AI shopping agents could shift India’s D2C battleground from traffic to trust
Tiger Analytics’ Pavan Kunchala says AI agents may increasingly compare products, recommend brands and complete approved purchases. For D2C players, the near-term implication is clearer product data, credible reviews, consent-led payments and retention strategies built for agent-mediated discovery.
What happened
Tiger Analytics says AI shopping agents could reshape Indian D2C commerce by selecting products and completing purchases. The shift raises the importance of
Key facts
- 73% of about 2,000 surveyed Indians use AI for shopping decisions
- 66% prefer AI for product comparisons
- 61% are comfortable allowing AI to complete approved purchases
- ₹1.2 lakh holiday budget example
- ₹6,000 monthly pet-supplies budget example
- 14 years building enterprise decision systems
- more than $500 million in marketing spending informed
Why this matters
Prioritize partnerships or acquisitions in catalog intelligence, review verification, identity and payment-consent infrastructure to build an AI-agent commerce stack before it becomes table stakes.
What to watch
- Major Indian AI assistants, marketplaces or UPI/payment apps launching consumer agent checkout with explicit merchant APIs.
- Growth in agent-referred sessions, assisted conversion rate, repeat purchase rate and basket size versus paid-search and social cohorts.
- Emergence of standard product-feed, review-verification, consent and merchant-reputation requirements for agent recommendations.
- Changes in sponsored-placement disclosure rules, AI-commerce liability standards or RBI/payment consent guidance.
- Rising consumer complaints around agent errors, misleading recommendations, counterfeit products or unauthorized pre-approved transactions.
- Marketplace ranking changes that privilege fulfillment reliability, structured catalog completeness or verified seller reputation.
- Build machine-readable product catalogs with complete specifications, ingredient/material disclosures, compatibility data, use cases, certifications, current availability and delivery SLAs.
- Create a verified-review and post-purchase feedback system; prioritize review authenticity, response times, return resolution and repeat-purchase evidence as agent-readable trust signals.
- Instrument first-party consent, saved preferences and pre-approved spending controls so customers can authorize agent-assisted replenishment without surrendering payment control.
- Audit product pages for comparison readiness: transparent pricing, variant logic, total landed cost, warranty, return terms and competitor-differentiating claims.
- Reduce reliance on broad top-funnel acquisition by developing retention loops: subscriptions, replenishment reminders, loyalty benefits and customer-service continuity across channels.
- Test distribution and data integrations with major Indian AI assistants, marketplaces, ONDC-linked sellers and payment ecosystems while retaining direct customer identity where possible.