As I work with more CMOs who are wading into broader responsibilities for driving company-wide customer experience programs, I am finding that many are slowing down because they feel they lack good enough customer data. As one expands from looking at mostly marketing data to taking a broader look across all customer touchpoints, especially those in call centers, physical channels, wearables, chat, and all the broader channels beyond core marketing ones, the data gets much more diverse in nature and often lacks a common identifier that can create a perfect single view of a customer.
But that doesn’t mean there aren’t other ways of stitching together a good enough picture for many purposes, especially for analyzing customer journeys. I’ll get into those in a moment, but let’s understand the blinders that marketers are often pushed into developing because of the tech stack they are used to.
Marketers have focused on acquisition and CRM programs that aim to coordinate the contacts out to a prospect and/or customer. They make sure the sequence and personalization across, mostly, direct mail, email, digital media, and websites create a coordinated acquisition, cross-sell, or engagement journey.
The systems that support this work hard to match any records via some form of common identifier, and many of the major enterprise CRM players are extending into broader Customer Data Platforms (CDPs), using this philosophy. This allows a clean orchestration of outbound touches across channels.
For managing marketing, this all makes good sense, but for managing customer experience more broadly, especially from a real-time journey analytics point of view, it creates significant constraints. Most non-traditional marketing channels do not have a common identifier, and as companies experiment with constantly adding new ones – moving to an expansive omni-channel experience – it takes much more effort to get the data into a shape where the marketing-oriented CDPs can cleanly ingest it, hence marketers feeling they “lack good data” to manage customer journeys.
Also, a CX program where you have cross-functional teams working together to manage customer journeys will want real-time (or near to it) views into what’s happening across customers bouncing from channel to channel.
Many CDPs only give analytic views when they crank out reports, since they are designed to manage outbound contacts and the analysis of them, but not necessarily to be hands-on immediate tools for transparency into customer behavior for teams wanting to make decisions in the moment or to pump journey history information to a rep.
Focused Customer Journey Analytics systems, and some newer CDPs that are not just extensions of CRM systems, are better designed from the ground up to manage all the complexity of customer journeys – adding new channels, dealing with inconsistent identifiers, providing real time views – because they start by matching customer records through associative AI that uncovers any kind of match method between any two databases and strings records together as different match methods accumulate across the enterprise.
They don’t create a golden record nor need a common identifier. One Customer Journey Analytics tool I am quite familiar with, Pointillist, uses this technique, and besides being a tool that can support journey management teams, can also be a feeder into more rigid CDPs, as it makes matches and provides a range of data descriptors that the CDP can’t easily find without significant data science support. There are others as well.
So when you think about your data potential for managing customer experience, take a hard look at whether you are creating artificial constraints because of the marketing legacy you are bringing into the broader CX context. Winning on customer journeys means constantly trying new channels, relentlessly testing and learning new experience designs, staying on top of customer behavior in real-time, and supporting teams operating in an agile mode to keep every journey moving forward.
It’s not quite the same as managing marketing programs, or even large-scale outbound personalization initiatives, so think about how to set up your tech stack and your processes to get going quickly on differentiating your branded experience.