The lack of automated models meant that the CRM business relies on business rules to extract target lists of customers. This method is ineffective in predicting churn nor future expectations of value, and is time-consuming for the data team to manually extract. This case study discusses the benefits of implementing an automated end-to-end customer journey with precision targeting made possible with predictive models.


  • Online wagering company

Time Frame

  • June 2017 – October 2017

Area of Expertise

  • Predictive Analytics

Responsible for

  • Select and build models using algorithms that are suitable for structured data
  • Automatic precision targeting for customers using their propensity to do X


  • Discovery: Benefits to various teams
  • Prototype: Accuracies, Post-Campaign Reviews
  • Production: End-to-end automation from raw data in SQL Server to predictions uploaded onto Salesforce
  • Monitoring: Accuracy reports

Outcomes and Benefits

  • Fully automated system
  • Improved efficiency of both the CRM and data team
  • Improved targeting and costs savings from not having to issue incentives to customers who don’t require it

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