It’s All About Ranking
If the list was not for prospecting calls, but for a CRM application where you also need to analyze past transaction and interaction history, the list of the factors (or variables) that you need to consider would be literally nauseating. Imagine the list contains all kinds of dollars, dates, products, channels and other related numbers and figures in a seemingly endless series of columns. You'd have to scroll to the right for quite some time just to see what's included in the chart.
In situations like that, how nice would it be if some analyst threw in just two model scores for responsiveness to your product and the potential value of each customer, for example? The analysts may have considered hundreds (or thousands) of variables to derive such scores for you, and all you need to know is that the higher the score, the more likely the lead will be responsive or have higher potential values. For your convenience, the analyst may have converted all those numbers with many decimal places into easy to understand 1-10 or 1-20 scales. That would be nice, wouldn't it be? Now you can just start calling the folks in the model group No. 1.
But let me throw in a curveball here. Let's go back to the list with all those transaction data attached, but without the model scores. You may say, "Hey, that's OK, because I've been doing alright without any help from a statistician so far, and I'll just use the past dollar amount as their primary value and sort the list by it." And that is a fine plan, in many cases. Then, when you look deeper into the list, you find out there are multiple entries for the same name all over the place. How can you sort the list of leads if the list is not even on an individual level? Welcome to the world of relational databases, where every transaction deserves an entry in a table.
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.