Business Impact

  • Data driven identification of churn factors

  • Enhanced customer lifetime value

  • Significant increase in retention rates

Customer Key Facts

  • Country : United States
  • Industry : CPG

Developing a churn prediction model to identify consumers most likely to churn

The customer, an American wholesale manufacturer, and distributor of bakery ingredients and products wanted a system to identify consumers who were more likely to dissociate with the brand so they can take preventive measures.


  • Limited data availability
  • Inconsistent variables
  • Weightage of unidentified factors affecting retention
  • The untuned model resulting in reduced accuracy

Technologies Used

Google Cloud Platform
Google Compute Engine
Google BigQuery
Google AI Platform
Google Ads
Cloud Storage
Cloud IAM
Cloud Monitoring
Cloud Logging
Vertex AI

Churn Analysis Model for an American Wholesale Bakery Goods Manufacturer and Distributor


Quantiphi segmented customers into various buckets through RFM analysis and developed a churn model which will help them understand the segment of customers most likely to drop off along the way.
This helped the client devise mitigation plans for factors associated with each segment.


  • Data-driven identification of churn factors
  • Enhanced customer lifetime value
  • Increased retention rate

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