2007, Wired magazine reported that, “Netflix published 10 million movie rankings by 500,000 customers, as part of a challenge for people to come up with better recommendation systems.” The data was anonymized removing personal details, but researchers at the University of Texas were able to de-anonymize that data using only a small set of public data from the Internet Movie Database (IMDb). Last month, the New York Times’ Privacy Project showed how cell phone company data of GPS pings from the cell phones of 12 million Americans could identify almost anyone when matched with public address data.
This has been a topic in the privacy field for decades and has been done with all kinds of data, including medical, financial and genetic. It’s scary and seems overwhelming, yet companies are still being asked to work to meet legal requirements laid out in legislation, including GDPR and the new California Consumer Privacy Act (CCPA) and to show they are putting appropriate privacy protections are in place.
While the nature of data security is that it can inevitably be breeched, regulators want to know that a company is being proactive in their compliance and consumers value companies that are transparent with their process.
This doesn’t mean you can’t use data for anything, but it does mean that you have to be careful to anonymize it effectively. Experts say there is no guarantee, but one powerful approach is a statistical technique called differential privacy, which is used by Apple, Google and other big tech companies via an algorithm that “adds random data into an original data set” (Digiday, April 2019). In September 2019, Google announced that they were allowing access to their differential privacy library to help developers at other companies achieve this.
Robin Röhm, chief executive officer of apheris AI GmbH, a start-up that develops artificial intelligence algorithms for biomedical data says, “what we’re addressing is really an operational question about whether a company has defined a top-down logic to ensure semantic-enabled data privacy protection. This means everyone in the organization needs to adhere to the process. Then you have privacy by design, in which the process is incorporated into the company’s architecture with algorithmic rules customized to its needs.”
We’re in early stages of understanding how best to ensure individual privacy both tactically and legally, but those who are focused on this area agree that it takes collective knowledge and ongoing diligence to evolve strategies that work in the present and over the long-term.
January 20, 2020
eTail 2020 Report: Retailers Are Relying on Personalization and CDPs to Go for the Win
Is retail finally getting serious about customer data? Are businesses growing impatient with getting only vague ideas about who their customers are, and how to maximize the chances they’ll buy, or buy again? And are they serious about using data throughout their organization as a strategic weapon in the fight for profitability?
A new retail report from eTail, WBR Insights, and Arm Treasure Data reveals an industry that’s betting big on leveraging data-driven technologies to drive more tailored and personalized customer experiences (CX). In doing so, retailers aim to better engage with their customers throughout the purchasing lifecycle. The responses suggest that for some retail marketers, using customer data to create customized customer journeys and experiences is not only a big part of their strategy, it’s the whole game. And to do all that, they’re increasingly turning to customer data platforms.
Customer Data Is Transforming Retail Operations
The report, “Retail CX and Data Management Strategies in 2020,” is based on a roughly even mix of department heads and senior executives at VP level or higher, all at retail companies with more than $1 billion in revenue.
Most of the surveyed executives believe better customer data, smartly applied, is critical to crafting customer experiences that lead to greater sales — and they plan to go all-in on data-driven customer experience (CX) in 2020. As one department-store VP emphasizes, his company’s investments are largely oriented toward personalized omnichannel CX, and “are mostly made to provide customers a more localized, personalized, and smarter shopping experience.”
When asked about their most important strategy for retail success, 29 percent of respondents said that empowering CX teams to build better relationships with customers was their highest priority. Over a fourth of respondents (26 percent) are prioritizing creating a single view of their customers across all their touchpoints, including physical and digital. Another 26 percent of respondents are prioritizing improving their analytics to unlock the full potential of their customer data.
Majority of Retailers to Implement a Customer Data Platform (CDP) in 2020
But making the right decisions about how to appeal to each individual customer is tough, especially when retail databases can include thousands or even millions of customer touchpoints. Data silos across businesses add to that challenge, creating an environment where retailers may unknowingly have multiple interactions with the same customer (for example, via an email campaign, social media reply, customer service engagement, etc.).
Perhaps that’s why more than three out of four retailers surveyed either already have a Customer Data Platform (CDP) or plan to invest in one in 2020 to help unify all of their customer data and build a single, holistic view of each customer. This is critical in enabling retailers to provide more personalized experiences, offers, and service to their loyal customers regardless of whether they shop on mobile, in-store, or online. But this trend is new, evidenced by the fact that so many respondents — nearly half — are apparently just starting to invest in CDPs, compared to only 31 percent of respondents that already have one.
Early Adopters Realize That All CDPs Are Not Created Equal
Advanced retailers are beginning to understand the benefits of implementing a CDP for delivering better customer insights, and they are prioritizing tailored solutions that address specific business needs. According to the study, more than half (51 percent) of retailers mentioned they look for a CDP solution that will accommodate the specific needs of their organization, compared to 31 percent who want an off-the-shelf solution.
CDPs and Customer Data Are a Critical Retail Link to Customers, Profitability
Retailers realize that creative use of customer data — particularly to drive customer experience — is their best chance for retail growth, and they see CDPs as increasingly central to their operations. The head of a large department store sums up the critical connection: “A Customer Data Platform is the most important link between digital tools and the customers themselves.”
Once retailers have unified their customer data, they can use it for better experiences such as clienteling, delivering more targeted and engaging experiences (for example, augmented reality) and providing choice of checkout methods (whether that’s human interaction or automated quick-checkout through use of mobile phones.)
With all of the potential of a single, unified customer profile, it’s no surprise that 64 percent of respondents with centralized systems were satisfied with the quality of their data, compared to only 52 percent of those that had decentralized systems. This speaks to the importance of choosing a flexible CDP, for easy integration with other existing martech solutions already in use.
Expect More Customer Data Collection in 2020 — Especially from Mobile Devices and Sensors
The trend toward collecting ever more data for tailored user experiences is accelerating, as retailers try to supplement the online shopping, loyalty, and demographic data they already have with more mobile phone and sensor data from IoT devices.
As one C-level executive at a specialty retailer puts it, “Our stores and examining devices will carry more sensors to help us provide the best possible product suggestions to our customers.”
Retail Moves Toward Data-Driven CX
It’s an approach that’s changing both customer experiences and all of the back-end operations needed to increase profitability. “Customer data forms the base of transforming retail operations,” wrote a C-level executive in specialty retail.
A different respondent wrote, “effective use of customer data does help in increasing store profitability.” And perhaps the best survey explanation of the trend toward data-driven CX is a C-suite responder’s comment that, “the goal is to connect customer data across our operational platform so that our people in operations can make quicker decisions.”
Another C-suiter from a specialty retailer has a much broader strategic goal: “Aggregating data from multiple channels and creating a single fabric of customer data is what we’re focusing on with our Customer Data Platform.”
That’s a sentiment that’s becoming increasingly common for companies undertaking major personalization initiatives. For example, a recent Forbes study on personalization found that retailers who are ahead of the curve in implementing successful personalization initiatives — people Forbes calls “leaders” — value both CDPs and personalization as important parts of their strategies. In addition, 76 percent of leaders believe a customer data platform is critical to their personalization initiatives.
Could a CDP Be a Part of Your Success This Year?
So as you plan your strategy for making your numbers in 2020 and beyond, consider the findings of this complimentary eTail report. The findings are clear: retailers are going big on customer data, and CDPs are central to the customer-experience strategies of the future. Are you ready too?
How an integrated marketing team can drive transformation
The idea of a “modern marketer” is always a moving target. New channels and new technologies fuel an ever-changing set of customer expectations. What’s clear is that in a world where no consumer is untouched by data-driven giants like Google and Amazon, the modern marketer needs a modern marketing team – one organized around producing holistic, data-driven, customer-centric experiences – to compete. But whether you’re a digital-native challenger or an iconic legacy brand, building the type of team that can deliver these types of experiences presents a challenge. At Simon Data, we focus on supporting great customer experiences everywhere. Along the way we’ve learned what it takes to build a team that can deliver.
Let go of legacy structures
At Simon, we’ve worked with numerous emerging and legacy brands. While it’s clear that they come at the challenge of building a modern marketing team from different places, they nevertheless encounter similar challenges. For established brands with legacies that extend to the pre-digital era or even simply to the pre-web 2.0 era, the marketing function likely didn’t spring into existence in a fully integrated form. Instead, as new channels emerged, marketing teams added new roles and functions to accommodate them, creating silos. For example, a team responsible for email communication may have no communication with the team creating advertising content. Even if those digital functions sit under the same umbrella, it’s possible that they have no meaningful contact with the group responsible for creating in-store experiences at a physical location and no way to share insights gathered from their respective efforts.
However, while much has been made of progressive brands’ talent for adapting to new channels, many of today’s digital-native challengers still struggle to integrate new channels and functions as they grow. In some cases this might mean learning to buy more traditional forms of media, or linking a digital experience to a newly launched physical retail space the siloing of new functions and new data channels is still common. In many cases the pace of progress means it’s hard enough to plug in a new capability let alone find a way to fully integrate it into a team.
Overcoming this history can be difficult since it requires taking on institutional inertia to rethink the marketing team org chart. But changes to team structure can often spur new ideas, facilitate better communication, and enable the kind of cross-functional data sharing required to execute a multi-channel campaign. Technology solutions can also play a role, using a customer data platform to unite and make actionable all of your customer data from new and legacy channels.
Build a team around experiences, not channels
Once we break down the silos between marketing functions, we’re still left with an open question: namely, how should the resulting team be structured to best take advantage of its newfound openness and cross-functional accessibility. The answer, for many of the most progressive brands that we’ve partnered with, is building their new marketing team around customer experiences and using stages of the customer lifecycle, rather than broad messaging channels, as their key organizing structure.
Instead of channel-based teams, these progressive brands create cross-functional pods set up around moments in the customer journey. For instance, a marketing organization might feature a pod solely focused on the experience of new customers. This group, encompassing advertising teams, performance marketers, brand strategists, creatives, data scientists, engineers, and in-store experience experts, would be responsible for all aspects of the new customer experience ensuring that all aspects of the onboarding process are consistent and oriented toward the way consumers are interacting with the business at that stage. Data collected from these similar interactions can be used to optimize for retention, upselling or moving customers into the next phase of their relationship.
Orient yourself toward behavior, not demographics
In the past it was useful to view customers demographically, as men or women, as 18–34, as urban or rural. Those descriptors spoke to customer behavior in a meaningful way, and marketing teams were built to think of customers as demographic segments. But modern consumers don’t fit so neatly into boxes. The rise of personalized experiences, from Netflix queues to Amazon recommendations, has changed the way consumers experience commerce and reset their expectations. Regardless of age, gender or location, customers are increasingly expecting experiences to be tailored to their individual needs and preferences.
Many businesses are finding it more advantageous to orient themselves toward a psychographic view of their customers. Looking to the way individuals interact with content rather than trying to form wide customer buckets based on external characteristics. It’s often more advantageous to think of customers as browsers with an interest in skincare products than as women aged 18–34. However, understanding all of these interactions requires a holistic view of your customers and a team that’s able to understand and apply data across all your channels and touchpoints.
Prioritize communication
Successful modern brands are more communicative on a one-to-one basis than ever before. They ask you, “What’s important to you? What information or experience is most valuable to you?” Asking these questions early and often, especially of new customers, takes some of the guesswork out of marketing communications. This questioning could take the form of explicit customer surveys and requests for feedback, as well as through testing and experimentation to determine which types of information and offers customers respond to. Regardless of the collection method, an agile modern marketing team can communicate with customers across channels, collect the right information, and use it to inform how and what each customer is served.
This type of responsive, real time communication with the brand is now possible at scale. You don’t need to call customers individually or survey them in the store. Everyone has access to websites and apps in their pocket that enable use to keep human customers in the loop and collect real-time feedback. And if you can’t get your customers the information they need, a real human contact can be a single touch away. The brands that are able to offer and clearly articulate this blend of automation and human interaction will be the most successful and creating fully integrated experiences.
Transformation is continuous
Finally, it’s important to remember that transformation is an active process, not a single end goal. Building a centralized plan isn’t enough to ensure that you continue to see the benefits of transformation. Modern marketing teams should be designed to test, experiment, pilot new programs, and iterate on an ongoing basis. For a modern marketing team, the ideal state isn’t one of endlessly future-proofing by adding new channels tools, channels, and skills, but rather on of consistent reinvention which delivers consistent results.
Whether they were hosting a dinner party or sending a wedding gift, the etiquette columnist Emily Post often advised her audience to add “a personal touch.” Emily Post authored most of her famous columns at the turn of the last century, so it might surprise her that the people most in need of her advice today aren’t society hostesses and housewives, but rather modern consumer-savvy businesses. Personalization is no longer just an aspirational goal for modern marketers. With each passing day, customized experiences are becoming more commonplace, whether they’re in our streaming queues and search results or on the retail floor. These customized experiences have the cumulative effect of raising the bar on consumer expectations. Brands that can’t clear that bar risk not only missing an opportunity but also looking a bit rude by comparison.
In truth, most brands know that personalization across all their channels is critical to success. What’s more, brands have access to more consumer data now than at any point in history. Most digitally progressive brands interact with customers across multiple digital and physical touchpoints, all of which yield signals and customers data points that can be used to fuel a personalization strategy. However, despite this abundance of data and a definite will to succeed, personalization efforts have run into some fairly persistent stumbling blocks. Here are the three biggest barriers preventing brands from fully hitting the mark in personalization:
Organizational Silos
One of the biggest challenges for brands looking to implement fully realized one-to-one personalization is the inability to access data. Recent years have seen many forward-thinking businesses drastically amp up their data collection efforts. As a result, much of the data required to execute a scaled personalization strategy is already available, but the siloed nature of many marketing teams makes it difficult to access, let alone build a unified view of the customer.
Today’s marketing teams are often assembled in a piecemeal fashion, over a period of years, to accommodate new channels and objectives. As a result, their data is collected and stored in a commensurately piecemeal fashion, distributed across different systems, tied to specific channels, and often managed by a variety of different teams. This siloing of data makes challenging to deliver consistent messaging across channels which can, in turn, lead to inconsistencies in communication both within teams and between brands and their customers. Since continuity between marketing channels, customer support, and on-site experience is increasingly key to customer acquisition and retention, it’s critical for savvy brands to unsilo their data and bridge gaps between teams. This is one of the primary challenges that customer data platforms have emerged to solve. By uniting data from disparate sources, a CDP can build a unified customer view of the customer enabling a more integrated approach to marketing.
Scale
While breaking down data silos is a significant step in the right direction, marketers do face other challenges on the road to personalization. Getting data into marketing systems is still tremendously difficult, even today. Many marketing systems were not conceived to handle the scale of data needed to implement a one-to-one personalization strategy and therefore can’t accommodate the scale of enterprise data or the complex forms in which it is represented. These technical limitations significantly hamper the way brands are able to leverage the data they already own.
Many of these challenges are endemic to businesses managing digital transformation. As data-driven growth strategies become paramount to the survival of businesses, it’s important to remember that there’s only so much one can achieve with a point solution based on cookie-cutter one-size-fits-all strategies. The most effective road to greatness is one that builds towards long-term strategies while also showing immediate ROI.
Technology and Innovation
The final hurdle standing between us and true one-to-one personalization is a little harder to clear. Personalization technology has evolved rapidly in recent years, producing breakthroughs that have made a more unified customer view possible for many brands. But going all the way is going to require another technological leap. Specifically, we’ll need the kind of next-generation AI technology that we’ve just begun to scratch the surface of. From today’s vantage we can see the beginning of the path that will ultimately lead us to the personalization promised land. Just seven years ago deep learning experienced a huge emergence, and today those same algorithms can drive cars and defeat human chess masters. We may be just one major innovation away from a similar AI explosion.
The marketing technology of the future will need to be scalable and flexible enough to account for rapid growth and to anticipate new phases of digital transformation. We expect these next generation solutions will focus more on actionable insights built on a wider range of data inputs including real-time behavioral, historical, product, and deep access to content.
What’s next?
Despite the stumbling blocks, true scalable one-to-one personalization is close at hand. Brands increasingly understand what their organizational and technological challenges are, and as a result they’re moving to solve them. By restructuring marketing teams to reflect a more integrated approach to customer interactions, the old channel-based silos are starting to come down. Similarly, a new generation of marketing technology has emerged to help scale in-house data operations and manage the ever-expanding array of data sources and inputs needed to create a complete unified customer view. Meanwhile, a string of promising advances in AI combined with the huge investment of resources the technology has attracted, promise to soon crack the type of next generation technology that will tie all these threads together.
December 23, 2019
How to keep your sock drawer (and your customer data store!) tidy this holiday season
Matching, de-duplicating, disparate storage locations… is this your customer data? Or your socks?
Have you ever been afraid to open your sock drawer because of the chaos you know will greet you? Tights tangled around socks, a potpourri of colours, types and brands, not to mention the dreaded assortment of single socks spilling over the edges, with no partner in sight.
An unkempt sock drawer is, in many ways, very similar to a messy customer database. Duplicates, mismatches, holes and the general wear and tear that happens each year… if you don’t regularly clear and maintain it, that drawer becomes a jumbled mess! And how many of us routinely put off the dreaded task of re-organizing our sock drawer, just as we do updating our database?
So, we thought the best possible holiday gift from BlueVenn would be some helpful advice on how to maintain a Single Sock View and keep your sock drawer tidy, functional and easy to navigate in the festive season.
Let the holiday sock incursion commence!
Just as the Yuletide spending spree provides retailers with a flood of new customer data, so the holidays bring a massive influx of new socks for everyone.
These traditional stocking fillers and token gifts are given to folk throughout the land, which means that it will soon be time to brace yourself for the dreaded annual reorganization of the cluttered sock drawer, an essential task if you’re ever to find a matching pair in the New Year.
Data Sock silos
Many of us retain so many pairs of socks that they overflow into multiple drawers – ‘sock silos’, if you will. Some in the allocated drawer, several pairs in the one below, others on the line, a soggy specimen left in the rim of the washing machine, not to mention the ones abandoned in yesterday’s shoes or kicked off in the night and buried under the covers. With socks, as with data, this is perhaps the most dangerous hazard to be avoided when trying to maintain order and cleanliness, making it far easier to end up with a muddle of useless single socks (with many of the intended partners left unwittingly languishing just feet away).
So, if you’re to have any hope of pulling out a matched and clean pair of socks in the morning when the celebrations are done, it’s time to knuckle down for a serious session of planning, sorting and pairing – something you’ll need to do with your customer data too, if you’re ever going to find the right piece of data at the right time to optimize your marketing campaign.
After extracting all the socks from their different drawers, you’ll need to search out fugitives hidden around the house (the hosiery equivalent of running a data planning project to uncover your data sources), then match them up. But not before you’ve removed the old socks that don’t fit any more, accommodated your spangly new festive pairs and thrown out the ones that have been vetoed as surplus to requirements or unfit for purpose by your fastidious partner (or in the case of your data, the ICO or California Attorney General).
BUT, without setting yourself a series of rules (your very own house sock retention policy) going forward, you’re just going to end up in the same shambolic mess again this time next year.
So, what rules do you need to follow to maintain an organized sock drawer (the single sock view)?
a. Ensure you match socks correctly before they enter the drawer. Remember, it can be difficult to pair up similar socks without defined matching rules (okay, they’re all black, but what about the different sizes, shades, materials and varieties of cuff). These will help sort your data too!
b. Merge matching socks using a joining rule that ensures they stay with their mates. There are several ways to merge your socks (e.g. balling, folding, gathering) and customer data (e.g. through the email address, postal code or Social Security number), but ultimately all achieve the same result.
c. Cleanse by removing unrequired or decayed socks (or data). It’s essential that you make a decision on how long to retain them. When did you last wear them? Is there a matching pair? How big is the hole? Similarly, when did your customer last buy something? Or open a communication? Or visit your website?
By following this simple schema you will ensure you remove the potential for multiple silos. Not only that, you will benefit from a new-found trust that your socks (customer profiles) will match, so that you will not need to spend an inordinate amount of time in the future sorting out the mess.
False positives
Like socks, customer data can be matched incorrectly, with ‘false positives’ and duplicates rearing their ugly heads. Plus, at certain times – the winter holidays, for example - we can have a high volume and velocity of socks (and data) from many new sources, and need to know how to fit it into our overcrowded storage facility. Otherwise, the deluge could result in additional silos that never really get used, containing solitary socks or unmatched, unusable fragments of data.
When it comes to your socks, ideally you’ll want just one drawer - the ‘single source of sock truth (SSOST)’ if you will - where you can rest assured the socks have been efficiently matched and merged.
Sound like hard work to achieve? Then perhaps you need to bring in a specialist to get your drawer in order and ensure you only have to manage the Single Sock View and that every pair inside is well kept, useable and accurately paired (notice your mother is trying to avoid eye contact at this point). Someone to build the rules and schema to ensure long-term maintenance, or perhaps make a checklist of core sock-sorter capabilities (we have a Real CDP Certification, so perhaps there should be a Responsible Sock Coordinator Classification).
The Single Sock Customer View
At BlueVenn we may not be masters of the Single Sock View, but when it comes to the Single Customer View, we’re experts. BlueVenn provides Customer Data Platforms that ensure your records are cleansed, matched, de-duplicated and kept free of holes (through patching with third party data enhancement), not just during the holidays but 365 days a year.
December 19, 2019
Getting ahead of privacy legislation is crucial for data-driven companies
The alphabet soup that began with GDPR is about to get thicker with CCPA in California (Jan 1 2020), LGPD in Brazil (early 2020) and forthcoming legislation in India and across APAC.
“Analyzing and implementing the California Consumer Protection Act (CCPA) in 2020 will be a major challenge for companies across virtually every industry sector, according to Kirk J. Nahra, writing in Bloomberg Law. “We can anticipate an impact that mirrors the European Union’s General Data Protection Regulation (GDPR) process in the spring of 2018, with less clarity, a shorter time frame to work with, and an even more confusing set of obligations.” In the U.S., Nahra envisions additional privacy regulation likely to follow from other states’ Attorneys General and possibly at the national level.
But despite knowing that CCPA was coming for some time, a majority of U.S. companies view themselves as unprepared. According to a report from privacy tech company, Ethyca, “88% of companies feel they have not reached an adequate state of compliance ahead of the implementation of CCPA.”
This is a microcosm of what’s happening globally as more laws come into effect. Companies are struggling to comply in their own regions and are even less prepared to evolve a global game plan. Yet data-driven companies must address this rapidly, both for reasons of compliance and to please consumers who increasingly want to know what’s being done with their information.
John Mitchison, director of Policy and Compliance at the Data & Marketing Association UK says “prior to GDPR, serving ads to people used to be impersonal,” but now the relationship with consumers is far different and they view sharing their data as part of an exchange. He says “when asked in a survey how happy they were with the amount of personal information they gave to organisations, sixty-one percent initially claimed to be happy with it and sixty-nine percent said sharing data is part of the modern society. But at the same time eighty-six percent said they would like more control over the data they give, and eighty-eight percent wanted more transparency about how their data is collected and used.” When asked who benefited the most from the data exchange, seventy-eight percent said the business, with only eight percent feeling consumers got more.
Companies have many decisions to make. First is to understand what laws are in their home country and what they need to do to comply. Secondly, they need to understand how outside laws may impact them, and thirdly, they need to decide how they will handle the data they collect and have.
Key areas of concern are:
· Access – who in marketing and in other areas will have access to specific customer data,
· Storage - how long will the company keep Personally Identifiable Information (PII), and
· Transparency – how will the company ensure customers are kept informed of how their PII data is used.
Christoph Bauer, of ePrivacy GmbH in Hamburg, says his consultancy sees a wide range of response from companies. “Some pride themselves on being very consumer friendly and want policy and their relationship with customers to reflect that. In marketing that becomes a selling point. Other companies want to know what they must do to comply but still are most concerned about maximizing use of the data they have.”
The World Federation of Advertisers identifies eight key areas of GDPR of relevance to marketing: extra territorial scope, basis for processing, children’s data, data breach notification, right to erasure, sanctions, data protection officer, and impact assessments.
According to Bauer, when companies were asked how they are doing with compliance, most estimated that they were at about the 30% level with some saying they were at 80% or more, but “the tendency has been to wait until more decisions are made” on ePrivacy and on GDPR enforcement. Bauer and Mitchison agree that companies need advice from their own legal team on all of their legal options and finally on compliance - and, going forward, will need advice on other countries’ legislation from local market experts there.
The International Association of Privacy Professionals (IAPP) is a key source for understanding what is happening globally and runs conferences, a list-serve and peer-to-peer networking events. They also publish extensive content for their members, who come from across the corporate landscape, including from marketing, product design, engineering, sales and, of course, legal.
According to Caitlin Fennessy, IAPP’s Research Director, “direct marketing folks are among the top people who should be looking at this today. Marketers should be proactive in making sure there is two-way, conversation between themselves and privacy experts at their company to ensure: 1) that they have a full picture of the data marketing is working with; 2) that they have a clear understanding of what is and is not personal data; 3) that it is determined what special protections should be put in place with regard to personal data; and 4) that there is a clear picture within the corporation of who needs to be informed with regard to how this information is handled and that a regular schedule is set up to check in.”
“You need to be clear who is touching that data inside and outside the company,” says Fennessy. She stresses that this is not a one-time process and that it requires diligence and persistence to ensure that you’re staying on the right side of privacy law.
With CCPA, there’s a lot of focus on the advertising industry and how data is being used by your company’s vendors. If customer data you collect is shared with a vendor or other company and there’s a chance data may be used for commercial purposes, the customer needs to be able to opt out with a “do not sell” button or something similar. “This gets complicated in the ad space,” Fennessy says, “where you drop a cookie that can be used by others.” She emphasizes the importance of enabling consumers to have access to their data, giving them the chance to opt out, and making certain all is clearly outlined in your privacy policies.
We’re in early stages of understanding how this will work, but those who are focused on this area believe that it is an issue for companies and marketers to address proactively particularly with CCPA and other new legislation. Fennessy also explained that CCPA 2.0, which is much closer to GDPR, is anticipated in the future as well.
Mitchison sums up saying, “I believe the data marketing industry is at a crossroads. We can be an industry that puts short-term profit above long-term loyalty, an industry that uses data, technology and creative to trick customers into a quick sale. Or we can be an industry that chooses to create truly engaging customer experiences…an industry that builds trust.”
December 16, 2019
Marketing Challenges of an Enterprise and Role of CDP
“The engagement rates showcase positive response, but my paid campaigns targeted at those audiences do not reflect high conversions. I am unable to focus on those important few who are truly loyal to the brand and leave out the rest. Some channels work well for older customer groups, While others work best for new and recent ones. I am unable to grasp the data in entirety in a single view. Designing exclusive campaigns for the high-converting groups is a challenge.”
Do you also go through a similar marketing experience? Such scenarios are not just a matter of frustration but loss of opportunity where marketers are six times more likely to increase profits (45 percent vs. 7 percent), and are five times more likely to achieve a competitive advantage in customer retention (74 percent vs. 13 percent) [1].
Many marketers who strive to create a great customer experience stumble upon similar problems related to customer data, integration and security. But, the early adopters of Intelligent Customer Data Platforms have been able to solve these problems.
A Customer Data Platform (CDP) allows a marketer to work in tandem with other data hubs within an enterprise and comprehend insights in unison with the rest of the enterprise. In short, only a CDP can help in creating personalized and yet consistent customer experience across all touchpoints that interact with the brand.
It gets more obvious that a CDP is a most sought after tool into a marketer’s toolbox to maintain better customer relationships. While a CDP’s importance is apparent, we have set out to answer how exactly does a CDP solve different problems of a marketer, both at the levels of strategy and implementation.
A marketer’s problems are directed towards Data, Systems, and the Customers.
Marketing Challenges of an Enterprise and Role of CDP
Data
Data Structure
How data is stored, archived, and retrieved within an organization is the beginning of a marketer’s obstacle lane.
Marketer’s Problem
Fractured data structures is not a recent challenge, it is an age-old maze that marketers struggle to stitch together. Siloed data across the organization fails a marketer to execute her top operational priority: analyze data to automate insight generation for its own team and for the sales personnel. Even though there is a quick response to this problem with the emergence of advanced predictive analytics, the real problem stays with the traditional methods applied to access data. 80% of marketer’s operational work involves acquiring and preparing data [2].
The CDP’s Role
The emergence of CDP has relieved marketers from accessing siloed data. A CDP ingests first-hand data coming from all data sources, which includes internal, external, and third-party sources. Even if any of the existing software vendors demand a data lock-in, such data could be ingested into your system of CDP. This simply means a good CDP can ingest both streaming and batch data.
A CDP uses this data to organize it for further use such as cohort management, cohort segmentation, campaign management, analysis and insight creation, and so on. The privacy of data is secured within a CDP that is designed to comply with data regulations.
Data Control and Accessibility
Though data exists within the organization, it is seldom at easy reach of a marketer. A marketer needs to travel through systems, processes, and people to access relevant data, and that means a loss in time.
Marketer’s Problem
Data has to change hands and a marketer fails to receive data in-time. Data accessibility and a marketer’s control do not, unfortunately, scale with the growth of the organization. A marketer faces political challenges in the data retrieval process. These cost money which lower ROIs.
The CDP’s Role
A marketer does not have to wait for exclusive data access with the use of CDP. A CDP provides complete control to retrieve relevant data in time. Further, data-crunching and processing are also automated to the extent of insight creation. The processed data can be put to use anytime to kickstart responsive campaigns, draw quick insights, and action other processes that add up to the bottom line. There is no dependency on technological or data specialists to execute these tasks.
Systems
Real-Time Response
With far-fetching data reach, real-time response to customer and real-time data availability is a challenge to every marketer. 43% marketers agree that they are not lacking data; they are missing the ability to transform data into real-time action [3].
Marketer’s Problem
With traditional forms of data access, marketers use and upload data using a batch cadence. There is no single system to anchor the data streaming activity for the entire organization. The customer stays away from the marketer and the brand for the same reasons. The time decay results in poor response and customer experience. Apart from this, adulterated, duplicated data cascades to create a poor quality of data bank within your organization.
The CDP’s Role
A CDP ingests data in real-time. There is no lag time that curbs the marketer from using data to respond to a customer. The response could be via a chatbot, an amendment to an ad campaign, revision to an on-ground offer, or an SMS. Automated triggers, auto-cohort segmentation powered by Artificial Intelligence and Machine Learning helps a marketer to make prompt revisions to the existing plan.
Marketing System Integration
Data silos also limit different marketing systems from talking to each other as well as with other systems within the organization. Data heterogeneity complicates further data management.
Marketer’s Problem
Identifying a customer or a high-value cohort is as good as owning a gold mine. With changing customer behavior across different marketing channels, predicting a path is very complex and expensive. To avoid complexity and high-cost, marketers settle with a generic persona or identity that helps them execute campaigns seamlessly. However, the lack of identity kills their ability to personalize customer experience, hence hurting the marketing ROI.
The CDP’s Role
A CDP integrates data from across different systems and creates a unique customer identity that is unified throughout several touchpoints. Every customer identity is enriched with each new action and interaction with the brand. The algorithm used to update each identity considers other factors that influence a customer’s journey and decision to purchase. These are demographic, geographic, campaign-specific, channel-exclusive, device-specific, behavioral, product-specific, and other similar factors.
Customer
Consistent Customer Experience
The number of marketing channels continues to expand, increasing the importance of highly personalized and relevant messages and offers [4]. The same number also throws the challenge to a marketer of mapping the touchpoints to maximize a personalized experience for each customer.
Marketer’s Problem
A customer expects different stimuli to purchase in each marketing channel. Each customer’s interaction is different on each channel. This leaves the marketer with so many permutations and combinations to design a campaign that is consistent across multiple marketing channels. Though a marketer achieves in doing so, the dynamic trends that have constant influence of a buyer’s journey distort the navigation that is determined by the marketer. The sheer number and increasing complexity constraints a marketer to bring out a consistent customer experience for every customer.
The CDP’s Role
The unique and unified customer identification that is generated by a CDP helps a marketer to tweak the experience for every individual customer. A layer of Artificial Intelligence stitched into the system allows a marketer to automate changes using stimuli as triggers and rules that generate the response to each interaction. It indicates the necessary changes in communication and budget that would be required to achieve a better customer experience for a high-value multi-channel campaign or a single-channel promotion. The single view of a customer that is inherent to and is provided by a CDP helps in retaining a consistent customer experience at any scale.
Diluted Customer Relationships
Either B2B or B2C, when ‘scale’ steps in, maintaining a strong and personal relationship with each customer is almost impossible for the brand. With personalization, names could be used, but marketers want to do more to ensure that relationships with customers are not diluted.
Marketer’s Problem
Marketers need to often fill the vacuum between the data management strategy and customer experience strategy. This means finding answers to scenarios such as “What exactly is the Customer looking for?”, “Predicting customer behavior”, “Identifying triggers that bind a customer community”, and many other abstract matters.
This needs a human eye and intelligence. The emotional connect that mere data lacks has to be compensated in the larger canvas of customer experience by the marketer intends to create. If these systems that drive the two critical strategies do not talk to each other, the marketer needs to manually instill intelligence, which adds to the complexity of building better customer relationships.
The CDP’s Role
A CDP behaves like a central system of intelligence that empowers other systems to interact in a unified manner. It eliminates the cumbersome activity of lengthy configurations, interpretations, and insight generation. In fact, a CDP plays the roles of a seamless Insights Engine that brings Intelligence to the network of systems within an organization.
While this answers about the role of CDP in an organization, you can read further about its Strategic Value to a Marketing Plan.
December 9, 2019
Real-Time Data Aggregates for Today’s Dynamic Customer Journeys
A customer who drives away from a home improvement retailer with a new refrigerator has little awareness of the domino effect the transaction generates, happy enough to have a working freezer. Unbeknownst to the customer, however, the purchase introduces a flurry of changes to the customer’s data aggregates, which have a material impact on the continuing customer journey. Aggregates that require updates will include categories such as date of last in-store transaction, last retail purchase amount, last purchase store number, last purchase date, last purchase amount, and year-to-date purchase amount, to name a few. Other potential changes could include data points such as a new physical address or email, or a customer’s status as head of household.
Within each of these broad categories there may be dozens of calculation rules that update a customer’s status, such as customer lifetime value (CLV), and others that determine the retailer’s next-best action based on the aggregations. Does the retailer send a thank you note? An offer for storage organizers and ice trays? Does the updated CLV warrant an invitation to a gold member club? Or does the purchase trigger free delivery and installation?
The permutations that kick into high gear with a single transaction underscore the importance of using automated, rules-based decisions that are updated and pushed out in real time to keep pace with a customer journey. Consider a scenario where the customer with the new refrigerator arrives home and sees an email offer for a discount on a water connection kit, with free hook-up. The offer is personalized, relevant, and in the context and cadence of the customer journey. Now consider the alternative where aggregates are updated using lists and batch data. At best, the same offer is made three days after the purchase – after the customer has already bought and installed the water connection kit.
Always Keep Pace with the Connected Customer
What differentiates the RedPoint Customer Data Platform from a list-based approach is that aggregates are not only updated in real time, they are also pushed out to subscribers in milliseconds. The master data management component eliminates lag time between data ingestion, processing, and calculation updates – ensuring a frictionless customer journey. This means that every engagement channel and external system that subscribes to the real-time updates is in synchronization with a customer at any stage of the customer journey.
To be considered an enterprise-grade CDP, a solution must ingest data from all sources, retain full detail of the data, store it, convert it into a unified customer profile, and make the profiles available to all external systems. Because these requirements do not specifically include real-time aggregation calculations, there are CDPs in the market today that lay claim to providing a single view of the customer through the aforementioned capabilities. In truth, however, the lack of rules-based aggregations means that they’re not equipped to manage an omnichannel customer journey.
Typically, a list-based CDP will instead ingest data, calculate some aggregates for a nightly batch upload to an FTP site, and a subscriber or external system will pick it up via web services or API integrations.
A Personalized Experience Requires a Rules-Based Approach
A traditional cadence sufficed when customer journeys were linear, static and rarely involved multiple channels. Marketers could safely rely on lists to generate outbound campaigns where there was little expectation from the customer for a personalized experience.
That’s not true today. According to the Harris Poll survey commissioned by RedPoint, 63 percent of consumers say that personalization is now part of the standard service they expect, with 37 percent of consumers going so far as to say they will flat out stop doing business with a company that fails to offer a personalized experience.
Consumers also expect a brand to know who they are – preferences, behaviors, permissions – across every channel. Satisfying the always-on, connected consumer requires two-way, real-time communication across a host of new and emerging channels. To achieve this, brands must have the capability to apply rules dynamically at any stage of the customer journey, producing a next-best action in the context and cadence of an individual journey.
The modern customer journey renders a list-based approach and list-based decision-making obsolete. To learn more about the benefits of real-time aggregates and what sets the RedPoint CDP apart from others in the market, I encourage you to read a recent BOSS Magazine article that goes into extensive detail on the shortcomings of what it called “out-of-the-box” CDPs with a “one size fits all approach” that mostly fail to match brands’ aspirations to target customers with highly personalized engagements.
In the article, my colleague George Corugedo explains how real-time aggregates provide a fast path between data and revenue, and why many RedPoint clients have declared that the RedPoint platform is the primary revenue generation platform for their organizations.
December 2, 2019
Customer Journeys are Dynamic, Your Engagement Technology Should Be as Well
Published in 2007, Tom Davenport’s groundbreaking work Competing on Analytics: The New Science of Winning explored the then relatively new science of harnessing customer data for competitive advantage. CIO Insight lauded it as one of the top 15 most groundbreaking management books.
The first chapter focuses almost exclusively on Netflix as an example of a data-driven company that parlayed business intelligence into a (then) innovative new business model – mailing DVDs to customers and recommending titles for their queue. As we now know, the book’s publication pre-dates several more innovations from Netflix, which underscores the rapid pace of digital transformation and the danger of inaction (see Blockbuster).
Rules-Based, Automated Machine Learning Models
The current entertainment streaming wars further illustrate how much has changed since the incipient times of 2007 with the use of analytics to compete on customer experience. With price and product largely commoditized, companies in retail, finance, healthcare, travel, and industries across the board recognize the urgency to use data and analytics to create a personalized omnichannel customer experience.
Creating a personalized customer experience for the always-on, connected customer requires two-way communication across every channel. The traditional approach to analytics that created a static list of customers for an outbound marketing campaign based on a fixed data model is incapable of keeping pace with an omnichannel customer journey.
To support a consistent, personalized customer experience across channels, decisions need to be rules based, not list based. While this entails a real-time element, which requires that a brand take the optimal action at the precise moment a customer appears in a channel, it also entails dynamic flexibility. Unlike static lists that that cannot respond to new or changed data, rules-based decisions can now account for the totality of customer behaviors up to the customer appearing in a given channel.
All of this is made possible through real-time data, or a golden record, for each customer coupled with automated machine learning. Lights-out, evolutionary programming makes dynamic flexibility possible. With a continual ingestion of customer data from every source and of every type, automated machine learning models (code-free) are tuned to optimize a dynamic, personalized customer journey without human intervention across channels.
Compete on Customer Experience
Dynamically managing a customer journey is required to provide customers with the type of online and offline experiences they increasingly demand from the brands they frequent. Results of a 2019 Harris Poll survey commissioned by RedPoint Global clearly show that competing on customer experience is an imperative; brands that ignore this reality run the risk of alienating core customers.
According to the survey, 63 percent of consumers agree that personalization is part of the standard service they expect. For instance, they expect a brand to know they are the same customer across all touchpoints (in-store, email, mobile, social, call center, etc.). Further, consumers were unsparing in their critique of brands that fail to deliver personalization, with 37 percent claiming that they will stop doing business with a brand that does not offer a personalized experience.
It’s easy to see why static lists, which are familiar to any marketer who has run an outbound, drip campaign based on segments, are unable to keep pace with a dynamic customer journey. A typical non-sequential, non-linear customer journey includes many touchpoints, and an ability to deliver a personalized experience throughout the journey, across any channel, requires knowing everything there is to know about the journey. Consider a customer who logs in to a retailer’s website. Which pages the customer views and page durations vary with each visit and for each customer. A list derived from an analysis of past behaviors will not account for subsequent behavior that may fundamentally alter the next-best offer, recommendation, or action, and thus will not by synchronized to a customer journey.
Personalization failures, which an Accenture study estimates costs US firms $756 billion annually, clearly do not go unnoticed – or unpunished – by the savvy consumer. A reliance on static, list-based analytics could easily result in a customer receiving an offer for a recently purchased product, or a recommendation that is irrelevant to the journey in that precise moment in time.
Keep Up with a Dynamic Customer Journey
Rules-based approaches eliminate these types of customer frustrations by being in pitch perfect sequence with a customer journey regardless of which direction it takes. One RedPoint customer, a specialty retailer, uses automated machine learning within the RedPoint Customer Data Platform to deliver an innovative, personalized buy-online, pay in-store (BOPIS) experience. Within minutes of purchasing a product on-line, the retailer is able to deliver a relevant, personalized experience to the customer even before the customer arranges for the in-store pick-up. Personalization touches include emails with pick-up instructions (directions, store hours, etc.), relevant offers for product accessories, and website content tailored to the customer’s preferences.
Another RedPoint client, a web services company, uses the platform’s millisecond response time to know everything there is to know about a customer in real time. When a customer calls into the call center, for example, an agent will be aware of every action that customer has taken – up to and including the call. If the customer is currently having an administrative issue on a hosted website, the call center agent will have a record of it and have the information needed to resolve the issue.
These outcomes stem from the ingestion of all customer data – every source and type – in real time, and rules-based, hands-free automated machine learning models that offer the same dynamism present in every customer journey today.
In 2007, the year it launched its streaming service, Netflix customers still happily waited a few days to receive DVDs in the iconic red envelopes. Today, of course, customers expect to be able to stream content of their choosing at any time on any device. Relying on list-based analytics is the equivalent of mailing red envelopes and hoping your customers are okay with an experience that may have been delightful a decade ago, but is archaic by the standards of modern technology. Customers demand a relevant, personalized, and seamless experience across channels. Meeting this expectation results in more satisfied, loyal customers which directly translates into revenue.
November 28, 2019
Customer Identity: Immediate conversion of sales targets & the revenue multiplier effect
While attending a recent technology conference, I knew almost everyone except for a few new attendees. My colleague, a new joinee, accompanied me to the event. I thought it was a good idea to bring her along so that we could network more! However, I noticed that each time she interacted with a person, it took longer for her to break the ice than for me, and I was able to get more leads and get new connections through known acquaintances.
To me, this difference was not because she lacked experience. She was very good at her job. But, the real gap was a lack of a connected network. She could neither recognize anyone nor could the attendees associate with her.
I had a stronger identity, which made socializing easier for me than it was for her.
This clearly showed me having a strong identity within my network contributed to my ability to have a more effective event. To me, the value that a ‘customer data platform’ brings to a martech stack is something very similar.
It has a direct impact on the sales conversions and bottom line.
A single customer interacting with your brand across multiple touch points and channels needs to be identified. She may choose to react in a different way each time. Identifying her helps you understand her intents irrespective of the channel.
You could have just started marketing with two known channels or two dozen of them. No matter what the number is, your customer explores and expects different things from each channel. It is interesting to spot how the same customer responds in a different manner on each of the channels that you exist.
Overall, how does it feel when you know that 57% of your customers stop buying from your company because a competitor provided a better experience? This statistic is backed by research. Connecting it back to my experience at the conference, I can conclude that it is all a matter of how well you relate, connect and identify your customer as much as you expect them to know your brand.
How is Customer Identity created?
While there are multiple micro-steps involved in creating a unique customer identity, they can be categorized into 6 steps.
Data Ingestion
Every brand relies on more than two channels to begin with. Each channel provides a variety of opportunities to communicate about your brand. Omnichannel is defined as the use of all channels available for service, distribution, and transaction within a unified experience. This includes online and offline channels. It could spread across these categories of channels — internet marketing, social media channels, physical marketing, paid advertising, public relations, event marketing, telemarketing, and so on.
The initial customer data is collected in the form of email IDs, name, phone number, social profiles, and so on. A unique customer ID is created for each customer. The ID stays as a reference point to add further attributes and secondary data to the ID. This reference point is used in further steps to make it foolproof for a marketer to enhance the customer experience.
Data Unification
Unique customer IDs enable a consistent brand image and unified customer experience across all channels of interaction, information, and transactions. For customer data unification, FirstHive uses AI-assisted techniques to complete each unique customer profile stitched to the respective customer ID.
As different sources provide information about a customer, it is tied to the unique customer ID in different database systems as well as the platform. It provides accurate information when the platform starts using it for real-time activity. It supports processes such as campaign management, cohort segmentation, personalization, and so on.
Data Enrichment
As much as furniture or silverware cannot be left alone in the shelf, it needs to be maintained, customer data also needs to be enriched. Data is organized during the steps of ingestion and unification. It needs to be cleaned to avoid duplication, attributed to the right data sources to help identify patterns, build data format consistencies, and to create consumable customer data.
During this step, IDs are enriched with other real-time data as well as organized based on automated rules and pre-configured processes. Further, related behavioral attributes, transactional updates, and product interactions are updated to that unique ID.
Persistent ID
A persistent ID is assigned to a unique ID. It is a long-lasting reference point that can be used to validate relative attributes. For instance, if you are a bank targeting two people, you may want to confirm if they are related in some way or not to ensure you share information that increases mutual and collective influence. Most often, telemarketers try to sell a product to the husband and wife at the same time or call the spouse of a person who has experienced a demise.
The persistent ID provides intelligent cues to avoid such dissonance in the customer experience. This is possible because FirstHive also ensures identity resolution. Historical data pertaining to a particular ID is not completely eliminated even after deletion.
Deterministic mapping
Using identifiers with deterministic mapping, marketers can connect the relevance of a campaign or offer to a target segment or an individual ID. This helps in gaining insights across cross-device usage, time preferences, product-device usage correlation, and others.
Probabilistic mapping
This is what differentiates our CDP from that of our competitors. With multi-source interaction and multiple accounts of a customer, the goal of a marketer tends to be to identify if the same customer is returning and interacting to achieve different objectives from different touch points. Probabilistic mapping takes into consideration other customer data across the universe that would eliminate any possibility of anonymity.
When sources provide less deterministic value to a customer’s data, statistical methods are applied under probabilistic mapping to identify the closest match. The customer ID includes these aspects, too.
What is Customer Identity used for?
Customer identity is no longer just a marketer’s magic bullet, it also serves the purpose of reputation managers, human resource personnel, anti-fraud managers and other counterparts within an organization. Here are some immediate use cases of customer identity.
Targeted communication
It is more efficient to eliminate guesswork and provide information packaged in the most preferred tone and voice when a marketer knows the customer better. Customer identity enables 1:1 communication building highly relevant context. A leading insurance company scrubbed its customer data using FirstHive. Over time, they did a splendid job of attributing data using the platform. They used this to target customers for upgrades and renewals.
Personalized experience
A leading salon and cosmetics consumer brand used customer IDs to track third-party influencers and of their own customers. Each influencer attracted different customer personas. The brand used customer IDs available on FirstHive to communicate offers for all their associated salons across the nation. They were able to provide coupons and offers that would deliver higher conversions.
Customer loyalty
It is not restricted to a single sector. Use of customer identity is very popular to roll out customer loyalty programs. My favorite one is how a hospitality and realty brand used customer IDs to improve customer retention using customer loyalty programs. Each customer assigned with an ID was tracked to provide highly customized cross-channel communication that led to personalized user experience. This was not just in the pre-acquisition, stage but also while delivering the service. Even before the customer made requests to the concierge, these brands had enough information to provide proactive recommendations to every customer. It included the use of taxi services, spa and gym, clubhouse, and so on.
Some interesting implementations were developed around concepts such as rebates, influence, and privileged access.
Identifying Fraud
Did you notice that when you created a profile for your company with generic email ID on Facebook, in about a day your account gets blocked because there is duplication? Fraud identification is more complex than this when it comes to banks and credit unions. Transaction duplication and fraudulent access to personal information can be prevented with the addition of customer ID.
Accurate customer identification is directly correlated to better customer experience. Forbes has mentioned that consistent brand presentation across all platforms increases revenue by up to 23 percent. However, great customer experiences are always tied to how well the brand knows its customers and how well each customer is able to associate with your brand.
We believe every great customer experience begins with a small step of identifying your customers and then building a relationship. We would be delighted to hear out your efforts to create better customer experiences. Drop a note to marketing@firsthive.com or share your story in the comments section below.
There is not a single multinational company thriving today that would deploy an integrated supply chain system that failed to account for volatility, disruption, and consumer demands for immediacy in the delivery of customized products and services. Putting the right product at the right time with the right price in front of a customer is simply too important to rely on static, list-based decisions and systems that fail to account for the dynamics of a global economy.
The same is true for keeping pace with always-on, connected consumers as they move through an omnichannel customer journey. Consumers demand a personalized customer experience, and evidence shows that delivering such an experience provides a direct line to revenue. Yet, unlike the universal acceptance of a dynamic and highly functional supply chain, many organizations are slow to embrace empowering marketers with the same power and flexibility under the spirit of “it’s just marketing” or “marketers just don’t think that way”. The hard truth, though, is that if “marketers don’t think that way”, they won’t be long for their jobs, as marketing is the direct line between data and revenue and expectations have risen both inside and outside the organizations for marketing to be the tip of the spear for the enterprise as a revenue-generating engine.
A New Mindset That Leaves Audience Lists Behind
Marketing, perhaps more than any other line of business, has complexities that require dynamic systems of engagement. An influx of communications channels. Online and offline engagement touchpoints and personas. Continuous updates to preferences and permissions. The bottom line is that no line of business deals with things like data lists anymore, and none should be bound by legacy technology designed for the age of outbound, batch, or drip communications through audience lists. Everything in marketing is now dynamic and enterprises cannot afford to have toys standing in for hardcore data-driven tools needed to achieve the ultimate marketing objective: omnichannel (all outbound and inbound channels) optimized messaging.
With this context in mind, here are 10 reasons why a customer engagement platform that derives audiences, channels, actions, and preferences dynamically at the time an action is taken is a requirement for keeping pace with a dynamic, omnichannel customer journey.
1. Rules Are Dynamic: In a rules-driven environment, marketers are not locked in by pre-defined, static lists of data, customers, or prospects that paint a picture of a moment in time at odds with the entirety of a customer journey. The dynamism and flexibility of a rules-based system underscores every reason why a rules-based approach is superior to a reliance on lists.
2. Rules Are Data-Model Agnostic: Being dynamic, rules can organize and re-orient themselves into whatever a changing business requirement or KPI dictates. Lists, being inflexible, are static in a table. Models based on lists will therefore become outdated the moment a business objective or KPI changes.
3. Rules Are Multi-Channel Driven: A marketing campaign based on a static list – such as sending a credit card application to a list of prospects – is likely not just impersonal, but it runs a risk of being irrelevant if a prospect signs up for the credit card after the list is created. Because a rules-based system is dynamic, eligibility for one channel versus another channel – or multiple channels – is decided at the time the communication is initiated based on the totality of a consumer’s actions.
4. Rules Are Journey-Based: A list-based system is locked in time and cannot keep pace with a customer in a non-linear, non-sequential journey. A rules-based system dictates a next-best action relevant to a customer’s journey at the precise moment and channel of engagement. It does not rely on a list that recommends an action for a customer based on an engagement that may have lost its relevance.
5. Rules Are Re-Usable: Lists are a one-and-done proposition. As soon as a customer takes an action – signs up for the credit card, buys the product, etc. – the list is obsolete. Rules are dynamic, which means they can be updated in real time based on a customer’s behaviors and re-used as a “living” document. A rule about what constitutes a gold customer means that the “list” of gold customers is fluid, and is re-usable within an automated machine learning model.
6. Rules Are Flexible: Once created and activated, a list cannot adapt to an unforeseen change in conditions. When an item on a grocery list is not in stock and the shopper makes a substitution on the fly, the list is relegated to the trash bin. Likewise, a static list of customers loses relevance when any change occurs that is not directly accounted for in the list.
7. Rules Are Not Batch-Driven: Running batch-based business processes means that decisions are – by definition – based on lists of data. Businesses – and connected consumers – move faster, which require that decisions rest on dynamic, analytically driven machine learning models.
8. Rules Account for Behavior Post-Campaign: An “if this, then that” dynamic rule builds on its own success, infusing value into campaign results by moving the chains, if you will, providing marketers with an – automated – logical next step to drive desired behavior. Results from a campaign derived from a list can be matched back to the list, but a new list must be created to advance any findings.
9. Rules Do Not Require Coding for Dynamic Personalization: A list-based email campaign set up for personalization requires scripts, as an example. The time-consuming, resource-intensive effort devalues the benefit of personalization because by the time a customer receives a personalized email, they may be steps ahead in a customer journey. Scripts simply cannot be written to keep pace or to account for every step of a dynamic customer journey. By not requiring coding, rules are unencumbered by manual intervention for dynamic personalization.
10. Rules Are Secure: A previous blog in this space focused on the importance of applying customer permissions dynamically in the customer lifecycle. The fact of the matter is that regulations frequently change; new provisions, bylaws, and legislation crop up continuously. A list of customer preferences and permissions becomes outdated with each new provision, introducing compliance risk and creating a self-defeating cycle where a new list becomes irrelevant almost the moment it’s published.
Applying rules dynamically at the time of each customer interaction seamlessly integrates all segment and preference data into the process at each stage of the journey, making it dynamic and eliminating the potential of frustrating a customer by ignoring or mishandling their preferences, or by ignoring where the customer is in their journey.
The RedPoint Customer Data Platform is rules-based. It creates rules for how, where, and when customer data is ingested. It creates rules for how it is managed, and for how it provides advanced identity resolution. It creates rules for how code-free automated machine learning models recommend a next-best action that is always perfectly in the context and cadence of a customer journey. These rules are the most powerful part of the solution, providing the foundation to enable segment-of-one marketing at scale. Any system that relies on lists is simply incapable of matching this power.
A rules-based platform embraces the enormous complexity of providing a personalized customer experience for every customer at every stage of a dynamic customer journey. The reward for tackling this complexity is to have a mission-critical solution that is accountable for generating revenue.
November 18, 2019
Key Questions to Ask When Evaluating an Enterprise CDP
The Customer Data Platform Institute created the RealCDP designation to clear up marketplace confusion about the true purpose and capabilities of a CDP. While over 100 vendors offer what they consider CDPs, the institute bestows the RealCDP label on just 44 of them, RedPoint Global included.
According to the institute, there are five qualifications for a CDP to earn the RealCDP distinction:
Ingests data from all sources
Retains full detail of all ingested data
Stores the ingested data as long as the user wants
Converts the data into unified customer profiles
Makes the profiles available to all external systems/li>
That’s a lot to unpack for companies that are evaluating CDPs as the single source of truth for customer data and as the foundation of a future-proof martech stack. To further clear up confusion, we will address five questions every company should ask during the evaluation phase.
Dynamic, Rules-Based Application of Customer Data
The five RealCDP qualifications do not explicitly state that a CDP must perform all of those functions dynamically, but that is perhaps the most important consideration for any company evaluating enterprise-grade CDPs. A solution can ingest, retain, store, and convert data and make it available to external systems, but done statically it will not keep pace with a dynamic customer journey.
A rules-based solution that offers an in-the-moment, real-time, flexible decision at the moment of interaction makes all the difference between a real-time, dynamic journey and a list-driven journey that will, by definition, always be at least one step behind the customer. Attaching static information to a persistently updated golden record devalues the ultimate purpose of a CDP, which is to guide a customer journey with segment-of-one personalization in the context and cadence of a unique, omnichannel journey.
Data Enrichment for Deeper, Richer Insights
An enterprise-grade CDP should offer a broad set of data enrichment capabilities. Data enrichment is the merging of third-party data from an external authoritative source with existing first-party customer data.
Data ingestion is a core requirement of a CDP, but data enrichment is needed to make raw data useful. It adds additional layers of detail to first-party data, generating insights that help marketers deliver a more hyper-personalized customer experience.
Data enrichment is a key element of integrated identity resolution. And while it is not expressly covered by RealCDP, it is nevertheless an important topic and an important distinction between an enterprise-grade CDP and an also-ran. Brands poised to deliver omnichannel personalization recognize that well-functioning data enrichment is a key CDP capability.
Advanced Identity Resolution for an Anonymous-to-Known Journey
Identity resolution is another question to ask when evaluating a CDP, because creating relevant, personalized interactions requires not just enriched records but accurate and timely understanding of the customer journey across all touchpoints. Marketers should expect the CDP to dynamically map partial sets of details from identified and anonymous records to resolve a customer identity across an unknown-to-known customer journey.
Identity resolution is foundational for building an accurate customer profile. It should automatically identify customers across databases and engagement systems. Companies evaluating CDPs must be wary of solutions claiming to offer advanced identity resolution, but providing little more than matching and stitching together known identities. Advanced identity resolution requires understanding, tracking, and relating the complete range of identity elements – offline and online, anonymous and identified, shared and individual – to build identities in the context of households, organizations, and changing preferences and goals.
Identity Resolution Done Right: Building a Golden Record
To provide the performance, agility and control marketers need for engagement at the cadence of the customer, a CDP must natively include all the tools required to convert raw data into a golden record. This includes handling normalization, validation, and transformation of raw data – names, addresses, numbers, business details – from both structured and unstructured sources, along with all kinds of “housekeeping” items, like handling nicknames, abbreviations, incomplete or missing fields, and data entry errors.
The RedPoint Customer Data Platform standardizes the data and uses probabilistic and deterministic matching – with more than 375 built-in functions – to decipher individuals, households, cookies, IP addresses, and IoT smart devices, among others, and handles both digital and offline identities.
This process turns raw data into an identity graph that marketers use to create a personalized customer experience in any channel. Relying on data scientists to convert data into a unified customer profile is an unnecessary, time-consuming step when trying to keep pace with a customer in an omnichannel journey.
Not Just Another Solution for the Martech Stack
An evaluation should begin with an understanding that a CDP is not just another martech solution, but is an enterprise tool. As such, a major consideration is whether a CDP offers pre-built connectivity to all enterprise sources of customer data, or whether its performance relies on limited martech and channel connections.>
An open garden architecture that supports connection to all enterprise systems is different than “open garden light” which limits connections to the martech stack. While the latter may integrate limited e-commerce and CRM data, or offer the “escape hatch” of file imports, it still requires the IT department to do the heavy lifting to bring all relevant back-office data into the CDP. A real open-garden CDP should provide high-quality, high-performance native connections to data sources above and beyond the martech world – first-party, second-party, and third-party data, batch and streaming, big data, databases, message queues – to ensure that keeping pace with an omnichannel customer journey does not require an IT project to make a new connection happen.
And for Good Measure …
There are two additional questions to ask when evaluating a CDP that are related more to underlying performance rather than off-the-shelf capabilities. First, it is important to confirm the CDP you are evaluating meets an IT department’s security, compliance, privacy and governance requirements. In a modern, customer-centric world where data is king for creating a personalized customer experience, the data that goes into a CDP is high-value, high-risk data centered on the relationship between a brand and the customer. It is vital that this data is handled appropriately (in transit and at rest) as a high-value asset.
Second, a CDP must offer performance that matches the cadence of the customer. Like the dynamic application of rules at the moment of each interaction with a customer, this requirement underpins the requirement for real-time throughout the data lifecycle, real-time at the data level, at the analytics level, and at the orchestration level. In fact, in describing the five qualities needed to be a RealCDP, the CDP Institute takes pain to say that there are topics not covered under the RealCDP umbrella – and real-time data processing is first and foremost on the list.
If your CDP checklist includes all five core capabilities of a RealCDP, kicking the tires of real-time ensures you’ll walk away with a solution that’s ready for the high-speed twists and turns of a dynamic, omnichannel customer journey.
November 11, 2019
In a Heartbeat: The True Value of a CDP’s Real-Time Capabilities
According to The Relevancy Group CDP Buyer’s Guide 2019, real-time targeting is a key CDP use case, used by 54 percent of marketers with a CDP to execute highly relevant campaigns, with messaging that reaches an intended target at the appropriate time to drive intended behaviors. According to the Guide, “Speed of data is critical to (these) marketers and the data agility that a CDP affords is a key toward reaching the right target, at the right time, with the right message.”
For retailers and brand marketers, “speed of data” translates to successful personalization through practical use cases such as delivering a perfectly timed offer the instant a customer appears in a channel, or delivering tailored content to a first-time visitor to a website tuned to their preferences and behaviors.
The benefits of real-time extend far beyond the retail sector, however, and nowhere is the benefit more important than care management, where life or death decisions may depend on offering real-time support to a patient. Using real-time patient data to manage the health of an at-risk population arrives at the very essence of CDP’s purpose.
The real-time processing engine that supports real-time engagement is ultimately what drives a CDP’s value, which makes it more than just a pillar of a successful martech stack. For retailers and brand marketers, that value is measured in driving revenue. For healthcare payers and providers, value is instead measured in better health outcomes, improved patient engagement and satisfaction, and lower total costs.
Value-Based Care and Telemedicine
Value-based care is a growing performance-based compensation model that rewards physicians, hospitals, and other healthcare providers on the quality of care they deliver rather than services rendered. Under the value-based care model, providers have a financial incentive for the long-term health of a patient.
As the leading cause of hospitalization for people older than 65, congestive heart failure provides a model value-based care use case. A treatment plan typically consists of regular monitoring, blood tests, medication, and lifestyle management. This makes telemedicine (defined as the distribution of health services and information electronically and with telecommunication technologies) ideal to help minimize hospital re-admittance rates and prevent disease progression – the two main treatment goals.
These were key metrics for an innovative telemedicine research program piloted by a hospital, with RedPoint Global as one of its technology partners. The hospital was managing care for 300 congestive heart failure patients, and wished to gather real-time data from connected devices to monitor their health and recommend care.
Real-time data such as vital signs could then determine a patient’s care pathway, unique for each patient based on real-time conditions. A pathway could consist of a call from a nurse, a motivational message, a visit from an ambulance, or scheduling a new treatment plan – all dependent on real-time signals from a connected device.
Optimizing Care Pathways with a CDP
Congestive heart failure patients typically have a team of providers invested in their long-term care, including primary care physicians, cardiologists, nurses, dieticians, pharmacists, exercise specialists, and social workers. For the research program to work, it had to centralize patient data into a single platform – a central requirement for real-time care management.
The RedPoint Customer Data Platform integrates data from any source or type into a central platform, providing care managers with a unified customer profile and a single point of control over data, decisions, and interactions to expertly manage a patient’s care pathway in real time based on up-to-date data.
Organizations benefit from a CDP’s real-time capabilities because it consistently provides a next-best action or recommendation in any channel in the context and cadence of a unique customer journey. In care management, a next-best action is similarly tied to a customer journey – in this case a patient journey or care pathway – based on everything that is knowable about a patient.
The pilot study provided invaluable patient data, but a real-time next-best action is not based solely on a real-time connected device, but rather a patient’s entire medical history. A unified customer profile, also known as a golden record, is persistently updated to include real-time data from every source. Combined with in-line analytics and automated machine learning, the golden record provides caregivers with a next-best action that is relevant to the patient’s journey at a precise moment in time. The analytics are key to scaling this to personalized engagement for thousands or millions of consumers.
A connected device could, for example, monitor and transmit a patient’s heartrate, respiratory rates, and physical activity. The device data would then be integrated in real time with a patient’s entire medical history and records – interactions with dieticians, exercise specialists, and other providers, test results – as well as second-party and third-party data, and structured, unstructured, and semi-structured data to become part of the golden record. An automated machine learning model programmed to determine risk factors, a re-admittance score, or another metric will intelligently orchestrate a next-best action optimized for the patient’s care pathway.
An Ally in the Healthcare Space
In addition to the heart research program, RedPoint is partnering with other healthcare providers to help advance value-based care strategies in an ongoing basis. Last year, RedPoint partnered with Lucerna Health to advance the transformation to value-based care through personalized engagement.
Patient behavior is the number one determinant of healthcare outcomes for several conditions, and the partnership was formed to help shape behavior through those personalized engagements. While the CDP was conceived as a marketing solution, and a real-time engine make it perfectly suited to personalizing a custom experience in the context of a unique customer journey, there is no more noble purpose than saving lives. With one heartbeat nearly every second, the impact of a CDP on real-time data and engagement clearly shows the linkage to tremendous value opportunities.
There is no doubt that a big pain that marketers face is having to string together data from multiple sources to access prospect, customer, and product information. In fact, according to Salesforce, “the number of data sources used by enterprise marketing departments is growing each year, and it’s growing fast. Research shows a 25% annual growth rate for significant data sources, but others are much smaller. If you look at this 25% growth rate and think ahead 10 years, you’re going to go from 15 to 40 data sources.” Marketers must have a way to unify this data in one place, and this is where a CDP can help.
What is a CDP?
A Customer Data Platform (CDP) is a software that centralizes your customer data from multiple sources into one place. Like the statement above, if you’re struggling with a variety of different data sources, a CDP is where you’ll want to gather your data at one source, but before you do this, there is one important step you should take: Clean your data.
Clean Your Data with Email Hygiene
Before you gather your data with a CDP, you’ll want to ensure that you maintain updated and accurate data. Whether you’re new to your role and inheriting a bunch of data sources that need to be organized, or you’re ready to discover new segments, build models, and deliver real-time personalization, you’ll want to make sure your data is in tip-top shape. To ensure your data is accurate, you’ll need email hygiene.
What is Email Hygiene?
Email hygiene is the process of identifying “problem” email accounts from email lists. Email hygiene can go above simple verification and validation by identifying harmful email addresses hiding in your data. Why does that matter exactly? An email list can be a ticking time-bomb, ready to explode at any minute. There are hidden threats within your email lists that can damage your ability to use your data properly, impact your sender reputation, and directly affect revenues. (Read What’s hiding in Your Email) Email hygiene can defuse the threats in your data and ensure that your next CDP will be happy.
Once your data is clean from harmful threats, you now want to gain more valuable customer insights from your previous lists with a Data Append Solution.
What is Data Appends?
Every month, about 3% of customer data becomes obsolete due to changing conditions as customers move, get married, or change names. Whatever the reasoning, this fact is why marketers are always in need of updated and accurate customer contact information. They can either obtain this information by searching for sufficient data or purchase new data. These options can lead to time lost, money wasted, and no guarantee that the information you have purchased is real. Data Appends is the best solution for marketers who need to fill in the missing or outdated information in their data lists.
Conclusion
Webbula can provide email hygiene AND Data Appends before you upload your data to a CDP. With our solutions, you will be able to clean your data with our industry-leading email hygiene and enhance your customer lists with missing information like emails, addresses, phone numbers, and demographic segments.
Now that your data is ready to go, you’ll have multiple ways to best use your CDP, not only for data unification, but for identifying resolutions, real-time engagement across all channels, people-based marketing, and most importantly to make better data-driven business decisions. Just think about the opportunity that you have now that your data is cleaned. You now can better segment the data for marketing and analysis purposes, and also personalize your messaging more efficiently now that your data has been enriched with expanded information.
Before you migrate your data over to a CDP, be sure to use Webbula’s email hygiene and Data Appends services to get your data in tip-top shape.
November 1, 2019
Want to Offer Exceptional Customer Experiences (CX)? Ask Your Data
One of the things I enjoy most about my role at Informatica is meeting with people in all types of organizations and from all sorts of industries around the world. And while their business models and geographic locations may be wildly different, one thing is universally understood: to keep current customers and gain new ones, you must be able to provide an exceptional customer experience.
More than ever, customer experience plays a critical role in both initial and repeat purchase decisions, as well as customer advocacy and public perception of your brand or your company’s products or services. Can you think of the last time you bought something or went somewhere new without consulting Yelp or asking a trusted friend or family member? Most of us can’t.
Which means that, as far as your current customers or your future customers are concerned, it all comes down to how well you remove the friction from your processes and interactions—and that comes down to not only how well you understand them, but also how well-informed your teams are. So you need to not only understand how and why your customers choose your product or service, but also if your customer service, sales, finance or operations areas—and even chatbots—have the information they need to deliver an exceptional experience when engaging with customers.
But what makes an exceptional customer experience? Believe it or not, your customers are telling you exactly what they want—whether it’s the type of pillow they prefer whenever they stay with your hotel, the mortgages they’ve researched over the past couple of months, or the best way to reach them if you need to alert them to a last-minute change at the doctor’s office.
It’s all in your data.
How Well Do You Know Your Customer?
You most likely feel that you already have quite a bit of information about your customer. And with every click or call, with each order or return, you can gain a little more knowledge and an opportunity to build a trusted relationship with them. Did they discuss a service issue with a chatbot? Register a complaint by email? Were they talking about your company on social media? Each interaction is a chance to discover just that much more about your customers. The trick is, of course, looking across these channels and stitching it together in a way that allows you to deliver intelligent and engaging customer experiences in real time.
For many companies, putting all of that data together—from the many different systems, channels and functions that exist throughout the organization, and in various stages of completeness and correctness—can be quite a daunting effort. And, to complicate things further, your customers are constantly changing how they interact with you. Every hour, more than 200 businesses will change addresses and 400+ business telephone numbers will change or be disconnected. In the next sixty minutes, nearly 6000 people will change jobs, and half as many will change their address. Before the hour is up, more than 500 people will get married and more than 250 will get divorced. It’s been calculated that nearly one third of your data degrades every year. This is a problem, because as data becomes less reliable, the people who use it lose trust in it, or will spend more time than they should to correct it. So, step one is creating a way to maintain your data and ensuring it’s up to date and remains trusted. Once that’s done, you have a foundation for a powerful 360-degree view.
A Spectacularly Powerful 360º View
Creating a data-driven customer experience means you are able to use relevant and trusted data to create an intelligent 360-degree view of your customer that resonates across your organization and is delivered to the teams that need it, where they need it, in their systems, channels, and functions. To create an even more powerful view of your customer, you can apply a hybrid of matching algorithms that mimic an expert human user to add real-time information to existing customer profiles—such as social, email, click stream, chat, analytics, or survey data.
This additional transaction, interaction and behavioral data offer up an even richer, more complete understanding of your customer, and give you better insights to create the next best experience—whether it’s being aware that your customer has a new baby and wants to make sure their insurance is up to date, knowing not to send an offer for a tourist attraction to someone traveling on business, or understanding that your customer doesn’t want to talk about refinancing a house that they plan to sell in three months.
Applying Intelligence for Relationship Discovery and Deeper Engagement
The good news is that these insights are more accessible than ever. Customer intelligence systems (also called customer intelligence platforms) are purpose-built to connect your data so you can connect with your customers. Because they contextually match and synthesize your interaction, transaction, and behavioral data with your customer profile, customer intelligence systems can transform each experience, and use AI-driven customer insights to help you deliver that next best customer experience—not just once, but at scale across the organization. And, with an added layer of data governance, synthesis, and identity resolution, you finally gain the context from your raw data so you can take action on what’s important to your customers and isolate the trends and preferences that enable relationship discovery and deeper engagement.
If you’d like to know more about how these digital strategies are delivering differentiated and consistent customer experiences, join us for our second annual Customer Experience VIP Summit.
October 21, 2019
Kids, Data and Privacy: If Your Company’s Not Worried, It Probably Should Be
Got data? Ask any company and you’ll get an affirmative. But most don’t realize they probably have kids’ data too, and that should be a concern. Knowing what data’s being collected, stored and utilized is critical and it’s not just about having children as customers. “Any company that has family profiles on employee health data, sponsors a “Take Your Child to Work Day,” or has an archive of photos that includes children, has kids’ data,” says privacy expert Kristina Podnar.
Today’s kids have an online identity that begins in infancy via parents, friends and family. By the time they are teens, 95 percent have a smart phone and spend close to half their time online. This raises important questions: What privacy rights should children have? Who will enforce those rights? What should a data protection framework look like? And, what do companies need to know to comply with current laws and mitigate future risk?
This has implications company-wide, stretching from the privacy policy to human resource data to the customer tracking and behavioral information that’s collected for marketing, advertising and sales. Podnar, an international privacy consultant who works with companies from global 1000 brands to small and mid-sized companies, says, “most companies think if they don’t produce a product or service that is for children this doesn’t apply to them,” yet she says it’s been applicable to 100% of her own clients. Podnar finds “organizations don’t realize what data they have and don’t segregate the data, so they can’t differentiate the kids’ data in the pool.”
Jarrod Craik, Vice President of Technology at BlueVenn, concurs saying, “This is a tricky area and a potential minefield for companies. Many don’t quite appreciate that holding children’s data is risky and may become a problem if the data gets into the wrong hands or is used for purposes that were not intended.” There are key questions of whether to keep it, for how long, and whether the benefit is worth the risks involved.
It is also complicated because laws protecting children’s privacy differ. Countries have different age designations regarding who is a child. The Children’s Online Privacy Protection Act (COPPA) in the U.S. “requires companies to obtain explicit, verifiable permission from parents before collecting, using or disclosing personal information from children under 13 or targeting them with ads,” according to the New York Times.
GDPR on the other hand, as explained by The Privacy Hub, “doesn’t set a single universally-applicable age. Instead, it gives the Member States the power to choose their preferred nation-wide age of data consent between 13 and 16. Most EU countries (including the UK) set this age at 13. But in Spain, the age is 14 and in The Netherlands, it’s 16.” In other places, South Korea requires parental consent when processing children’s data, Brazil has a law going into effect in 2020, and Australia and India are looking at adding children’s privacy protection regulation.
Customer Data Platforms can help significantly by ensuring that company data is unified, consistent and up-to-date. CDPs make it easier to identify what kind of data the organization is holding and whether data is being accessed. In consulting with their clients, Craik says that at BlueVenn, “we’ve always taken the view that holding children’s data is risky, and companies should only hold what has been agreed to and use it just for the stated purpose. It’s good to have a time limit after which you either get rid of the data if you’re not using it, or renew the agreement for a new installment of time. I generally advise taking as little information about children as possible, for example a birthdate to know age, but not gender or other information you don’t need.”
Podnar recommends companies get ahead of the problem now, saying, “if you define guardrails ahead, you won’t have to wonder what data can be used or whether you’ll get in legal trouble”. She says future forward companies are doing this and that “smart marketers are proactively sharing what they’re doing with their customers” as a way to show transparency in their process and instill trust. She believes this will be an increasingly important issue and one that is “ideal for companies to look at while they’re retooling, and before it becomes more heavily regulated,” which Podnar feels certain will come over the next 5 or more years.
Every month about 3% of customer data becomes obsolete due to changing conditions. Customers move, get married, change names, pass away, or leave the grid in search of a simple life in the woods. Whatever the reason, this fact is why marketers are always in need of updated and accurate customer contact information. They can either obtain this information by searching for sufficient data or purchasing new data. These options can lead to time lost, money wasted and absolutely no guarantee that the information you have purchased is real. Data Appends is the best solution for marketers who need to fill in the missing or outdated information in their data lists.
But What is Reverse Data Appending?
While Reverse Data Appending is not an “official” industry term, it typically refers to customer data that only includes email addresses. Reverse Data Appending can add matching names and postal mailing addresses to your existing data, among other data sets. This not only gives you more options for contacting your customers, but also allows for even further segmentation. Now you’ll have geographic information and can market to them via direct mail. According to the experts, “businesses can witness an average increase of 30% in their ROI while using email appending in their marketing campaign”.
Updating your Email Data
It is important to update your data lists frequently; sending to a list that is even 4 to 6 months old opens you up to considerable risks, including spam traps, disposable domains, and poor conversions. If you fail to keep up with your data lists, you will lose the existing customers and will also tarnish your opportunity to attract new customers. Data Appends and Reverse Data Appends give you more information about your customers and contacts, allowing you more flexibility when it comes to marketing and communication.
Best Practices
Appending your data will help freshen up your lists with updated and accurate information like name, phone number, email address, and mailing address, as well as information such as interests, hobbies, financials, mortgage, political, and other specific data. Data Appends and Reverse Data Appends can help you communicate with your audience more smoothly. It also can…
Add missing information ranging from names, phone numbers, and email addresses to interests and hobbies.
Give you a better understanding of your customers and prospects with more complete information at your fingertips.
Boost your response rate by eliminating emails to invalid or outdated addresses.
Keep your data clean, which can help increase deliverability and avoid landing in spam folders.
Give you options for segmented and personalized marketing and communications based on enhanced data.
Why Webbula insightData Appends?
When it comes to appending your existing data, there is simply no other option that compares to Webbula insightData. Our data is constantly updated and certified with our industry-leading cloudHygiene filters that ensure accuracy and mitigate potential fraud, creating a truth-set that is unparalleled. Because of the strength of our dataVault, we are able to cross-reference against a greater variety of fields that often go back in time over a decade. At Webbula our passion is truth in data and we strive for the utmost quality, always choosing it above quantity.
How does insightData Appending work?
Conclusion
Grow and enhance your customer list through Webbula’s insightData append solution so you can understand your customers, their interests, and cross-channel behaviors while adding missing information like emails, addresses, phone numbers, and demographic segments. Deliver relevant ads at the right time to the most relevant audiences with industry-leading, premium data quality.
Visit Webbula’s insightData page to learn more. You can request a free trial to see how our data can help your business grow. Webbula insightData is just one of the reasons Webbula is THE Data Quality Expert.
The investment in analytics has accelerated. In fact, for most organizations the investment in analytics has outpaced the investment in data management. Organizations have been in an ‘arms race’ to fuel their digital transformation with Next Best Action, Next Best Experience, and Next Best Offer analytic engines. Most have received mixed results. Insights have been described by some users as “slightly better than common sense” as well as “misleading on occasion”. While helpful, most organizations have concluded that those solutions need to mature and that something is missing.
But so what?
Here’s an important question that you should answer – what data drives those analytic solutions? One banking organization invested in a modern Next Best Offer analytic solution that was, analytically speaking, very sophisticated. And they fed it data from their data warehouse, which was fed from account systems with a minimum of data quality and matching applied. The results were mixed. While some offers were relevant, many others were off target and based on historical information. A wealth management company rolled out next best action to their advisors, identifying actions such as sending a “guide to savings for millennials” to their customers who fit into that segment. Again, the results were mixed, and the organization concluded that they had taken a step forward, but it would ultimately be a long journey towards truly personalized Customer Experience. There is clearly more work to be done. Significant improvements will be gained by simply feeding better data to the existing analytic solutions.
Here’s a second important question to ask – if you believe that data is the problem, do you think the answer is a consolidated customer data foundation that is integrated with those analytic solutions? It isn’t. Complete customer data is a necessary foundation, but it contains an overwhelming amount of data that would flood the analytics tools. It will often contain contradictory and time-sensitive data points that may lead the analytic solutions astray. What is required is a Customer Insight System that leverages Artificial Intelligence to serve up Next Best Data (NBD) for each unique customer. From the 360 (a complete identity and relationship graph of customer and related data), Next Best Data utilizes analytics and Artificial Intelligence to determine the context of the usage; it then presents only relevant data for the moment and for the specific interaction that is occurring. Rather than analyzing the products a customer owns to determine that a rewards credit card should be offered, NBD takes in all Customer 360 data and understands relationship patterns, campaign responses from household members, allowing the Next Best Offer engine to correctly determine that a credit card offer will fail, but it is the right time to offer a brokerage account. Contextual data is the rocket fuel for Next Best Action, Next Best Offer, and your data-driven digital transformation.
Many organizations that have invested significantly in analytics are now investing in Next Best Data and utilizing new technology to bring all data together and determine what is relevant, or contextual for each interaction, each offer, each action. These new technologies drive deeper customer analytics, omni-channel personalization at the individual level, and marketing transformations. Market analysts such as 451 Research are referring to this new class of software product as customer intelligence systems or customer intelligence platforms.
To learn more about how intelligent, trusted data fuels engaging and relevant customer experiences, join us in New York on December 3, 2019 for our second annual Customer Experience VIP Summit.
If you happen to run a local retail store, or are the marketing director for a national fortune 500 retail giant, communicating with your customers is priority number one. And collecting your customer’s contact information is the first step to doing that effectively. But collecting customer data is easier said than done. No matter what processes you use, the results can be rife with input errors, misinformation, or worse. You may end up with a valid email address, but not a valid name or phone number. The bad data ends up collecting dust and never getting used, leaving a portion of your customer base in limbo.
What if there was a solution that could help put that bad data back to work for you? Instead of tossing aside unknown data after you go through the email verification and hygiene process, retailers may also want to start considering having another look by appending incomplete “dusty” data. But what does that mean exactly? Is there really a way to take a small piece of customer information and add more to it? Yes, there is; keep reading to learn more.
What is Data Appending?
Data Appending is simply filling in the missing pieces of your data, completing the puzzle you’ve already started. As long as you have some sort of information such as an email, phone number, or name, we can often get you the rest, revealing the complete customer picture and essentially saving data that might otherwise have been lost or set aside.
What is Forward Append?
A forward append means that you already have a name or address on file of an individual and you are seeking more information such as a phone number, email, demographics, property data, or vehicle information.
Forward appending is considered the most common type of append because companies usually have a name, but need to fill in the missing information.
What is Reverse Append?
Reverse appends is when you obtain one field of data and use it to return all other data that can be tied to it. Let’s say, for example, a business wants to send out a marketing campaign but wants to personalize it. They have a list of valid emails, but no full names. What can they do? They can reverse append their data using the email addresses, and receive the names, and any other information they may need to make the email campaign more personalized such as demographics or interests.
Reverse appending is open for almost any dataset, from names, to email addresses or phone numbers. Reverse appending match rates are not as good as forward appending. Forward appending is known to be more accurate and the process takes less time than the reverse appends.
What is Fractional Appending?
Fractional Appending is a mix between both forward and reverse appends. For example, if you have a name and an email, you can get a postal address, which can be extremely useful for reaching out to your customers with a direct mail campaign.
Webbula for Data Appending
If you’re a marketer working in retail, you’ve most likely found yourself scratching your head after an email data verification and hygiene test wondering why so much data turned out to be incomplete, wondering why you wasted so much marketing effort collecting customer data only to be left holding an incomplete picture. The good news is, the next time this happens, don’t panic. Webbula insightData can append your troublesome data and enhance your list, making you better prepared for your next email campaign.
So clean your email lists with Webbula cloudHygiene, and append the rest of your data with Webbula insightData. InsightData will enhance and append your customer list with missing information like emails, addresses, phone numbers, and demographic segments. With insightData you will truly understand who your customers are, their interests, and their cross-channel behaviors to better enable sending targeted and effective email campaigns.
Don’t be afraid, append your data with Webbula insightData. To learn more about insightData visit Webbula.com
September 18, 2019
TFM 2019: How Bacardi uses data to top up the bottom line
The Technology for Marketing conference is among the world’s largest. Speakers will include CDP Institute Founder David Raab. You can click here to register.
Also speaking with be Brenda Fiala, Bacardi’s Global VP of Strategy, Insights and Analytics. Techerati interviewed her ahead of her TFM & ECE Keynote panel appearance.
When Bacardi was founded 157 years ago, rum wasn’t considered a refined drink. To transform the sugary-spirit into something sellable in upmarket Cuban taverns, Bacardi founder Facundo Bacardí Masso had to “tame” the drink by filtering it through charcoal and ageing it in white oaked barrels.
Over one and a half centuries later, Bacardi’s products are merrily swigged by millions across the world. Alongside its namesake rum, Bacardí, the company’s core brands now include a mouth-watering selection of the world’s most well-known tipples, including Grey Goose, Patron, and Bombay Sapphire — brands which have all flourished thanks to data and its impact on decision-making.
Actionable Insights
Today, we are undeniably in the dual age of big and small data. In fact, it’s hard for multinationals like Bacardi to know what to do with all data at their disposal. To transform its growing supply of data into actionable insight, Bacardi had to return to its origins. It had to tame data and refine it to enable better decision making across the company.
Data is fundamentally ore that needs to be refined before it can be made into a product, which in Bacardi’s case includes informed omnichannel marketing, business and brand strategies. At TFM 2019, Brenda Fiala, global VP of strategy, insights and analytics at Bacardi, will detail how her 33-strong insights and analytics team turns data into a competitive advantage.
As Fiala explained, turning a company into a data-driven engine not only requires leveraging technical tools but enacting structural and cultural changes so that data becomes the beating heart of almost every core process.
When Fiala arrived at Bacardi Ltd, one of the first changes she implemented was increasing the volume of meaningful data points so they came in weekly, rather than quarterly. The goal was to gain 100 percent visibility across advertising and promotional activity spend so brands and markets could see the effectiveness of their marketing. Her team’s guiding principles are simple but effective:
“It’s not just about looking at financial data. But ensuring one, we keep the consumer at the heart; two, we keep our brand health strong. And if we can do those two things, then the finances should follow,” Fiala said.
“Now that we have our data house in growing order, we’re able to move to predictive analytics. For the past two years, we’ve been making recommendations and validating our advertising spend.”
Fiala has extended her team’s influence across the organisation and united commercial, company and consumer data.
“Usually, there are silos in companies that look after those different types of data. And we’re breaking those silos to connect the data together, which allow us to run the business in a more agile manner,” Fiala said.
Often when data is connected with other data that was previously siloed, counterintuitive results glare back at you. Fiala says part of her company-wide data education involves making clear the heuristics that can be used to figure out if results are actually telling you something you need to know, or if a gremlin in the assumptions line is throwing up false flags.
“Number one, ‘Is the data wrong?’ So you take them through the data model. Number two, ‘Is the analytics wrong? Is the math wrong?’ So you discuss what the math can and cannot do. Number three, “What other factors are not in the model of the data that could be influenced?’ And then number four is ‘Is something wrong with our strategy? Or is this something that just needs to be optimised?” Fiala said.
“The data never tells you what to do. The analysis helps inform our conversations as business leaders, to help us make business decisions — and ideally faster.”
By using data to establish the properties of the numerous channels on which Bacardi Ltd can promote their brands, Fiala’s team have helped the company move from a “peanut butter” (or evenly spread) strategy to one that customises and optimises spending on every channel for maximum impact. By identifying channel-specific thresholds and their correlations to brand strength and potential sales, Bacardi brands can target each channel with confidence.
“Whether it’s the work that the team did on Sound of Rum last year with Major Lazer, to our work with Swizz Beatz and Anitta, we now have insight through our data to maximise the integrated channels by which they’re working.”
Another variable Bacardi Ltd has identified as making a significant difference to the bottom line is the degree to which people are talking about their products, which the team measures in conjunction with its PR companies, social listening techniques and other consumer research.
From these insights, Bacardi is able to identify ways of speaking to consumers directly, the knock-on effect of which being more consumers talk about their brands. Observing how consumers were increasingly making reference to Grey Goose-based cocktails, Bacardi created a cocktail recipe section on GreyGoose.com that also informed visitors where the cocktails could be purchased in several of its major markets.
Secrets to Success
No company would readily give away their secrets to data success, and Fiala is no exception. But she did reveal one quality that she believes helps to make data doyens out of those who possess it: creativity — something she first observed at Caltech during her chemistry degree.
“I could see the scientists who really interrogated the data from different angles and who connected data sets. The most important ingredient of all was creativity, even in analytics. If you’re going to make a difference and get people to notice you, get people to adopt your behaviour and buy your product in the moments that matter, then you need a great dash of creativity.”
Fiala says Bacardi’s global brand platform maximises business objectives by using creativity to understand consumers and their emotions better each year, with data as the platform. The key to success is then optimising PR and media teams so they can address consumers directly.
“The key to creativity is being able to see consumers as they are, through their data and behaviour. And to understand consumers as they want to be understood, through their emotional drivers and their aspirations. Putting those together into actionable insight is what you’re looking for.”
Fiala finishes by addressing the one law of big data – that big data is getting bigger. It’s not so much the volume of data that’s a challenge for Bacardi, she says, but the speed in which they can analyse it and align it in real-time.
One way Bacardi has sped-up this process, an idea born from the company’s cross-company “next-generation programme,” is unlocking understanding through data visualisation. People working at the company are now trained so that they have an intuitive understanding of what kind of information is understandable when it’s displayed. Fiala herself suggested they take inspiration from Edwin Tufty, the forefather of data visualisation.
“When I pointed them to him, they looked at me with blank eyes. But Edwin Tufty has been doing this for years with his wonderful books on the quantitative display of information. So I think there’s something that holds a lot of the keys to making this usable for people to make decisions.”
Customer service executives are alive to tech opportunities rating AI, personalisation and omnichannel capabilities as key to transformation in the future.
Across Incite Group’s new 2020 State of Customer Service Report, which is free to download now, there is a clear theme of customer service executives leaning into the possibilities of new technology. Principal among these are Artificial Intelligence (AI) and Machine Learning (ML) technologies that can help to automate simple tasks and drive a more personalised experience.
More than 900 respondents to the report’s survey gave their opinions about emerging tech. They said that they believe personalisation technologies will have the most potential in 2020 to drive improvements in customer service, with 46.8% choosing it as a key technology. This was followed by AI, which 44.3% feel is a major technological opportunity.
Direct communication tools were considered to offer major opportunities as well, with 43.1% seeing real-time messaging tools as offering big possibilities and 40.3% also seeing potential in chatbots and virtual assistants.
Furthermore, when asked what they thought the most talked-about topic would be in 2020, customer service execs chose the automation of simple tasks by AI, followed by a data driven customer experience and the transition of new digital channels and messaging services to the core of customer service.
While all these technologies offer opportunities in themselves, their true power will lie in combining them for maximum effectiveness. Chatbots, voice applications and virtual assistants will all be exponentially more powerful with personalisation driven by underlying analytics and AI. That same underlying structure can also help empower agents to be more effective at reaching solutions more quickly and accurately no matter what medium they are using through dashboards that can locate customer history or diagnose ongoing issues immediately and present them to the agent.
The ultimate goal of this is to drive a personalised, seamless omnichannel experience for the consumer. In the longer term, respondents to the report’s survey rate the development of a seamless omnichannel experience as the most transformative application of innovation in the next decade. Some 34% chose this as their trend to watch, followed by tools that can empower customer self-service (29.7%), improvements to automated responses (20.1%) and faster response times (16.2%).
This means that customer service departments will need to make major investments into data gathering and standardisation and the development and deployment of AI.
For more insight, click here to download the full report, which features key trends across more than 30 detailed charts and graphs taken from one of the biggest industry surveys out there across more than 40 pages of analysis. This report features hundreds of insights into how customer service is changing and innovating, including takes on artificial intelligence, automation, data management, measurement, personalisation, omnichannel and optimising organisation structures. Download the report now to get the data as well as views from top customer-service minds representing huge brands, including:
· Mastercard
· Microsoft
· Zurich
· Uber
· Plusnet
September 9, 2019
Clear Signs of B2B CDP Market Maturing, with Weaker Players Radius and Mintigo Exiting
There has been a lot of evidence in the last few months that the B2B Customer Data Platform (CDP) category is maturing. There was the recent Forrester report, the dramatic increase in RFPs and customer adoption of CDPs, and even Salesforce’s recent announcements about the pivotal nature that CDPs will play in their future business strategy.
We have also seen both Radius and Mintigo go away, subsumed into other relatively unrelated businesses. This reflects a trend where, as a category matures less successful players exit — in this case, companies either with limited functionality like targeting SMBs, or a narrow focus on a specific feature such as automated predictive modeling — while more complete platform strategies dominate. We saw similar patterns in categories like Marketing Automation and DMPs over the last decade. This is supported by Gartner’s Hype Cycle mode, where there is a focus on performance and results as a category approaches the “Slope of Enlightenment,” and providers survive by focusing on customer satisfaction.
Salesforce has also played a part in this maturation. They chose to sunset Data.com — which was a single-threaded data provider with D&B & Jigsaw data — to adopt a vendor agnostic, more comprehensive CDP strategy as their go forward plans. However, their focus is on B2C business, so there remains a significant gap in B2B for their customers.
Having been recognized as a leader in this space, we at Leadspace are encouraged by the evidence that the B2B Customer Data Platform (CDP) category is maturing. We’ve long believed that predictive AI and analytics is more a feature of a well-constructed B2B CDP. We’ve preached the value of a robust data foundation, powerful AI, and real-time, cross-channel activations combining in a holistic customer data solution that powers real B2B go-to-market success. And we’re seeing incredible customer success with SAP, HPE, Extreme Networks, and many more of the top (award-winning) B2B marketing and sales organizations.
It seems clear that CDP is becoming a critical component in the B2B tech stack now and into the future. Of course, with any critical and transformational technology, there is complexity and change to deal with. That’s why we’re here, and are committed to helping you take back control of your data, using the power of AI to fuel better engagement and relationships with your customers.
August 16, 2019
Groupon and CarMax Discuss Mobile and Digital Product Strategy at the Open Mobile & Digital Experience Summit
In 2019, an exemplary customer experience strategy will be the difference between your brand and your closest competitor. In fact, a recent Gartner study revealed 81% of executives believe they’ll compete on CX alone, but only 22% have claimed to have developed the right product, digital and marketing strategy that exceeds customer expectations.
Ahead of this year’s Open Mobile & Digital Experience Summit, we spoke with leading Digital, Product and Marketing leaders to uncover their goals, as well as how they are putting customer expectations at the center of their business.
Digital, Product and Marketing must all work together to deliver personalized digital experiences, timely and relevant marketing and on-the-go mobile access. Hear what these pioneers had to say…
For 2019 Incite Group has made a bold statement: ‘Customer First. Digital Second.’ What are your thoughts on product’s role to leverage the voice of the customer to build impactful product experiences?
Anne Yauger, AVP, Digital Products, CarMax: “One of CarMax’s core values is ‘We Put People First,’ and we believe the only way to create a differentiated and impactful customer experience is to see this experience from the perspective of the customer. The voice of the customer is central to everything we create.
While technology is a prominent enabler of customer experience in today’s digital world, innovative solutions don’t always involve technology. In many cases, we need to look beyond digital for the best solution to meet customer needs. Customers who are shopping for a large, complicated or emotionally-driven purchase (a car is all these things!) are often looking for a real human connection in addition to an enabling digital experience.
Ultimately, I believe that Product’s role is to discover, validate and build the iconic experiences that truly solve customer problems -- whether they are digital, physical or a combination of the two.”
CarMax will delve deeper into their product and CX strategy at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8th) uniting digital, product and marketing leaders to discuss the latest tools and strategies you can implement across digital experience, product, mobile, growth marketing and more. Find out more here: https://events.incite-group.com/oms/
For 2019, we have identified the importance of building impactful experiences that add value to consumers’ lives beyond a transaction. What are your thoughts on this?
Bryan Ennis, VP of Product, CarMax: “The car buying transaction is an inherently complex and emotional experience – this is the second largest purchase most of us ever make, after a home. Knowing this about our customers’ journey, we focus on enabling our customers digitally alongside building a relationship with the customer that transcends the transaction.
For example, the center point of CarMax’s new omnichannel experience (which allows the customer to do as much or as little of the car buying process from home as they’d like) is our customer check out process we call the customer hub. The customer hub allows customers to take control of their shopping experience and progress digitally. Customers can see all the steps they’ve taken, whether that’s saving their favorite vehicles, getting prequalified for financing, or receiving an offer on their existing vehicle. Most importantly, the hub allows customers to easily seek out help from a CarMax consultant when and how the customer prefers (through text, e-mail or a phone call). CarMax consultants also have visibility to the hub, which enables us to know exactly where the customer is on their shopping journey and provide customized support. As we continue to build and evolve this product, our goal is to enable customers digitally, while continuing to build the relationship that’s critical to the experience transcending the transaction.”
Learn more from CarMax and 400+ other big brands at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8th) which will shape your digital, product and marketing strategy for 2020. Find out more here: https://events.incite-group.com/oms/
As consumers engage across multiple channels, mobile remains a critical customer touchpoint. How is Groupon differing their mobile experience for 2019?
Sarah Butterfass, Chief Product Officer, Groupon: “We’re aggressively transforming our mobile experience so that it’s evolving with the customer. While we strive to make our app and mobile web experience great, we’re taking an extra step. Instead of focusing solely on how we can create a seamless experience on Groupon, we’re thinking about how our seamless experiences can help make someone else’s experience better and more valuable. In other words, how do we enable more great experiences, regardless of where they start? Meeting the customer where they’re at is an important part of how we’re continuing to be a mobile-first company with over 200 million downloads of our mobile apps.”
Groupon will join Mastercard, Hilton, Uber, Amazon and Walmart at this year’s Open Mobile & Digital Experience Summit SF (Nov 7-8th) which will shape your digital, product and marketing strategy for 2020. Find out more here: https://events.incite-group.com/oms/
August 12, 2019
What CDPs Must Do in APAC -- the World’s Fastest Growing Tech Region
The Asia-Pacific region is the world’s fast-growing economy. Organizations in many areas are investing in technology and retooling in response to rapid growth in consumer digital usage. Plus, there is increased concern about data use and privacy. Customer data platforms can be the right tool at the right time in APAC, according to executives already in the market from Lexer, Meiro, Segment, Tealium, and Venntifact, but it will take a concerted effort in education, marketing and region-specific customer support to make that happen.
CDP adoption in APAC is varied and behind the U.S., the UK, and EMEA. But according to the CDP Institute’s latest Industry Update, the industry is growing faster in APAC than any other region. There’s confusion and often a lack of knowledge there about what CDPs are and how they compare with other technology, particularly DMPs. This is due in no small part to other types of technology claiming to incorporate CDP capabilities, regardless of the degree to which they can deliver on that claim. APAC is also an exceedingly complex market with different economies, business hierarchies, and capabilities spread over great distances, so staffing and servicing it takes a lot of planning and investment.
Joseph Suriya, senior director of marketing for APAC at Tealium, calls it “an incredibly diverse and non-homogeneous market with regions that have many cultural and operational differences,” so, “there is no one-size-fits-all solution.” Pavel Bulowski, co-founder of Meiro, a CDP software company based in Singapore, concurs, saying, “if a company thinks it can go into business in Asia by setting up a Singapore office to ‘conquer Asia’, they’ll find they do not have a well worked out strategy.”
However, for those who know the APAC market, or are in a position to enter and operate effectively, the potential is vast. Prakash Durgani, global head of sales engineering at Segment, says, “the digital economy in SE Asia alone will triple to $240b by 2025. India’s outlook is similar, and the combination of these suggests that overall consumer technology growth across APAC could outperform Europe both in number of end-users as well as aggregate wallet share.”
In terms of awareness of CDPs in APAC, vendors and consultants have said it has been scant, but that’s changing now as well. Damon Etherington of Venntifact, a CDP consultancy based in Sydney, says with “large marketing cloud vendors such as Salesforce and Adobe now actively promoting their CDP products for a 2020 launch, the hype and excitement is taking hold in the market. Demand from brands to understand their existing marketing technology ecosystem…and to understand the gaps and requirements for their technology stack is becoming common.” Suriya at Tealium notes that, “Asia has a tendency to be very peer driven, so if their competitors are exploring something, more often than not, this will encourage other companies to look into the same thing.”
In terms of current drivers, surveys have indicated that APAC customers are concerned about how their data is being handled, so technology leaders there view ensuring data privacy and consumer trust to be very important.
Bulowski at Meiro says, since APAC is “a tremendous growth market,” customer retention is not necessarily the main focus now, but going forward when there is “any kind of economic slowdown, there will be an increased emphasis on maximizing current customers, so those will become very important to retain as a customer base. And, this will drive further interest in CDPs.”
Dave Whittle, CEO of Lexer, a CDP with offices in New York, Los Angeles, Sydney, and Melbourne, says that the current CDP market in Australia vs the U.S. is impacted by two drivers: the smaller population (about 1/13th of the size) and the 1-2 year lag in adoption of new technologies. “That said, we are seeing innovative Australian companies recognize the opportunity to implement a CDP to provide an immediate advantage against their slower moving local competitors. Clients are beginning to reorient their systems, processes and teams away from channel centric to the customer centric approach that Lexer enables.”
While it is hard to categorize the larger APAC market, Etherington of Venntifact explains that APAC is comprised of “a number of regions varying greatly in digital maturity. Broadly speaking, Australia and Japan are relatively advanced, South East Asia is still early in its digital sophistication, Hong Kong, Singapore and South Korea have pockets of maturity, while India and China are their own class altogether - huge, complex and rapidly expanding.”
Consensus from these executives on operating in APAC is that a company should either focus on one region or set up regional offices in multiple key locations. They stress that this is a market that takes a lot of time to cultivate and gain the trust of businesses there. But there is a lot of opportunity. Etherington observes that, “Australia has only a handful of active CDP vendors educating and actively selling in region. Those tend to be the larger, pure play CDP vendors such as Tealium and Segment,” so he thinks there’s a clear opportunity in the mid market where there are no active CDP players. For servicing, he says, “specialist consultancies such as Venntifact are emerging to help brands navigate the challenges, and develop effective use case driven roadmaps for CDP adoption.”
August 1, 2019
Are digital marketers losing sight of the value in connecting online and offline identity data?
It will come as no surprise to this group that the options for and use of Customer Data Platforms (CDP) are on the rise. Marketers need a central place to hold, access and manage their vast customer data as well as personalize and better target their ad programs to reach the right consumers with the most relevant messaging.
As part of this dynamic, consumer identity markers (names, physical addresses, phone numbers, emails, hashed emails, mobile ad IDs) and identity resolution have become a key enabler for CDP users to fully realize the value of these platforms. How else can you ensure that the consumer data in your CDP is comprehensive, linked and up to date? At any given time, up to 30% of consumer records in brand databases are incomplete, duplicative or incorrect. So it’s critical that marketers have access to dynamically updated identity data. While many marketers assume this type of identity resolution is baked into CDPs, that isn’t always the case. And of the CDPs that do offer integrated identity resolution, there are often some gaps – specifically as it relates to critical offline data.
Enterprise-grade identity resolution offers the ability to resolve all identity markers to a single person, regardless of which enterprise system they reside within. This ensures that there is a single, linked view of the consumer. Typically utilizing an identity graph, or known truth set, to resolve all identity markers to a single view of a consumer, foundational identity resolution enables critical marketing functions such as personalization, segmentation, analytics, outbound activation, compelling inbound engagement in real-time -- you name it.
While the advent of CDPs has generally risen out of digital marketing, marketers today who solely focus on a consumer’s digital identity and digital footprint may be missing out on key offline indicators that provide a more comprehensive view of that person, and a critical way to use that information to link online identities. Offline indicators can include name, physical address, emails, phone numbers (mobile and landline) as well as historical auto/home ownership attributes. As consumers live their lives, these markers are constantly changing (marriages, moves, phone changes, email address changes, etc.).
To capture both online and offline consumer data, identity markers that power CDPs must be linked together with a persistent identifier through identity resolution. These identity markers provide an essential way for brands to integrate and update first and third-party consumer records in multiple, siloed datasets, e.g., CRM, billing, delivery, customer care, etc. This empowers marketers to maintain a single, 360-degree view of customer contact, transaction and attribute intelligence for de-duplicated and personalized outbound and inbound transactions. Identity resolution also helps improve outbound marketing scale by enabling multiple identity markers to be tied to a consumer, improving onboarding match rates and driving outbound omnichannel scale. Plus, integrated data enables cross-device and platform tracking and helps optimize campaigns, engagements and channel attribution.
Digital intelligence like browser cookies, tags and mobile ad IDs are by design, anonymized, but many can be tied back to offline data using privacy-friendly hashed emails. Physical addresses and phone numbers are another key component to match to digital identifiers to implement omnichannel marketing. And, some marketers overlook the fact that on average, U.S. consumers maintain three email addresses, and use them differently across platforms (e.g. one address for social accounts, another for retail platforms, another for personal use). Being able to access and maintain a record of multiple, linked email addresses helps ensure that the right consumers can be reached on as many platforms and channels as possible.
Foundationally, marketers need to leverage identity linking and ensure identity resolution is implemented across their entire internal dataset. This is essential to maintaining linked, de-duplicated and enhanced records for use within their CDPs. Whether identity resolution is already part of their CDP’s functionality or accessed through real-time APIs, marketers who leverage on-demand identity resolution, can do so on a record-by-record basis (even from fractional identity marker data), enabling them to minimize data decay to maintain their ability to reliably reach the right consumers at scale.
Since identity markers are constantly changing, those changes must be consistently captured in an organization’s CDP to ensure marketers are delivering relevant and compelling messaging and offers as their target consumers’ lives evolve and change.
July 29, 2019
The Future of Customer Data Platforms Needs to be Vertical
Last year, especially towards the end, saw a lot of debates and discussions on Customer Data Platforms, their utility and future, and whether they truly represent the “holy grail” of centralizing disparate data sources and simplifying complex orchestration processes.
In a matter of weeks between October and November, three fairly divergent views presented themselves:
– Forrester’s Joe Stanhope came up with a critical review of B2C customer data platforms. The central premise of the report was that CDPs have “over promised and under delivered”.
– Michael Katz, mParticle’s CEO, wrote a defense to Stanhope’s article and in the process tightened the definition of CDPs. His article defines and builds on the notion of “foundational CDPs” that focus exclusively on building and maintaining customer-centric data pipes. It says that CDPs are only just scratching the surface of their potential.
– Last year’s DMS West had a panel on CDPs and how “everyone is a CDP”. It was easily one of the most engaging panel discussions of the event and one that added more fuel to the fire. The inevitable consensus was: Let the CDP space evolve.
Startups will obviously glorify and hype up the category. And it behooves analysts to temper expectations and bring buyers back to reality.
Here’s what we know for sure.
Barring rare exceptions, practically every company out there is struggling with exploding data. And barring rarer exceptions still, no one has been able to solve this problem at scale. Companies are either ill-equipped or are failing in massively high percentages in their attempts.
And so by extension, this is a vacuum that innovation will fill. CDPs are the right start and are here to stay. So what’s the real deal?
There are two obvious insights that everyone seems to miss but can give clarity on where CDPs are headed.
1. Poorly structured and disconnected data is not just a “Customer Experience” or “Activation” problem
In fact, it’s not even an exclusive digital marketing problem. Sure, the nature of early data sets getting captured is marketing-oriented. This is understandable. Marrying high volume, high velocity behavioral data from mobile and web assets with other high variety data sources brings incredible targeting benefits.
And so, cross-device analytics and experience/orchestration activation has naturally emerged as leading CDP use cases.
But digital marketing is not the only use case for well-structured data.
Let’s take the example of banks. Structuring bank data sources can have so many other benefits:
– Product teams start getting high quality real-time data as they design new products or create up-sell/cross-sell offerings
– Data governance across the organization becomes centralized and simplified
– Analytics teams can start developing nuanced, long-term understanding of users and start offering products to a user that they may otherwise drop or ignore
2. None of the players - large marketing clouds, identity resolution vendors or Google - would want to solve these vertical problems. They may not even be foundational CDPs.
This second insight derives by extension from the first. Large marketing clouds will keep solving horizontal digital marketing problems across various industries - and package them vertically for a particular market.
Vertical growth requires a deep-dive into an operating market. This means that an organization gets a chance to scale up its martech product within their line of business; as they are well-versed with the market dynamics and customer expectations./p>
Google probably couldn’t care less about whether core banking data can be processed and streamlined into the CDP. This means that all of these players will HAVE to be treated as “data sources” and not the central Customer Data Platform.
CDP companies need to start picking industries not called “E-commerce” and solve their digital marketing problems. They need to move to use cases beyond ones that involve DMPs (Data Management Platforms), DSPs (Demand Side Platforms) and other exotic three letter digital marketing acronyms.
And that’s where the CDP promise will get delivered.
Where the data rubber will firmly meet the value road.
Where founders need to stick to a hard vision.
Where easily large TAMs (Total Addressable Markets) will shrink, widely cast sales nets need to be replaced with fishing rods, account engagements will have to deepen. That’s very difficult.
Does this sound boring? Like a lot of hard work?
Great, welcome to 2019 and the future of the CDP!
July 25, 2019
Successful Marketing Campaigns Begin with Customer Data and Machine Learning
If you’re a data scientist, you’re familiar with the many processes data must go through before it can be used for machine learning models and analytics. If you’re a marketer, you may not be aware of the lengthy journey customer data takes before it reaches you. This post highlights some of the processes customer data goes through before it is made available to marketers for customer analytics and marketing campaigns.
Customer data is collected from multiple sources
You can’t have a marketing campaign without customer data, and there is a wealth of customer data available to marketers. Common customer data sources include CRM platforms, social media sites, company websites, and mobile applications. Collection is only the first step. Every system that stores customer data formats and stores that data in different ways - data could be structured, unstructured, or semi-structured and contain different data types such as numeric, text, or categorical. The data must be properly processed before it can be used for marketing campaigns or any customer data-driven business process for that matter.
The data is processed
Customer data must be cleaned, normalized, integrated, and deduped before it can be fed to machine learning models or used for analytics. And customer data often requires enrichment, where missing or out of date customer data fields are appended with new or updated information. For example, a customer record that includes only a name and email would be appended to include company name, job title, and phone number. Data scientists are usually the ones responsible for preparing data for analysis, and it takes about 80% of their time - unless they have tools to automate the process. For example, our customer data platform (CDP) saves data scientists time by cleaning and standardizing data automatically. Our platform also enriches customer data and can be integrated with existing martech solutions using our Enrich API. Once customer data is processed, it must undergo segmentation before it can be used effectively for marketing campaigns.
Customer data is fed to models for segmentation
Creating successful marketing campaigns is a difficult prospect without having a granular understanding of your customers. So, once customer data has been processed, it is segmented typically using one or more clustering models. Clustering allows customers to be segmented based on demographic attributes as well as behavioral patterns. The data can be segmented in hundreds of ways providing organizations a detailed understanding of customers. Segmentation is often used by organizations for lead scoring and propensity prediction. While organizations can build machine learning models for lead scoring and predictions in-house, they don’t have to. Our CDP includes a Lead Scoring API and a Predict API so that organizations can quickly integrate lead scoring and prediction capabilities into existing martech platforms.
The foundation for successful marketing campaigns
Customer data and machine learning form the foundation for successful marketing campaigns because they enable martech platforms to feature capabilities such as advanced customer analytics, contextual personalization, and timely customer engagement. However, it is impossible for marketing campaigns to be successful if the customer data powering them has not been processed and segmented properly.
July 22, 2019
The Asian Financial Services Industry Needs a Customer Data Platform
Customers in the financial services industry want personalized experiences. They, in fact, expect and demand them from their service providers. They prefer to stay loyal to a company as long as they receive this special treatment. As a result, personalization has become the number one priority for marketers in the industry today. They are waking up to the realization that delivering personalized experiences highly depend on understanding customer data.
Very few companies have the means to understand this data and use it to enrich the customer experience. The technology that has been recently making waves in every industry is known as the Customer Data Platform (CDP). A CDP is a packaged SaaS (software-as-a-service) product that is designed to build a unified customer database for an organization. Implementing a CDP can help achieve consistent customer engagement, increased loyalty, and higher sales.
David Raab, CDP evangelist and Founder of the CDP Institute, was invited as a chief guest at the Customer Data Summit 2018 event organized by Lemnisk.
David is a widely recognized thought leader in marketing technology and analytics. He was one of the first people to recognize that digital marketing systems were not just proliferating but also the data that these systems were throwing up were getting grouped into silos, making it really hard for marketers to understand customers holistically.
David also realized that there was a tremendous opportunity if he could bring these disparate systems together. Around this insight, David coined the term CDP and founded his institute in 2016. The CDP Institute’s work has been seminal in helping marketers understand the need for a CDP and the ways that they can derive value from it.
David’s thought-leadership session imparted the following key insights:
• The most challenging barrier to Marketing Automation success is data integration between the various marketing systems of an organization. Financial marketers in Asia face the same challenges as their peers elsewhere, which include unifying customer data, providing superior customer experience, working within compliance constraints, and finding the budget to pay for solutions.
• The CDP industry has seen a good growth rate of around 73% over the last 12 months. Two-thirds of the growth is attributed to new vendors and the remaining to existing vendors. The adoption rate has been high for B2C marketers as their businesses depend highly on user engagement and digital conversions. Companies that opt for a CDP prefer to have a complete packaged solution that includes the core CDP functionality along with analytics and engagement.
• A CDP works well when all marketing systems are interconnected. One interesting observation is that one-third of CDP users lack an integrated technology stack. Companies that claim to have a CDP do not have this system integration and, therefore, do not fall under the CDP-classified vendors.
• Things such as churn prediction and predictive modeling are a set of classic algorithms that thrive on good data. Artificial Intelligence (AI) is totally data-driven and works well with data that is highly detailed. A CDP can play a major role in developing custom algorithms and advanced intelligent systems such as AI. One of the things that it can do is create a standardized variable or model score and make that shareable to all systems that it connects to.
• Of its various capabilities, a CDP also enables cross-device personalization by associating each device with the customer’s master ID when they log in. The master ID is used to build a unified customer profile with all device data. The right message for each master ID is selected and shared with all devices. The unified and complete customer profiles help financial marketers in selecting the right message and deliver a consistent experience across all devices.
It is still early days for a CDP in Asia. Many organizations are still at the stage of learning for themselves why options such as DMP (Data Management Platforms), Enterprise Data Warehouses, and marketing clouds won’t solve the problem that a CDP addresses. The core technologies used in Asian financial institutions can support any level of marketing sophistication that their users are ready to deploy. The early CDP adopters are touted to have an advantage over others in the industry.
Over the years, I’ve had the opportunity to work with a number of companies that lie at the intersection of audiences, data, and technology. As time has passed, many of the challenges associated with using data to reach potential customers have been solved. Amassing audience information? Check. Recognizing a prospect across platforms and devices? Check. Integrating information and insights into a range of sales and marketing technologies? Check, again. Many of the fundamental problems of data-driven marketing have been recognized and addressed.
Now, we’re dealing with harder problems. Which isn’t to say the earlier challenges were easy, but rather that the new problems weren’t initially foreseen. For example, now that marketers have access to so much data, how can they evaluate its quality? How is data quality maintained over time? How can it inform smart planning, support decision-making, and fuel marketing programs that are engaging but not creepy?
Much of the thinking around using audience data comes from the B2C world, whose brands and marketers were among the first to see the potential for reaching and engaging customers online and through social channels. That is rapidly changing and more B2B brands are not only thinking about their customer data, but are also rethinking the ways they put that data to work.
Companies like VanillaSoft are making it easier for inside sales teams to use social channels to reach business buyers - and set up a cadence and process for efficient interactions. Even just a few years ago, the idea of reaching a B2B purchaser via SMS or Facebook or Twitter would have raised eyebrows. Now, they have become part of the daily routine and are recognized as valid channels for engagement.
This type of engagement depends on having accurate contact data, the type supplied by companies like DiscoverOrg. They do a great job of constantly vetting and refreshing their information but contact is just one piece of the puzzle; what about the rest of a marketing team’s data? That data also needs to be evaluated and refreshed on a constant basis, while also bearing in mind customer privacy, their preferences for engagement, and what approaches are most likely to be effective.
The challenge is that the current volume and velocity of data simply can’t be managed manually. To be successful, marketers need to rely on AI and machine learning to do the heavy lifting. While in its early stages, this approach is already paying dividends to forward looking marketing organizations.
These systems can do so much more than simply ensure data quality. Technologies, like those from Zylotech - a self-learning customer data platform that enriches audience data, predicts purchase behavior, and helps increase sales - are able to ingest, normalize, append, and segment customer data in new and interesting ways. These platforms are becoming incredibly sophisticated, transforming audience data into customer intelligence and putting that intelligence to work for sound business results.
That jump is a game-changer. For the first time, B2B marketers have an intelligent virtual ally in their corner. This doesn’t mean that marketers can take a hands-off, automated approach - not by a long shot. Marketers need to apply their own intelligence and creativity to develop campaigns and programs that harness machine learning and AI while preserving customer trust. It’s a fine line and one that is constantly shifting. Thankfully, there is a feedback loop that allows marketers to see what’s working or what might be causing prospects to flee.
AI can learn those business engagement boundaries, but human intelligence needs to trace their outlines for the AI to learn and operate effectively. That’s a higher level function for both marketers and AI. It may sound daunting, but it will soon come to seem obvious. Machine learning and AI - coupled with our own creativity and intelligence - will have a huge upside and are poised to rewrite the rules of marketing.
July 15, 2019
How CDPs Can Bridge the Gap between Financial Marketers and Customers
As marketers, we constantly wear the hat of our customers to understand the motivation, buying behaviors, and rationale for making purchase decisions. Marketing decisions are often made with fractured customer data, which means expecting positive outcomes from marketing campaigns is like trying your hand at the slot machine. Financial marketers today are sitting on huge piles of customer data that is siloed in various martech databases and tools. They are unable to leverage this data for deploying targeted campaigns to customers.
How Financial Marketers Leverage Customer Data
Consider this scenario: Mark visits his bank’s website anonymously and visits the credit card section on the website. He views the Gold credit card and drops off the website. Three days later, Mark returns to login to his bank account and immediately sees a homepage personalized banner showing the benefits of getting a Gold credit card. Mark is a Senior Executive with a multinational firm and has his salary account here.
After logging into his account, he is given an offer to get a Gold credit card along with many more offers and benefits which he previously did not see. The bank sends Mark an email with a custom offer on a Gold credit card. Mark opens the email and signs up for a credit card. What began as a simple task of viewing a credit card anonymously, ended up being an upsell to a customer for the bank. The bank was able to bridge the gap between themselves and Mark by leveraging a Customer Data Platform (CDP).
Interest in data-activated Marketing is driving the need for CDPs
Data-activated marketing based on a customer’s needs, intent, and behavior in real-time is becoming a vital part of digital growth for a financial organization. This can boost total sales easily by 15-20% and improve the ROI on the marketing spend across various marketing channels. A growing number of financial marketers are turning to CDPs to resolve their scattered and siloed customer data problems.
Here are 3 ways by which a customer data platform can bridge the gap between financial marketers and customers:
Understand each customer intimately
A customer data platform helps in un-wrangling customer data that is spread across various data sources. It aggregates and unifies this data for each individual customer. By getting a unified customer view, financial marketers can deeply understand customer preferences, their online behavior, transactional details, buying intent, etc. This ensures that financial marketers are in a much better position to market and engage with customers/prospects.
Deliver personalized messages to millions of customers in real-time
Coupling a CDP with Artificial Intelligence can do wonders for a data-driven financial organization. Customer data platform enables marketers to understand each customer. However, a CDP paired with Artificial intelligence ensures that each customer is delivered a unique 1:1 personalized experience in real-time.
Target customers with personalized offers through cross-device
Personalization is limited if it is restricted to a single device. Research has shown that almost 35% of customer conversions happen on a device other than the one where the first impression occurred. A CDP helps capture user details cross-device and unifies them to provide a single customer view. Financial marketers increase the marketing campaign effectiveness with personalized offers through cross-device.
Conclusion
In this digital age, financial institutions are struggling to increase digital engagement and growth. The gap between them and the end customer is growing wide day after day. Financial marketers must, therefore, bridge the gap by exploring, identifying, and leveraging new technologies such as CDPs and AI. This will help them to stay in the game and attract new business growth and increase digital customer engagement.
July 11, 2019
Rightsize Your Martech Stack with These 4 Essentials
As of this writing, there are over 7,000 martech solutions available, compared with only 150 in 2011. Having a big martech stack isn’t that big of a deal anymore. If you’ve got the budget, you can purchase loads of martech to do just about anything. But going on a martech shopping spree will cost you more than money: it may disrupt your focus. Try telling your customers, “we’re too busy buying and implementing martech to focus on you,” and see how that goes. Martech exists to help your B2B marketing team drive improved engagement with customers.
You need a martech stack that provides you with a personalization engine which you can use to drive more relevant content to your customers, something a customer data platform (CDP) helps give you (more on CDPs later).
Rightsizing your stack
The “right” size of your martech stack depends upon your team’s objectives and existing resources. You don’t have to be a minimalist like Marie Kondo to understand that “less can be more.” Start by clearly defining your goals. You don’t want more martech: you want the “right” martech to do the work your marketing team needs done to drive more personalized customer engagement. Understand that the “R” in ROI stands for the return you expect.
Rightsizing your stack is a careful process that requires: (1) analyzing your goals; (2) auditing existing capabilities/resources (including martech); (3) identifying gaps in your capabilities; (4) evaluating what’s available (including martech) to close those gaps; and (5) implementing (and integrating) solutions to enhance your capabilities -- which may mean buying the “right” martech to get your personalization engine revving. Defaulting to “just buy more martech” wastes money and potentially gets your team lost in a martech hairball, where goals and martech are so misaligned that you spend more time untangling the mess than driving ROI.
With all that said, there are basic martech tools that every marketing organization will want to have to drive personalization and relevance.
4 Martech stack essentials
MAP. Back when they became popular in the early 2000s, marketing automation platforms were viewed as “all-inclusive” solutions which marketers could just set up and watch perform every marketing task. Well, MAPs haven’t quite eliminated the need for human marketers. According to MAP vendor Marketo (competitors include Eloqua and HubSpot), MAPs allow B2B marketing teams “to streamline, automate, and measure marketing tasks and workflows, so they can increase operational efficiency and grow revenue faster.”
CMS. A content management system is an application that allows marketers to create and manage digital content in order to engage their customers. A CMS (WordPress and Drupal are popular vendors) is typically divided into two parts: a content management application (CMA) and a content delivery application (CDA). The CMA is a graphical user interface that allows marketers to easily manage the creation, modification and removal of content from a website. The CDA provides the back-end services that support content management and delivery. If you produce and distribute content, you need a CMS.
CRM. A customer relationship management system manages your relationships with customers and potential customers so you can drive ROI. A CRM helps everyone in your organization streamline and coordinate customer interactions, and improve KPIs such as customer retention, cross-selling, and profitability per customer. As CRM vendor Salesforce (competitors include Zoho and Insightly) explains, a CRM “gives you a clear overview of your customer” through “a simple, customizable dashboard that can tell you a customer’s previous history, the status of their orders, any outstanding customer service issues, and more.”
CDP. As the old data science truism goes, “garbage in, garbage out.” A customer data platform takes raw, unfiltered data and enhances its quality and usability in order to provide you an always-on engine to drive personalization and ongoing marketing relevance (and ROI). Unfiltered data goes into the CDP and comes out as a unified and complete view of your customers, as business intelligence, and as actionable insights that enable you to (among other things) personalize engagement with customers, build precise customer profiles and segments, optimize campaign performance, and boost marketing ROI. With a CDP, you can drive marketing relevance in real-time, at every step of the customer journey.
A CDP enables multiple approaches to B2B marketing, from customer segmentation to ABM, and beyond. CDPs with AI/machine learning (AutoML) enable you to identify customer behavioral patterns and apply predictive analytics to boost marketing ROI. You might “get predictive” to tackle customer churn and drive retention by anticipating when and why some customers leave your funnel, taking proactive marketing action to prevent leakage.
Through adopting an ABM strategy, you could identify, profile and segment a select group of your key accounts for your ABM team to target with messaging that better meets their pain points and expectations, thus driving more ROI per key account. You could also identify up-selling and cross-selling opportunities, or craft special offers for specific customer segments whether inside or outside your ABM programs, to drive relevance and profitability. The use cases for a CDP are massive.
Once you have these 4 martech essentials, how you build the rest of your stack depends on your goals, your capability gaps, and your budgets. With a CDP and these other tools, you can build an always-on personalization engine to continually engage your B2B customers and grow your revenues. If you want to learn more about the what, why, and how of CDPs, then visit www.zylotech.com today.
July 8, 2019
7 Use Cases of a Customer Data Platform for Financial Services Marketers
One of the biggest challenges faced by Financial Services marketers is to set up a systematic utilization of their vast database across different systems. A Customer Data Platform (CDP) is the answer to the woes of all financial marketers who want to make the best use of their customer data and leverage them for marketing and lead conversion purposes. As per CDP Institute, “A Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.”
Here are 7 use cases where CDPs can be used to enhance customer engagement and increase lead conversions by Financial Services marketers:
1. Collection of data in its original form
Frequent modifications on the original source data make it difficult for marketers to pre-determine the use of data for their marketing strategies. So it is important to store customer data in its original form to ensure that the data can be useful in the future. A Customer Data Platform designed for financial services collects data in its original form from all sources. The data ingested could be structured, semi-structured, and unstructured data and it is stored without any modification. This data can be transformed, unified, and re-formatted by marketers to use in marketing programs and for analysis. CDPs include functions that simplify the addition of new data sources and transformations.
2. Creating customer one view
A CDP unifies the data captured from different sources into a single view for each customer. It brings together data from sources such as first-party behavioral data, second-party partner data, third-party audience persona data, CRM data, next best offer (NBO) data, and any other customer data touch points. The data is structured into a single customer profile with the help of a unique identifier (CRM ID, Email ID, Contact number, etc.). These unified profiles will provide a 360-degree view of each customer and help marketers in segmented targeting for customer engagements.
3. Powering different marketing systems
The collective data in the unified customer database of a CDP is easily available to other marketing systems, typically through an API or database query. It can be used to power multiple marketing stacks to improve customer engagement across channels. A CDP can be used as the central integration point for all other systems.
4. Cross-device data coordination
User interactions with different devices are captured by the CDP and a unified customer view is formulated to create a unique journey for each user. The unified database is used to identify the user’s preferred experience on each device by tracking their online behavioral patterns. Personalized messages and offers are sent to the preferred devices of each user based on their individual journeys.
5. Real-time personalization
A CDP uses real-time triggers to power personalized customer engagement. Users are targeted with messages based on their online activities and behavioral data. For example, a user visits the website of an insurance brand and visits the life insurance page but then drops off from the website without taking any action. So the next time when the user visits the website, he is shown offers based on his previous activity on the website, i.e. offers on life insurance products.
6. Call center integration
CDP can be used by marketers to tie their online and offline lead generation activities together. Data flow can be both ways to ensure that user engagement is optimized from both ends. Marketers can share real-time website revisits of a lead with a call center agent to contact the user immediately. On the other hand, call center data of a lead can be ingested into the CDP and this data can be used to send out emails or browser-push messages to the user as per their last conversation with the agent.
7. Leverage AI for channel marketing
A CDP can leverage Artificial Intelligence algorithms and can pull historical information about the user’s buying intent and calculate the next best offer on products based on a user’s interest. Financial marketers can create marketing campaigns based on such data and target users with the calculated price offer and products. AI can enable marketers to send push notifications, emails or SMS to users who have never made any purchases from the website. Personalization is based on look-alike modeling using the historical data of the visitor’s online activities.
July 4, 2019
PARCO Fights the “Retailpocalypse” with Omnichannel Personalization
“Data-driven company” is a title that many businesses covet. But PARCO Co. Ltd., which owns a chain of retail malls in Japan, has actually earned that title, in part through its innovative use of a CDP to leverage data from far-ranging sources. It’s on the front lines of successful efforts to combine CRM and in-store sales data with some of the sexiest, cutting-edge sources of data. Geolocation information, Internet of Things (IoT) data, loyalty program information, cell phone app data, and even weather sensors on mall rooftops feed real-time data into PARCO’s strategy for omnichannel personalization, which relies heavily on Arm Treasure Data’s CDP.
These data-driven marketing and sales innovations are getting results that deserve to be the envy of many retail organizations facing the brick-and-mortar “retailpocalypse” that has already taken out or taken down some of the most familiar names in retail.
Omnichannel Personalization Competes with Big Online Retailers
With the expansion of ecommerce caused by the widespread use of smartphones, the market has seen the emergence of big players such as Amazon and Rakuten, exposing retail businesses with brick-and-mortar stores to what is often called the “e-commerce threat.” But Naotaka Hayashi, Executive Officer for the Group ICT Strategy Office of PARCO, has a philosophy about competing with these giants.
“I do not think that e-commerce and physical stores conflict with each other. We have just embarked on a journey to find a way to utilize digital data and our physical stores, to increase customer engagement,” Hayashi says.
How does PARCO execute on its long-term strategy for strong brick-and-mortar stores in the digital age?
Increasing Customer Lifetime Value with Technology-backed Customer Service
PARCO’s core business is the operation of shopping centers, meaning that those who actually sell products are the staff members working at about 3,000 tenant shops in PARCOs nationwide. In other words, PARCO’s marketing mission is to support the customer service and sales of such tenant staff, acting as a bridge between customers and tenants.
That’s a much closer relationship than most American mall owners have with their tenants, and it means that PARCO’s strategy is far more tightly coupled with its tenants’ strategies.
“The tenant sales volume is the sum of customers’ lifetime values, while it also means the sum of the lifetime value of customer service by tenant staff. It is very important to increase their numbers,” says Hayashi.
The role of technology in PARCO is “expanding customer service through technology,” as Hayashi puts it. Instead of considering the digital channel as the complete opposite of physical stores, the company uses the digital channel as a tool to enhance the customer service in person.
PARCO’s omnichannel retail strategy was born in 2013 as an effort to expand customer service in its physical shops. First, the company optimized shop websites for smartphones and left all the communications on the website at the hands of tenant staff, rather than PARCO’s. Along with these shop blogs, PARCO released its official smartphone app, “POCKET PARCO” several years ago, with the aim of further fortifying digital touchpoints.
Mobile Apps: A World of Omnichannel Personalization Possibilities
The POCKET PARCO mobile app comes with a feature that distributes information about events, promotions, and coupons from PARCO and tenant shops. It also provides shopping support features during actual store visits. In return, it gathers a treasure trove of data that fuels new insights.
The POCKET PARCO app has provided a wealth of data and insights for omnichannel retailing, especially from geolocation data.
“Before, shopping centers such as ours did not have a way of knowing the entrance a customer takes to enter the store, which shops the customer stops at, and whether or not any purchases were made there. But the use of the app is helping us make sense of customer activity and personalize our interactions for each customer,” said Hayashi.
According to Hayashi, data marketing using POCKET PARCO is roughly divided into two big efforts: the visualization of reward-motivated customer behaviors and understanding customers’ satisfaction level after shopping.
The company’s reward program uses “coins.” Customers are rewarded a coin when they clip (as a favorite) blog articles of tenants, check into shops, make payments using registered credit cards or prepaid cards, and so on. These coins serve their purpose as motivation: once a customer collects a certain number of coins, the customer can get a gift certificate in exchange. Through POCKET PARCO, customers’ shopping information and behavior in the store can be visualized as statistical information.
What Affects Customer Behavior Most?
Hayashi emphasizes the importance of a marketing tactic that uses a POCKET PARCO function to clip a shop’s blog. According to Hayashi, data analysis revealed a correlation between clipping a blog article at a shop and an increase in the number of shoppers or purchases in that shop. PARCO uses this insight, along with individual customer data profiles from its customer data platform (CDP), to optimize the offers for each customer.
By using such an “equation,” PARCO optimized recommendations in blog articles according to the customers’ preferences and behavioral data. Specifically, they use artificial intelligence (AI) and machine learning (ML) to analyze which shops’ articles customers frequently clip and where the customers go after clipping each blog. The results are used to decide which articles to show customers.
“Our one-year analysis revealed that a customer who clipped a shop article had a 35 percent higher tendency to visit a shop and make a purchase than a customer who did not clip. Such a big difference in subsequent actions depends solely on whether an article is attractive enough to induce a tap or not, so it is very important to increase recommendation accuracy by AI and other tools,” said Hayashi.
Geolocation Data, Smartphones, and Omnichannel Personalization
The company has also introduced a new POCKET PARCO function called “PARCO WALKING COIN” that uses the step counter of a smartphone and measures the number of steps a user takes in a PARCO store. The function essentially gamifies the literal customer journey through a mall; when customers achieve the target in-store step count, they get a reward coin for redemption toward purchases. To get the coin, customers often visit stores they’ve never gone to, making extra purchases and increasing customer lifetime value.
“The effect of coin promotions on purchases is great,” says Hayashi.
PARCO also sends push notifications to those who have not visited the store for a while after their first purchase. As a result of A/B testing, the company realized that customers who received such push notifications had an 8 percent higher repeat purchase rate than those who didn’t get the promotion. “Data-based customer segmentation and personalization of app notification are generating great effects,” says Hayashi.
Hayashi believes in the entwined, dual-power relationship between physical stores and digital channels in PARCO, a belief that has been reinforced with CDP insights into the data collected from so many shoppers.
It’s a vision similar to that of many experts such as Forrester, who say that successful retailers will combine interesting experiences that can only be done in person, with data from many different digital channels — such as social media, loyalty programs, online purchases, even weather and geolocation data — to give each customer what he or she craves, encouraging them to buy more overall. It’s a data-driven vision that PARCO is embracing wholeheartedly in its operations to “seize the day” and create its own destiny.
“We cannot change the past, but we can change the future,” says Hayashi. “We will steadily work to collect and analyze past data and use what we learn to create our future.”
It’s no longer a secret that a customer data platform helps marketers achieve a unified view of their customers and improves their day-to-day efficiency. Or that they help marketers cut down on the time they spend cleaning and aggregating their customer data from various systems. Or that their segmentation capabilities help marketers create detailed customer profiles, and in turn help them create personalized marketing communications that resonate with their customers.
You get the picture.
What many people may not realize, however, is that there are unintended consequences to using customer data platforms. Sure, they do all the useful things mentioned above (and more), but here’s a look at three things marketers will definitely miss doing once they start using a customer data platform.
#1: Frequent chats with IT
One of the nice things about customer data platforms is that they make running database searches easy and storing customer data a breeze. Their simplicity is a large part of their appeal, in fact. So, marketers will be disappointed to learn that they will no longer have to rely on their favorite member of the IT staff to run database queries and update data for them. The data stored in the platform is easy to access and update, and running customized reports and performing list pulls doesn’t require any complicated SQL commands.
A close relationship between IT and marketing is important, though, so make sure to say hello to your friends over there every now and then. In fairness, though, they’re probably tired of hearing from you and are likely very excited about your newly embraced self-reliance.
#2: Killing time waiting for your query to finish
Traditional customer databases typically contain massive amounts of information in them, and as a result don’t process customized queries at the lightspeed pace most marketers work at. This can be a nightmare for those looking to quickly shift gears on existing campaigns or get a new one started. Luckily, customer data platforms can process queries nearly instantly, delivering easily digestible results. They also allow marketers to easily create custom queries, allowing marketers to experiment with and explore their data.
Unfortunately, this means marketers won’t be able to take their usual long lunch while waiting for their queries to finish. This in turn will lead to an increase in productivity, better overall campaign planning, and better campaign results. A real nightmare scenario.
#3: Remembering all your login credentials
Most marketing teams take advantage of a variety of systems to stay connected with their customers. A typical marketing technology stack often features different systems for automation, customer relationship management, email management, social media engagement, and more. Customer data platforms are integrated with these systems and pull in customer data from all of them to help create a more detailed view of their customers and contacts.
So there goes the fun game of trying to remember which Pa55w0rd has the ‘!’ at the end and which childhood pet was the favorite the day the account was setup. Marketers will have to turn to daily crossword or sudoku puzzles in order to keep their minds fresh, or look to Tom Brady’s brain training regimen to ensure their place as the GOAT.
Businesses that create a true omnichannel experience can exceed customer expectations and move ahead of the competition. At the heart of it, omnichannel is about using customer (and potential customer) data wisely to create a seamless customer experience, regardless of how the customer chooses to contact the brand.
There are a few good reasons that marketers should be leading the charge to omnichannel. First, the marketing department is responsible for many of these channels, including email and social media. Second, a great deal of the data that makes omnichannel work really lives with the marketing department.
To help guide the organization to a more profitable customer experience, marketers need to understand true omnichannel: what makes it work, how it differs from multichannel, and how to upgrade from one to the other. Read on to get started.
What Does Multi-Channel Really Mean?
Multichannel customer service means that customers can communicate with your business in a variety of different ways. They can send an email, call you on the phone, text you or tweet at you and expect a prompt response.
This type of availability is a good place to start. It’s certainly an improvement over insisting customers use fax machines or landlines to get ahold of your business. But, just being available on multiple channels doesn’t guarantee a consistent customer experience across them.
In practice, customers often feel like they’re starting over whenever they switch channels. The person answering email doesn’t know about the phone call with customer service. The phone bank has no access to the customer’s chat history. It all adds up to a frustrating experience that is only multiplied with each new channel.
What Is an Omnichannel Customer Experience?
Omnichannel marketing is a step up from multichannel. It goes beyond being available on multiple channels; instead, the goal is that channel shouldn’t matter to the customer experience. Regardless of how a customer chooses to contact your business, they can pick up the conversation where they left off. Think of it like a home entertainment system that lets you move from room to room, watching your shows on whatever connected devices are there, flipping from device to device.
Customers prefer this type of seamless customer experience, but they rarely get it. Think about your experience as a customer: Aren’t you pleasantly surprised when you don’t have to repeat the same information ad nauseum? Most businesses struggle to retain information on the same channel — for example, across multiple phone calls — to say nothing of maintaining consistency across all channels.
Imagine if the next time you called a customer service line, they greeted you by name, knew all about your order history, and had a record of every previous interaction. That kind of personalization, that level of customer care, is what inspires raving fans. And that’s what true omnichannel really means.
Upgrading from Multichannel to Omnichannel Customer Experience
At the heart of it, omnichannel means a shift from an incident-based system to a customer-based one. This may seem like a daunting task. But with the right change in mindset and some minor investments in resources and technology, it can be done. Here are the crucial considerations for developing omnichannel in your customer experience:
Data Management
First, you have to know who your customer is, what data they’re generating, and where it’s stored. It’s important to have a complete picture of your data landscape to start breaking down silos and making customer data seamlessly available across customer service. Companies already using a CDP have a real advantage here, since they’re already collecting and integrating data into unified customer profiles that can be used for targeting, segmentation, personalized offers, and more.
Single View of the Customer
Once you have mapped your data landscape, work on bringing data together into a single dashboard. The right data management software can help you create a holistic view of the customer for every individual customer journey, uniting channels into one seamless conversation.
Organizational Buy-In
Once you have customer information consolidated and readily available, it’s important to make sure the team has a will to use it. From leadership on down, it should be clear that the organization prizes an omnichannel customer experience, and that everyone should use the available tools to achieve it.
Focus on Customers, not Channels
In the end, how the customer chooses to interact with the brand will almost be irrelevant. Anywhere the interaction takes place, the data will be added to the conversation. Your team can view each customer action in full context, not as a series of isolated issues.
Start the Omnichannel Conversation
Is your customer experience omnichannel, or just multichannel? It’s a crucial question. By the year 2020 customer experience will overtake price and product as the key differentiator for businesses. It’s high time to map your data landscape, break down silos, and create a consistent customer experience on every channel.
June 26, 2019
Seven Signs You Should Build a Customer Data Platform (CDP)
Companies and marketers often face challenges while understanding their customers, giving rise to ineffective marketing experiences. Many of them end up marketing products to customers who already have those products or have no interest in them. This is often due to fragmented data sources and the inability to utilize them in a smart manner. So, how does one create an integrated view of their visitors and customers?
This is where a Customer Data Platform steps in. A CDP gives marketers a unified view of their customers, with real-time interactions and data stitched in from multiple databases, compiling readable customer profiles to study, analyze and market to.
The best thing about a Customer Data Platform (CDP) database is its ability to plug it into other systems, allowing marketers to access and use it for further use. So, how do you find out if your brand can benefit from a Customer Data Platform? Here are a few signs you need to look out for.
1. You have an incomplete or limited view of your customers
Every smart business generates and collates data through multiple channels, such as websites, call centers, email and social channels, to name a few. All this raw data amounts to vast volumes of records spread across different systems. This data is a veritable mine of information on consumer behavior for marketers, if assembled and presented correctly. A Customer Data Platform automates this difficult process, eliminates inaccuracies and duplicates, and seamlessly merges, cleans, matches and combines data to form a comprehensive single source in real time that can be consumed by marketers to maximize sales opportunities.
This is how a Customer Data Platform provides a single, accurate and 360° view of your customers. It stitches different customer data sources on web, social, POS systems, mobile wallet apps, customer interactions, web ordering systems, SKU tracking, and in-app ordering together with ease to create a single customer profile, which is updated in real time. The data is accessible and actionable to any system, allowing you to drive innovation with the latest datasets.
2. Your online advertising is not providing the expected ROI
If your online advertising isn’t providing the right results, there’s a high probability that you’re reaching out to the wrong audience or advertising to them at the wrong time. By implementing the CDP and integrating it with your advertising platforms, you can significantly increase the effectiveness of online advertising through very pointed retargeting in real time.
3. You find the task of sifting through available customer data daunting
Marketers often face the challenge of cleaning and sifting through large amounts of data, which is a tedious and formidable process. A CDP allows the unification of data from different sources and automates this task, thereby reducing the need for data cleaning and prep. Now, you can segment, assess, manage and generate reports with ease.
4. You are not building campaigns and communications based on behavior
Are you unable to trigger campaigns to individual customers based on historical, locational and behavioral data? Are you facing difficulties in tracking customer behavior across all channels and providing intelligent recommendations based on customer-journey triggers? If you answered yes to both, then Customer DP is the solution for you. It allows you to set up targeted campaigns focusing on underperforming areas and enticing the right customers, instead of blind marketing.
5. You are not tracking consumer behavior and usage online
Several marketers admit that they do not have any analytics in place online or on their provided apps and services. If you are among those, you need to start tracking your consumer’s online behavior and usage now to successfully predict their behavior! With access to multiple internet accessible devices, being online has become an integral part of our lives and marketers need to keep pace with where their consumers are. A CDP efficiently enables observing and listening to your customers and their needs online in real time. Customers are very vocal and expressive on the web and this information can be accurately interpreted to swiftly serve them relevant content that is more likely to pique their interest.
6. Real-time personalization is missing from your brand’s website
Wouldn’t you want to know if a potential customer arrived on your website? Wouldn’t you want to alter the customer experience accordingly? To offer this level of one-to-one personalization, you need real-time information about your visitors. Customization is the key to successful interaction, and this is exactly what you can work towards when you choose to adopt a Customer Data Platform.
7. You have abandoned your omni-channel marketing strategy
To conclude, a Customer Data Platform orchestrates relevant engagements and streamlines all elements across web, email, mobile and social for a seamless and profitable experience for all involved. By building the right CDP, you’re not only able to personalize the experience, but can also reduce the cost of advertising.
The Customer Data Platform has the potential to transform the marketing industry and customer engagement.
Want to learn how other organizations are building their CDP? Reach out to me at cdp@hgsdigital.com.
June 24, 2019
How The Motley Fool found more high-value customers at a lower cost
If The Motley Fool’s goal is to make the world smarter, happier, and richer, to do that, they need to scale — not just by creating more content or offering more newsletters, but also by personalizing for their readers based on their individual context. Here’s how they pulled it off.
If you’re in the financial space, you probably already know the name The Motley Fool.
The brand is on a mission to make the world smarter, happier, and richer, and over the years, they’ve been doing just that — and gaining a lot of reader trust and brand recognition along the way.
But as with any successful company, sticking with the same old strategies year after year isn’t a recipe for continuing success. Which is why as customers began demanding more and more personalization and companies like Amazon started seeing huge returns on their investment in customer data, The Motley Fool took note and started down their own path to data-driven personalization success.
We wrote about the brand before, telling you how they reduced cost per acquisition by 20%. And recently, we were thrilled to co-present a webinar with their Channel Marketing Manager, Sylvia Sierra.
She shared a few more insights about how The Motley Fool is finding high-value customers at low cost. Today, we’d like to pass some of those insights along to you. We recommend watching the whole webinar (you’ll find it here), but here are some of the highlights:
The problem of scaling relevance
If The Motley Fool’s goal is to make the world smarter, happier, and richer, to do that, they need to scale — not just by creating more content or offering more newsletters, but also by personalizing for their readers at scale.
The core question they came to Lytics asking was this: As customers and prospects multiply, how do we make sure we’re serving them all in the right channel at the right time?
The answer started with identifying what was holding them back from doing that. According to Sylvia, the three core challenges were siloed data (which created scarcity and inefficiency), a disconnected digital journey, and inefficient access to the insights they needed across the business.
As Sylvia explains in the webinar: “[The storage of client data] had scarcity and inefficiency built into it. We had a number of different [data silos] that were accessible not by the marketers but by the business information unit…Our technical staff was tasked with accessing the data. That was not an efficient way to do things. The data was not democratized…
“I think we [marketers] are all going through these same issues. Our technical staff are in demand all the time and trying to wait for business intelligence teams to pull out insights on a campaign-by-campaign basis makes scaling very difficult.”
And so The Motley Fool knew they needed insights and answers from their data that would allow them to scale relevancy. They needed to connect digital journeys to individual clients to serve them better. They needed to better understand the interactions of their highest value clients and identify more prospects like them. They needed to uncover real user intentions.
Before Lytics, that data wasn’t easy to come by. But once they had Lytics set up, things started to change.
Centralizing data to understand customers
The process started, as most data processes do, by centralizing customer data and making it directly accessible to the marketing team. No more asking business intelligence to uncover insights and deliver reports. No more waiting days or weeks for results.
Now, the data was at their fingertips and right away they could start understanding who their highest value clients were and what those clients had in common.
Converting prospects to high lifetime-value memberships
According to Sylvia, three huge, immediate benefits came out of this process. The first? The brand was able to decrease the cost of acquisition by 20% for their highest value members.
They did it by partnering with business intelligence to figure out who they believed were more likely to convert. They looked at frequency of interactions with content, what led up to conversions, and what touch points seemed vital along the way.
Then, using those insights, they created three audiences — platinum, gold, and silver — and used the data on those audiences (including what actions they took before purchasing) to identify who was likely to become a high-lifetime-value customer and focus their time, budget, and energy on those highest-value customers.
“The theory panned out,” Sylvia says. “We developed the three audiences and tested on Facebook, Google, and Yahoo! Finance. We observed and enjoyed a 20% lower cost of acquisition for some of our highest value members.”
Finding more of their best customers through lookalike audiences
Using tools like Facebook and Google, The Motley Fool also took that data on high-lifetime-value customers and created lookalike audiences to identify more potential customers who looked like their best.
The more they understood customer behaviors, interactions, and content affinities, the better their targeting got.
Serving members content they love
The third key benefit of The Motley Fool’s new customer data strategy and toolset was all about giving existing customers the best experience possible.
“We do this,” Sylvia explains, “through the personalization side of Lytics. We [used to] serve ads based on…the campaign of the day…[But] what if [instead] we served people based on what we know they’re actually reading?”
“If people are interested in investment vs. retirement, how do we serve the content differently [to those different audiences]? [For those who] react to messages about Amazon…how about sending them an exit pop-up [about ‘the next Amazon’]?
Serving more relevant content to people…creates more stickiness. More returns to the content. And a higher engagement with the brand…Good content drives purchases. So surfacing more of that content has been very positive for us.”
Results like these for your business
These results aren’t unusual for Lytics clients. A 20% decrease in acquisition cost. An uptick in purchases driven by surfacing the best content. Better customer data and insights from that data often result in big wins for marketing.
For advertising to work, it needs to be accurately targeted. Good ads speak to customers about their personal needs and interests, which is why they generate sales. But accurate targeting is notoriously difficult and it’s common to waste half a marketing budget on directing ads to people who simply aren’t suited to the product or service on offer. Many marketers are now trying to overcome the challenge of targeting by using Data Marketing Platforms (DMPs) to create lookalike audiences with limited understanding of their current customers. While DMPs are effective at providing aggregate-level data, they often fall short when it comes to improving targeting – and they don’t necessarily help marketers understand their customers across a lifecycle, encompassing their first visit, research, purchase and post-purchase.
Programmatic advertising is another emerging industry which marketers have been experimenting with varying degrees of success. Programmatic advertising helps buy ads in real-time and target to specific individuals. While programmatic advertising can seem efficient, it’s actually more expensive. If you simply rely on DMP data on whom to target, you still are making advertising decisions on how a 3rd-party company (DMP) categorizes and defines the user persona. This lack of transparency can make it difficult for brands to understand exactly where their budgets are going and whom they’re targeting.
Programmatic advertising and DMPs are both valuable tools for marketers. But a system that brings together all relevant data for targeting a known profile can lead to best possible results.
Solution to the Challenge of How to Achieve Accurate Targeting
There is a great opportunity for marketers to use big data to develop a better understanding of customers and improve targeting. The trouble is, most marketers lack the knowledge to do this effectively. Customer Data Platforms (CDPs) help marketers understand their customers by bringing together all relevant data already available within the organization to create a 360-degree customer profile. And unlike DMPs, which collect anonymous data, CDPs collect data that can be tied to an individual, which makes them extremely useful for marketers who want to target customers with personalized content. Perhaps unsurprisingly, an increasing number of brands recognize the benefits of CDPs. According to a report by the Customer Data Platform Institute, CDP vendors reported more than $300 million revenue in 2016, and the industry is expected to reach more than $1 billion by 2019.
Here’s a closer look at how CDPs can be used to increase advertising ROI:
Step 1: Bring Together Data from Various Systems
CDPs collect, collate and centralize all incoming data across several sources and channels, helping marketers ensure they are targeting the right audience. The ability to view such a broad set of data for a profile makes it easier to segment the audience based on whom you’re targeting.
Step 2a: Target the Right Visitors
The data about the audience from CDP is sent to a DMP to gather additional data points where possible and create a larger data set for the audience. This enables better targeting of the audiences. CDP data is then used for retargeting ads in conjunction with programmatic advertising platforms and demand-side platforms (DSPs).
Step 2b: Target Look-Alike Audiences Using DMP
The targeted data set from CDP data is fed into a DMP to create a look-alike audience set. This audience is then targeted with advertising through integration with a DSP.
Step 2c: Feed CDP Data into Other Social Advertising Platforms
CDP data is used for highly accurate retargeting on Facebook, Twitter, Instagram, YouTube, LinkedIn, and other social networks. CDP data is used to generate look-alike models on these sites.
Taking Customer Targeting to the Next Level
CDPs fulfil the marketing industry’s need for data-driven analytics while providing multi-channel integration that makes it much easier to provide customers with a seamless experience. CDPs consolidate all existing data, update it with the help of artificial intelligence and apply real interactions so marketers can chart micro-decisions that can make the difference between gaining and losing a customer. CDPs also solve several issues that have plagued advertising for years, including outdated or fragmented data, incomplete or incorrect information and a lack of customer behavior tracking. Marketers can now personalize their products and services with sophisticated algorithms supported by AI and machine learning – all thanks to CDPs. What’s more, information can be continuously fed into a CDP, ensuring that customer profiles are constantly updated.
Additional Best Practices to Further Enhance Advertising ROI
Here are a few more best practices that can help improve advertising ROI:
Keep experimenting with advertising segments – perform A/B testing
Measure results for each experiment and act on them
Look-alike audiences can be expensive, which is why marketers should experiment with a smaller data set first before trying out larger data sets
Create a feedback loop to take the activity data from various ads and input it back into CDP – this enables continuous fine-tuning of ads
Create multi-stage, in-depth, multi-channel campaigns and leverage CDP, DMP, programmatic advertising, data sciences to achieve the best possible ROI
For help or more information on how to achieve better advertising ROI, reach out to me at cdp@hgsdigital.com.
June 19, 2019
Better late than never: Salesforce acquires Tableau… 3 years after Manthan-Tableau integration
Manthan is always looking for ways to make data consumption easy and natural for those who need it the most and are pressed for time. From AI-driven intuitive interfaces to self-serve customizable dashboards, we are constantly pushing the bar to provide the B2C segment insights into their data using our analytics software.
In line with this vision, Manthan partnered with Tableau over 3 years ago to bring together Manthan’s deep expertise in pre-built, industry specific analytics with Tableau’s rich visual analytics.
This week, Salesforce came to the same conclusion, when it announced its intent to buy Tableau. Salesforce’s decision to acquire Tableau highlights their shift from CRM into the analytics field.
Is Salesforce just patching up a flop?
“The acquisition will test Salesforce’s focus”, said Zane Chrane, an analyst at Sanford C. Bernstein & Co. According to Chrane, Business Intelligence is “not Salesforce’s core competency and there is much Tableau does that doesn’t pertain to the CRM world, making the fit slightly imperfect.”
According to this Bloomberg article, the acquisition is also an “implicit admission” that Salesforce’s analytics product, Wave, “was a flop.”
Understanding the drive towards self-serve analytics
Manthan customers can directly access data using the Manthan Customer Data Platform through Tableau and publish dashboards. The solution merges the benefits of strong data governance and pre-packaged data sciences (served by Manthan), and business dashboards (served by Tableau), all in one holistic solution.
Manthan had a CDP in 2012, even before the term was formalized in 2013. Earlier this year, Salesforce recognized its need, and they are now building a CDP.
With over 6 years of serving both business and data science users in the retail industry, and now successfully venturing into the QSR segment, Manthan has long understood how critical customer data management is, and the need for democratizing data and analytics.
Before there was a Customer Data Platform, there was…
…Manthan Customer360, a unique customer data and AI platform to serve as the foundation for personalized marketing. It made the life of the marketer, analyst, data scientist, and IT team easier by helping them organize customer data effectively. Out of the box analytical capabilities directly helped in creating better and targeted campaigns much faster.
Manthan has been a pioneer in the CDP space and has been offering the vertical specific CDP with advanced analytics capabilities to retail businesses globally long before the term was even coined.
Salesforce is behind us on both counts. The way I see it, they’re playing catch up with Manthan.
Let’s not forget the AI-driven power-up
Last year, we added AI capabilities to our Customer Data Platform for B2C enterprises; this enabled the system to constantly upgrade unified customer profiles based on every action, generate astute customer insights and action them with personalized product and offer recommendations.
With Manthan Maya, these insights are made available through an easy conversational interface to business users. This is AI on your desk, and in your pocket on your mobile phone – you ask a question in English and get the answer, without having to sift through lengthy reports. This means the ability to proactively act on opportunities and respond to market.
Predicting what’s needed for a future-proof offering is something that comes easy to Manthan.
According to TechCrunch, post the Tableau acquisition, Salesforce is now looking into the possibilities of expanding their AI-based initiatives.
Any bets on who Salesforce will acquire next to bring Einstein to life?
June 17, 2019
Leveraging Customer Analytics to Reduce Churn Rates and Grow Marketing ROI
Customer retention, often measured by “churn” rate (the percentage of existing customers who leave in a specified period of time), is the most important success factor/KPI for any business. When customers stay, your business can build long-term profitability through repeat purchases, as well as cross-selling and up-selling opportunities. When you retain customers and optimize their lifetime value, you also create brand ambassadors who give you priceless word-of-mouth marketing and referrals. “Churn,” on the other hand, is a revenue killer.
As a recent Forbes article explains, “it can cost five times more to attract a new customer, than it does to retain an existing one. Increasing customer retention rates by 5% increases profits by 25% to 95%.” Not to belabor the point, but your business simply must retain existing customers in order to grow. This post will explain how customer intelligence via analytics can help you reduce churn.
Customer analytics for the customer journey
Customer analytics can drive retention and engagement throughout the entire customer journey, from generating top-of-funnel leads to the mid-funnel nurturing of leads to bottom-of-the-funnel conversion and closing, turning leads into revenue. Knowing more about your customers, which customer analytics enables, is the best way to engage your customers with relevant messaging that keeps them moving through the funnel to conversion (and greater lifetime value).
For example, you can improve lead generation with bots backed by machine learning who perform quick engagements with your customers, answering basic questions and routing them to relevant products, services, and salespeople. Contrast this real-time, bot engagement with a website form that a prospect might fill out, only to wait weeks for a busy salesperson to answer. Which of those two scenarios drives more engagement, faster?
Customer analytics can help “solve” customer churn
The best way to leverage customer analytics is to map out your entire customer journey and identify places where analytics can help (places where your funnel is “leaky”). In its simplest form, the process would work as follows:
Identify a particular problem that customer analytics can address.
Ensure that your data is ready to be used, that it’s actionable and can be deployed on the particular problem.
Deploy machine learning to address the problem and learn from the data you’re collecting, so your approach becomes “smarter” over time in addressing the problem.
Let’s identify customer “churn” as a big problem-to-be-solved (see step 1), which it certainly is. In step 2, you would need to collect and then leverage relevant data to better understand where and why customers are leaking out of your funnel. Then you’d build a model based upon this data, deploying machine learning, in order to help you predict the moments and the reasons your customers leave. So instead of passively watching customers leak from your funnel, you would know why they leave and be able to proactively engage them to prevent churn.
Machine learning basically uses math, statistics and probability to find connections among variables in your data, helping you optimize important outcomes such as retention. These machine learning models get even smarter at making predictions by constantly integrating new data. The result? You get data-driven insights that lead to marketing actions that retain your customers.
In another example of driving retention and ROI, you might apply customer analytics to better understand your customer’s past purchasing or browsing history, and then build a predictive model that could anticipate the next product a customer (or customer segment) might be interested in buying. This predictive model, using machine learning, would then help you identify the right customers (and the right times) for up-selling or cross-selling opportunities.
To do all this and more, you need the capacity to build models based upon quality data and deploy machine learning so these models get smarter over time, driving relevance and stronger customer engagement. You need a data and analytics platform that allows you to make your data actionable (the old saying remains true, “garbage in, garbage out”). By leveraging an automated customer data platform with machine learning analytical capabilities, you can leverage your data to reduce churn and boost ROI.
June 14, 2019
How Hyper-Personalization Helps Build Loyal Customer Relationships
Businesses and brands have always strived to segment and target their customers to be able to adapt their communication to the exact client needs. With the advent of the digital era, the customer segmentation is now undergoing an enormous transformation.
Today, it’s the age of hyper-personalization, a paradigm shift in the marketing industry that’s made the biggest businesses sit up and take notice. Hyper-personalization demands pushing the boundaries of personalization to an individual level and adapting functionalities and interactions in real time to offer each and every customer a truly unique experience that delights them and keeps them coming back for more.
For effective hyper-personalization, customer data is a prerequisite and rightly so, since it is one of the industry’s most valuable assets. So, what do customer satisfaction, customer loyalty, and hyper-personalization have in common, you may ask. Well, everything.
How does data help with hyper-personalization?
True one-to-one personalization requires a lot of data about every aspect of customer behavior. Mature brands have been pushing the envelope with hyper-personalization thanks to the help of advanced data management and data-driven marketing. And, this is exactly where a good Customer Data Platform (CDP) steps in to take over the reins. It is the Customer Data Platform that processes large amounts of information flows, analyzes this data and converts it into actionable insights that can be utilized by marketers to tactically anticipate a customer’s needs. Personalizing your business’s communication and campaigns can mean making or breaking a brand. Today’s consumers are tech-savvy and demand experiences that are relevant to their requirements, making hyper-personalization one of the best marketing investments you can make.
Gartner predicts that over 50% of companies will redirect investments towards customer experience innovations in 2018.
What’s more, today’s customers are comfortable, to a certain extent, with their favorite brands knowing their individual information, such as product and personal preferences so they can enjoy a tailored experience crafted specifically for them. Customers also value their time, and pre-set buyer journeys allow them to get to their destination faster. This means organizations can use data to hyper-personalize experiences, albeit with customer consent where needed.
How does hyper-personalization help achieve customer satisfaction and drive revenue?
Research supports hyper-personalization, with studies stating that reaching out to a customer personally drives engagement, brand loyalty and therefore, revenue. For instance, studies by VentureBeat displayed results that a simple change such as using a customer’s name in emails pushed the email open rate up 29.3 percent on average. The same study also stated that websites using personalization drove up their page views as well as conversion rate by a large percentage.
Once you segment your customers using behavioral data, you can understand each segment and tweak its respective buyer journey. This is easily achieved by a CDP that integrates data from multiple channels, online and offline. Marketers can study these segments, and the results can then be utilized to create a number of micro-segments, which can then be used to personalize journeys further, much to the customer’s delight.
By focusing on defining your customers, their preferences, issues and challenges, you can access intricate details about the pain points in their purchasing journeys and find out what exactly it is that affects their buying decisions.
Now that you have the capability to listen as a business, you automatically have the means to respond based on the intentions of a customer. For example, if a customer has abandoned their cart after adding products to it, you can deliver a relevant and timely communication with a form of incentivization, like a discount, which can then act as a trigger to the targeted customer. The best part about all this? It’s all automated, thanks to an effectively plugged in CDP.
What is big data’s role in hyper-personalization?
A large number of businesses manage to personalize their communication and product offerings; however, there are many companies who continue to struggle to scale and seamlessly stitch data across multiple channels. This is where technology has a vital role to play, and big data is what helps marketers tie it all together.
To put it simply, hyper-personalization can only be achieved through advanced analytics as this is precisely what allows businesses to react and reach out to customers individually and in real time. It is big data that holds the key to understanding the customer, their personality, attitudes, geographic locations and other underlying factors that can offer marketers the ability to drive personalization in an omni-channel and digitally connected environment.
Big data means nothing if not used correctly. The benefits need to be translated to the consumer and consequently, to the brand. According to McKinsey, personalization can reduce acquisition costs by as much as 50%, lift revenues by 5-15%, and increase the efficiency of marketing spend by 10-30%.
What are the challenges to hyper-personalization?
Well-thought-out customer personalization requires businesses to redefine their strategies to optimize results. A business serious about taking advantage of personalization needs to understand the state of their industry and build the required technological capabilities that support it.
One of the biggest challenges marketers face when it comes to personalizing customer journeys is gleaning relevant information fast enough. CDP steps in here with the help of Artificial Intelligence and Machine Learning to tackle this challenge head on.
Another challenge often faced by businesses is the security and compliance required to manage large amounts of data entrusted by customers to the company. Today, consumers are open to trading their personal information for free or improved services which leads to a large amount of Personally Identifiable Information (PII) data that your business is directly responsible for securing. Adding to this is the IoT (internet of things) and its hyper-connected nature, which can turn out to be a real security nightmare. With a CDP, businesses can control what data is accessed by whom, leading to better governance of data, leading to increased data security.
So, there you have it; to conclude, hyper-personalization doesn’t have to feel like a daunting and overwhelming undertaking that requires big bucks to implement. Successful businesses often begin small, creating a big impact quickly in the Martech ecosystem. Taking the time to incorporate a CDP into your business process will reap your organization awards in the short and long run.
Want to learn how other organizations are building their CDP? Reach out to me at cdp@hgsdigital.com.
June 12, 2019
The difference between real-time and instant data is the difference between personalisation success or failure
In the context of technology marketing, the term real-time has been ubiquitous for as long as I can remember. And for those who are responsible for evaluating and making decisions to procure technology solutions, real-time has become a concept to be wary of. So why the scepticism? Real-time is perhaps the most over used and certainly the most abused phrase in the history of technology. Quite simply, the number of technology vendors who have laid claim to being real-time and the vast range of their capabilities, has made the term meaningless.
Perhaps we should draw a parallel with another industry whose marketers are obsessed with speed – the car industry. This highly regulated market obliges manufacturers to publish auditable performance figures relating to the speed and economy of their products, and these figures are a significant/important evaluation consideration to would be customers. Ironically in the tech industry, where purchases involve much higher levels of investment, and in theory more sophisticated and professional evaluation and procurement processes, there is no such objective performance data available in many cases. If the car industry was like this, budget low powered shopping cars would be claiming supercar performance.
In a market where anyone can claim to have real-time, it is very hard to accurately establish whether a software vendor’s solution is actually capable of enabling your intended use case. Of course, many uses of CDP technology does not require real time capability. For example, most analytics projects do not require data instantaneously because the insights from analytics are generally not applied in-the-moment. Similarly, if your customer data is being used to personalize email or social media campaigns, the data simply needs to arrive before the campaign is launched rather than within milliseconds of the data being captured.
However, if you are aiming to activate customer data for use in-the-moment, it’s hard to deny that you will need the data instantly. For example, if you are using your data to personalize the content of a web page or mobile app, the data needs to arrive within milliseconds, in significantly less time than it takes for the page to load. Even if the data arrives in one second, this in-the-moment personalization, because the data needs to arrive within a decisioning solution which generates a next best action and personalized content must then be created and served to the CMS. All of this needs to happen in significantly less than a second, because the page content will load in less than this time.
Now, it seems reasonable to assume that a vendor claiming real-time capabilities would be able to achieve what I have described above, but not everyone’s definition of real-time is quite this exacting. In reality, the marketing departments of many vendors make bold ‘real-time’ claims for solutions that take multiple seconds, minutes or even hours to connect data. That is why we at Celebrus have found a better term to describe the speed of our data connections. Because true real time capabilities should involve zero latency. In-the-moment personalization requires data to be available instantaneously, and Celebrus is one of a very exclusive selection of solutions that can deliver instant data.
Celebrus instant data is connected within less than 500 milliseconds of the customer interaction taking place. Celebrus’ unique tagging free technology captures the interaction data, identifies and profiles the customer, detects key signals of opportunity or threat within the customer behavior and connects either these signals or the entire data stream within the customer profile to the chosen campaign end point or decisioning solution.
We think the instant data term makes our capabilities a little less ambiguous for anyone considering which CDP to implement. But ultimately it’s not the name that the CDP vendor uses to describe their capabilities that’s important. It’s about defining your use case and determining how quickly you will need data to be available in order to achieve your desired outcome. If you want to achieve genuine, real-time personalization across digital channels, you will need a CDP that can capture and connect customer data within significantly less than a second.
Find out more about Celebrus instant data capabilities. Click here to contact our expert team.
You may have heard the expression, quality doesn’t cost -- it pays. A more precise formulation applies to business in the form of the 1-10-100 rule of data quality. The idea is that while it could cost you $1 to corroborate the data upon entry, it costs $10 to clean it later and $100 to leave it uncorrected due to the various losses that will result from it. How to prevent that happening? Adopt a CDP solution.
Losses due to poor data quality cost the US economy $3.1 trillion annually, according to IBM’s 2016 estimate, and concern about data quality has risen since then. According to Dunn & Bradstreet’s 6th Annual B2B Marketing Data Report, it grew from 75 percent in 2016 to 89 percent this year. It also found that only half of those surveyed express confidence in their own data.
As we discussed here, the CDP is not merely a customer database. A CDP enables businesses to automate best practices for data quality, assuring complete, integrated, updated, and cleansed data. Essentially, a CDP puts into action the core best practices for maintaining data quality: cutting through silos, synthesizing data for strategic marketing, and eliminating the headaches that result from outdated or duplicated data.
1. A CDP solves the silo problem. Bhavesh Vaghela was among the experts whose customer data management tips were shared in an article on best practices. He identified his top customer data management concern as “siloed internal structures.” The problem it poses is not just a lack of convenience in one centralized repository of data but a setup that “stifle[s] collaborative learning and prevent[s] organizations from getting a better understanding of the customer journey.”
Vaghela expounded on why breaking through silos is so crucial: “With a myriad of touch points connecting customers to brands, it’s now more important than ever for these different touch points to connect and form a cohesive brand experience. But when data is not shared between departments, this cripples the ability for companies to make the most informed decisions about their customers.”
2. Getting all the data streams to run together is the first step toward what Joe Pino identifies in the same article as his primary concern: “to focus on centralization, particularly with the fast, furious, fragmented nature of cross-channel customer data.” Achieving that gives you a much more complete picture of what your customer is about. Pinto adds, “In turn, this approach allows marketers to become more strategic, gain greater understanding and have greater engagements in order to deliver relevant messaging.” Ultimately, your centralized data enables you to “start generating powerful results from it.”
3. There is such a thing as too much data that can actually cost you in terms of wasted resources and a less informed look with your customers. Accordingly, Mathew Boaman’s top tip for the article is to “avoid duplicate data.” Even if the bit of digital bandwidth is of no concern, it is a source of “frustration for employees using the data and also potential customers who are receiving inquiries as a result of the duplication.”
Boaman explained why the problem is such a common one:
Duplicity happens very frequently as companies change business names, move addresses or transfer phone numbers. Imagine that both Company A and Company B are both in the same outbound marketing campaign and are simultaneously receiving letters in the mail. This is a waste of money, and also makes the company sending the letter look bad.
To combat that problem, the CDP ascertains that the data is clean and resolves any duplicates that will impede optimal results for your marketing team.
The Zylotech platform assures data quality because it collects and merges both anonymous and known user profiles back to a single record through an automated process in real time. AutoML comes into play to probabilistically match two seemingly different records and create truly complete customer profiles.
On that basis, it gathers together unified customer data, drawn from all relevant sources to build highly intelligent and centralized segments that can be delivered downstream to all your marketing channels. As activations happen across all your connected marketing tools, new events are picked up in real-time by Zylotech, creating the most up-to date 360 view of the customer from which to derive insight through the embedded analytics engine.
A CDP solution makes it easy to avert the $100 losses and even the $10 cost of data quality cleanup. Like quality in general, the investment in data pays.
June 7, 2019
Open the Door for Customer Exploration: Uncovering CDPs’ Irrevocable Powers
Turning your ideation to execution process from a long, stressful, multitouch point one into a smoother, self-sufficient, result-oriented procedure is not a fantasy. Learn what Optimove can do to help you uncover data insights.
I often encounter situations at work that bring me to this invaluable conclusion: Even the smartest marketer’s hands are frequently tied by the lack of technology and shortage of execution tools, that prevent her from achieving her goals and showcasing her skills as an experienced professional.
I want to share an interesting story: One of my closest friends currently works for a B2C enterprise as a marketing manager. We recently met up during the long Easter weekend. While catching up, she shared about the 10-step journey she had to go through to send her Easter promo campaign.
First, she needed to ask the Business Intelligence team to query the database and export the list of customers who fit her desired target audience. It took just two hours to receive the list. Unfortunately, the audience size was smaller than expected, so she needed to rethink and redefine her audience.
“If only I was able to query the DB myself,” she told me. “I could have been way more productive without having to depend on the other team.”
That’s just one aspect of being dependent on others - the data analysis team will then need to analyze the campaign, share the result in an Excel file, and who knows if it will take a day, two days, or arrive by sometime during the following week. From my experience - it’s usually the latter.
You can probably identify with her frustration. How could she turn the ideation to execution process from a long, stressful, multitouch point process that requires having to push most of her work to the last minute into a smoother, self-sufficient, result-oriented procedure, she could have full control over.
Untying the marketer’s hands
In recent months, Customer Data Platforms (CDPs) nabbed the spotlight they deserve in the martech conversation. These solutions allow marketers to collect, analyze and act upon all customer data within one interface among the “must have” tools in the current marketer’s toolbox. And one application inside the CDP I’ve benefitted greatly from is the customer insight discovery.
Marketers face immense pressure to increase their company’s revenue. Many times, we fall into the trap of focusing on acquiring new customers in order to meet these goals. Other times, marketers put an emphasis on maximizing the value of existing customers.
No matter which of these groups you fit into, much of your success will be tied to your ability to uncover insights about your targeted customers. Below, I offer two of my favorite examples (with short videos made with the help of my colleague) on how to leverage your CDP to uncover valuable insights.
1. Which promotion should you offer valuable customers at risk of churn?
As marketers, there’s a fine line we need to tiptoe around when creating promotions to stave off churn. On one hand, we want to provide customers with a juicy offer they can’t resist. On the other hand, we don’t want to cannibalize our revenue with an aggressive offer. By storing all customer data in one place, you can easily access your segment’s history and confidently answer this question:
In the example above, the average order value for this “High-Value High Risk” customer segment was $331. The average price paid per item was $116.3, and the average items per order was 3.4. The marketer who can easily find and act upon this information will probably offer either a 10% discount for orders above $380 (for a total order value just above the segment’s average), or a quantity discount for orders that contain more than four items. As dealing with high-value customers usually involves outliers, exploring the groups key characteristic and reviewing their range could also determine whether this group should be split to more granular groups. In this example, the average and median are relatively similar, meaning this group is pretty homogenous.
2. Are you acquiring valuable customers?
Sometimes, marketers aren’t interested in discovering insights to create promotions, but rather to understand if different actions or strategy changes had a positive impact. One example of positive impact that could result from a strategy shift could be whether customers acquired after the change had higher future values than before. With Facebook ad spend projected to continue growing in 2019, we imagine marketers are trying to understand if new strategies are improving their results. Leveraging a combination of historic and predictive values from your CDP could answer this question in minutes:
As seen in the example above, marketers can quickly compare the future value of customers acquired before and after a Facebook acquisition strategy change. In this case, the average went from $48.3 to $54.2, a massive uplift.
Focusing on insight discovery
Insight discovery is not a new obsession for marketers, but only CDPs allow marketers to do this on their own terms. Your discovery could focus on identifying the ideal segment to engage with, the precise manner in which you should do so, or even what you should offer. And once you’ve run your campaigns, it should focus on seeing whether your efforts impacted company revenues in the short and long term.
This can’t be mastered after quickly glancing at a few how-to articles, which is why our webinar offers in-depth, easy to grasp examples that prove the benefits of customer exploration capabilities.
Demand for customer data platforms has grown internationally in the last two years, particularly in Europe and the UK, so this seemed like a good time to look at the APAC market. Leaders there report Asia-Pacific marketers are acutely aware that they need the functionality of Customer Data Platforms but knowledge of CDPs as a distinct class of software is still in the very early stages, according to local experts interviewed by the CDP Institute. We spoke with executives at Knowesis, Lemnisk, Manthan and MarketSoft to learn more about the state of the market and trends they see evolving.
As in the U.S., the UK and Europe, the main industries to watch for CDP are retail, telecommunications, ecommerce and finance along with government and travel. These industries are feeling pressure to move rapidly to find new ways to meet customer and corporate expectations for improved identity recognition, personalized customer service, increased marketing efficiency, and ROI.
“We are seeing emerging awareness [in Australia/New Zealand] with early adopters of genuine CDPs coming on board,” says Daniel Cummins, CEO of Marketsoft, a consultancy based in Sydney. “At the same time, there are a number of players claiming to be CDPs, but who have products that only offer a portion of the functionality, or without the underlying strategy, so there’s inconsistency about the concept’s definition itself.”
Amit Agarwal, Senior Vice President at Manthan Systems, concurs that “awareness isn’t there, and there’s a big need to evangelize about CDPs and be more aggressive.” Manthan is headquartered in Bangalore with offices in U.S., Singapore and UAE, and serves global retailers and restaurant businesses, hosting over 250 million customer profiles worldwide. Agarwal said Manthan has had significant success making inroads, including with a large-scale implementation for Future Group, a top food, fashion and consumer goods conglomerate with 1,800 stores across India and sends 200 million customer communications every month.
Nathan Rae, chief business development officer for Knowesis, agrees that “customer awareness is still very low,” but he says, “awareness is building quite quickly, and vendors are pushing” the technology. Knowesis is a Singapore-based CDP company with offices in Thailand, Malaysia, Indonesia, Australia, India and the UAE, and 13 enterprise customers. “We still don’t see a lot of RFPs asking for ‘CDP’,” reported Ajith Kumar, Knowesis’ vice president for professional services. Kumar said he’s seeing a lot of analytics providers using some CDP terminology and believes that, “once Gartner and Forrester begin talking more about CDPs, it will have a big influence with enterprise customers here.” He expects this to make it easier for marketers at companies to categorize the RFPs and to tell higher ups why they are shopping vendors in this specific domain.
Rahul Thomas Mathew, Director of Marketing for Lemnisk, observed that the recent Salesforce and Adobe announcements on their CDP plans have sparked a lot of curiosity. Like the others, he sees this as the right time to broaden understanding about CDP in the APAC market. Lemnisk is a CDP company specializing in financial services, headquartered in Bangalore with offices in the U.S., Singapore and the UAE.
On the sales side, there is an additional challenge in APAC companies because while marketers recognize the value having a CDP, IT departments play a major role in decisions and often have other priorities.
“The best case studies and projects are when the decision sits with marketing and is made with customer use cases in mind” says Marketsoft’s Cummins. “If we can get involved at a company supporting CDP from the top down, that’s ideal. We try to position CDP as a service as much as possible via marketing, and then allow IT to assume ownership once they build the capability.”
Corporate decision makers tend to be CMOs, and in some companies, the chief digital or chief experience officer, said Cummins. Pricing in Australia ranges from AU$150,000-300,000 for technology spend, and from AU$100,000 on the low end to AU$300,000-600,000/year for services on the upper end.
All agreed that privacy is a small factor in APAC countries at present, but thought it was likely to grow as a concern. Cummins observed, “GDPR is more of a perception than a reality here, but one of the biggest benefits for us has been that it has pushed awareness. There’s definitely a new level of IS [Information Security] awareness in terms of risk, adoption, and best practices, but there’s a long way to go.”
Rae and Kumar said a lot of governments in their market are cautious when it comes to PII (Personally Identifiable Information) data being stored on cloud, especially for local companies of significant size.
APAC locations mentioned as most engaged in CDP were Australia/New Zealand, Hong Kong, India, Singapore, and the UAE.
As for trends to watch for, Agarwal at Manthan believes educating the market via the demarcation of CDP and RealCDP, as planned by the CDP Institute, will help to offset efforts by other marketing automation players to position other types of products as Customer Data Platforms. Those often sell customers short by just addressing two or three data sources and channels, rather than providing true unified customer data.
Mathew at Lemnisk believes that “CDP’s tightly coupled with other vertical focused offerings can drive significant traction and value in vertical markets.”
Finally, Rae sees a key trend in the movement evolving in APAC companies from on-premises data stores to the cloud, since that will require “refreshing platforms and being willing to look at new tools.”
June 3, 2019
How AI and Machine Learning Are Impacting B2B: 3 Great Use Cases for CDPs
An earlier Zyloblog post described the multiple benefits CDPs offer technology companies, benefits that go way beyond “just” the marketing function. This post will explore why so many B2B companies are now choosing CDPs in the noisy marketing technology/martech landscape (now with over 7000 vendors), what CDPs offer them, and how they’re implementing CDPs for three important, marketing-related purposes: Account Based Marketing (ABM), ID resolution, and GDPR compliance.
Unlocking the value of data: 3 key questions
Collecting raw data, by itself, provides almost zero value in B2B or anywhere else. Data needs a strategy and a structure to unlock its massive potential. How should you begin?
Start by defining exactly where your B2B company wants to go (i.e., map your goals), and define how you’ll navigate to get there (i.e., defining your key performance indicators/KPIs aligned with those goals). You must then answer 3 key strategic questions: (1) what data is most relevant to the business outcomes (goals and KPIs) you seek to drive (hint: it’s usually connected to ROI/return on investment) and (2) how you can leverage data to drive organizational decision-making, including around what products you make and how you engage customers?
Answering that second question will have you converting prospects and building customer engagement/lifetime value, while creating great, full-funnel customer experiences. Only by answering these two “data-needs-a-strategy” questions above can you begin asking the third, “data-needs-a-structure” question: (3) what particular technologies, tools and processes can help our B2B company reach our strategic goals? That’s where a CDP comes in.
3 great B2B use cases for CDPs
CDPs with machine learning give you complete, actionable visibility into your customers’ behavior: you can engage them across multiple channels in real-time, plugging leaks in your funnel (i.e., driving customer retention), segmenting customers to drive ROI, and leveraging predictive customer analytics to identify (and take advantage of) cross-selling and up-selling opportunities. An automated CDP helps you become truly customer-centric in how you run and market your B2B business. What follows are 3 of many great B2B use cases for CDPs:
1. Account Based Marketing. ABM is a B2B strategy that concentrates sales and marketing resources on a defined set of target accounts within a market and leverages personalized campaigns designed to resonate with each account. It’s a top trend in marketing today, and your CDP complements and enables it, helping you deeply understand your key accounts and key personas to drive more ABM revenue.
Your CDP provides actionable, timely account intelligence, customer profiling, persona matching, and ongoing data enrichment of target accounts that will drive ABM success. You can leverage a comprehensive view of each account, while your contact data updates throughout the process. Learn who is connected to who, and how, with key account data that tracks your account’s different business units and organizational hierarchies. Know how they make purchasing decisions that impact your revenues, then influence those decisions through ABM.
2. ID resolution. The first rule of sales and marketing is “know thy customer,” but that can be challenging when it comes to digital channels. A CDP can help by organizing collected data points, making them actionable throughout the funnel. A CDP’s data enrichment function will simply fill in missing data fields such as customer names, email addresses, mailing addresses, phone numbers, and more. You can better know your customers through their unique identifiers, which helps you engage them.
A CDP’s data enrichment function can also sift through your web traffic and transform anonymous customer data into actionable assets. It does this by resolving identities and enriching relevant information through the use of cookies, device IDs, or IP addresses. With now-relevant customer data in hand, you can more engage with known customers, not strangers.
3. GDPR compliance. When B2B marketers gain a deeper understanding of who customers are and what they need, as CDPs enable, those customers tend to give you “permission” to market to them. This “permission” approach to marketing, pioneered by Seth Godin, is necessary in a world where data privacy has never been more important (especially to customers).
The General Data Protection Regulation/GDPR means you’ll need customers to actively opt-in, giving you permission to market to them. Marketing to customers you don’t know with offers they don’t find relevant will ultimately lead to customers refusing to stay “opted-in.” Know your customers and their needs, or risk violating a growing regulatory framework around data privacy, of which GDPR and California’s Data Privacy law are two recent examples. A CDP lets you manage and maintain customer permissions, so you retain them.
May 30, 2019
The Differences Between Attracting Customers into Your Store, and Catering to Them While They’re in: Combining Fast and Slow Data
Luring the right people with the right offers requires a rich set of data and sophisticated customer modeling – but it’s a different set than what is needed to react to their signals when they’ve stepped in the door. The magic happens when you combine.
In today’s digital realm, we should always aspire to attract the most relevant people to our business and guide them to the right sections with the right offers to maximize the chances they’ll make a purchase. In order to do so, we need to understand their preferences and their willingness to buy, to learn best what makes them tick.
Some customers always look for bargains and sales, while some get most excited about new arrivals. In order for us to identify these preferences and tendencies, we need to have a broad set of data about each customer, such as their demographic characteristics and purchase history. This data must be integrated from multiple systems, cleansed and matched, in order to allow for segmentation models, predictions and recommendation engines to produce reliable results.
That said, we should make sure we know the difference between the sets of data we work with, to come to the holistic, 360° view of our customers. Most data used for profile creation, segmentation and predictive modeling is historical data, and therefore these processes are non-real time by nature. We call them “Slow Data.” Real-time engagement using live signals is what we call “Fast Data.” Both kinds serve us for different purposes, and they go hand-in-hand. In this piece, we will clarify how to use these two sets of data.
Nice and slow
Look at this example: When a user clicks the purchase button on a website, it doesn’t necessarily mean that this purchase will be completed, and it does not indicate whether or not the customer is happy about the newly purchased product. We know that a transaction could be declined, that the customer may return the item or even submit a negative review.
Because we do have these scenarios happening, and more often than not, we cannot rely solely on engagement data obtained in real time. Doing so, we will misclassify customers who returned their last purchase and placed a bad review as “repeat customers.”
The more accurate data required to create reliable customer profiles can only be obtained from “systems of record,” and then this data must be processed and modeled. More than that, some insights can only be generated and validated after a customer’s actions were compared to that of a different customer. All this does not happen in real time.
Once we have the right data, we can create multiple customer segments and apply predictive models that will help us tailor the right message to the right audience.
Going faster than a roller coaster
There’s an important point that needs our attention here: We’ve talked about how automation without orchestration leads to chaos – and the more granular our customer segments are, and as we add more execution channels, the harder it becomes to effectively manage all these communications.
In many cases a single customer could be eligible for multiple campaigns, and if these are all sent out on the fly, without considering what other campaigns were already sent or what other campaigns are going to be sent later that day, the same customer could get contradicting offers or a less-relevant campaign that could result in a missed opportunity. We should always plan our marketing campaigns in advance and set a framework of priorities and exclusion rules, so the system can orchestrate all these scheduled campaigns and optimize the communication to each customer.
Once we manage to get our customer into the store, we want to switch to a different mode of operation: We still have all our pre-processed customer profile, but now we also have live activity feed from the store that we need to use in order to respond in real time to specific signals coming from the customer. For example, if we know that a certain customer is a deal seeker and we see her currently looking at non-discounted products, we can point her to the clearance section of the store. Another example could be based on our modeling of the customer’s preferred product category: We can welcome a customer in our store by highlighting a promotion for a product in that specific category.
The tortoise and the hare
So far, we talked about how in-store engagement is a real-time process, while profile creation and smart orchestration are not real time. But there is also a third option in between, in which the marketing plan is built in advance but adapts itself to short-term updates. Consider the following scenario: We plan a campaign for some of our one-timer customers, trying to encourage them to make a second purchase, and we schedule this campaign to be sent this afternoon. Let’s also assume our system already completed the modeling and prioritization and has identified the optimal list of customers which should receive this campaign today, with their specific product recommendations. But right before execution, we find out that a couple of these customers just returned their last purchased items – which significantly changes their profile and the way we want to approach them. For them, we are no longer trying to activate one-timers but rather save them from churning. In this case, and even though we already established the optimized list of the one-timer campaign recipients, we need to re-evaluate the audience just before the actual execution time in order to make sure we exclude any customer that no longer matches the desired profile. This example shows how a combination of fast and slow data processing and modeling can help reach the optimal result.
In conclusion
Identifying a customer’s profile is based on different data sources – integrating and cleansing them, aggregating, and running various predictive and AI models on top of them. But to get the full picture and cater to the ultimate goal of reaching the right customers with the right offers at the best time requires not only to know the difference between slow and fast data, but more importantly, that they always go hand in hand.
May 27, 2019
Encouraging Customer Loyalty - Making the First to Second Purchase
The ability of a business to retain its customers is crucial for its growth and success. In order to do so, many include in their business objective decreasing the percentage of single purchasers, while increasing the number of multiple purchasers and therefore the overall customer lifetime value (CLTV).
Knowing that people returning from holiday are keen to book their next trip so soon (and also knowing from personal experience how the feel-good factor of a great break can make you anxious for the next one) it’s easy to see how you might encourage that next booking.
You may start, for example, with a “welcome home” message – hoping they enjoyed the holiday, but sympathetic to the truth that it’s Monday again (ugh). Then, a few days later, you can start suggesting new destinations based on where they’ve been before, maybe with a discount code to use in their next booking? Little nudges that show how much you care can encourage your customers to come back, again and again.
But – oh yes, there’s a “but” – not all sectors find one-time purchasers thinking about their next purchase in the same way as holidaymakers. After all, if I buy a new pair of trainers, I’m not immediately thinking about buying my next pair having worn them a couple of times (unless they’ve fallen apart, in which case, I’ll be looking for a refund...)
Let the numbers guide you
For a first to second campaign to be profitable, the best you can do is to maximise your resources, distinguishing between the customers that are worth pursuing to those that are not. How? Not based on your gut instinct, that’s for sure! You have something in your hands that’s much more powerful and precise: your customer database.
The insights from customer data are your best chance to get to know your customers on a deep level and, therefore, to create communications that will engage and interest them.
Data can help you build a profile of the customers most likely to make a second purchase, analysing details such as how much time passes between the first and the second purchase, or how much discounts and peer reviews influence them. Of course, all of this is the more efficient the less your silos are separated, but more on that in a future blog post.
Say you have the optimal database at your disposal, one that can deliver you a single customer view on all channels for each one of your customers. Let’s take a look at the next steps:
First, you need to define the timeframe for when a customer typically will purchase again from your brand.
Then ask yourself, does the purchase value or product, customer type, time of year, lifestyle profile or any other factor affect whether the customer will purchase again? Look carefully for these correlations.
Now you can build upon your previous analysis and create a full profile of first to second purchasers.
Congratulations! You have now a complete understanding of your customers. The final stage is to move to a predictive model, whereby customers are assigned a likelihood to become first to second purchasers, allowing you to target those with high and low likelihood with more advanced tactics.
Automation in action
Once you have a picture of likely second purchaser built by data, you don’t gain just a deeper understanding of your customers and of how your business works. You can use these insights to create marketing automation strategies which will further maximise your resources. One way of increasing customer loyalty, for instance, is to make sure you maximise the excitement of their first purchase with you. That’s something we’ve been working on with Travelodge, and you can read more here.
Ultimately, the devil is in the detail. The more you get to know, and we mean really know, your customers, through data insights as opposed to “gut feelings”, the more likely you are to build a strong and lasting relationship with them and, therefore, increasing their CLTV and your revenue.
For some tangible advice, take a look at this infographic our in-house strategy team have put together, with tips on converting your new customers into loyal multi-purchasers.
Mobile marketing has become a key part of modern digital infrastructure. How does this industry operate in 2019, and what strategies are currently at play?
For almost two decades, tech experts claimed the future would be mobile. With an estimated 2.7 billion smartphone users around the world, it seems that future has finally arrived. Advertisers and marketers have clearly adapted to this new reality, seeking out new ways to turn traditional marketing strategies into effective mobile campaigns. Even major brands like Amazon have introduced customizable ad services that are gaining momentum in online and US retail spaces.
In 2019, mobile marketing has turned into a multi-channel discipline that supports massive segments of our online ecosystem. Yet, this future arrived so quickly that it can be challenging to explain or even grasp many of the seismic shifts this field experiences on an annual basis.
For that reason, it’s important to take a step back and broadly discuss mobile marketing in 2019, the various techniques at play, and the effectiveness of some prominent strategies.
In this new PostFunnel series, Nuts and Bolts, we’ll delve into the Martech world in 2019, trying to shed some light on main tools and best practices being used by you, our fellow marketers, in your day-to-day strategies. Every month, our experts will sink their teeth into another aspect of this fascinating field, with hope to inspire you to elevate your business through some smart marketing.
As the term implies, mobile marketing is a technique where advertisers deliver communications to users via smartphones and tablets. As simple as that description sounds, mobile marketing encompasses a broad range of delivery channels including email, SMS messaging, push notifications, in-app advertising, QR codes, and many more.
Thanks to the prevalence and accessibility of today’s smartphones, mobile marketing has a higher potential to target specific audiences than perhaps any prior marketing discipline. Advertisers can deliver personalized messaging, deploy ads based on time of day or location, and design interactive ad formats that effectively engage specific demographics.
Why use mobile marketing?
Mobile phones are so globally ubiquitous that you’d be hard-pressed to find a more effective marketing platform. The overwhelming majority of adult populations worldwide own some kind of mobile device, while the global median for smartphone ownership is 43%. Customers use mobile devices to play games, watch movies, and communicate via social media — all fertile ground for marketing opportunities.
The significance of mobile devices is even higher in emerging economies, where cell phones have become the easiest method of gaining internet access. Meanwhile, in the developed world, the volume of online content accessed using smartphones has eclipsed traditional platforms such as desktop computers.
What is in-app mobile marketing?
In-app mobile marketing, sometimes referred to as app-based marketing, refers to the deployment of advertisements directly within an app itself. Since over 90% of time spent on smartphones is used to view apps, this is perhaps the most effective and cost-efficient marketing technique available to advertisers today.
The easiest way to deploy in-app marketing is through one of the titans of the mobile advertising space, namely Google’s AdMob or Facebook, or through a specialized in-app advertising network like Tapjoy. In order to monetize their apps, developers often integrate ad network SDKs that display ads when certain conditions are met. Some app publishers like Facebook even use Promoted Post services that seamlessly integrate ads into news feeds across all devices.
One important variant of in-app mobile marketing is in-game marketing, where advertisements are deployed directly within a mobile game. While there are certain ad formats and deployment considerations when delivering messaging to gaming audiences, marketing SDKs function in fairly similar ways to in-app mobile marketing on a technical level.
What is SMS mobile marketing?
SMS mobile marketing is the earliest form of the technique, first implemented when SMS and shortcodes launched in the early 2000s. It requires advertisers to obtain or capture mobile phone numbers and directly communicate with users via SMS messaging services. SMS mobile marketing can refer to both inbound marketing strategies for lead generation and outbound strategies to communicate promotions and events.
While SMS mobile marketing has been overshadowed by in-app advertising, it still remains a powerful strategy. On average, SMS marketing ads have a 98% open rate, a 45% conversion rate, and are typically read within three minutes of deployment. That makes it an impressively effective strategy for rapid engagement with a large volume of potential customers.
More importantly, SMS mobile marketing is widely used internationally, especially in regions like Europe and Southeast Asia. This broad reach is largely thanks to compatibility with non-smartphone cellular devices. SMS marketing is more strictly regulated than other marketing channels, but tends to benefit from having clearly defined best practices that are standardized through cellular carriers.
What are push notifications?
Push notifications are a type of message displayed on mobile devices by third-party apps that aren’t currently running. These notifications serve a variety of purposes, most commonly to inform users of incoming messages from social media apps. From a marketing perspective, push notifications are an ideal format for keeping users in the loop about new promotions or app features.
Above all else, the primary driver behind push notifications is customer retention. It’s easy for users to install and forget about an app, but push notifications let publishers and advertisers continue to communicate once the app is closed. Studies consistently show that push notifications can increase 90-day user retention from 3x to 10x depending on the effectiveness of your messaging.
What are QR codes?
QR codes are a type of matrix barcode that can be scanned by a mobile camera, usually activating a web link in the process. In mobile marketing, this allows advertisers to combine physical and digital marketing techniques by displaying QR codes in the real world. For example, a retail chain could place unique QR codes on receipts to link a customer’s online and offline identities, or a viral marketer might leave codes in public places as part of an augmented-reality game.
In the hands of mobile marketers, QR codes are unique tools that appeal to human curiosity can be placed anywhere, and are easy to track. Unfortunately, QR codes are also not as intuitive as other marketing strategies on this list, and tend to be used by a smaller subset of mobile users. That said, QR codes can be useful when deployed effectively, and are especially popular in regions like China.
What are mobile search ads?
Mobile search ads are standard search engine advertisements that are indexed and optimized for mobile devices. They can be displayed through a web page or search engine like Google, and typically integrate with smartphones to use features like “click to call.”
When search ads are optimized to match Google’s search interface, they have a higher chance of appearing when users search for related products or services on their mobile devices. Depending on the advertised business, a smartphone’s location service can also narrow down the search to relevant local companies. Google search ads can also feature a click-to-call button or click-to-install button as a call to action for your customers.
What are some mobile marketing best practices?
Always keep your audience in mind. A mobile marketing strategy that’s effective on social media won’t necessarily carry over to mobile games.
Be concise. Smartphones have limited screen space for deploying your message, and there are literally thousands of things users could do instead of viewing your ad. Get straight to the point, and give them a reason to engage with you.
Optimize websites for mobile devices. Much like our last point, transferring desktop-optimized web pages to mobile devices usually means your marketing efforts are lost to clutter and noise. Design mobile-specific versions of your sites that are optimized for on-the-go smartphone users, and build your marketing campaign around them.
Adopt multiple marketing strategies. There are many mobile marketing strategies available to advertisers in 2019. Don’t be afraid to experiment with models that show potential and reflect your brand.
Benchmark your results. Keep track of how users interact with each of your mobile marketing strategies. Follow conversion, retention, and engagement metrics to maximize your ROI.
Mobile marketing is a far more complicated field today than it was in 2000, but there are also far more ways to engage with your audience than ever before. By adopting the strategies listed above, your business will be well underway to expanding your reach across a variety of active channels.
May 20, 2019
Behavioural and Engagement Data Key to Success for Predictive Analytics
At a recent event I presented six case studies about the successful (i.e. they made money) application of Predictive Analytics. All the case studies were based on the application of Predictive Analytics to help target customers or prospects at various points along the customer journey, for instance, identifying the single buyers most likely to make a second purchase or which VIP customers were at greatest risk of lapsing.
At the end of the presentation I was asked, ‘What is your definition of Behavioural Data?’ I had repeatedly talked about the importance of accurate and complete data to drive Predictive Analytics and described 1st party customer data as falling in 3 types: transactional, engagement and behavioural. But I had fallen into the trap of failing to explain what I meant by each of the definitions I was using. So, very briefly:
Transactional Data. Or RFM. Or RFV. What a customer has bought, how much, when, where… Data that is used to build RFM models (i.e. identifying prospects, single or multi buyers, VIPs etc.)
Engagement. Points of interaction with the brand, from opening an email to receiving a catalogue to visiting the website.
Behavioural Data. Usually related to website activity, what the prospect or customer has browsed, their recency frequency across devices, what they’ve clicked, if they visit the website direct from social, if they click on a discount offer, etc.
Behavioural data has always been critical. It is the core of data-driven personalisation. By building up, and allowing marketers to react to ‘behaviour’, from marketing the products someone is interested in through to identifying if the consumer is an offer junky or full price buyer, this is what underpins RedEye’s approach to Predictive and AI. Using this information to work in combination with engagement and transactional data identifies prospects and customers in terms of what someone will do next and when.
But despite presenting six case studies all showing conversion and revenue improvements, we can’t escape the fact that there is a little bit of market weariness to the subjects of Predictive Analytics and AI! Gartner are now stating in their Hype Cycle that Predictive Analytics has fallen into the trough of disillusionment! And with so many marketing tech businesses out there are talking about it, but not many are able to demonstrate the real value it can bring for retailers.
Back in 2016, Forbes research showed that 89% of marketers had Predictive Analytics on their roadmap. Fast forward to 2018 and 93% of consumer-facing businesses are unable to use Predictive Analytics. This really shows the disparity between the desire to implement Predictive Analytics vs. the actual implementation.
The aim of the presentation was to try to reinforce the potential value of these tools to the market, but the key is to start with data. Customers are getting more and more difficult to understand with the proliferation of marketing channels. Just recently WhatsApp was added into the fold, yet another channel that marketers can target their consumers through.
Brands are losing touch as they struggle to track all their customer’s moves, which in turn leads to a decline in customer loyalty. Consumers can feel their favourite brand just doesn’t understand them. As humans we just can’t keep up! This is where AI comes in!
Start by understanding your data. Where is it coming from? I recently joined a panel with the Head of CRM at Domino’s; he told me their journey began by creating a Single Customer view by collating their offline data and combining it with their online channels.
He was right: putting in the leg work at the beginning and creating a true Single Customer View was key. A Single Customer View means you can tie together transactional, engagement and behavioural data, allowing you to paint the full picture of your customers.
Finally, it is key to apply AI and Predictive Analytics to something tangible. At RedEye, our predictive models are based on the customer lifecycle. By making incremental improvements at each of the key customer moments, you can see substantial increases in overall customer value.
One may not assume that a customer perceives a ‘customer experience’ as was intended by the provider. Conversely, an experience that is favorable for the customer does not always contribute a company’s performance. The challenge is to unite both perspectives.
Anno 2019, Customer Experience or ‘CX’ is omnipresent. The CX concept was introduced in 1982 by Holbrook and Hirschman as a holistic construct. In the meantime, both academics and practitioners believe that a favorable customer experience not only positively impacts customer satisfaction, customer loyalty, and word-of-mouth behavior – something customers themselves have known all along – but that it also is a compelling precursor of the much-coveted competitive advantage.
Despite this consensus, the CX concept remains foggy because the holistic construct has diverged into two mostly unconnected schools of thought. The main reason is that academics and practitioners tend to look at customer experience through one of two opposing lenses. One is the organizational lens and the other is the customer lens. This is one of the conclusions of a review of customer experience research since 1982 drawn by Kranzbühler et al. (2017).
The one who looks through the organizational lens assumes that experiences can be designed and that all customers will perceive stimuli alike. The one viewing through the customer lens ascertains that firms cannot deliver value since the customer is always a co-creator of value. While the former focuses on organizational structure, strategy, and customer-employee interactions, the latter considers individual customer journeys, cognition, affect, and senses.
Static and Dynamic Customer Experiences
A distinction is made between static and dynamic customer experiences. A static CX describes how an individual evaluates one or more touchpoints with an organization on a cognitive, affective, and sensory level at one specific point in time. A dynamic CX considers the evolving cognitive, affective, and sensory evaluation throughout the entire customer journey.
The organizational lens points to the design of static CX and to the management of dynamic CX, yet the focus on static CX tends to dominate. The customer lens analyzes customers’ perceptions in three planes: the static CX itself; how dynamic CXs are formed; and how cognition, affect, and the senses impact both static and dynamic CXs.
The organizational lens focuses on what is within a company’s control; the customer lens looks at the whole picture.
As one can see, the famous customer journey is a key component of the customer lens. The ultimate goal of a customer journey is to “teach companies more about their customers in order to market better, sell faster and serve more effectively” (Milbrath, 2019). Being taught requires a willingness to learn and to shift to an outside-in view. Only then can one look inside one’s own processes and, with lessons learned from customers, improve them to match expectations.
Yet most organizations fail to truly master the art of customer journey mapping. Three reasons account for this fact. First, looking through the wrong lens sets one up for failure. Very fe
