Recommendation System & Churn Reduction
6% Churn Reduction for a Major Broadcasting Company
The Challenge
A major broadcasting company faced rising subscriber churn and stagnating engagement. Their content recommendation approach was not personalized enough, and the company lacked predictive tools to identify at-risk subscribers before they churned. They needed a data-driven solution to increase revenue through better content recommendations and targeted retention efforts.
Our Approach
We designed and deployed recommendation systems combining collaborative filtering with content-based features, optimized for the broadcasting company's content catalog and user behavior patterns. The system personalized content surfaces for individual subscribers based on viewing history, preferences, and engagement signals.
Uplift modeling was used to identify subscribers most likely to respond to retention interventions. Rather than predicting churn alone, the uplift approach measured the incremental effect of each intervention — ensuring marketing spend was directed at subscribers where it would make the most difference.
The entire ML pipeline was built and deployed on GCP, with automated feature engineering, model training, and serving. We leveraged BigQuery for data processing and built monitoring dashboards to track recommendation quality and business KPIs in real time.
CI/CD pipelines were put in place to automate model retraining and deployment. Data drift and feature drift were monitored continuously; when thresholds were exceeded, retraining was triggered automatically so recommendation and uplift models stayed aligned with changing viewer behavior and content catalog.
The Results
At scale, a 6% churn reduction translates directly into millions in retained subscription revenue. The uplift approach was key — rather than targeting all at-risk subscribers equally, interventions were directed only at those where they would actually make a difference, making retention spend significantly more efficient.
6% Churn Reduction
Measurable lift Revenue Impact
87%+ Failure Detection
2+ weeks Prediction Horizon
Technologies Used
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