Predict, Prevent, and Retain
Objective
To identify at-risk customers and build a data-driven retention strategy that improves customer lifetime value and reduces churn.
Business Problem
A subscription-based company was losing customers faster than it could acquire them. The retention team lacked clear insight into why customers left or which actions would drive renewals.
Analytical Approach
- Modelled churn probability using logistic regression and random forests.
- Analysed payment behaviour, tenure, and support interaction data.
- Scored customers into risk tiers and simulated retention campaign outcomes.
Key Metrics
- Churn Rate: 12%
- Retention ROI: 3.2×
- Revenue Saved: $425K CAD
- Accuracy: 85% prediction rate
Insight
Churn was concentrated among month-to-month customers with frequent billing issues. Retention offers and proactive service engagement reduced churn by up to 9%.
Business Value
The churn model transformed guesswork into targeted retention. It provided a playbook for intervention, enabling the business to invest retention dollars where they yield maximum ROI.
💡 Coming Soon: Predictive notebook, retention strategy guide, and validation workbook.
Tags: #PredictiveAnalytics #CustomerRetention #MensahInsights