Platform · DashMart

DashMart Data Scraping

Q: What does DashMart collection cost?

We quote individually, driven by addresses times SKUs times observations per day. Adding DashMart to an existing panel with Gopuff, DoorDash and Instacart costs materially less than commissioning it alone, because the address panel and schedule are already designed. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

A DoorDash-owned store inside the DoorDash app. Same surface as the marketplace, entirely different economics.

DashMart data scraping collects pricing, availability and fee data from DoorDash's own convenience stores. The discipline that matters: DashMart is not the DoorDash marketplace. It appears in the same app, but DoorDash owns the inventory and sets the price, where marketplace prices are set by merchants and frequently carry a markup over in-store.

This is the same trap as Flipkart Minutes and Amazon Now, in a US setting: one app, two commercial models, and a feed that blends them describes neither.

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

dashmart.jsonl LIVE FEED

{"sku":"EX-4471","surface":"dashmart", "address_id":"CHI-SUBURB-031","city":"Chicago", "store_id":"dm-1180", "price":5.99, "delivery_fee":2.99,"service_fee":1.40, "serviceable":true,"in_stock":true, "promise_minutes":29, "price_setter":"doordash"} {"sku":"EX-4471","surface":"marketplace", "price":7.49,"price_setter":"merchant", "note":"same item, same app, merchant-set price — delivered separately"} {"sku":"EX-9902","address_id":"CHI-EDGE-007", "serviceable":false,"in_stock":"null", "caution":"outside store radius — not a stockout"}

3 of 2,884,010 sku-address rows addresses: 380surface flag on every record · never blended with marketplace · schema v1.0

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

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DashMart at a glance

How we handle DashMart specifically

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

Model: DoorDash-owned inventory, inside the DoorDash app
The trap: Not the DoorDash marketplace. Different price setter, different economics
Surface flag: surface: dashmart on every record
Granularity: Address or zone — store catchments, not postal areas
Price setter: DoorDash. Marketplace prices are set by merchants
Markup: Marketplace items often carry markup over in-store. DashMart pricing is direct
Fee stack: Delivery, service and small-order fees as separate fields
Refresh: Several times daily; hourly on priority SKUs

Platform specifics

What separates DashMart from DoorDash marketplace

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

Two commercial models sharing one app

On the DoorDash marketplace, a restaurant or retailer sets its own prices, and those prices frequently sit above what the same item costs in that merchant's own store. The platform takes a commission.

On DashMart, DoorDash owns the stock and sets the price directly. There is no merchant, no markup relationship, and no commission layer.

Why blending them produces nonsense

Price movements mean different things. A marketplace price change is a merchant decision; a DashMart change is DoorDash's own.
Assortment logic differs. DashMart carries a curated convenience range; the marketplace carries whatever merchants list.
Availability behaves differently. DashMart stockouts are inventory; marketplace unavailability is often a merchant pausing.

Every record carries surface. If you also take our DoorDash marketplace feed, the two arrive as separate records against the same product key. This is the same rule applied to Flipkart Minutes and Amazon Now.

The comparison buyers actually want

The inquiry that prompted this page named four platforms together — Gopuff, DoorDash, DashMart and Instacart — and asked how ETAs and fees vary by time of day, day of week and distance from store.

That is a well-formed question and it needs all four on one panel:

Two owned-inventory players — DashMart and Gopuff — where price is direct and fees carry the variation.
Two marketplace models — DoorDash and Instacart — where the item price itself may carry markup.

Comparing them on headline item price alone would rank the marketplace players as more expensive for reasons that have nothing to do with competitiveness. We deliver item price, each fee, the promise and the distance basis as separate fields so the comparison can be constructed honestly.

Store catchments and the address panel

DashMart stores serve delivery radii, so the same rules apply as on Gopuff: catchments cut across postal boundaries, and two nearby addresses can see different assortments and different fees.

We observe at address level using a designed panel, record serviceable as a state distinct from in_stock, and capture store identity where the platform exposes it.

The panel design is agreed with you and stated in the deliverable, because a series is only comparable if the observation points do not move. A panel that changes between months makes the months incomparable, and nothing in the data would tell you that had happened.

Scope

What we collect on DashMart, 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

Item price at address level with the surface flag on every record
Delivery, service, small-order and peak fees as separate fields
Minimum order value and free-delivery thresholds
Serviceability as a distinct state from out of stock
Delivery promise as displayed, with the observation timestamp
Store identity where the platform exposes it
Pack architecture parsed, with unit price on a stated basis
Promotional mechanics as displayed, separate from base price
Category as the app presents it

❌ What we do not, and why

A blended price across DashMart and DoorDash marketplace
Merchant markup inferred rather than observed
Sales, order volumes or store throughput
Customer, dasher or order data
Anything behind a signed-in session

Core DashMart fields

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

Field	What it is on this platform
sku / product_key	Your identifier and our matched identity
surface	Constant: dashmart. Never blended with marketplace records
address_id / zone / city	Address panel point and its geography
store_id	Where exposed, so assortment differences are attributable
price	Item price, set by DoorDash rather than a merchant
delivery_fee / service_fee / small_order_fee / peak_fee	Each fee separately
min_order_value / free_delivery_threshold	Basket thresholds
serviceable / in_stock	Two distinct states
promise_minutes	Promise as displayed
pack_size / pack_unit / price_per_unit	Parsed pack and unit price
observed_at / daypart	Timestamp and derived daypart

Use cases

What teams do with DashMart data

The four-platform delivery economics panel

DashMart, Gopuff, DoorDash and Instacart on one address panel and one schedule, with item price and each fee separate, so owned-inventory and marketplace models can be compared without the markup distortion.

Owned-inventory price positioning

DashMart against the marketplace on the same products, delivered as separate records, showing where DoorDash prices its own stock relative to the merchants on its platform.

Fee variation by daypart and distance

Each fee tracked as its own series across dayparts and address distances, which is where the competitive action sits on owned-inventory models.

Assortment by catchment

Store-level range across catchment types, with serviceability separated from stock so a coverage gap is not read as a sell-out.

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

Send us a DashMart 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.

DashMart is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. DashMart 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 quick commerce data covers, and a DashMart-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

DashMart data scraping: frequently asked questions

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

No, and treating it as such is the main error here. DoorDash owns DashMart inventory and sets its prices. On the marketplace, merchants set prices and those frequently sit above in-store, with a commission layer in between.

Every record carries surface: dashmart, and marketplace records arrive separately against the same product key.

We can show you the marketplace price and, where you supply it or where we also collect the merchant's own channel, the in-store price alongside. The difference is then yours to compute and it is an observation.

What we will not do is infer a markup percentage where we cannot see both sides. That would be a model presented as a measurement.

Because store delivery radii cut across postal boundaries. Two nearby addresses can be served by different stores with different assortments and different fees.

We use a designed address panel and hold it fixed, because a series is only comparable if the observation points do not move.

Yes, and it is the only way the comparison holds. The same address panel, the same observation schedule, and item price and each fee kept as separate fields.

Comparing headline item prices across owned-inventory and marketplace models would rank the marketplace players as expensive for reasons unrelated to competitiveness.

No. No names, no ratings, no identities, no order records. The unit of analysis is a product at an address.

We quote individually, driven by addresses times SKUs times observations per day. Adding DashMart to an existing panel with Gopuff, DoorDash and Instacart costs materially less than commissioning it alone, because the address panel and schedule are already designed.

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

See real DashMart 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.

Get a free sample Talk to an engineer

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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