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Trust & CompanyAmazon India Thrice a Week Product, Seller & Specification Dataset
A thrice-weekly Amazon.in extract built around who is selling and what the listing actually claims — 22 columns including seller name, brand, product code, a full specifications block and a product_details block alongside price, MRP, discount and the sold-out flag.
This is the file to take if your question is about sellers rather than prices. In the supplied sample a single footwear brand appeared under five 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. If any of them is not what you expected, the sample will show you before you spend anything.
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, MRP, discount and sold-out together
Enough to see whether a seller is holding price or clearing stock, and whether the listing is buyable at all.
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 — this list is 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
1.84 million listings per run, weighted towards categories where multiple sellers compete on the same product.
| Category | Listings tracked | Median sellers seen | Share sold out |
|---|---|---|---|
| Footwear | 284,000 | 6 | 31% |
| Fashion accessories | 261,000 | 5 | 24% |
| Home & kitchen | 248,000 | 9 | 19% |
| Electronics accessories | 236,000 | 11 | 22% |
| Beauty & personal care | 214,000 | 8 | 17% |
| Sports & outdoor | 192,000 | 7 | 26% |
| Toys & baby | 168,000 | 6 | 21% |
| All other categories | 237,000 | 5 | 20% |
Only tracking your own brand and its resellers? Send the brand list and we will price a narrower file, which costs considerably less than the full catalogue. We can also move this to a daily capture if you are running automated enforcement.
Historical data
The last 30 days come with the dataset. Beyond that we hold Amazon India records from February 2025 onwards. Tell us the brands or categories 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 your own ASIN list captured more often. Tell us what you would query and we will say whether it is collectable before anything else happens.
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 Amazon India seller data
On a marketplace, the listing and the seller are different things. The same product can be offered by a dozen parties at a dozen prices, and the one a customer sees changes through the day. Most Amazon 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 MAP 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 one footwear brand appeared under five 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.
Why three times a week
Seller rotation on a contested listing happens faster than weekly and slower than hourly. Three captures a week is enough to see a reseller appear, undercut and disappear, without paying for readings that repeat. If you are running automated enforcement, take the daily feed instead.
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.
