AI-Powered GTM: How FMCG Brands Are Maximising Store Profitability

India’s FMCG industry has long relied on wholesale-led distribution to achieve scale. That approach worked when reach mattered more than nuance. But today, every neighbourhood tells a different story: incomes vary, consumption habits differ, and infrastructure can be inconsistent. A one-size-fits-all distribution model no longer suffices.

Recognising this, brands are moving closer to the last mile, building direct connections with retailers. And the objective is no longer just reach, it is making every store count, turning granular insights into real, measurable profitability. For companies operating in millions of micro-markets, the difference between broad coverage and targeted execution can mean the difference between growth and wasted investment.

The first challenge is deceptively simple: do the stores in your network actually exist? “Ghost stores,” as FMCG companies call them, are entries in databases that never appear on the ground. In some markets, they account for nearly a third of reported outlets. Each wasted sales visit drains resources, while inflated coverage metrics distort assortment planning, inventory allocation, and broader strategic decisions. Artificial intelligence is now providing a practical solution.

By combining geolocation with image recognition, brands can verify store existence in real time. A quick photograph of a storefront or a scan of signage, cross-referenced with mapping data, confirms whether a retailer is genuine. This ensures that sales teams focus their efforts where it matters most, equipping real stores with the right SKUs and protecting the distribution investment.

Verification, however, is only the beginning. The next step is understanding what kind of store has been validated. Is it a premium outlet serving affluent households, or a high-footfall shop catering to value-conscious consumers? At one leading FMCG brand, through our solution, sales teams capture scans from nearly 1.35 million shops every month. One analysis showed inventory presence in 95% of images, while 42% feature clearly identifiable signage.

By decoding shop facades, signage quality, and shelf-level objects, AI maps each store’s profile, differentiating between smaller, value-driven shops and outlets positioned to push premium SKUs. This enables precise allocation of inventory and sales effort, preventing overstocking in low-potential stores or missed opportunities in high-value locations.

Beyond SKU allocation, these insights also reveal a retailer’s operational capability, helping brands gauge whether a store has the working capital to handle stock and determine appropriate credit capacity. Onboarding thus becomes more than expanding reach, it becomes a strategic move to ensure every store added can execute effectively and contribute to profitability.

Yet stores do not exist in isolation. Their performance is inseparable from the neighbourhoods they serve. AI allows brands to decode micro-markets with remarkable granularity. By triangulating GST turnover, footfall data, and broader locational signals, companies gain a nuanced understanding of each outlet’s catchment.

The impact is tangible, a convenience store in Vasant Kunj, Delhi, serving affluent households, is better suited for full-sized, premium SKUs of a haircare brand, while a high-footfall shop in Seelampur demands a sachet-heavy assortment of the same product. Misreading these neighbourhood dynamics can lead to understocked premium items or wasted inventory in low-affluence areas, reducing profitability and eroding customer trust.

Moreover, AI continuously monitors these micro-markets over time, allowing brands to adjust assortment and investment as neighbourhood demographics and consumption patterns evolve. When store-level profiling meets neighbourhood intelligence, brands gain a holistic picture of retail reality, allowing them to stock the right products, optimise investment, and unlock growth aligned with actual consumption potential.

AI’s impact on FMCG go-to-market strategies is no longer hypothetical. It is already eliminating ghost outlets, refining assortments store by store, and decoding neighbourhood demand. Yet for these insights to scale, the industry needs common standards. Uniform benchmarks for verifying outlets, assessing neighbourhood potential, and interpreting image data can ensure insights are consistent, actionable, and reliable across markets. Without standardisation, even the most sophisticated AI risks producing fragmented guidance, limiting the very profitability it seeks to enhance.

Ultimately, AI equips brands to rethink distribution not just in terms of reach but in real profitability. By turning intuition into data-driven decisions, companies can make every store count, optimise investments, and respond to India’s diverse retail landscape with precision.

Standardised practices, combined with advanced analytics, can transform these tools from isolated solutions into a foundation for a truly data-driven retail ecosystem, where every store tells its real story, every SKU has a purpose, and every decision drive measurable profit.