Retention Pricing Models to Prevent Customer Attrition

A Dutch Teleco was struggling with excessive churn at the end of the first contract year. 

Challenge

Excessive Churn at the contract end year.

Sales wanted to know :

  • Which customers are likely to churn after 1 year?
  • Is the cost of retaining churning customers likely by running promotion exceeds the likely benefit?
  • Type & Extent of promotion to be used to retain clients?

Approach

Understanding drivers of attrition to prevent it in time

Historical consumer specifics were used to determine drivers that were most commonly linked with the likelihood of customer switching providers or churning at the of the term. These drivers ranged from their product basket, usage behaviour to complaints logged. Customers that were predicted as likely to churn were assessed for their value to the organization by computing their customer lifetime value. Those with a high CLV were then marked to get promotions
from marketing such as bundled discounts, vouchers, etc.

Methodologies 

  • Neural Network
  • Customer lifetime model

Results

  • Reduced churn by 19% and increased up-sell revenue by roughly 1.2 million.
  • Enabled the marketing department to focus their effort on high potential customers
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