There is a tendency within organizations to apply big-data analytics to “big” problems. This is understandable. Everyone wants a big idea, a major breakthrough, something brilliant to ship up to the C-suite. But that’s rarely where the big money is made.
The big money is made by refining the 10’s or 100’s or 1,000’s of decisions that are by themselves low value, but cumulatively huge value—decisions about pricing, customer selection, next best actions to target customers. And the new ideas need to be simple—to understand and implement.
Here’s an example of “keep it simple” gone wrong. A couple of decades ago, segmentation was big in retail. Marketers loved the idea of giving colorful names to different types of people who responded to different types of messages and products. Meanwhile, merchandisers used totally different metrics to choose products for the shelf: 1) top sellers; 2) high-margin products; 3) bang for the amount of shelf space. Call it a dollars-per-square-inch metric.
Adding 6 or more qualitative segments to those 3 quantitative criteria meant that the data was not easily sortable. Merchandisers went from using a tried-and-true formula to making judgment calls—on hundreds of products. And they were already busy. Bottom line, it didn’t work out so well.
The same “keep it simple” principle applies today. Insights need to be better, faster, and simpler. If it’s not better, why bother. If it’s not faster, people won’t have time to do it. If it’s not simpler, people will just ignore the analysis because it’s too complex. Translating data to insight to action requires a clear focus on a useable output.
Think of it as a SIMPLE-COMPLEX-SIMPLE pathway. Ask a simple question, like what’s the best price? Apply complex analytics and algorithms to produce a result, and translate that into a simple answer. Applying this “simplicity trinity” to, say, customer selection—who’s valuable and who isn’t—and you have a real competitive edge. Think small and simple, thousands of times a day. “Keep it simple” still has meaning.