Telecom Churn Prediction

Churn Prediction for Telecom Customers

Objective

    To develop a 'Churn Probability' scoring model and an accompanying DSS to enable the client to:

  • Improve customer retention
  • Improve ROI on recovery efforts
  • Effective targeting in reaching and retaining high value customers
  • Cross-selling or up-selling based on specific customer needs

Solution

    Built a DSS system to drive better decision-making for one of the largest telecom companies in India with annual revenues of $110 millions

  • Identify significant variables
  • Build prediction model
  • Test and validate models
  • The techniques used were Neural Network, Decision Tree and Logit. The DSS allows managers to control and analyze using a 'What if' simulator, the return on investments in churn management.

Challenges

  • The company had subscriber base of more than 2.6 million spread across 209 cities. Each month's data comprised of more than 150 variables and 65,500 records, wherein each record represents an individual customer. The DSS provides a comprehensive comparative assessment of customer profitability as measured by 'Average Revenue Per User' (ARPU) vis-à-vis customer loyalty indicated by the 'Tenure Base' of the customer on its network.

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