Dark-store siting, category demand modelling, basket-size forecasting, and city-tier expansion sequencing. Real signals replacing census-and-population proxies.
Q-commerce throughput economics live or die on dark-store demand modelling — and most operators today are using census-based proxies that get inflection wrong. Our pincode-precise affluence and lifestyle posture signals deliver materially better demand forecasts, both for siting new dark-stores and for category and SKU expansion within existing ones.
The boardroom questions we're already answering across this industry.
Illustrative sample below — anonymised, for format reference only.
Pincode-level demand forecasts replacing census-and-population proxies. Real signals that drive throughput economics — predict orders before the dark-store opens.
Where to launch high-AOV categories — premium beauty, electronics, home, alcohol — with data on the pincodes that actually convert versus those that just generate orders.
Affluence and lifestyle posture as forward indicators of basket size. Plan inventory mix and SKU allocation pincode by pincode for margin optimisation.
Which Tier-2 and Tier-3 cities are ready, and in which pincodes within them — sequencing the next wave of geographic expansion with evidence rather than intuition.
Model delivery radii precisely enough to know, before you commit capex, whether two dark stores would end up competing for the same demand pool.
Track rival dark-store openings and ad intensity by geography to spot the corridors competitors are quietly building density in — before it shows up in your own order data.
The product lines this industry uses most — explore each in depth.
Send us one decision you're working through — a launch market, a branch site, a network gap, a campaign plan. We'll come back with a snapshot from the analytics spine within 48 hours.