Business Situation
- A leading digital publishing house faced significant challenges with high subscriber churn rates, resulting in revenue loss.
- The company struggled to understand the key drivers of churn among its readership and lacked personalized strategies to target and retain at-risk subscribers effectively.
SGA Approach
Technology
- Leveraged Python with libraries such as Pandas, NumPy, and Scikit-learn for data preprocessing and machine learning modeling
- Integrated the solution with the client’s existing CRM system for seamless deployment
AI
- Developed ML models, including logistic regression, random forest, and gradient boosting, to forecast subscriber churn probability
Data
- Consolidated subscriber data from multiple sources, including subscription databases, website activity logs, and email engagement metrics
- Engineered features to identify key indicators of churn risk, such as content consumption patterns and engagement frequency
Key Takeways
- Implemented a machine learning-based churn prediction model integrated with the client’s CRM system, reducing the monthly churn rate from 6.6% to 4.8%
- Developed personalized engagement strategies based on subscriber behavior and content preferences that increased CLTV by 15% through targeted retention campaigns
- Reduced the customer service team’s manual effort by 30%
- Enabled real-time monitoring of churn rates and engagement metrics for proactive countermeasures and achieved 90% accuracy in predicting high-risk subscribers
