Case Study: TVS Motor Company achieves 2x faster sales-lead follow-up and 30% more service appointments with Databricks

Accelerating revenue growth with AI-driven automotive experiences

TVS Motor Company, the third-largest two‑wheeler maker in India operating in 70+ countries, needed to modernise its data platform to drive profitable global growth. Sales and after‑sales data lived in multiple siloed systems, forcing teams to spend time consolidating inconsistent information and preventing a unified view of customer interactions across web, dealer and call‑centre channels.

Using the Databricks Lakehouse on Azure — including Delta Lake and MLflow — TVS built a centralised, single source of truth and sped up data engineering, ML development and deployment. Consolidating ~150 tables enabled ML models for lead prioritisation and service‑visit prediction, delivering a 2x faster sales‑lead follow up, a 30% rise in bike service appointments, and demonstrated ROI within 18 months while creating a scalable platform for future AI use cases and international expansion.

TVS

Anand Das

Head of Data Science and Engineering, Customer and Retail