Retention Pricing Models to Prevent Customer Attrition/Churn
A Dutch Teleco was struggling excessive churn at the end of the first contract year.
In oder to prevent customers from churning, Sales needed to know:
- Which customers are likely to churn and the likely reasons for churn?
- Which customers can be retained with a price promotion and if it was worth retaining a customer?
- What kind of promotions – discounting or bundling – should be offered?
- What was the right amount of discount or the right price for a bundle to retain a customer?
Retention Pricing & Customer Basket/Bundling Pricing to reduce attrition
Historical and transaction customer data was used to determine drivers that were most commonly linked with the likelihood of customer switching providers. These drivers ranged from their product basket, usage behaviour to complaints logged. Customers that were predicted as likely to drop out were assessed for their value to the organisation by computing their customer lifetime value. They were also assessed on their likelihood to respond positively to a price promotion. For those with a high CLV and likelihood of positive response to price promotions, predictive price recommendations were provided to sales to negotiation new contracts.
- Neural Network
- Customer Lifetime Value Model
- MRD Pricing AI
- Reduced Churn by 19%
- Increased up-sell revenue by roughly 1.2 million.
- Enabled the marketing department to focus their effort on high potential customers