retention-analysis

Churn analysis uses survival analysis techniques like Kaplan-Meier curves and Cox regression to quantify customer lifetime and identify retention patterns. Load timestamped user-activity data with churn indicators, compute survival metrics, and generate retention curves to spot at-risk cohorts and optimize engagement strategies.

Churn prediction combines cohort analysis, feature engineering from user activity, and machine learning models (scikit-learn) to score churn risk. Train on historical data, identify high-risk segments, and generate actionable recommendations for intervention before customers leave.

LTV modeling integrates survival curves with revenue data to estimate long-term customer value and ROI. Combine Kaplan-Meier survival estimates with cohort-based retention matrices to quantify the financial impact of retention improvements across user segments.

Yes. Cohort analysis segments users by signup date and tracks retention rates over time in heatmaps, while survival analysis (Kaplan-Meier, Cox regression) models time-to-churn. Used together, they reveal both short-term retention trends and long-term survival patterns across customer groups.

Retention analysis applies to any subscription or engagement-driven business. The methodology—survival modeling, cohort analysis, churn prediction—generalizes across SaaS, membership programs, e-commerce, and gaming apps using standard event-based data with timestamps and churn indicators.

Prepare user-activity data with columns for user ID, event timestamps, and a churn indicator (binary flag or last-activity date). pandas handles data cleaning and aggregation; numpy and matplotlib support computation and visualization. Survival analysis requires event times and status flags for Kaplan-Meier and Cox fitting.