E-Commerce Churn Risk Identification Using Behavioral Segmentation

• Analyzed 74,817 user events to identify at-risk customers before churn using behavioral segmentation

• Found that financially identical users showed divergent behavioral signals, proving spend monitoring alone is insufficient

for retention

• Published full analysis and code on GitHub