Getting Started With Data-Driven and Database Marketing
Put another way, we’re quantifying customer value. This is strategically very important and for good reason, because:
“That which can be measured, can be maximized.”
This brings us two the last big focuses that your database marketing solution can help you answer, “Who is the Customer?”
Completing The Picture: Who? (Is The Customer?)
Continuing to build from the bedrock we started with in a raw transaction file, we can move on to learning about who the customer is who bought, repeat purchased, established loyalty and possibly went dormant. Now we match those customer records to data about the customer and the lifestyle.
This requires more matching logic, spinning through your database and matching individual customers based on unique identifiers or combinations of fields you may already have. Even then, you or a service provider will then need to perform iterative matching to maximize your coverage of these data fields.
Some of the more valuable and important categories to focus on of data enhancement include:
When this data is identified and completed, we “extend” the customer record so we can now answer questions that inform messaging, creative and even product selection/assortment.
Bonus Question: How Did They Do That? (Math and Models)
The element of database or data-driven marketing that generates the most interest is utilizing statistical methods to forecast or predict behavior and customer value. These are the least accessible methods but may offer the most sustainable competitive advantage.
With a well-organized database, we can now begin working with statistical methods and running calculations called models or model scoring.
These methods answer questions like: Who is likely to try an aggressive new design on the product? Who is most likely to drink brown liquor at dinner? What customers have the highest probability of attrition?
While these methods may be considered the most “glamorous” of the data-driven marketing sciences, it is perhaps most important to realize that they are only possible or cost-effective when they are built on a robust foundation. That is, a modeler will spend 90 percent of the time and effort on getting data into a format to be able to develop sensible and useful statistical outcomes ― which drives up cost and time to value if the fundamental database design doesn’t support this use case for the data.