Perspectives Matter in Analytics
As I emphasized numerous times in this series, analytical questions must be formed based on business questions, not the other way around. But too often, marketers seek to find answers to their questions within the limited data and reports they get to see. It is not impossible to gauge the speed of your vehicle based on the shape of the fur of your dog who is sticking his head out the window, but I wouldn’t recommend using that method when the goal is to estimate time of arrival with a margin of error of less than a minute.
Not all analytics are the same, and different types of analytical objectives call for different types of data, big and small. To understand your surroundings, yes, you need some serious business intelligence with carefully designed dashboards, real-time or otherwise. To predict the future outcome, or to fill in the blanks (as there are lots of unknown factors, even in the age of Big Data), we must change the perspective and harness different sets of data. To determine the overall destination, we need yet another types of analytics at a macro-level.
In the world of predictive analytics, predicting price elasticity, market trends or specific consumer behaviors all call for different types of data, techniques and specialists. Just within the realm of predicting consumer behavior, there lie different levels of difficulties. At the risk of sounding too simplistic, I would say predicting “who” is relatively easier than predicting “what product.” Predicting “when” is harder than those two things combined, as you may be able to predict “who” would be in the market for a “luxury vacation” with some confidence, but predicting “when” that person would actually purchase cruise ship tickets requires a different type of data, which is really hard to obtain with any consistency. The hardest one is predicting “why” people behave one way or the other. Let’s just say marketers need to listen to anyone who claims that they can do that with a grain of salt. We may need to get into a deep discussion regarding “causality” and “correlation” at that point.
Stephen H. Yu is a world-class database marketer. He has a proven track record in comprehensive strategic planning and tactical execution, effectively bridging the gap between the marketing and technology world with a balanced view obtained from more than 30 years of experience in best practices of database marketing. Currently, Yu is president and chief consultant at Willow Data Strategy. Previously, he was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, Yu was the founding CTO of I-Behavior Inc., which pioneered the use of SKU-level behavioral data. “As a long-time data player with plenty of battle experiences, I would like to share my thoughts and knowledge that I obtained from being a bridge person between the marketing world and the technology world. In the end, data and analytics are just tools for decision-makers; let’s think about what we should be (or shouldn’t be) doing with them first. And the tools must be wielded properly to meet the goals, so let me share some useful tricks in database design, data refinement process and analytics.” Reach him at email@example.com.