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Business Impact & ROI

Predictive modeling is only as valuable as the business decisions it enables.

The Problem with Blanket Campaigns

When marketing teams notice an uptick in churn, the typical response is to send out a 20% off coupon to all inactive members. However, many of those users would have renewed at full price anyway.

We lose margin on the "Loyalists" to save the "Flight Risks."

Targeted ML-Driven Campaigns

This project solves this by scoring every customer with a Churn Probability between 0.0 and 1.0.

Simulation Results

Below is the comparison of a simulated retention strategy targeting the entire user base versus targeting only users identified by our model as high-risk within high-lifetime-value clusters.

ROI Comparison

The Strategy Breakdown

  1. Segment Users: We use K-Means on historic billing and engagement to find our top tier users (Whales).
  2. Predict Churn Risk: We apply our XGBoost classifier to get a 30-day flight risk probability.
  3. Targeted Intervention: Only users who are in the Top 20% of probability and belong to high profitability clusters receive the expensive marketing intervention.

Impact: The ML-driven strategy creates a significant gap in wasted spend, fundamentally raising the ceiling on marketing ROI.