Two trends challenging business thinking today – Big Data and customer centricity – seem at first to be antithetical. Driving decisions from more and more data raises the specter of dehumanizing business interactions. But the real value of Big Data for business is the opportunity to learn about our customers at such depth and speed that we can truly put them at center stage.
Still, answers to the most important questions, aren’t just there to be scooped up from Big Data, such as:
- How is this customer likely to respond to this action?
- What new needs can we anticipate?
- What does this changing behavior mean?
Next Generation Analytic Learning
Companies that become very good at next generation analytic learning -- by this I mean data-driven learning enabled by computing infrastructures and analytic techniques that make it practical to examine data of very high volumes – will be able to orient their entire operation around their customers. They’ll engage customers and build win-win relationships with such insight, innovation and efficacy that they’ll be very difficult to dislodge as providers of choice.
Big Data can help or hinder us on the way to customer centricity. Today we have the means to capture and analyze much bigger quantities of data than ever before, and to make meaningful connections between different types of it. We can analyze data in-stream for real-time decisions. We can distribute analytic tasks in a massively parallel manner across many processor nodes, then algorithmically assemble their outputs into a single result. But is any of that helpful for achieving customer centricity?
It’s most helpful when we can systematically extract the most valuable analytic insights – causal relationships – from Big Data. These insights enable us to understand individual customer behavior and sensitivities, anticipate needs, and predict likely responses to offers and treatments. In some situations we must find and act on such insights as data is streaming in. In others we can use out-of-stream methods to dive deeply for them.
Big Data computing infrastructures are making it practical to employ automated machine learning algorithms for this purpose – but human expert oversight is essential to ensure results make business sense and are useful in operations. And ultimately, whether any of these insights have an impact at all on customer centricity depends on how quickly we can pump them into operations so that they drive and inform every decision we make and every interaction we have with our customers.
These are essential capabilities for turning Big Data into an enabler for customer centricity. They’re the fundamentals of what I call “next-generation analytic learning.” Next-generation analytic learning starts with what you want to know about your customers – in other words, with business questions like “Which of my customers are most sensitive to discount coupons?”
Starting with the business questions helps you target the right data. I call this approach “next generation” because it elevates test-and-learn methods to a new level of efficacy. These improvements to the traditional champion-challenger method were not triggered by Big Data; they’ve evolved in response to increasingly granular customer treatments and rapidly changing customer behavior. Still, next generation learning is certainly suited to the challenges and opportunities Big Data presents. It’s a systematic, highly efficient way of continuously advancing what we know about our customers and improving how we use those insights to interact with them.We will continue this conversation next week on the Banking Analytics Blog. I also explore into next-generation learning in my recent Insights white paper: "When Is Big Data the Way to Customer Centricity?" (registration required).