Using a dataset of over 100,000 customer records, the authors developed a predictive model that analyzed behavioral patterns, purchase history, and engagement levels to determine factors associated with churn. The model achieved an accuracy rate of 89% in predicting customer churn, allowing businesses to proactively target at-risk customers with retention strategies.

Key Findings

• Customer engagement frequency, response time to support tickets, and purchase regularity were the strongest indicators of potential churn.
• Proactive retention measures, when applied to high-risk customers, resulted in a 15% improvement in retention rates.