Data Scientist: Chart The Customer Journey
Your customers really don’t care how smart your data scientists are. Customers don’t spend much time contemplating how much work those data scientists might have put into tuning the analytic models that power your channels. And they probably wouldn’t listen if you tried to impress them with the size and sophistication of your big data platform.
The average consumer focuses on the day-in day-out of his or her own life, which is a long-running saga in which your company may, at best, have a supporting role. Normal people rarely pause to parse the moments in their routines into distinct “experiences” that you may or may not be optimizing. Likewise, they rarely take time out to re-assess their distinct relationships with you and the dozens of other merchants who support their way of life. The critical exception, of course, is when you either surpass their fondest expectations or repeatedly rub them the wrong way.
The notion of an ongoing customer life “journey” is at the heart of modern best practices in customer relationship management (CRM). In the business world, many data scientists focus on marketing, sales and service, all of which are distinct CRM subdomains of the customer’s never-ending journey. Many data scientist projects focus on optimizing journey-relevant engagement touchpoints, including offers, interactions, transactions, notifications and outcomes.
However, it would be a stretch to claim that you, the data scientist, are optimizing the customer journey in its entirety. Traditionally, data scientists have been mere plumbers in the ebb & flow of commercial interactions in customers’ lives. The data scientist builds and maintains a panoply of analytic models and rules that tune the various moments within the customer journey. From the point of view of the data scientist and the subject-domain specialists in various CRM disciplines, each of these moments is a distinct next best action. But from the customer’s perspective, it can, in the worst-case scenario, all feel like a bewildering blur of stuff being orchestrated around them by some impersonal brand.
Who, if anybody, is mapping the customer journey from end to end? Well, there is a substantial new specialization in the business world for “customer experience management” (CEM) professionals, who have their own approaches and mapping tools. Here’s an excellent 2010 article from Harvard Business Review on the art of customer journey mapping, as well as a link to a wide range of sources of guidance on this hot topic. You should also check out the CEM offerings of Tealeaf Technology, a best-of-breed solution provider that IBM acquired a few months ago.
If you have CEM professionals charting the customer journey, are your data scientists and CRM subject-area specialists using their maps to guide ongoing modeling initiatives? Even if one group of data scientists is considering the end-to-end customer journey, they probably work in siloed isolation from data scientists supporting other functions, channels and partners in your CRM ecosystem.
CRM silos are persistent stumbling blocks in the customer journey. Absent an enterprise-wide customer journey map to guide their fragmented efforts, silos risk stoking a steady flow of sour experiences, which are aggravated by mistargeted messages, impersonal interactions, and awkward cross-channel hand-offs in the flow of the customer relationship.
From the data scientist’s perspective, every analytic optimization trick in the book will fall flat if you can’t ensure the one thing that matters most: customer loyalty. You will go belly-up if you don’t retain satisfied customers who decide to stick with you through all the twists and turns of their various journeys through life.