TVS Motor Company centralizes sales and service data on Databricks to drive AI-driven automotive experiences
TVS Motor Company used the Databricks Data + AI Platform on Azure, with Delta Lake and MLflow, to centralize siloed sales and after-sales data from 150 different tables into a single source of truth, building ML models for sales-lead classification and predicting service center visits, achieving 2x faster sales-lead follow-up and a 30% increase in customer appointments for bike service.
Overview
TVS Motor Company used the Databricks Data + AI Platform on Azure, with Delta Lake and MLflow, to centralize siloed sales and after-sales data from 150 different tables into a single source of truth, building ML models for sales-lead classification and predicting service center visits, achieving 2x faster sales-lead follow-up and a 30% increase in customer appointments for bike service.
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Inspect the highlighted sourceThe challenge
TVS Motor's sales and after-sales lifecycle data sat in multiple, difficult-to-access data platforms, causing inconsistent insights across teams; sales teams spent significant time consolidating data instead of developing growth strategy, and the business lacked a consistent view of customer interactions across its website, dealer showrooms and call centres.
The solution
TVS Motor built a centralized data platform on the Databricks Data + AI Platform on Azure, using Delta Lake for fast, scalable pipelines and MLflow to simplify model development and deployment, to create a single source of truth for prospect and retail engagement data and develop ML models for sales-enquiry lead classification and predicting service centre visits.
Reported business value
The centralized platform delivered 2x faster sales-lead follow-up and a 30% increase in customer appointments for bike service, and TVS Motor demonstrated return on investment for Databricks within 18 months.
Sources
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