Predicting Departures Before They Happen
For subscription businesses, churn is the silent killer. We developed a Churn Prediction Model that goes beyond simple usage metrics. By analyzing product adoption depth, support ticket sentiment, and billing history, we calculate a daily 'Health Score' for every account. This allows Customer Success teams to intervene proactively, turning potential cancellations into renewal opportunities.
The Challenge
The Solution
Key Features
Health Scoring
Composite score (0-100) indicating the likelihood of renewal.
- Usage frequency
- Feature breadth
- NPS score
Risk Factors
Explainable AI showing *why* a customer is at risk.
- Low login rate
- Unresolved bugs
- Price sensitivity
Revenue Impact
Forecast potential revenue loss and prioritize high-value accounts.
- ARR at risk
- Expansion opportunity
- Contract health
Technology Stack
Project Timeline & Results
Data Unification
Created a 'Customer 360' view in Snowflake.
Ingested data from Mixpanel (usage), Stripe (billing), and Zendesk (support). Cleaned and joined the datasets to create a time-series record of customer behavior.
Modeling & Validation
Model correctly identified 70% of churners 30 days in advance.
Trained the survival model. Identified that 'lack of integration activation' was the #1 predictor of churn in the first 90 days. Validated the model on a holdout set.
Operationalization
Reduced churn by 15% in the first quarter.
Pushed risk scores to Salesforce. Set up alerts for CSMs when a key account's health dropped below 60. Launched automated email campaigns for lower-value at-risk accounts.
Related Services
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