Tata Motors Smart Search: Turning B2B Survey Data into a Product Feature

Manasvi joined Tata Motors' strategic planning team at a point where the commercial vehicle division was developing a new product for a segment where the customer experience was significantly less developed than on the passenger vehicle side. The brief was to understand what B2B customers actually needed.

She designed a qualitative survey and helped run it across more than 150 B2B customers. The responses were entirely qualitative, which meant the synthesis challenge was not statistical but interpretive: understanding what customers were really asking for beneath the surface of their stated preferences.

The harder problem was that most customers could not articulate a specific truck they wanted. What they could describe were behavioral patterns and requirements: payload needs, mileage expectations, specific feature combinations. Mapping those behavioral inputs to the right product in a large catalogue was a machine learning problem.

Manasvi worked with the ML team to develop a concept she called a smart search vehicle feature. The feature allowed customers to input their requirements rather than a specific truck, and the system would surface the best match. She also sat with the product team to validate the feasibility of the features she was recommending.

The synthesis work took her from over 150 qualitative inputs down to three to four concrete feature recommendations, one of which became the smart search concept.