SaaS Customer Churn Prediction Model
Built a machine learning model predicting customer churn with 87% accuracy, enabling proactive retention strategies that reduced churn by 18%.
The Business Problem
A B2B SaaS company was losing 8% of customers monthly with no early warning system. Retention efforts were reactive and often too late.
Why It Matters
Each lost customer represented £15K in annual recurring revenue. The compounding effect of churn threatened long-term growth.
Data & Tools Used
Analysis Process
Collected and cleaned 24 months of customer behaviour data. Engineered 45 features from usage patterns, support tickets, and billing data. Trained and validated multiple ML models. Deployed the best model with automated scoring.
Findings & Recommendations
Key churn indicators: declining login frequency over 3 weeks, support ticket sentiment shift, reduced feature adoption after month 3. Customers with fewer than 2 integrations had 3x higher churn risk.
Deliverables
- Churn Prediction Model
- Feature Importance Analysis
- Retention Playbook
- Automated Alert System
- Executive Summary
Business Value
87% prediction accuracy. 18% reduction in monthly churn within 6 months. Estimated £450K in preserved annual revenue.
Skills Demonstrated
End-to-end ML project from data engineering through model deployment. Demonstrates practical application of data science to business problems with measurable ROI.
If you're losing customers without understanding why, predictive analytics can identify at-risk accounts before it's too late.
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