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Trust & CompanyAmazon India Thrice a Day Pin-Code Pricing & Availability Dataset
The highest-frequency file we run on Amazon.in — three captures a day, keyed on city and pin-code. Twenty-two columns covering MRP, selling price, discount percentage, availability, brand and grammage parsed as its own field, in a flat structure you can load without parsing anything.
Take this one if intraday movement is the thing you are measuring. Prices and stock on fast-moving consumer goods change between morning and evening, and a single daily reading records whichever state happened to exist when the crawler ran.
The file already exists, because this pipeline runs three times a day 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.
Three readings every day
Morning, afternoon and evening, each with its own datetime. Enough to see a price move within a day rather than between days.
City and pin-code on every row
Availability in particular is a local fact on Amazon.in. The file records which pin-code a reading applies to rather than averaging across a city.
Grammage as its own column
“450 Grams”, “1.84 Kilograms” — parsed out rather than left inside the product title, so price per kilogram is arithmetic instead of regex.
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 a full day of captures with all 22 columns.
Coverage
Captured three times a day across the pin-codes on your feed. The supplied sample covers a single Mumbai pin-code; the live feed scales to as many as you need.
| Capture slot | Local time | What it catches | Share of daily price changes |
|---|---|---|---|
| Slot 1 | 06:00 – 08:00 | Overnight repricing and restocks | 41% |
| Slot 2 | 13:00 – 15:00 | Midday deal activations | 22% |
| Slot 3 | 19:00 – 21:00 | Evening peak and stock-outs | 37% |
Need more cities or a wider pin-code set? That is the main cost driver on this dataset — tell us which pin-codes matter and we will price the file around them. We can also add a fourth capture slot for a specific sale window.
Historical data
The last 30 days come with the dataset, at full three-times-daily granularity. Beyond that we hold records from April 2025 onwards for the pin-codes on this feed. Tell us the cities and we will confirm exactly what exists.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 22 columns — more cities and pin-codes is the usual request, followed by seller name, category hierarchy and ratings joined in. Tell us what you would query and we will say whether it is collectable before anything else happens.
Request custom fields“Our competitor was dropping price at about seven in the evening and raising it again overnight. On a daily feed we never saw it once.”
What people use this dataset for
Pricing teams
Catch intraday repricing by competitors, including moves that reverse before the next day's capture.
FMCG brands
Track price per kilogram across pack sizes using the parsed grammage column, without cleaning titles first.
Availability monitoring
See stock-outs appear and clear within the same day, at the pin-code where they actually happened.
Demand and forecasting teams
Feed intraday price and availability into models that a once-daily series cannot support.
About intraday Amazon India pricing
Pricing on fast-moving consumer categories is not a daily decision. Deals activate at fixed times, competitors reprice in the evening peak, and stock-outs clear overnight. A once-daily crawl records one frame of that and calls it the day.
What three captures a day actually buys you
In our own measurement of this feed, roughly four in ten price changes happen overnight and another four in the evening window. A single morning reading catches the first group and misses the second entirely, which is why a daily series can show a competitor as stable while they are moving twice a day.
Why grammage is a separate column
Because comparing a 450 Grams pack against a 1.84 Kilograms pack is the entire point in FMCG, and doing it from the product title means writing parsing rules that break on the next listing. Here the pack size is already extracted, so price per kilogram is a division.
Empty price fields on out-of-stock rows
When a listing is out of stock there is no price to capture, so mrp, Selling_price and discount_percent are empty rather than zero. In the sample, one row sits at Out Of Stock with all three price columns blank. Treat an empty as absent rather than as a measurement, or your averages will drift for no real reason.
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
datetime, so you can work with actual capture times rather than the nominal slot.
type, Darkstore Id and Tier — came through empty across the whole sample. They are populated on dark-store sourced rows and are not applicable to standard Amazon listings. Check the sample against your requirement before buying.
