Reducing Customer Churn

Reducing Customer Churn

Build a high-performing customer churn model to improve customer loyalty, realize sustainable revenue growth, and optimize business processes.


  • Historical customer data is dispersed in multiple system environments and cloud providers across jurisdictions.
  • Data centralization commingles user data, requiring operations to anonymize and duplicate data which reduces signals and increases storage costs.
  • Data science teams struggled to quickly normalize data to report on critical business objectives, often resulting in high turnover rates for data science talent.


  • Devron’s forward-deployed engineers help data science teams deploy and train satellite models on data at each of the provider’s 23 regional offices.
  • Insights from satellite models are aggregated and the updated models sent back out so models at every data store can learn from each other.
  • Budgets reallocated to decentralized (federated) data science initiatives following trends in cost reductions and performance of highly accurate ML models to reduce customer churn rate with machine learning.


Increased Customer Retention
Improved Customer Loyalty
Greater Model Accuracy
Reduced Data Centralization Time & Costs