Every Figure Must Be Good, Bad or Ugly
Last month, at the end of my article “Stop Blaming Marketing Problems on Software,” I listed nine high-level steps toward insight-driven analytics. Let’s dig a little further into the process.
Qualifying numbers into good, bad and ugly is really the first step toward creating solutions for the right problems. In many ways, it is a challenging job — as we are supposed to embark on an analytical journey with a clear problem statement. During the course of the investigation, however, we often find out that the original problem statement is not sufficient to cover all bases. It is like starting bathroom renovation in a house and encountering serious plumbing problems — while doing the job. In such cases, we would have no choice but to alter the course and fix a new set of problems.
In analytics, that type of course alteration is quite common. That is why analysts must be flexible and should let the numbers speak for themselves. Insisting on the original specification is an attitude of an inflexible data plumber. In fact, constantly “judging” every figure that we face, whether on a report or in the raw data, is one of the most important jobs of an analyst.
And the judgment must be within the business context. Figures that are acceptable in one situation may not be satisfactory in another situation, even within the same division of a company. Proper storytelling is another important aspect of analytics, and no one likes to hear lines out of context — even funny ones.
It may sound counterintuitive, but the best way to immerse oneself into a business context is to figure out why the consumer of information is asking certain questions and find ways to make her look good in front of her boss, in the end. Before numbers, figures, fancy graphics, statistical methodologies, there are business goals. And that is the key to determining the baselines for comparisons.
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 firstname.lastname@example.org.