Data Consolidation and Pre-processing
We consolidated just under 100 GBs of data, everything from socioeconomic and geographic data to payment behavioral data. Our data science team filtered and cleaned it for subsequent analysis.
Behavioral Clustering and Factor Analysis
Our data science team used machine learning clustering models to discover structures and hidden patterns within the client’s data. After a set of highly correlated customer features were identified, they were passed through a clustering algorithm to distill four previously unknown customer segments. A regression analysis of each segment’s features revealed what factors were most likely to contribute to certain outcomes—that is, paying on time or enrolling in APA.
Testing and Validation
Guided by insights from the data, different versions of ad content aimed at improving payment timeliness and APA enrollment rates were quantitatively tested and confirmed.