Every Figure Must Be Good, Bad or Ugly
You get to hear “actionable insights” whenever analytics or roles of data scientists are discussed. It may reach the level of a buzzword, if it hasn’t gone there already. But what does it mean?
Certainly, stating the obvious doesn’t qualify as insightful reporting. If an analyst is compelled to add a few bullet points at the bottom of some gorgeous chart, it has to be more than “The conversion rate decreased by 13.4 percent compared to the same period last year.” Duh, isn’t that what that plot chart is saying, anyway? Tell me something we can’t readily see.
And the word “actionable” means, “So, fine, numbers look bad. What are we supposed to do about it?” What should be the next action for the marketers? Should we just react to the situation as fast we can, or should we consider the long-term effect of such an action, at this point? Shouldn’t we check if we are deviating from the long-term marketing strategies?
Many organizations consider a knee-jerk reaction to some seemingly negative KPI “analytics-based,” just because they “looked” at some numbers before taking action. But that is not really analytics-based decision-making. Sometimes, the best next step is to identify where we should dig next, in order to get to the bottom of the situation.
Like in any investigation, analysts need to follow the lead like a policeman; where do all of these tidbits of information lead us? To figure that out, we need to label all of the figures in reports — good, bad and ugly. But unlike policework, where catching the bad guy is the goal (as in “Yes, that suspect committed a crime,” in absolute terms), numbers in analytics should be judged in a relative manner. In other words, if the conversion rate of 1.2 percent seems “bad” to you, how so? In comparison to what? Your competitors in a similar industry? Last year’s or last quarter’s performance? Other similar product lines? Answering these questions as an analyst requires full understanding of business goals and challenges, not just analytical skillsets.
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 principal and chief product officer at BuyerGenomics. Previously, Yu was the head of analytics and insights at eClerx, and VP, Data Strategy & Analytics at Infogroup. Prior to that, he 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 firstname.lastname@example.org.