Case Study: US Foods achieves faster insights and reduced customer churn with DataRobot

US Foods Analyzes Transactions From 300,000 Customers with Snowflake and Datarobot

US Foods, a leading U.S. foodservice distributor serving roughly 300,000 restaurants and operators, struggled with an on‑premises data warehouse that was costly to maintain, limited in capacity, and slow for reporting—forcing analysts into siloed Excel/Access workflows and making predictive modeling impractical. To modernize analytics and enable churn prediction, US Foods moved transaction data to Snowflake’s cloud data platform and adopted DataRobot’s enterprise AI for end‑to‑end predictive modeling.

By centralizing data in Snowflake and using DataRobot to train and deploy churn models, US Foods cut report times dramatically (one report went from five hours to three minutes), achieved about $100,000 in annual savings, and gained actionable forecasts to identify at‑risk customers for targeted retention. The Snowflake + DataRobot solution supports daily large‑scale ingestion, analysis of millions of historical records, elimination of data silos, and freed analysts to explore new data sources while improving merchandising, marketing, and supply‑chain decisions.

US Foods

Steve Griswold

Data Scientist