Case Study: Dealer Tire achieves 14% conversion lift and 21% revenue increase with Domino Data Lab

Accelerating Innovation in the Automotive Industry

Dealer Tire, a century-old Cleveland-based automotive distributor that partners with manufacturers and dealerships, set out to grow dealership parts and service sales by turning its rich data into productized analytics. The small data science team faced DevOps and data-engineering constraints—limited infrastructure, tooling and resources—which made it difficult to build, deploy and scale predictive models quickly, even as leadership demanded rapid, measurable results.

To solve this they implemented Domino in 2017 to standardize environments, automate pipelines and operationalize models. Using Domino, the team built Tire Trigger—an ensemble of individualized models (tread-wear, miles-driven, routing of model selection, plus NLP data cleaning) and A/B experiments—to predict when each customer needs new tires and drive targeted outreach. Tire Trigger produced a 14% lift in conversion versus control (corresponding to a 21% revenue increase), while also simplifying deployment, improving talent acquisition and positioning Dealer Tire as an analytics partner to OEMs and dealers.

Dealer Tire

Chris Schron

Director of Data Science