“Big data” — the explosion of quantifiable information, much of it generated by people’s behavior on the Internet and social media — has captured the imagination of companies, academics, and the media. Executives are rightfully intrigued by the idea of drawing conclusions about their customers’ buying propensities from details of their activity: who they’re connected to, what they like. This promise is born partly of the fact that the data for Internet-based analytics is already being captured on computers, there to be sorted, filtered, and modeled.
Undoubtedly, big data will be the next game changer for marketers, but some will gain more advantage from it than others. Because of the automated tools that are available for mining data today, many executives assume that they will be able to easily uncover trends that weren’t previously visible. But analytics involves more than just knowing the facts. It requires the right analysts asking the right questions to make the right decisions. Any analysis of data that stops after asking “what,” which is already a big undertaking, isn’t analytics. You have to ask “why?” and “what next?”
To answer these questions, and to leverage big data’s full potential, companies need to go back to basics. Having been closely involved in the evolving marketing analytics landscape over the past 25 years, I’ve learned three pragmatic lessons that have always been at the core of a strong analytics program, and should guide your initiatives today: Rely on theory-based approaches, not blind data mining; strive for a holistic view of your customers and markets; and learn by doing.
It Starts with a Theory
Without a theory about how consumers form preferences and act on them, an analyst will quickly be overwhelmed by the amount of data available, and all the processing power in the world won’t help. The starting point should be an explicit hypothesis about the needs of your customers and how you create value for them. It may be a new product in the lab that you think has the potential to be a runaway hit. Or it may be that there are customers in your market who aren’t really loyal to anyone — “undecided voters” whom you could capture with some slightly altered proposition. Once you’ve gathered the data required to test your hypothesis, the analysis will usually lead you to specific ideas for developing winning value propositions and taking them to market. Superior segmentation — clustering your customers and prospects based on similar behaviors or preferences — can lead to much more effective targeting strategies.
One pharmaceutical company trying to increase sales of a drug that was in decline, and for which the sales staff had been severely cut back, decided it might benefit by deploying its remaining salespeople more strategically. Company leaders’ hypothesis was that their current sales plan was not effectively targeting physicians with the highest potential to prescribe their drug. To test this, the company gathered a large data set on all the physicians treating the medical condition for which this drug was prescribed — how many prescriptions the physicians wrote each year, whether the number of prescriptions they wrote was growing or declining, and to whose formulation (the company’s own or that of their main competitor) the physicians were loyal. This allowed the company to identify a sweet spot in the market: physicians who wrote a lot of prescriptions to begin with; who were increasing the number of prescriptions they wrote each year; and who were not loyal to either manufacturer’s formulation, dispensing prescriptions for both about equally. The sales team made a beeline to pursue this opportunity, with results that exceeded their expectations.
