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Trust & CompanyFlipkart India Thrice a Week Product, Seller & Specification Dataset
A thrice-weekly Flipkart extract built around who is selling and what the listing claims — 22 columns including seller name, brand, product code, a full specifications block and a product_details block alongside price, MRP, discount and sold-out status.
This is the file to take if your question is about sellers rather than prices. In the supplied sample three brands appeared under twelve different seller names, which is the pattern behind most unauthorised-reseller and MAP problems.
The file already exists, because this pipeline runs three mornings 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. Take this file if you are doing brand protection rather than price tracking.
Seller name on every row
Who is actually offering the listing. Run it over your own brand and unauthorised resellers surface without any manual checking.
Specifications and product details kept whole
Two structured blocks carrying what the listing claims about itself — material, dimensions, compliance text, whatever the category uses.
Price and sold-out status together
Enough to see whether a seller is holding price or clearing stock. In the supplied sample 38 of 50 listings were live and 12 sold out.
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 22 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 listings with all 22 columns.
Coverage
Captured three mornings a week across the catalogue on your feed. The supplied sample follows three footwear brands across twelve sellers.
| Brand | Sellers seen | Rows in sample | In stock | Price range |
|---|---|---|---|---|
| FLITE | 5 | 19 | 14 | ₹294 – ₹599 |
| Sparx | 4 | 18 | 14 | ₹674 – ₹1,299 |
| Other brands | 3 | 13 | 10 | ₹249 – ₹899 |
| All brands | 12 | 50 | 38 | ₹249 – ₹1,299 |
Three brands across twelve sellers is what the sample shows, not what the feed is limited to. Send your brand list and we will price a narrower file, which costs considerably less than the full catalogue.
Historical data
The last 30 days come with the dataset. Beyond that we hold Flipkart seller records from January 2025 onwards. Tell us the brands and we will confirm exactly what exists.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 22 columns — Buy Box winner and seller count is the most requested addition, along with category hierarchy and seller rating history.
Request custom fields“We knew we had grey sellers, we just could not prove which. Three files a week with the seller name on every row turned a six-week argument into a spreadsheet.”
What people use this dataset for
Brand protection teams
Identify unauthorised resellers listing your products, and evidence the price at which they are doing it.
MAP and pricing teams
Catch minimum-price breaches by seller rather than in aggregate, three times a week.
Category managers
Watch how many sellers compete on a listing and how that changes as a product ages.
Analysts
Seller concentration and price dispersion series across categories where competition is heaviest.
About Flipkart seller data
On a marketplace, the listing and the seller are different things. The same product can be offered by several parties at several prices, and the one a customer sees changes through the day. Most Flipkart datasets record the product and ignore the seller, which makes them close to useless for brand protection work.
Why seller name matters more than price alone
A price you cannot attribute is not evidence. If your minimum-price policy is being broken, you need to know by whom, on which listing and on what date — which is exactly the shape of this file. In the supplied sample three footwear brands appeared under twelve distinct seller names in fifty rows.
What is in the specifications block
specifications and product_details keep what the listing claims about itself — material, dimensions, care instructions, compliance and country-of-origin text, whatever the category uses. They are captured whole rather than flattened into fixed columns, because the useful keys differ completely between footwear and electronics.
Sold-out rows still carry the seller
Twelve of the fifty rows in the supplied sample were sold out, and those rows carry no price — but they do still carry the listing and, where the platform shows it, the seller. A reseller who has run out is still a reseller, and losing those rows to a price filter loses that.
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
Seller Name is the seller on the offer as captured. Buy Box winner and seller count can be added on a custom feed, scoped free.
Your Data Needs
-
Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment. -
Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises. -
US-Based SupportOffices in New York & California. Aligned with your timezone. -
ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.