Customer Churn Prediction Retaining $12.3M ARR
Built ML model predicting churn 90 days in advance, enabling targeted retention campaigns.
Customer Churn Prediction Retaining $12.3M ARR
Built ML model predicting churn 90 days in advance, enabling targeted retention campaigns.
Founder note: this is written to be practical. We focus on what we learned in delivery, what changed for the team, and what results held up after rollout.
The Real Problem
1. Monthly churn rate of 8% was losing $120K ARR monthly
2. No visibility into which customers would churn
3. Reactive support instead of proactive retention
4. Churn was causing negative unit economics
5. No way to segment retention efforts by risk level
What We Actually Changed
Built churn prediction and retention system:
- ML model predicting churn 90 days in advance (92% accuracy)
- Customer segmentation by churn risk
- Automated intervention campaigns for high-risk customers
- Win-back campaigns for churn prevention
- Product recommendation engine to increase engagement
- Support escalation for high-value at-risk customers
- Churn analytics dashboard
- Integration with CRM and email systems
TOOLS AND METHODS USED
Delivery Journey
Historical Cohort Analysis
Analyzed 3 years of customer data and churn patterns
Feature Engineering
Built predictive features from product usage
Model Development
Built churn prediction models, tested accuracy
What Improved
This churn prediction system transformed our business model. We went from losing customers to learning why and fixing it. The $12M we saved in ARR is life-changing. WEBOPS Limited understood SaaS metrics better than our internal team.
FOUNDER BRIEF
DELIVERY STACK
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