Understanding What a Customer Data Platform Needs to Be

Marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed. So, how can marketers achieve such an advanced level of personalization?

Modern-day marketers try to achieve holistic personalization through all conceivable channels in order to stand out among countless marketing messages hitting targeted individuals every day, if not every hour. If the message is not clearly about the target recipient, it will be quickly dismissed.

So, how can marketers achieve such an advanced level of personalization? First, we have to figure out who each target individual is, which requires data collection: What they clicked, rejected, browsed, purchased, returned, repeated, recommended, look like, complained about, etc.  Pretty much every breath they take, every move they make (without being creepy). Let’s say that you achieved that level of data collection. Will it be enough?

Enter “Customer-360,” or “360-degree View of a Customer,” or “Customer-Centric Portrait,” or “Single View of a Customer.” You get the idea. Collected data must be consolidated around each individual to get a glimpse — never the whole picture — of who the targeted individual is.

You may say, “That’s cool, we just procured technology (or a vendor) that does all that.” Considering there is no CRM database or CDP (Customer Data Platform) company that does not say one of the terms I listed above, buyers of technology often buy into the marketing pitch.

Unfortunately,the 360-degree view of a customer is just a good start in this game, and a prerequisite. Not the end goal of any marketing effort. The goal of any data project should never be just putting all available data in one place. It must support great many complex and laborious functions during the course of planning, analysis, modeling, targeting, messaging, campaigning, and attribution.

So, for the interest of marketers, allow me to share the essentials of what a CDP needs to be and do, and what the common elements of useful marketing databases are.

A CDP Must Cover Omnichannel Sources

By definition, a CDP must support all touchpoints in an omnichannel marketing environment. No modern consumer lingers around just in one channel. The holistic view cannot be achieved by just looking at their past transaction history, either (even though the past purchase behavior still remains the most powerful predictor of future behavior).

Nor do marketers have time to wait until someone buys something through a particular channel for them to take actions. All movements and indicators — as much as possible — through every conceivable channel should be included in a CDP.

Yes, some data evaporates faster than others — such as browsing history — but we are talking about a game of inches here.  Besides, data atrophy can be delayed with proper use of modeling techniques.

Beware of vendors who want to stay in their comfort zone in terms of channels. No buyer is just an online or an offline person.

Data Must Be Connected on an Individual Level

Since buyers go through all kinds of online and offline channels during the course of their journey, collected data must be stitched together to reveal their true nature. Unfortunately, in this channel-centric world, characteristics of collected data are vastly different depending on sources.

Privacy concerns and regulations regarding Personally Identifiable Information (PII) greatly vary among channels. Even if PII is allowed to be collected, there may not be any common match key, such as address, email, phone number, cookie ID, device ID, etc.

There are third-party vendors who specialize in such data weaving work. But remember that no vendor is good with all types of data. You may have to procure different techniques depending on available channel data. I’ve seen cases where great technology companies that specialized in online data were clueless about “soft-match” techniques used by direct marketers for ages.

Remember, without accurate and consistent individual ID system, one cannot even start building a true Customer-360 view.

Data Must Be Clean and Reliable

You may think that I am stating the obvious, but you must assume that most data sources are dirty. There is no pristine dataset without a serious amount of data refinement work. And when I say dirty, I mean that databases are filled with inaccurate, inconsistent, uncategorized, and unstructured data. To be useful, data must be properly corrected, purged, standardized, and categorized.

Even simple time-stamps could be immensely inconsistent. What are date-time formats, and what time zones are they in?  Dollars aren’t just dollars either. What are net price, tax, shipping, discount, coupon, and paid amounts? No, the breakdown doesn’t have to be as precise as for an accounting system, but how would you identify habitual discount seekers without dissecting the data up front?

When it comes to free-form data, things get even more complicated. Let’s just say that most non-numeric data are not that useful without proper categorization, through strict rules along with text mining. And such work should all be done up front. If you don’t, you are simply deferring more tedious work to poor analysts, or worse, to the end-users.

Beware of vendors who think that loading the raw data onto some table is good enough. It never is, unless the goal is to hoard data.

Data Must Be Up-to-Date

“Real-time update” is one of the most abused word in this business. And I don’t casually recommend it, unless decisions must be made in real-time. Why? Because, generally speaking, more frequent updates mean higher maintenance cost.

Nevertheless, real-time update is a must, if we are getting into fully automated real-time personalization. It is entirely possible to rely on trigger data for reactive personalization outside the realm of CDP environment,  but such patch work will lead to regrets most of the time. For one, how would you figure out what elements really worked?

Even if a database is not updated in real-time, most source data must remain as fresh as they can be. For instance, it is generally not recommended to append third-party demographic data real-time (except for “hot-line” data, of course). But that doesn’t mean that you can just use old data indefinitely.

When it comes to behavioral data, time really is of an essence. Click data must be updated at least daily, if not real-time.  Transaction data may be updated weekly, but don’t go over a month without updating the base, as even simple measurements like “Days since last purchase” can be way off. You all know the importance of good old recency factor in any metrics.

Data Must Be Analytics-Ready

Just because the data in question are clean and error-free, that doesn’t mean that they are ready for advanced analytics. Data must be carefully summarized onto an individual level, in order to convert “event level information” into “descriptors of individuals.”  Presence of summary variables is a good indicator of true Customer-360.

You may have all the click, view, and conversion data, but those are all descriptors of events, not people. For personalization, you need know individual level affinities (you may call them “personas”). For planning and messaging, you may need to group target individuals into segments or cohorts. All those analytics run much faster and more effectively with analytics-ready data.

If not, even simple modeling or clustering work may take a very long time, even with a decent data platform in place. It is routinely quoted that over 80% of analysts’ time go into data preparation work — how about cutting that down to zero?

Most modern toolsets come with some analytics functions, such as KPI dashboards, basic queries, and even segmentation and modeling. However, for advanced level targeting and messaging, built-in tools may not be enough. You must ask how the system would support professional statisticians with data extraction, sampling, and scoring (on the backend). Don’t forget that most analytics work fails before or after the modeling steps. And when any meltdown happens, do not habitually blame the analysts, but dig deeper into the CDP ecosystem.

Also, remember that even automated modeling tools work much better with refined data on a proper level (i.e., Individual level data for individual level modeling).

CDP Must Be Campaign-Ready

For campaign execution, selected data may have to leave the CDP environment. Sometimes data may end up in a totally different system. A CDP must never be the bottleneck in data extraction and exchange. But in many cases, it is.

Beware of technology providers that only allow built-in campaign toolsets for campaign execution. You never know what new channels or technologies will spring up in the future. While at it, check how many different data exchange protocols are supported. Data going out is as important as data coming in.

CDP Must Support Omnichannel Attribution

Speaking of data coming in and out, CDPs must be able to collect campaign result data seamlessly, from all employed channels.  The very definition of “closed-loop” marketing is that we must continuously learn from past endeavors and improve effectiveness of targeting, messaging, and channel usage.

Omnichannel attribution is simply not possible without data coming from all marketing channels. And if you do not finish the backend analyses and attribution, how would you know what really worked?

The sad reality is that a great majority of marketers fly blind, even with a so-called CDP of their own. If I may be harsh here, you are not a database marketer if you are not measuring the results properly. A CDP must make complex backend reporting and attribution easier, not harder.

Final Thoughts

For a database system to be called a CDP, it must satisfy most — if not all — of these requirements. It may be daunting for some to read through this, but doing your homework in advance will make it easier for you in the long run.

And one last thing: Do not work with any technology providers that are stingy about custom modifications. Your business is unique, and you will have to tweak some features to satisfy your unique needs. I call that the “last-mile” service. Most data projects that are labeled as failures ended up there due to a lack of custom fitting.

Conversely, what we call “good” service providers are the ones who are really good at that last-mile service. Unless you are comfortable with one-size-fits-all pre-made — but cheaper — toolset, always insist on customizable solutions.

You didn’t think that this whole omnichannel marketing was that simple, did you?


Session Spotlight: Learn How Brands Can Take Advantage of CDPs at FUSE Summit

In a few short years, customer data platforms (CPDs) have made a big splash on the marketing technology scene. Gartner named them one of six marketing technologies to watch in 2018 and the category is expected to reach $1 billion in revenue by 2019. Adoption is expected to grow based on marketers’ growing interest in permission-based marketing, first-party data, and providing unified cross-platform customer experiences.

CDPs are expected to be a hot topic at the FUSE Digital Marketing Summit this November. During a session led by Customer Data Platform Institute founder David Raab will explore how CDPs can enable marketers to better gather and act on customer data, how CDPs fit into the martech stack, tips for sizing up potential vendors, and examples of how marketers are growing sales with CDPs.

Check out the video below to learn how CDPs differ from other customer data solutions and whether a CDP might be a fit for your martech toolbox.

Learn more about the agenda for FUSE Digital Marketing here.



How a CDP Can Be Used to Build Consumer Trust & Comply With GDPR

How a CDP can be used to ensure accurate first-party data and consistent brand messaging – which help build consumer trust – while also maintaining compliance with consumer data protections such as GDPR.

For anyone who has ventured into the “Quotes” section of Pinterest, you’ve seen thousands of quippy memes dealing with loss of trust. The gist is once trust is lost, it’s hard to regain. Although mostly focused on romantic relationships, the same can be said for relationships with brands and business.

Consumer trust in businesses is low and dropping. According to the industry standard measure of consumer trust, the Edelman Trust Barometer, overall consumer trust dropped 10 full percentage points during 2017 from 58% to 48%. Coincidentally 2017 was a record high point for US data breaches (1,579 data breaches in all), as well as ushering in the birth of the Cambridge Analytica/Facebook debacle.

In this series on specific customer data platform (CDP) use cases, you’ll see the core competencies of CDP’s go a long way toward maintaining consumer trust. In this post we’ll look at how a CDP can be used to ensure accurate first-party data and consistent brand messaging – which help build consumer trust – while also maintaining compliance with consumer data protections such as GDPR.

Managing First-Party Data

All communication from a brand/business to its customers and prospects is an expression of its brand. Many brands and businesses have relied heavily on third-party sources to provide targeting options for reaching prospects and customers.

Understanding the flaws in this method is as simple as creating an account at https://aboutthedata.com. Sponsored by Axciom, the leading aggregator of third-party targeting data, this portal will allow you to access your digital profile. Each of the characteristics in this profile identifies how you are being targeted. Now think about brands and marketers crafting messages directed to YOU based on this data. A mismatch between messaging and targeting will chip away at authenticity and brand trust.

First-party data collection and activation are the reasons the CDP exists. By ingesting, organizing, reconciling, segmenting, and activating first-party data across all customer data siloes, the CDP creates the opportunity to communicate around specific data gained from the direct, first-party relationship between brand and consumer. Imagine the following:

  • Adjusting the content of your website based on the user’s past content tastes and interests. Right message.
  • Determining the appropriate channel for your message based on the behavior of an individual target. Right channel.
  • Choosing the appropriate timing of your message based on the intensity of your customers behavior. Right time.

GDPR and Data Management

Aside from creating more consistent and authentic conversations between customers and brands, a CDP also creates a potentially smoother path to compliance with recent privacy policy legislation including GDPR and the California Privacy Act. Key to compliance are two factors, both of which should be core capabilities of any CDP system.

  1. Choice: A core capability of CDP technology is the identification and reconciliation of known and unknown users. As unknown users are accessing your site, the ability to offer them the appropriate experience (cookies for tracking or not) can be offered or directed and the preference maintained. More and more tools in the marketing technology stack are offering this capability, but maintaining these preferences in one environment that is used for all customer data collection and interaction makes the most sense.
  2. Transparency: The portability aspects of the GDPR and California Privacy Act specifically relate to delivering a comprehensive profile of all data points and their use for an individual. Whenever asked, an organization must be able to produce a succinct and complete picture of the user’s data and how it might be used within the organization. There is really no better place to create and extract that comprehensive picture than the CDP.

Being a steward of your customer data is not just a nice thing to do but an absolute requirement in an age where consumer trust is rapidly eroding and regulations on data protection are mounting. Adopting a philosophy and discipline in growing and activating first-party data from customers and prospects pays off by creating more authentic relationships grounded in trust. Statistically speaking, a highly-personalized relationship steeped in authenticity converts and performs optimally every day of the week. To cite one of those Pinterest quotes, “To be trusted is a greater compliment than being loved.” For marketers, trust is the pathway to business success.