Perspectives Matter in Analytics
When we observe a certain phenomenon, we should never do so from just one angle. We’ve all heard the fable about blind men and an elephant, where each touched just one part of the animal and exclaimed, “Hey, this creature must be like a snake!” and “No, it feels like a thick column!” or “I’m sure it is like a big wall!” We certainly don’t want to fall into that trap.
In the world of marketing, however, so many jump to conclusions with limited information from one perspective. Further, some even fool themselves into thinking that they made scientific conclusions because they employed data mining techniques. Unfortunately, just quoting numbers does not automatically make anyone more analytical, as numbers live within contexts. With all these easy-to-use visualization tools, it’s equally easy to misrepresent the figures, as well.
When we try to predict the future – even the near future – things get even more complicated. It is hard enough to master the mathematical part of predictive analytics, but it gets harder when the data sources are seriously limited; or worse, skewed. When the data sources are contaminated with external factors other than consumer behavior, we may end up predicting the outcome based on the marketer’s action, not on consumer behaviors.
That is why procuring and employing multiple sources of data are so important in predictive analytics. Even when the mission is to just observe what is happening in the world, having multiple perspectives is essential. Simply, who would mind the bird’s-eye view when reporting a high-speed car chase on TV news? It certainly enhances the picture. On the other hand, you would not feel the urgency on the ground without the camera installed on a police car.
I frequently drive from New Jersey to New York City during rush hour. (I have my reasons.) I have been tracking the number of minutes in driving time between every major turn. Not that it helps much in reducing overall commuting time, as there isn’t much I can do when sitting helplessly on a bridge. But I can predict the arrival time with reasonable accuracy. Now armed with smartphone apps that collect such data from everyone with the same applications (crowd sourcing at its best), we can predict ETA to any destination with a margin of error narrower than a minute. That is great when I’m sitting in the car already. But do such analytics help me make decisions about whether I should have been in the car in the first place that morning? While it is great to have a navigator that tells me every turn that I should make, do all that data tell me if going to the city on the first day of school in September is the right decision? Hardly. I need a different perspective for that type of decision.
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.