Accelerating revenue growth with AI-driven automotive experiences
Faster sales-lead follow up
Increase in customer appointments for bike service
As the third-largest two-wheeler company in India, TVS Motor Company needed to find a way to modernise their data platform to drive revenue and generate profitable business growth globally. Existing sales and after-sales lifecycle data sat in multiple data platforms, which caused inconsistencies when different teams across the organisation had to create meaningful insights for reporting and analysis. Together with Databricks and the Azure cloud, TVS Motor transformed their data processes and digitised the new customer data acquisition. The organisation leveraged the Databricks Data Intelligence Platform to modernise their data engineering infrastructure and centralise data. This provides a single source of truth for all the prospects and retail network engagements and interactions happening within the organisation. With the power of data and analytics, TVS Motor can improve sales funnel effectiveness and thus, achieve their overall objective of driving profitable revenue growth.
Inconsistent view of siloed data sources across the organisation
Operating in more than 70 countries and with a customer base of millions globally, TVS Motor Company aspires to become one of the top original equipment manufacturers (OEM) for two-wheelers and three-wheelers globally. They understand the importance of data and AI transformation in enabling this vision. However, their current teams were hindered by a lack of insights to inform their decisions due to siloed systems and data sources.
“Analytical data sources existed in multiple and difficult-to-access data platforms. Sales teams had to spend a lot of time consolidating data and insights rather than focusing on developing and activating their sales growth strategy,” said Anand Das, the Head of Data Science and Engineering, Customer and Retail, at TVS Motor Company.
In addition, the business wanted to keep the customer at the centre of their data transformation journey as they pivoted online. Having a consistent view of new customer interactions from various sales channels — brand website, dealer showrooms or call centres — was necessary for the organisation to address them on time and improve sales effectiveness.
A single source of truth to understand sales effectiveness better
TVS Motor homed in on Databricks as their platform of choice to enable reliable, secure and scalable collaboration among their data teams. They leveraged the Databricks Data Intelligence Platform on the Azure cloud, which comprised different components such as Delta Lake to build fast and scalable data pipelines, and MLflow to simplify model development, training and experimentation, and deployment.
“Delta Lake has been instrumental in accelerating our transformation as it embodies the best of data lakes and data warehouses. It centralises data and automates cluster lifecycle management, enabling our data teams to run multiple workloads at scale with low latency,” said Satya Mohapatra, Lead Data Scientist at TVS Motor Company.
MLflow has dramatically streamlined the end-to-end machine learning lifecycle and reduced the time to market considerably. The standardised tools, processes and commands on MLflow simplify recording and tracking ML experiments. This allows the data teams to develop various ML models that track customer feedback to improve customer satisfaction.
Faster time to market for ML models to drive revenue growth
With the help of Databricks, TVS Motor was able to synchronise data and enable a shorter time to market for their ML models.
One of which is for sales enquiry lead classification. Business teams had to find a way to prioritise customer inquiries and follow up accordingly based on the propensity to retail. The centralised data platform allows the teams to consolidate data from 150 different tables for all the features, resulting in a significant improvement in sales lead conversion.
With front-end service advisers at the heart of the business, the organisation is always looking for ways to bring efficiency to the service centre. They have developed ML models, which predict when a customer will visit the service centre and the type of service to be rendered based on historical data — empowering service advisors with the insights they need to support their customers proactively. In the future, they will also develop chatbots and voice-bots to automate the entire inbound and outbound processes of the service centre for a better customer experience.
“We were able to demonstrate the return on investment for Databricks within 18 months. It was made possible through the faster time to market for unified data lake development, AI models and business intelligence products that were developed using this centralised data platform,” said Anand.
Looking ahead, TVS Motor will continue to leverage Databricks to enable more use cases through the unification of data engineering, machine learning and data analytics. They are also on the journey to horizontally scale the data lake to serve international markets where they operate to better pivot their business online.
“It is an exciting journey ahead, and we are looking forward to using Databricks to run more deep learning models at scale as our existing use cases mature,” said Budigam Nagaraju, Lead Data Scientist at TVS Motor Company.
