A 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.
Requirement
Our client wanted to increase customer satisfaction by improving service quality and consistency. To help 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.
Solution
- Data model predicts the likelihood of a customer leaving and a ranking of most ‘at risk’ customers.
- Explanations for each risk rating, giving sales the opportunity to research possible corrective action 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
• OS: Red Hat Enterprise Linux
• DB: Oracle
• Middleware: Apache Tomcat, Java
• Frontend: Angular
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