Customer loyalty

Reducing churn with predictive analytics

Our client was concerned at high customer churn rates – they lost approximately 15% of their accounts each year. Their brand image suffered and sales teams spent a lot of time resolving customer complaints instead of selling.

The requirement

The client wanted to increase customer satisfaction by improving service quality and consistency. To achieve this they required a predictive tool that identified ‘at risk’ customers & the reasons for the at risk status so they could resolve issues and prevent customer churn rather than merely respond to it.

The solution

  • Data model predicts the likelihood of a customer leaving
  • Ranking of most ‘at risk’ customers
  • Explanations for each ‘at risk’ rating
  • Customer account teams have the opportunity to develop corrective actions before meeting the customer
  • Customer loss rates fell by 18% in the pilot country (USA) and the solution is being rolled out to other regions.
Technical specifications:
VMWare Virtual Server
Operating System: Red Hat Enterprise Linux
Database: Oracle
Middleware: Apache Tomcat, Java
Frontend: Angular

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