Accurately segmented prospects without historical data
Case Studies / Banking / Price Optimization
A large financial services organization was launching a new product that would be marketed directly. However, they did not have time and resources to conduct extensive testing before the launch.
Without having any historical experience, our goal was to identify ways to identify and target profitable prospects.
Using a hypothesis driven approach, we developed 26 segments (such as affinity for the brand, use of other products, credit acceptable, credit risk, etc.) to divide prospect pool into three groups: attractive, exploratory, and unattractive.
Post campaign analysis revealed that the segmentation was the single most important variable in response modeling.