Propensity score modeling, also known as behavioral scoring, is a popular method of descriptive analytics which aims to calculate the likelihood of a user taking a particular action.
In my first loyalty segmentation article, I wrote about how to segment frequent flyer loyalty members based on demographics, account profile data and status levels which they had obtained within your FF program.
Once the framework for a segmentation engine is established, chances are it’s beginning to look more like a data storage, business intelligence machine, rather than a static member database. That is the idea! For loyalty programs, the member database should be driving all marketing activity and acting as the center for all decision-making around what campaigns are displayed to which members at any given time.
Traditional segmentation by audience type.
Audience segmentation becomes increasing more powerful when understanding consumer behavior, or why someone transacted with product A instead of product B.
As a sub-set of prescriptive and predictive analytics, propensity modeling, or scoring, is a powerful addition to the data intelligence toolkit in which to use for marketing purposes. When combined with traditional segmentation techniques (i.e., demographic), the result is greater accuracy than ever before in ensuring the right user receives the right message at the right time.
By stacking these audience segmentation models on top of each other, your customer profile becomes highly granular and allows for intelligence such as: