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Trust & CompanyBlinkit India Twice a Week Keyword Rank, Share of Search & Ad Placement Dataset
A twice-weekly file that answers a question price data cannot: when a shopper searches on Blinkit, who appears, and who paid to be there? Thirty-three columns covering keyword, search rank, organic versus sponsored placement, brand visibility, price and availability at pin-code level.
In the supplied sample, 17 of the 45 placements found were sponsored — and on one brand's own name the rank-one slot was held by a competitor's paid listing. That is the thing this dataset exists to surface.
The file already exists, because this pipeline runs twice a week whether you buy it or not. Most vendors start collecting after you order — which is why they quote a lead time. Here the most recent file is in your account within minutes of payment, with an API key issued at the same time.
What you get, in plain terms
Four things. This is a search visibility dataset rather than a product extract — if you want Blinkit store pricing, take the daily Q-Commerce file instead.
Rank on the keyword, not just the product
One row per keyword, per pin-code, per position. search_rank tells you where a product actually appeared to a shopper typing that term.
Organic separated from sponsored
type marks each placement Organic or Inorganic, with separate placement counters. Paid and earned visibility never get mixed together.
Results differ by pin-code
The same keyword returns different products in different delivery areas. Ten pin-codes in the supplied sample, and the feed scales to whatever set you need.
However you want it
Direct download, REST API, Amazon S3, Google Cloud, Snowflake or SFTP. The API key comes with the dataset.
Fields included in this dataset
All 33 columns, in the exact order they appear in the file — taken straight from the sample, not from a brochure. The free sample ships with a data dictionary giving an example value for each one.
Sample rows from the real file
Real rows from the sample file, not an illustration. The free sample is 50 keyword-by-pin-code rows with all 33 columns.
Coverage
Captured Monday and Thursday across the keywords and pin-codes on your feed. The supplied sample tracks 5 keywords across 10 pin-codes.
| Keyword type | Keywords in sample | Rows | Sponsored share of placements |
|---|---|---|---|
| Brand | 3 | 39 | 41% |
| Generic | 1 | 9 | 22% |
| Competitor | 1 | 2 | 0% |
| All keywords | 5 | 50 | 38% |
Five keywords and ten pin-codes is what the sample shows, not what the feed is limited to. Keyword count multiplied by pin-code count is what drives the price — most clients run a few hundred keywords across ten to thirty pin-codes.
Historical data
The last 30 days come with the dataset. Beyond that we hold Blinkit keyword records from February 2025 onwards for the keywords on this feed — enough to show how sponsored share moved through a festive quarter.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 33 columns — share of voice calculated per keyword is the most requested addition, along with banner and category placements, competitor brand mapping and daily capture.
Request custom fields“We found out we were paying to appear on our own brand name, and still losing the first slot to a competitor who was paying more. Nobody had been looking at the search page itself — only at sales.”
What people use this dataset for
Brand and media teams
See who is bidding on your brand name, how often they take the top slot, and in which pin-codes it happens.
Retail media buyers
Measure what your spend actually bought — sponsored position by keyword, rather than a platform-reported summary.
Category and SEO teams
Track organic rank on generic terms like “sunscreen spf 50 for oily skin”, where winning without paying is still possible.
Analysts
Build a share-of-search series across brand, competitor and generic keyword types.
About Blinkit search and retail media data
Blinkit is the largest quick-commerce platform in India, and its search results page is now a paid media surface as much as a discovery one. Sponsored listings sit above organic results, the mix changes by delivery area, and none of it appears in a product or pricing dataset — which is why this file exists separately.
Why organic and sponsored have to stay separate
A brand at rank one has either earned that position or bought it, and the two mean opposite things for your budget. type records which, and the placement counters track them independently. In the supplied sample 17 of 45 found placements were sponsored — well over a third of everything a shopper saw.
What keyword type tells you
Keyword Type marks each term Brand, Competitor or Generic, and the three behave completely differently. Defending your own brand term is a cheap, high-intent fight you should always win. Bidding on a competitor's name is an offensive move. Generic terms are where category share is actually decided. Averaging the three produces a rank that describes none of them.
When a keyword returns nothing
Product Status carries either “Found” or a message of the form “No results for {keyword}”. In the supplied sample that happened on 5 of 50 rows. It is a real signal — either the product is not distributed in that pin-code, or the term is not being matched at all, and both are worth knowing before you spend money pointing shoppers at it.
Is collecting this data legal?
Collecting publicly visible product and price information is generally lawful in most jurisdictions. Actowiz collects only public pages, respects robots.txt and platform terms, holds no personal data, and aligns with GDPR and CCPA. We are ISO 9001 and ISO 27001 certified, and this dataset carries documented provenance so your legal team can review the source before you buy.
Frequently asked questions
location, which reads as “Bengaluru, Karnataka 560013, India”, but if you need it as its own column say so and we will populate it.
