Every Figure Must Be Good, Bad or Ugly
Then, analysts must check if these figures are moving in different directions for different segments. Look at these figures and ratios by:
- Channel — separately for outbound (what marketers used) and inbound (what customers used)
- Product line/Product category
- Time Periods — Year, month, month regardless of the year, date, day of the week, daypart, etc.
Now here is the kicker. Remember how we started the journey with the idea of baseline comparisons? Start creating index values against them. If you want to compare some figures at a certain level (say, store level) against a company’s overall performance, create a set of index values by dividing corresponding sets of numbers (numbers in question, divided by those of the baseline).
When doing that, even consider psychological factors, and make sure that “good” numbers are represented with higher index values (by playing with the denominators). No one likes double negatives, and many people will have a hard time understanding that lower numbers are supposed to be better (unless the reader is a golfer).
Now the analyst is ready to mark these figures good, bad and ugly — using various index values. If you are compelled to show multiple degrees of goodness or badness, by any means, go right ahead and use five-color scales.
Only then, analysts should pick the most compelling stories out of all of this and put them in less than five bullet points for decision-makers. Anything goes, for as long as the points do matter for the business goals. We have to let the numbers speak for themselves and guide us to the logical path.
Analysts should not shy away from some ugly stories, as those are the best kind. If we do not diagnose the situation properly, all subsequent business and marketing efforts will be futile.
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.