India's retail market is set to cross ₹210-215 trillion by 2035. Here's what marketers should do next
BCG and the Retailers Association of India project the retail market to more than double from ₹90-95 trillion in 2025 to ₹210-215 trillion by 2035. With GDP growing at 8% in 2025 and India on track to become the third-largest economy by 2030, consumption is getting stronger-and more digital.
The takeaway for marketers: growth won't be won by blanket reach or generic campaigns. It will be won by sharp consumer focus, AI-driven experiences, and operating models that can move fast without breaking the business.
What's changing: from clicks to AI-guided choices
Internet adoption has more than tripled since 2016, creating high "adoption elasticity" for new tech like Generative AI. The next phase isn't about basic ecommerce. It's about agentic commerce-AI agents assisting how people discover, compare, and buy.
That means fewer linear funnels and more context-led decisions. Relevance, timing, and personal utility will beat pure brand recall.
Why this matters for performance
According to BCG, end-to-end AI transformations across merchandising and supply chains can drive 40-60% performance gains. Isolated pilots tend to top out around 10-15%.
Translation: scattered tools won't cut it. Integrated data, models, and workflows will.
New rules for growth
- Choose your shopper, then choose your trade-offs. Don't try to serve every segment. Define your core use cases and over-serve them.
- Build AI into the full experience. Discovery, evaluation, pricing, availability, payments, service-treat it as one connected system.
- Let agents do the heavy lifting. Product finders, conversational advisors, replenishment nudges, and post-purchase support should be AI-assisted by default.
- Redesign talent and ops. Marketing, merch, supply chain, and data need shared goals, shared metrics, and shared backlogs.
Practical plays for retail marketers
- Agent-led product discovery: Deploy AI advisors on high-intent categories. Train on your catalog, reviews, returns, and A/B-tested content. Measure assisted conversion and return rate lift.
- Demand sensing meets media: Connect inventory and price elasticity signals to paid strategy. Suppress SKUs with poor availability; boost lines with healthy stock and margin.
- Context-driven lifecycle: Trigger messages based on use cycles, seasonality, and local events-not just time since last purchase.
- Offer and assortment testing at scale: Use multi-armed bandits to rotate creatives, bundles, and landing pages. Optimize for contribution margin, not only ROAS.
- Return reduction as a KPI: Enrich PDPs with AI-generated fit/usage guidance. Track "kept revenue" as a core metric.
Data and measurement that make AI useful
- First-party foundation: Clean product data, consistent taxonomy, and consented customer profiles. No shortcuts here.
- Event streams over snapshots: Feed real-time signals (browsing, stock, promo states) into models that update recommendations and bids.
- Outcome-linked scoring: Score creative, content, and audiences by profit, LTV, and return probability-not vanity metrics.
Team model that actually ships
- Pod structure: Cross-functional pods for priority categories (marketer, merchandiser, data scientist, engineer, CX lead).
- Shared backlog: Weekly prioritization based on impact and confidence. Sunset low-yield experiments fast.
- Enablement: Upskill marketers on prompts, agent design, and measurement so they can brief-and challenge-AI work.
Fast tests you can run this quarter
- Launch a conversational advisor on one high-traffic category. Target a 5-10% lift in assisted conversion.
- Connect stock signals to paid suppression and measure wasted spend reduction.
- Replace static size/fit guides with AI-generated guidance from returns data; aim for a measurable drop in size-related returns.
- Pilot auto-generated PDP variants and let an optimizer allocate traffic to the best performer in real time.
Guardrails
- Accuracy and trust: Keep a human-in-the-loop for regulated or high-risk categories. Log agent responses for review.
- Bias and fairness: Audit recommendations and pricing for unintended bias.
- Privacy-by-design: Keep consent clear and data minimization standard.
As Kumar Rajagopalan of RAI put it, the next decade's gains won't be secured through sales growth alone. They'll go to retailers that make explicit business model choices, embed AI across the shopper experience, lean on agent-led functions, and rebuild talent and operating models to match.
For context, the report-Winning Codes for Retail 2035: Capturing the ₹200 Trillion Prize-was presented at the Retail Leadership Summit 2026 in Mumbai. You can learn more about the organizations behind it here: BCG and Retailers Association of India.
Level up your team's AI skill set
If your roadmap includes agent-led experiences and AI-augmented marketing, consider structured upskilling. A practical starting point: AI Certification for Marketing Specialists.
Bottom line: India's retail growth is real, and so is the shift in how people buy. Marketers who build for AI-guided decisions-backed by clean data, tight ops, and clear trade-offs-will take more than their fair share of the ₹200 trillion opportunity.
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