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Platform · DMart

DMart Data Scraping

A retailer whose strategy is not to move prices, and whose online presence is deliberately not the business.

DMart data scraping collects product listings, pricing and availability from DMart's online operation. Two strategic facts shape what a feed can be: DMart runs everyday low pricing rather than a promotional cycle, so prices move slowly and a high refresh rate buys nothing; and its online presence is deliberately thin relative to its stores, so online data is not the assortment.

Most Indian grocery data assumes a promotional retailer with a full online catalogue. DMart is neither, and a feed designed for Blinkit will produce very little here.

Free pilot on your own DMart list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

dmart_2026-08-25.jsonl LIVE FEED
{"product_id":"dm-4471","ean":"89012*** redacted", "price":148.00,"currency":"INR", "price_scope":"online", "price_changed_since_last":false, "days_since_change":63, "pincode":"400097","serviceable":true, "note":"63 days unchanged — EDLP. daily observation would be 63 identical records"} {"product_id":"dm-8812", "price_changed_since_last":true,"days_since_change":0, "note":"THIS is the informative event on an EDLP retailer"} {"product_id":"dm-9902","pincode":"400051", "serviceable":false, "caution":"online footprint is narrower than the store estate — this is NOT store pricing"}
3 of 44,220 product rows · online footprint onlyprice_scope constant: online · weekly cadence · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to DMart or its owners. DMart and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
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udaan
Food Delivery
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Taxi Aggregator
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Tmall
DMart at a glance

How we handle DMart specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Retailer
DMart — everyday low pricing, India
Pricing model
EDLP, not promotional cycles. Prices move slowly
Consequence
High refresh buys nothing. Weekly is usually right
Online
Deliberately thin relative to the store estate
So
Online range is not the assortment, and we say so
Fulfilment
Pickup-oriented in many areas rather than pure delivery
Comparison value
A price floor reference that moves rarely
Never
Store assortment or store pricing inferred from online
Platform specifics

What EDLP and a thin online presence mean for a feed

These are the reasons a DMart dataset needs its own handling rather than a shared retail schema.

A retailer that does not move prices changes the refresh economics

Most grocery and quick-commerce collection is priced on observations per day, because prices move within the day and you need to catch the movement.

DMart runs everyday low pricing. It sets a low price and holds it, rather than running a promotional cycle.

  • Daily observation catches almost nothing that weekly would miss.
  • Hourly is money spent on identical records.
  • The interesting event is a price change, and those are rare enough that a weekly cadence catches them adequately.

We will quote for daily if you want it, and we will tell you it is unlikely to earn its cost. This is one of the few retailers where we actively recommend a lower refresh than a client asks for.

Which makes DMart useful as a reference

A price that rarely moves is a stable floor. For a brand or a competitor tracking Indian grocery, DMart's price on a staple is a benchmark that does not need constant re-observation — which is a genuinely useful property rather than a limitation.

The online range is not the assortment, and this matters more here

Every retailer's online range differs from its store range. On DMart the gap is wider and more deliberate, because the online operation is a supplement to a store-first business rather than a parallel channel.

  • The online catalogue is a subset, and not a representative one.
  • Fulfilment is pickup-oriented in many areas, which shapes what is listed.
  • Serviceability is limited relative to the store footprint.

We collect what is published online, record serviceable as its own state, and do not infer store assortment or store pricing from it.

Why we are firm about that

A client asking for "DMart prices" usually means store prices, because that is where the business is. Delivering online prices labelled as DMart prices would answer a different question than the one asked, and the difference would not be visible in the data.

So we state it on the page, in the fields, and in the pilot. If store-level pricing is what you need, extraction is not the route.

Where DMart fits in an Indian panel

Most Indian grocery engagements centre on quick commerce — Blinkit, Zepto, Instamart and the newer entrants — where prices move constantly and pincode-level collection is the design problem.

DMart sits at the other end of the same market and answers a different question.

  • Quick commerce shows what a shopper pays for convenience, at speed, in a pincode.
  • DMart shows the value floor that convenience is priced against.
  • The gap between them is the interesting number, and it is not obtainable from either alone.

Where a client is running a quick-commerce panel, adding DMart at weekly cadence is inexpensive and gives the panel a reference point it otherwise lacks. That is the scoping we would usually suggest, rather than DMart as a standalone engagement.

The pincode discipline from our India coverage page applies to the serviceable areas, though the panel is smaller because the online footprint is.

Scope

What we collect on DMart, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

  • Online listings, price and availability
  • Serviceability as a distinct state, with the online footprint stated
  • Pack parsed, with unit price on a stated basis
  • Price change events, which are the informative ones on an EDLP retailer
  • Own label flagged where a category carries it
  • EAN where published, for cross-retailer joins
  • Category as the retailer presents it
  • Weekly cadence recommended, with the reasoning stated
  • Explicit statement that online is not the store assortment

❌ What we do not, and why

  • Store assortment inferred from the online range
  • Store pricing inferred from online pricing
  • A high refresh rate sold where it buys nothing
  • Sales, volumes or footfall
  • Anything behind a signed-in session

Core DMart fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field What it is on this platform
product_id / ean / ean_missing_reason Identifiers, with nulls reasoned
product_name As published
price / currency Online price. Not store price, and flagged as such
price_scope Constant: online. Never presented as store pricing
price_changed_since_last / days_since_change The informative fields on an EDLP retailer
pack_size / pack_unit / price_per_unit Parsed, with the basis named
is_own_label Where a category carries it
serviceable / pincode Online footprint, narrower than the store estate
in_stock Online availability
fulfilment_type Where the platform distinguishes pickup from delivery
observed_at Timestamp
Use cases

What teams do with DMart data

Value floor reference in an Indian panel

DMart's price on staples as the floor that quick-commerce convenience is priced against — a reference point a quick-commerce-only panel does not contain.

Price change detection on a slow-moving retailer

Change events rather than continuous observation, which is the informative signal when a retailer's strategy is not to move prices.

Convenience premium measurement

The gap between a DMart price and the same product on quick commerce in the same city, which is the number neither feed produces alone.

Cross-retailer joins on branded lines

EAN where published, which works better here than at a discounter because DMart carries substantial branded assortment.

The 24-hour sample — run on your sources, not ours

Send us a DMart item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

  • Real extraction from your actual sources
  • Returned within 24 hours
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

DMart is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. DMart data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what grocery data scraping covers, and a DMart-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

DMart data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Probably not, and we will say so. DMart runs everyday low pricing rather than promotional cycles, so daily observation catches almost nothing weekly would miss and hourly is money spent on identical records.

This is one of the few retailers where we actively recommend a lower refresh than clients ask for.

No. These are online prices, and price_scope is a constant marking them as such.

A client asking for DMart prices usually means store prices, because that is where the business is. Delivering online prices labelled as DMart prices would answer a different question, and the difference would not be visible in the data.

No. The online catalogue is a subset and not a representative one, because the online operation supplements a store-first business rather than mirroring it.

If store-level range or pricing is what you need, extraction is not the route.

Because it answers a question quick commerce cannot. Quick commerce shows what a shopper pays for convenience in a pincode; DMart shows the value floor that convenience is priced against.

The gap between them is the interesting number and neither feed produces it alone. Adding DMart at weekly cadence to an existing India panel is inexpensive.

Better than at a discounter. DMart carries substantial branded assortment with published EANs, so joins to quick-commerce platforms and other grocers work on shared identifiers for a meaningful share of the range.

We report the EAN fill rate per category in the pilot as usual.

We quote individually, and this is among the cheapest grocery engagements — narrow online footprint, slow price movement, weekly cadence.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real DMart data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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