Customer loyalty
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
Customer loss rates fell by 18% in the pilot country
- 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
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Operating System: Red Hat Enterprise Linux
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Database: Oracle
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Middleware: Apache Tomcat, Java
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Frontend: Angular
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