Predictive Churn & LTV Modeling
Acquiring a new customer is 5x more expensive than retaining an existing one. We build machine learning models that analyze historical user behavior to accurately predict which customers are highly likely to churn in the next 30 days, allowing you to intervene with targeted retention offers.
Core Features
Early Warning Churn Scores
Assigning a daily 'Churn Probability Score' to every active user based on micro-changes in their engagement, login frequency, and support ticket history.
Lifetime Value (LTV) Forecasting
Predicting the total future revenue a new customer will generate over their lifetime, allowing you to optimize your Customer Acquisition Cost (CAC) bidding.
Explainable Drivers
Not just predicting *who* will churn, but extracting *why* (e.g., 'User experienced 3 bugs in the last week'), so your success team knows exactly how to save them.
Automated Intervention Triggers
Connecting the ML output directly to your CRM (HubSpot/Salesforce) to automatically trigger discount emails or VIP account manager alerts when churn risk spikes.
Our Process
Historical Data Aggregation
Week 1-2Combining disparate data sources: CRM logs, product usage telemetry, billing history, and support tickets into a unified customer view.
Feature Engineering
Week 3Transforming raw logs into predictive features (e.g., 'Days since last login', 'Ratio of successful to failed payments').
Model Training & Selection
Week 4-5Training survival analysis and gradient boosting models (XGBoost, LightGBM) to find the most accurate predictor of customer attrition.
Explainability Integration
Week 6Applying SHAP values to the model so the output isn't just a number, but a ranked list of reasons why the customer is at risk.
CRM Deployment
Week 7-8Deploying the model via API and building the two-way sync with your CRM to push daily scores to the customer profile.
Technologies We Use
FAQ
How much historical data do we need?
Can't we just set up rules, like 'if they don't log in for 30 days, flag them'?
How does the model handle seasonality?
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