Case Study
Prevent and Predict Customer Churn
The Challenge
The client was facing a major challenge with high customer churn rates, which directly impacted revenue and profitability. Since customer acquisition costs (CAC) were already significant, every lost customer not only meant wasted marketing spend but also a sharp reduction in customer lifetime value (LTV). Retention, therefore, became a critical business priority.
The main issue was that the client had no visibility into which customers were most at risk of churn. As a result, their retention strategy was inefficient, they were forced to target all users with generic offers and discounts. This “one-size-fits-all” approach significantly increased costs while delivering minimal improvement in customer retention.
Our Solution
To solve this, we developed a churn prediction model using historical data, customer behavior, support interactions, and app engagement metrics. This model allowed us to identify specific customer segments with the highest churn probability, rather than treating the entire user base the same.
Once the at-risk cohorts were identified, we automated interventions by delivering personalized retention strategies, such as targeted offers, loyalty rewards, and promotional campaigns tailored to each segment. By aligning the right incentive with the right user group, the client was able to optimize retention efforts, reduce promotional costs, and maximize ROI.
Our Process
Data Integration
Feature Engineering
Model Development
Operationalization
Retention Actions
1. Data Integration
Consolidated data from multiple sources: transactions, demographics, support tickets, app engagement, and ratings
Cleaned and standardized records, ensuring data quality and privacy compliance
Outcome: Built a single source of truth for customer behavior, enabling accurate churn analysis
2. Feature Engineering
Created behavioral features: purchase frequency, time since last login, support interactions, and customer ratings
Designed demographic and lifecycle variables to capture churn drivers
Outcome: Generated 100+ meaningful features, improving model input quality and predictive power
3. Model Development
Built a supervised machine learning model for churn prediction
Validated accuracy using cross-validation and AUC/ROC metrics
Outcome: Achieved 80–85% accuracy in identifying at-risk customers before churn occurred
4. Operationalization
Integrated churn scoring into client workflows via automated data pipelines
Enabled real-time risk scoring for continuous monitoring of customer health
Outcome: Reduced manual intervention and provided daily updated churn lists for the business team
5. Retention Actions (Next Best Steps)
Customer Segmentation: Grouped customers into high-risk, medium-risk, and healthy buckets
Action Playbooks: Tailored interventions by risk group
High risk: Personal outreach, discount offers, support check-ins
Medium risk: Re-engagement campaigns, product education emails
Low risk: Loyalty rewards, upsell opportunities
Feedback Loop: Tracked which interventions worked and fed results back into the model for continuous improvement
Outcome: Turned insights into actionable retention strategies, closing the loop from prediction to impact
In short, the churn model transformed the client’s approach from broad, expensive retention tactics to a data-driven, cost-efficient, and personalized retention strategy — improving both customer satisfaction and business outcomes.
Let's Build Your
Retention Success Story.
Want to know which of your customers are most likely to churn?
Let’s build a prediction model that helps you act before it’s too late.
Trusted by Leaders
