Difference Between an Operational and a Marketing Database, Part 2

Last month we observed that many people think they have a Marketing Database when, in reality, they have nothing more than an Operational Database. We noted that an Operational Database supports "nuts and bolts" tasks such as fulfillment, but does little if anything to assist data-driven marketing.

Also last month, we provided the following test that you will be able to pass with flying colors if you have a Marketing Database: 1) examine the history of each of your customers as of a year ago; 2) rank your customers from best to worst, as they would have been ranked a year ago; 3) divide the ranked customers into deciles; and 4) for each decile, calculate average per-customer revenue and average per-customer promotional spend from a year ago to the present.

To know for sure if you have a Marketing Database, you need to be able to do one last thing: simultaneously for each of three additional past-points-in-time - two, three and four years ago - create a standard File Inventory Report. The specifics will vary by the type of business you are in, but invariably will include: 1) permutations of customer counts, purchase rates and dollar amounts, and 2) year-over-year absolute as well as percent changes. Components of your File Inventory Report will also double as Key Performance Indicators ("KPI's") that are closely tracked throughout the organization.

If you passed last month's test, and can simultaneously create the three File Inventory Reports, then congratulations, you really do have an environment worthy of being called a Marketing Database! And, you probably know the reason why a Marketing Database needs to be able to do all of this:

Database marketing is, by definition, driven by deep-dive data mining. Deep-dive data mining, in turn, requires the ability to rapidly recreate past-point-in-time ("Time 0") views, and then manipulate and report on the data within these views. In fact, it is common for multiple such views to have to be simultaneously recreated. Without this ability, you will not - for example - be able to quickly turnaround any cohort analysis, including lifetime value. Nor will you be able to build any statistics-based predictive models.