Customer Churn Prediction Telecom operator in US

A US based telecom operator was looking for a solution to prevent customer churn based on their spending patters.

CoreView’s solution using AI/ML helped to analyse the customers’ behaviour patterns and identified potential churn, so that they can prevent it by taking right steps.

The Challenges

The key challenges were -

  • To predict the corporate customers who are going to churn in next 3 months
  • What are the drivers for the churn and how to reduce the churn?
  • No direct measure for effectiveness
  • Parameters and their impact on results is not well defined

Coreview’s Solution

CoreView built a solution with below features.

  • Analysis of 100K B2B customers data for last 24 months
  • Feature engineering – Calls data, Billing data, Care tickets data etc
  • Python – Data processing and time-series forecasting
  • Models based on RandomForest, Clustering and LSTM
  • ARIMA time series forecasting

The Results - Reduced Customer Churn & Increased Revenues

This helped the company to bring a great customer experience with -

  • 8% reduction in Customer churn for the 2 quarters
  • 5 % increase in revenue by Cross sell and up-sell
  • Increase in Customer satisfaction

8% reduction in Customer Churn and 5% increase in Revenue

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