Churn Prediction for Subscription Services

DataPro’s team worked closely with the client to build a custom churn prediction system starting from their existing analytics and enhancing it with AI modeling. Our goal was to give their small team a powerful tool that could plug directly into their workflows.

We followed a 4-step development process:

1. Behavioral Data Mapping

First, we audited the client’s existing data stack including Firebase events, Mixpanel logs, CRM, and Stripe billing data. Then we mapped a full picture of user behavior signals, such as:

• Session frequency and duration
  
  
• Time to first value (TTFV)
  
  
• Feature usage patterns
  
  
• Course completions vs. drop-offs
  
  
• Payment failures and grace periods
  
  
• Customer support interactions
  
  

We helped the client unify all this information in a lightweight data warehouse and structured the events for ML-readiness.

✅ Result: Centralized user behavior signals for churn modeling.

2. Churn Modeling with Machine Learning

Our data scientists built and tested several churn models using historical data. We focused on classification models that predict the probability of a user churning within the next 30 days.

We evaluated models like:

• Logistic regression
  
  
• Random forest classifiers
  
  
• Gradient boosting (XGBoost)
  
  

Features were engineered around recency, frequency, and engagement trends. We also built cohort-level features (e.g. users who subscribed during a promo vs. regular users) to refine predictions.

The best model reached an F1 score of 0.82, with prediction confidence bands the client could act on.

✅ Result: A live model that flags high-risk users 2–4 weeks before they cancel.

3. Actionable Churn Dashboard

Instead of overwhelming the team with raw data or model outputs, we built an interactive churn dashboard:

• User-level churn risk scores
  
  
• Segmentation filters (plan, cohort, device, etc.)
  
  
• Behavior trend graphs per user
  
  
• Automatic flagging of “high-risk” accounts
  
  
• CSV export for campaign targeting
  
  

This gave both the customer success and product teams a clear view of who needed attention and why.

✅ Result: Non-technical staff could now identify users to target with win-back strategies.

4. Intervention Workflow Integration

Finally, we integrated churn alerts into the team’s CRM and email tools. Using Zapier and custom webhook triggers, we enabled:

• Automated outreach to high-risk users (email or in-app)
  
  
• Personalized messages based on feature drop-off
  
  
• Dynamic discounts or support prompts for re-engagement
  
  

We also provided simple API hooks so they could evolve the system as their automation stack matured.

✅ Result: Early interventions became systematic instead of manual and random.

The Impact: Retention Up, Stress Down

Within the first 2 months of go-live, the app team saw meaningful impact:

• 18% reduction in churn across 3 renewal cycles
  
  
• 20% increase in user retention for flagged cohorts
  
  
• Faster support response to at-risk users
  
  
• More targeted marketing with churn risk data
  
  

Perhaps most importantly, the small team no longer felt in the dark about who might leave. Instead of scrambling after cancellations, they were finally ahead of the curve.