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Trust & CompanyMyntra India Thrice a Week Product, Seller & Specification Dataset
A thrice-weekly Myntra 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.
One thing to plan for before you buy: in the supplied sample 40 of 50 listings were sold out, and sold-out rows carry no price and no seller. That is correct behaviour, but it means a filter on price will keep only a fifth of the file.
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 or assortment work rather than keyword tracking.
Seller name where the platform shows it
Who is actually offering the listing. Run it over your own brand and third-party sellers surface without manual checking.
Specifications and product details kept whole
Two structured blocks carrying what the listing claims about itself — material, care, fit, whatever the category uses.
Sold-out status tracked explicitly
In fashion, a listing that stays visible after selling out is normal. 40 of 50 rows in the supplied sample were in exactly that state.
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 six sellers.
| Field | Filled in sample | Why it is not always there |
|---|---|---|
product_name |
50 of 50 | Always present |
Brand |
50 of 50 | Always present |
is_sold_out |
50 of 50 | Read at the moment of capture |
mrp / product_price |
10 of 50 | Empty on sold-out listings — there is no price to record |
Seller Name |
10 of 50 | Shown only on listings that are currently buyable |
discount |
10 of 50 | Follows price, so it is present on the same rows |
These are counts from the 50-row sample, not guarantees. Download it and check the fields you actually need — if your work depends on seller name, note that it comes with the buyable rows.
Historical data
The last 30 days come with the dataset. Beyond that we hold Myntra records from February 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 — size-level rows with their own stock status is the most requested addition in fashion, along with category hierarchy and seller rating history.
Request custom fields“Four fifths of the listings we track were sold out and still live. Our assortment reporting had been counting every one of them as available.”
What people use this dataset for
Brand protection teams
Identify third-party sellers listing your products, and evidence the price at which they are doing it.
Assortment and merchandising
Separate listings that are live from listings that are actually buyable — in fashion those are very different numbers.
MAP and pricing teams
Catch minimum-price breaches by seller rather than in aggregate, three times a week.
Analysts
Seller concentration and sell-through series across fashion categories.
About Myntra seller and assortment data
Myntra is India's largest specialist fashion platform and operates a mixed model — its own inventory alongside brand and third-party sellers. That makes seller-level capture more useful here than the platform's own reporting suggests, because the same product can appear under more than one party.
Sold-out listings stay visible, and that distorts everything
In the supplied sample 40 of 50 listings were sold out while still live on the platform. Fashion works that way — a style stays up while sizes clear. But if you are counting live listings as available assortment, you will overstate it by four times in a sample like this one. is_sold_out is the field that separates the two.
Price and seller come with the buyable rows
Sold-out listings carry no price, no discount and no seller name, because the platform stops showing them. That is correct rather than missing — but it means a filter on price silently keeps only the buyable fifth of the file. Use is_sold_out as your filter instead, and treat empty price as absent rather than zero.
What is in the specifications block
specifications and product_details keep what the listing claims about itself — material, fit, care instructions, country of origin, whatever the category uses. They are captured whole rather than flattened, because the useful keys differ completely between footwear and apparel.
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
is_sold_out rather than on price, or you will keep only a fifth of the file.
is_sold_out is at listing level. Size-level rows with their own stock status are the most requested addition in fashion and we can build it on a custom feed, scoped free.
Your Data Needs
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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.