Churn Prediction Case Study
Boosting Subscriber Retention Through Churn Prediction Models for a Telecom Company
Introduction
A prominent telecom company faced escalating subscriber churn rates, impacting revenue and profitability. Identifying subscribers at high risk of churn became a priority. To proactively address this, we implemented a churn prediction model to identify at-risk subscribers and enable targeted retention efforts.
Key Challenges
The telecom company struggled with:
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High Churn Rates: A significant portion of subscribers were discontinuing services, affecting revenue projections.
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Reactive Retention Efforts: The company's retention strategies were primarily reactive, addressing churn only after it occurred.
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Limited Customer Insights: Lack of actionable data on churn drivers hindered targeted interventions.
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Inefficient Resource Allocation: Broad, untargeted retention campaigns were costly and ineffective.
Our Approach
Data Integration
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Collected data from billing systems, customer service interactions, network usage, demographics, and marketing campaign responses.
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Integrated and cleaned the data to create a unified customer view.
Feature Engineering
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Identified key predictors of churn, such as call frequency, data usage, payment history, and customer service complaints.
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Engineered new features, including recency, frequency, and monetary value (RFM) scores, to enhance model accuracy.
Model Development
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Built a machine-learning model using algorithms like logistic regression and random forests to predict churn probability for each subscriber.
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Trained and validated the model using historical data, ensuring high accuracy and reliability.
Segmentation and Targeting
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Segmented subscribers into risk categories (high, medium, low) based on their churn probability scores.
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Developed tailored retention strategies for each segment, including personalized offers, proactive customer service, and targeted communication.
Implementation and Monitoring
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Integrated the churn prediction model with the company’s CRM system.
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Provided a real-time dashboard for tracking churn predictions and campaign performance.
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Continuously monitored and refined the model to maintain accuracy and adapt to changing market conditions.
Results Achieved
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Reduced Churn Rate: The subscriber churn rate decreased by 10% by the end of the year.
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Improved Retention ROI: Targeted retention efforts led to a 15% increase in ROI compared to previous mass marketing campaigns.
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Enhanced Customer Satisfaction: Proactive engagement and personalized offers improved overall customer satisfaction.
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