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Trust & CompanyFlipkart India Daily Coupon & Applied Offer Dataset
A deliberately narrow daily file: product identifier and the full coupon structure attached to it, captured every morning. Two columns, one of which is rich JSON carrying every coupon on the listing with its title, description, type and rupee value.
This is not a pricing dataset and it is not trying to be. It answers one question properly — what is actually being taken off the price at checkout — which the listed selling price never tells you.
The file already exists, because this pipeline runs every morning 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 file is two columns wide on purpose — if you need price and catalogue data, take the twice-a-day feed instead.
Every coupon on the listing
Not just the headline. Coupons_1, Coupons_2 and so on, each with its own title, details and rupee value.
Coupon type is captured
pre_applied_coupon, additional_coupon, selective_coupon — they behave differently at checkout and the file keeps them apart.
Stacking rules in plain text
“Buy for ₹25 or more get 10% off your Next Buy”, “Add 7 items to unlock offer”. The conditions are captured verbatim rather than resolved away.
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
Both columns, exactly as they appear in the file — taken straight from the sample, not from a brochure. coupon_details is a JSON object; the free sample ships with a dictionary of every key it can contain.
Sample rows from the real file
Real rows from the sample file, not an illustration. The free sample is 50 products with both columns.
Coverage
Captured every morning across the products on your feed. The supplied sample covers 50 distinct products, every one of them carrying at least one coupon.
| Coupon type | What it means | Applies automatically | Seen in sample |
|---|---|---|---|
| pre_applied_coupon | Already deducted when the page loads | Yes | Most rows |
| additional_coupon | Unlocks on a basket condition | No | Common |
| selective_coupon | Applies to selected items only | Sometimes | Occasional |
| All coupon types | — | — | 50 of 50 rows |
Fifty products is what the sample shows, not what the feed is limited to. Send your product list and we will price a narrower file, or take the full catalogue — product count is what drives the cost.
Historical data
The last 30 days come with the dataset. Beyond that we hold Flipkart coupon records from January 2025 onwards — enough to cover two Big Billion Days cycles, which is usually what people want it for.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the two columns — the coupon JSON flattened into named columns is the most requested change, along with the selling price joined on so you can compute the true post-coupon price in one place.
Request custom fields“Our reported discount and the customer's actual discount were eight points apart. The gap was entirely pre-applied coupons that nobody in our team was tracking.”
What people use this dataset for
Brands on Flipkart
See what is actually coming off your price at checkout, not just what the listing says.
Pricing and revenue teams
Measure realised discount rather than listed discount — the gap is routinely several points.
Promotion planning
Track when competitors turn coupons on and off, and what conditions they attach to them.
Analysts
Build a coupon-intensity series across the category to see how promotional the market really is.
About Flipkart coupon data
Flipkart runs a layered discount system. The listed selling price is one number; on top of it sit pre-applied coupons that come off automatically, additional coupons that unlock on a basket condition, and selective coupons that apply only to certain items. A shopper who knows how to combine them pays meaningfully less than the price on the page.
Why this is a separate file
Because the coupon structure is rich and the pricing structure is not. Flattening every coupon into a pricing extract would either lose information or produce a very wide, mostly empty table. Keeping it as its own file with a structured coupon_details object means nothing is thrown away, and it joins to any of our other Flipkart datasets on product_id.
Why listed discount understates real discount
In the supplied sample, coupon values ran from ₹4 to ₹157 on individual products. A brand benchmarking against the listed selling price will consistently overstate realised price. If you are setting your own floor or measuring competitiveness, this is the field that closes that gap.
What you will need to do with the JSON
coupon_details is a JSON object with numbered coupon keys. It is captured as the platform structures it rather than flattened, because the number of coupons per product varies. If you would rather have named columns, that is a custom feed and we scope it free.
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
product_id.
Coupons_1, Coupons_2 — each carrying a title, a details string, a coupon type and a rupee value, plus summary fields for how many were applied.
product_id.
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.