Strategizing Against High CAC User Retention & Cohort Analysis
In this project, transaction data was meticulously cleaned by handling missing values, duplicates, and outliers to ensure high-quality analysis. A suite of new features was then engineered, including date extraction and monthly activity identification.
Subsequently, a Cohort Analysis was constructed using the first purchase month as the grouping baseline. Retention rates were calculated and presented in a retention matrix visualized via heatmap, providing clear visibility into customer behavior.
💡 Key Findings from Heatmap
- Steep First-Month Drop-off: Retention drops significantly after the first month across all cohorts. Specifically, the Jan 2010 cohort saw a drop to 36% retention in Month 2, meaning 64% of customers churned immediately.
- Long-term Stabilization: Despite the initial drop, retention stabilizes over time. The Jan 2010 cohort maintained a consistent 35-47% retention rate up to the 12th month, indicating a strong base of loyal customers.
- Varying Cohort Performance: Cohorts from Feb & Mar 2010 showed slightly lower long-term retention compared to Jan 2010, suggesting potential issues with acquisition quality or campaigns during those months.
- "Smile/U-Pattern" Effect: A slight retention increase was observed in later months (e.g., Jan 2010 increased from 38% to 43% in Month 10), likely driven by seasonal factors or successful reactivation efforts.
🚀 Actionable Recommendations
Since the biggest churn occurs in Month 1, focus on the first-purchase experience. Implement immediate incentives like a "Discount for 2nd Purchase" or personalized product guides to secure early retention.
To lift the flattening retention curve for older cohorts, implement automated re-engagement emails with special offers targeting users who have been dormant for 3+ months.
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