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Trust & CompanyBlinkit India Weekly Q-Commerce Pin-Code Availability & Pricing Dataset
A weekly file built for breadth rather than frequency — 23 columns across 47 pin-codes and 47 dark stores, with grammage parsed out, inventory as a unit count, and both a Status and an Availability field.
This is the file to take if the question is where you are listed at all, rather than how price moved this week. In the supplied sample 38 of 50 rows were out of stock — which at national spread is a distribution finding, not a pricing one.
The file already exists, because this pipeline runs every 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 file trades frequency for geographic spread — 47 pin-codes in the sample against 12 stores in the daily file.
Wide pin-code spread
47 pin-codes and 47 dark stores in the supplied sample, from Thane to Patna to Chennai. Built to answer where you are listed, not how price moved today.
Inventory as a unit count
Not an in-stock flag. inventory carries the actual number of units the store holds — 0, 1, 2, 4 — which is the difference between knowing a product is low and finding out it has gone.
Grammage parsed as its own column
Pack size sits in grammage rather than inside the product title, so price per unit is arithmetic instead of a parsing exercise.
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 23 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 product-by-pin-code rows with all 23 columns.
Coverage
Captured weekly across the pin-codes on your feed. The supplied sample spans 47 pin-codes and 47 dark stores nationally, deliberately wide rather than deep.
| Region | Example pin-codes in sample | Dark stores | Share out of stock |
|---|---|---|---|
| Maharashtra | 400601, 421302, 440020 | 9 | 81% |
| Delhi NCR | 110045, 110085 | 7 | 71% |
| Karnataka | 560013, 560100 | 8 | 75% |
| Tamil Nadu | 600119, 600096 | 6 | 83% |
| Bihar & East | 800023, 700107 | 5 | 80% |
| Other states | 12 further pin-codes | 12 | 74% |
Forty-seven pin-codes is what the sample shows, not what the feed is limited to. Pin-code count is what drives the price — tell us which areas matter and we will price the file around them.
Historical data
The last 30 days come with the dataset. Beyond that we hold Blinkit pin-code records from February 2025 onwards. Ask and we will confirm exactly what exists for your pin-codes.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 23 columns — a populated city and tier column is the most requested addition, along with category hierarchy, competitor SKU matching and a wider pin-code set.
Request custom fields“We were arguing about pricing when the real problem was that we were not listed in two thirds of the pin-codes we thought we were. Seeing it across forty-seven at once ended the argument.”
What people use this dataset for
Brands checking distribution
Find the pin-codes where you are absent or permanently out of stock, before the sales number tells you.
Sales and channel teams
Verify that coverage claimed by distributors matches what a customer in that pin-code actually sees.
Category teams
Compare your availability against competitor brands across a wide geographic spread, week over week.
Analysts
Build a national availability series for the largest quick-commerce platform in India.
About Blinkit pin-code availability data
Blinkit fulfils from local dark stores, so whether a product exists for a customer depends entirely on their pin-code. A weekly file covering many pin-codes answers a different question from a daily file covering a few stores, and most brands need the first before the second.
Why out-of-stock rates look high here
In the supplied sample 38 of 50 rows were out of stock. That is not a data problem — at national spread, across a long-tail category, most pin-codes simply do not carry most SKUs. It is exactly the finding the file exists to surface, and it is invisible if you only sample the metros where your distribution is strongest.
Status and Availability are two different fields
Status records whether the product page was found at all; Availability records whether it can be bought. A product can be listed and unbuyable, which is a different problem from not being listed. Keeping them separate is what lets you tell a distribution gap from a stock-out.
What the empty columns mean
city and Tier came through empty across the whole supplied sample. The pin-code is present on every row, so city can be derived, but if you need it as its own column say so and we will populate it.
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
Status says whether the product page was found; Availability says whether it can be bought. Listed but unbuyable is a different problem from not listed at all, and keeping the two apart is what lets you tell them apart.
