ML-Powered Churn Prediction Model for Telecom Company

Aiplified > AI Use Cases > ML-Powered Churn Prediction Model for Telecom Company


A leading telecom company aimed to reduce customer churn by proactively identifying at-risk customers and taking targeted retention actions. They needed a custom AI solution using AWS AI services, such as Amazon SageMaker, to develop and deploy a churn prediction model that could analyze customer data and predict the likelihood of churn.

Key Components of the Solution:

  1. Data Collection: Historical customer data, including demographics, usage patterns, and billing information, was collected to create a comprehensive dataset for model training.
  2. Data Preprocessing: The dataset was cleaned, transformed, and normalized to ensure optimal model performance and feature engineering was applied to create relevant features for churn prediction.
  3. Model Development: Using Amazon SageMaker, multiple machine learning algorithms were trained and tested to create a churn prediction model with the best performance.
  4. Model Deployment: The chosen churn prediction model was deployed in the AWS environment using Amazon SageMaker, enabling real-time churn prediction for the company’s customer base.
  5. Churn Prediction and Retention Actions: The system analyzed customer data and generated churn risk scores, allowing the telecom company to proactively identify at-risk customers and implement targeted retention strategies.


The churn prediction model powered by AWS AI services significantly improved the telecom company’s ability to identify and retain at-risk customers. Key results included:

  • A 25% reduction in customer churn, as the company was able to proactively identify at-risk customers and take targeted retention actions
  • A 20% increase in customer lifetime value, as a result of improved retention rates and reduced customer acquisition costs
  • Enhanced customer satisfaction, as the company was able to address customer needs more effectively and offer personalized incentives to retain at-risk customers
  • Improved data-driven decision-making capabilities, allowing the company to allocate resources more effectively towards customer retention initiatives

This example case study demonstrates the potential of leveraging AWS AI services, such as Amazon SageMaker, to address business challenges and drive growth. By implementing a churn prediction model, businesses can proactively identify and retain at-risk customers, resulting in improved customer satisfaction, reduced churn rates, and increased revenue.