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Reducing Customer Churn


  • 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.
Reducing Customer Churn
Reducing Customer Churn
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