Big Basket India Twice a Week Keyword Rank & Competitor Visibility Dataset
A twice-weekly keyword file built for competitor term tracking — what comes back when a shopper searches a rival's brand name on Big Basket. Thirty-three columns covering rank, organic versus sponsored placement, brand visibility, price and availability.
The supplied sample follows two competitor beverage terms and returns 11 different brands across them, with 10 of 50 placements sponsored. Searching a competitor's name and finding a crowded page is the clearest signal that the term is worth buying.
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 Big Basket pin-code pricing, take the daily 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. In the supplied sample 10 of 50 were paid — a fifth of the page.
Built around competitor terms
Keyword Type marked every term in the supplied sample as Competitor. Eleven distinct brands appeared across just two such searches.
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 follows 2 competitor beverage terms across 6 pin-codes.
| Keyword | Keyword type | Rows in sample | Sponsored | Distinct brands |
|---|---|---|---|---|
| fanta | Competitor | 26 | 6 | 7 |
| 7up | Competitor | 24 | 4 | 6 |
| All keywords | Competitor | 50 | 10 | 11 |
Two keywords across six 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 terms.
Historical data
The last 30 days come with the dataset. Beyond that we hold Big Basket keyword records from February 2025 onwards for the keywords on this feed.
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 placements, competitor brand mapping and a wider keyword set.
Request custom fields“We ran our two biggest rivals' names through it and found eleven brands fighting over those pages. Two of them were ours, competing with each other.”
What people use this dataset for
Brand and media teams
See who appears on competitor terms, whether they earned the slot or paid for it, and in which pin-codes.
Retail media buyers
Find the competitor terms that are cheap to win and the ones already crowded with paid placements.
Category teams
Measure how many brands genuinely compete on a term before committing budget to it.
Analysts
Build a share-of-search series across brand, competitor and generic keyword types.
About Big Basket search data
Big Basket's search results page carries both organic and sponsored listings. Because it operates a scheduled-delivery model alongside quick commerce, its result pages behave a little differently from the pure quick-commerce platforms — assortment is wider and the paid share is currently lower.
Why competitor terms are worth tracking first
A shopper typing a rival's brand name has already decided what kind of product they want but not which one. That makes competitor terms the cheapest conquest traffic available, and the first place to look when you are deciding where to spend. In the supplied sample two competitor beverage terms returned eleven different brands between them.
A lower paid share than the quick-commerce platforms
Ten of fifty placements in the supplied sample were sponsored — about a fifth. On Zepto, a generic category term in our own sample ran at over half. That gap is the argument for spending here: the same position costs less because fewer people are bidding.
A note on the column name
This file spells the field Organic placement with a lowercase p, where our Blinkit and Zepto keyword files use Organic Placement. The contents are identical. It is being normalised, but if you are joining these files across platforms today, map both spellings.
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
Keyword Type marks them as Competitor so you can report on them separately from your own brand terms.
Your Data Needs
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Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
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Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
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US-Based SupportOffices in New York & California. Aligned with your timezone.
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