Snapdeal deploys AI-driven platform to expand non-metro fashion e-fommerce penetration

Snapdeal deployed a multi-solution artificial intelligence suite to streamline fashion discovery for value-focused apparel buyers across regional markets. The platform’s personalized recommendation engine, StyleMatch, influences 76 per cent of all customer transactions by analyzing browsing behavior, location, and style preferences in real time. To eliminate search barriers in regional apparel retail, the platform introduced Large Language Model (LLM) conversational search, which processes over 5 million natural language queries monthly for specific clothing preferences, occasionwear, and budget parameters.

Image-based sourcing bridges regional fashion terminology gaps

With over 80 per cent of customer orders originating from non-metro cities, visual discovery tools mitigate text-search limitations in value fashion segments. Snapdeal’s expanded Snap&Shop feature processes over 1 million monthly image-based searches, allowing shoppers to upload photos or screenshots to identify equivalent apparel items. Deploying natural language and visual search allows non-metro consumers to access curated apparel lines without navigating technical fashion vocabulary, states Achint Setia, CEO,Snapdeal.

E-commerce infrastructure scaling across secondary markets

Online fashion platforms face rising customer acquisition costs and logistics complexities when servicing dispersed regional demographics. Integrating machine learning models across customer support, seller enablement, and inventory logistics reduces operational friction while widening product selection across Tier-II and Tier-III commerce nodes.

Founded in 2010, Snapdeal operates as a pure-play value e-commerce platform targeting price-conscious shoppers across non-metro India. Specializing in budget fashion, footwear, and lifestyle merchandise, the company serves customers across 2,500 towns. Snapdeal targets sustainable revenue growth through expanded seller enablement and AI-driven retail infrastructure.