No, this is not an article about fracking, drilling for gas and oil in shale. This is about drilling down into big data. We’ve been using the term for a long time and it provides a useful metaphor for data analysis. But we’ve conditioned ourselves to think of drilling down only to a superficial degree and that needs a rethink.
When data wasn’t big and analytics relied on less robust hardware we were only able to scratch the surface of our data, a practice that survives to this day. Scratching often means looking for insights only at the end of business processes. So for example, we look for signs of churn next week or the next best offer today, or to forecast the next sales deadline. All of this is valuable but not enough. If we’re doing our jobs right, we should be using powerful analytics to perform root cause analysis to better forecast events so that we can either avoid them entirely or further enhance our likelihood of success.
What if you could go further into your data so that rather than simply discovering someone or some business that was about to leave your service (churn, non-renewal) you could find those moments of incipient danger and correct a problem at the source? You can but it requires change, not more hardware or better software though those things are always welcome, but a different way of framing the challenge in front of you.
Too often we make assumptions about some aspect of business and then collect and analyze data about it. That’s a good approach as long as the assumptions are valid and accurate but too often they are not. When we assume something we are building an ad hoc model of what we believe reality is and that’s a good thing. Modeling is the heart of all kinds of progress in any number of fields of human endeavor but it’s not something we do particularly well in business with some exceptions.
As Nate Silver writes in The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, “We need to stop and admit it: we have a prediction problem. We love to predict things — and we aren’t very good at it.” You might recall that Silver called 49 out of the 50 states correctly in the 2012 presidential election. This man does not have a prediction problem.
Retailers might be an exception; they model heavily and they do a good job. They collect customer and store data so that they can model the ways they set up stores and plan the assortments that they stock. Those models mirror very closely the clientele and traffic for an individual store. When it comes to online business and B2B business we aren’t there yet because it’s both a different and a harder challenge.
Finding a solution in the online world starts with figuring out your model before you make any assumptions and before you implement something. (This is not your business model but your approach to customers, which is part of the business model.) It’s surprisingly easy to do if you take a two-step approach to analytics.
Step one, build a realistic model of your business by asking your customers. I call this discovering your moments-of-truth and I write about it in my new book, Solve for the Customer, which will be available shortly. As you know if you read this space often, a moment-of-truth is simply any time your customers expect you to live up to a promise whether that’s a product, company, or brand promise irrespective of whether the promise is expressed or implied.
Knowing your moments-of-truth you can build customer facing processes in Step Two. Your processes and supporting software will meet customers where they live, so to speak. The best way to do this is with journey mapping software because it lets you examine all of the contingencies and define sub-processes as appropriate. It’s also the logical place to define metrics that will tell you if you are meeting your goals in your moments-of-truth.
For example, customer onboarding is a good example of a moment-of-truth and there are many analytics vendors that focus on customer health as a function of how quickly customers get down your learning curve. People at Scout Analytics, for instance, tell me that there is a direct correlation between customer longevity and how fast they onboard. Knowing this, smart vendors deploy customer success managers to ensure that onboarding is swift and trouble free.
You can identify moments-of-truth like this throughout your customer lifecycle and often those moments do not automatically require expensive human intervention. But having a moments-of-truth approach plus good analytics, rather than assumptions, enables a vendor to deploy resources where they’ll be most beneficial to both the customer and to the vendor.
None of this is hard. In fact once you change your frame of reference (a.k.a. your paradigm) from ad hoc assumptions to dedicated and conscientious modeling, it flows. When we move from random approaches to modeling, which incorporates a bit of statistics (and that is what analytics is about to a great degree) we pass from a framework of art to one of science. That’s what’s happening right now in many areas of front office business and it’s why I’m saying we’ve arrived at a new science, Customer Science.
(Cross-posted @ Beagle Research, LLC)