Blinkit Keyword Rank Tracker

Captures the full Blinkit search results slate for a keyword in a specific Indian location, with every product's rank, the ad slots marked, and the campaign holding each paid slot. Quick commerce is store level, so each pincode resolves to the dark store that actually serves it and the ranks match what a shopper standing there would see.

What you get

• One row per search result position: position, product_id, name, brand, price, mrp, in_stock and inventory.
• is_sponsored on paid slots, read from Blinkit's own AD badge, plus ad_campaign_id naming which campaign holds the slot. You learn who is buying position one, not merely that position one is paid.
• Full location context on every row: location_label, pincode and store_id, so ranks from different cities never get mixed up.
• captured_at on every row, so a time series can be built by appending runs.
• Ranks for the whole slate, not just one brand, so competitor rank and ad share are derivable later without rescraping.

Input

Give the keywords and at least one Indian pincode.

{
  "keywords": ["atta"],
  "locations": [
    {
      "lat": 28.4595,
      "lon": 77.0266,
      "label": "Gurgaon-DLF",
      "pincode": "122002"
    }
  ],
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "IN"
  },
  "pages": 1
}

• keywords (required): search terms to rank. Duplicates are ignored.
• pincodes: Indian 6-digit pincodes, for example ["122002"]. Each is resolved to coordinates and Blinkit picks the store serving that point.
• locations (advanced): exact store targets as {"label","lat","lon","pincode"}, for when you want a precise point rather than a pincode centroid.
• pages: result pages to walk per keyword. Blinkit returns roughly 12 cards a page.
• proxyConfiguration: egress for the run. An Indian residential proxy is the default and is required.

Output

One dataset row per search result position, per keyword and location.

{
  "captured_at": "2026-09-11T09:01:28+00:00",
  "platform": "blinkit",
  "keyword": "atta",
  "location_label": "Gurgaon-DLF",
  "pincode": "122002",
  "store_id": null,
  "position": 1,
  "is_sponsored": true,
  "ad_campaign_id": "596175",
  "product_id": "7",
  "name": "Aashirvaad Select 100% MP Sharbati Atta",
  "brand": "Aashirvaad",
  "price": 299,
  "mrp": 381,
  "in_stock": true,
  "inventory": 14
}

A live run of one keyword in Gurgaon returned 12 rows, 3 of them sponsored. ad_campaign_id is null on organic rows, and store_id is null when Blinkit does not name the serving store.

Use cases

• Keyword rank tracking for your own SKUs, city by city, from keyword, position and location_label.
• Ad-slot share: how many of the top positions carry is_sponsored true, and which ad_campaign_id values hold them.
• Competitor share of search, counting brand across the whole ranked slate.
• Price and MRP monitoring at rank, pairing position with price and mrp.
• Availability alerts from in_stock and inventory, per store.

Notes and limits

• An Indian residential proxy is required. Quick-commerce platforms refuse datacenter addresses.
• Roughly 12 cards a page. Raise pages to walk deeper into a keyword's slate.
• Blinkit silently redirects an unserved point to a default store. The actor detects that and fails the capture with a clear message rather than returning another city's ranks.
• A pincode that cannot be resolved is skipped, and a run with no usable location fails fast.
• A keyword that matches nothing is reported as an empty capture with a warning, never as a clean zero.
• India only.

FAQ

Do I need a proxy? Yes, an Indian residential one, which is the actor default. Datacenter addresses are blocked.

How many results can I get? About 12 positions a page per keyword and location. Set pages higher to walk deeper.

Does it need a login or an API key? No. No account and no key.

How does a pincode pick a store? The pincode is resolved to coordinates, those coordinates are set as the delivery location before the search, and Blinkit selects the dark store serving that point. The resulting store is reported back in store_id and location_label.

Can I capture several cities in one run? Yes. Pass several pincodes or several locations, and every row is stamped with the location it came from.