Customer Churn Prediction for a Leading Telecom Firm

1. Introduction

A leading telecom firm engaged VerticalServe to develop a customer churn prediction model to identify and retain customers at risk of leaving. The project included gathering data from various sources, model training, deep learning, and the creation of sticky attributes for customer retention campaigns.

2. Data Sources

VerticalServe gathered data from multiple sources, including customer account information, marketing campaigns, demographic data, Call Detail Records (CDR), device usage, service subscriptions, competition data such as plans, campaigns, offers, and payment information.

3. Model Training with Old Switched Users

The team used historical data of customers who had already switched to a different provider to train the churn prediction models. This data allowed the model to learn patterns and identify factors contributing to customer churn.

4. Deep Learning with TensorFlow Models

VerticalServe employed TensorFlow, a deep learning framework, to develop advanced neural network models for predicting customer churn. These models were capable of identifying complex relationships between customer attributes and churn behavior.

5. XGBoost

The team also implemented XGBoost, a powerful gradient boosting algorithm, to improve the prediction accuracy and efficiency of the churn models. This approach helped the telecom firm identify at-risk customers with greater precision.

6. Churn Prediction on a Scaled Time Horizon

VerticalServe developed churn prediction models that operated on various time horizons, allowing the telecom firm to identify and address customer churn risks in both short-term and long-term scenarios.

7. Creation of Sticky Attributes for Customer Retention Campaigns

Based on the insights from the churn prediction models, VerticalServe identified “sticky attributes” — factors that increased customer loyalty and reduced the likelihood of churn. These attributes were used to design targeted customer retention campaigns.

8. MLOps

VerticalServe implemented MLOps practices to ensure seamless integration of the churn prediction models into the telecom firm’s operations. This included model deployment, monitoring, and continuous improvement to maintain the highest level of prediction accuracy.

9. Results

The customer churn prediction models developed by VerticalServe enabled the telecom firm to:

• Identify at-risk customers with high accuracy
• Develop targeted customer retention campaigns using sticky attributes
• Improve customer satisfaction and reduce overall churn rates
• Optimize marketing spend by focusing on high-risk customer segments

10. Conclusion

VerticalServe’s customer churn prediction solution empowered the telecom firm to proactively address customer churn risks and improve customer retention. By leveraging advanced machine learning techniques and MLOps practices, the telecom firm was able to enhance customer satisfaction and maintain a competitive edge in the market.

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