Quick Commerce Data Scraping Services

Because a national q-commerce catalogue does not exist. Each dark store carries two to five thousand SKUs selected for its micro-market, so assortment, price, stock and delivery promise all differ by zone at the same moment.

A national figure blends zones where a SKU is ranged with zones where it never was, and averages a forty-minute stock-out with zones that never went out. The result describes no shopper's experience anywhere. Every record we deliver carries its pincode so you aggregate afterwards, on your terms.

Two separate fields, and this is the most important distinction in the schema. listed says whether the SKU is in that zone's catalogue at all. in_stock says whether it is buyable where it is listed.

Datasets that merge them cause real damage: a brand sees 40% unavailability, escalates to supply chain, and discovers weeks later that most of the gap was never-ranged zones. We compute availability only across the listed population, and deliver range gaps as their own metric — which is commercially more useful, since a list of zones where competitors are ranged and you are not is an actionable category argument.

Within the hour on the hourly tier, with the stock-out start timestamp recorded so duration is measurable rather than just flagged. Recovery time after restock is captured the same way.

This matters because q-commerce stock-outs are short. A dark store holds hours of cover, not weeks, so a daily snapshot misses most of them entirely and systematically understates the problem. If availability is your primary use case, hourly on a focused SKU-pincode set beats daily on everything.

Usually far fewer than clients first assume. Cost scales with zones times SKUs times frequency, so zone selection is the main lever — 500 SKUs across 1,800 pincodes hourly is roughly nine times the cost of the same SKUs across a well-chosen 200.

We design the set around revenue-weighted metros, sample one pincode per dark store cluster to remove redundancy, deliberately include income-tier variation, and add a rotating low-frequency sweep to validate that the dense sample still represents the wider picture. That design conversation happens before we quote.

Yes. Shelf position within category and search results is captured per zone, along with a sponsored flag where the platform marks paid placement.

This is one of the higher-value uses of q-commerce data, because trade and retail media spend is otherwise verified only by the platform selling it. Zone-level and hour-level capture shows not just whether placement appeared, but how consistently — which is usually where the discrepancy sits.

Platform terms generally restrict automated access, and we say that plainly rather than implying the question is settled. Our practice: publicly accessible catalogue pages only, generic location input to set the zone, no account creation, no customer credentials, no orders placed, low request rates.

You receive a written methodology document per platform describing exactly what is accessed and how, and a DPA before signature, so your counsel can assess your specific use case. Any vendor telling you this is entirely without consideration is not being straight with you.

Yes — displayed delivery minutes, handling fee, surge fee and free-delivery threshold, all per zone and captured at the moment of collection.

These move with time of day and platform load, so a single daily reading is not representative. Where delivery economics are the focus, we collect at several fixed times daily so peak and off-peak behaviour separate cleanly rather than being averaged into a meaningless midpoint.

India is the deepest by a wide margin, with genuine pincode-level differentiation across several national platforms. The GCC is strong and growing. Parts of Europe have meaningful coverage in metro areas, though the sector has consolidated significantly there. US rapid delivery exists but with different economics and less zone-level catalogue variation.

We give you an honest per-market assessment during scoping. In some markets q-commerce is thin enough that grocery delivery data answers your question better, and we will tell you that rather than selling you the more expensive collection.

We quote individually, and here the quote is almost entirely zones × SKUs × frequency. Nothing else moves the number as much.

A focused SKU set across a well-chosen metro zone sample at several-times-daily refresh sits at the lighter end. Broad SKU coverage across thousands of pincodes at hourly refresh across multiple platforms sits considerably higher. Because zone design has such leverage, we do that design with you before quoting. One scoping call, a free pilot on your own SKUs and pincodes within 48 hours, then a fixed monthly quote. Request a quote.