AJIO chatbot targets fewer returns, wrong sizes

The smartest move is to stop thinking like a chatbot builder and start thinking like a margin tool; discovery is nice, but AJIO will pay for fewer returns and fewer tickets.

• –AJIO already appears to have ZIP, a conversational discovery assistant, so the pitch needs to be fit assurance and post-purchase rescue, not generic chat: https://www.kaily.ai/customer-stories/ajio
• –The market is already moving toward AI shopping agents; Amazon, Flipkart, Myntra, AJIO, and others are being pushed to optimize for AI search and conversational commerce: https://www.livemint.com/technology/amazon-meesho-race-to-crack-llm-powered-shopping-chatgpt-claude-ai-search-flipkart-11766987283517.html
• –Virtual try-on is quickly becoming table stakes in Indian fashion e-commerce, so the real edge is pairing it with return-risk prediction and warnings on items that run small or fit inconsistently: https://m.economictimes.com/industry/services/retail/googles-new-virtual-try-on-lets-indian-shoppers-see-outfits-on-themselves-just-by-uploading-a-photo/amp_articleshow/125741225.cms
• –Size advice needs richer signals than height and weight: add brand-specific fit memory, fabric/stretch heuristics, past return reasons, and a confidence score with clear size-up or size-down explanations.
• –Post-purchase automation is probably the biggest ROI lever: order tracking, delivery exception handling, exchanges, and proactive refund updates will save more money than another discovery feature.
• –If you want a pitch AJIO can buy, instrument the business case with return-rate reduction, size-related return share, ticket deflection, conversion lift, and time-to-resolution.