Platform · DoorDash

DoorDash Data Scraping Services

Where the same merchant can be reached two ways, and the two do not always carry the same prices.

DoorDash data scraping is the automated collection of publicly visible DoorDash data — marketplace menus at item and modifier level, merchant-site ordering powered by the same network captured separately where identifiable, convenience and grocery verticals kept apart, and the fee stack decomposed.

This is a logistics network as much as a marketplace. The same restaurant can be ordered from on the platform or through its own website powered by the same network, and those two front doors do not always show the same prices.

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doordash_paths.jsonl LIVE FEED

{"doordash_store_id":"dd-771204", "order_path":"marketplace", "vertical":"restaurant", "city":"Austin","delivery_zone":"78704", "serves_zone":true, "menu_item_id":"mi-4481", "base_price":14.95, "modifier_price":2.50, "delivery_fee":3.99, "service_fee":2.24, "small_order_fee":0.00, "membership_waiver_terms":"delivery fee waived over $12", "min_order_value":10.00} {"doordash_store_id":"dd-771204", "order_path":"merchant_site", "path_link_confidence":0.86, "base_price":13.50, "note":"same item, 9.7% lower on merchant site"}

2 of 5,412,700 rowsmerchant-site path inferred · zones: 310 · schema v2.2

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

Our Data Powers

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Food Delivery

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E-Commerce

Tmall

DoorDash at a glance

How we handle DoorDash specifically

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

Platform: DoorDash marketplace, with merchant-site ordering captured where identifiable
Distinctive structure: Two front doors to one merchant, priced independently
Verticals: Restaurant, convenience and grocery kept separate
Fee stack: Delivery, service and small-order fees decomposed as displayed
Membership: Membership fee waivers captured where publicly displayed
Geography: Delivery zone per city, since coverage and fees vary
Refresh: Daily standard; sub-daily during peak hours
Region: United States

Platform specifics

What makes DoorDash data different from other delivery platforms

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

Two front doors to the same merchant

DoorDash operates a consumer marketplace and also powers ordering on merchants' own websites. A restaurant can therefore be reached two ways, and the prices are set independently.

Why that matters commercially

Marketplace and direct prices can differ on the same menu, because commission structures differ.
A merchant driving traffic to its own site may price lower there, which is invisible to marketplace-only collection.
Fee structures differ between the two paths.
For a chain, this is a channel-consistency question they frequently cannot see across their own franchisees.

We capture order_path as marketplace or merchant_site where the merchant site is identifiable as network-powered, and match menu items across the two with confidence scoring.

The honest limit: identifying a merchant site as network-powered is inference from publicly observable signals, not disclosure. Where confidence is low we report the two paths separately with the possible link flagged rather than merging them — the same discipline as virtual brand detection on Deliveroo.

Three verticals, three schemas

DoorDash spans restaurant delivery, convenience and grocery. As on every multi-vertical platform we collect, they share an app and share nothing that matters to a schema.

Restaurant is menu items with modifier trees and kitchen-driven availability.
Convenience is packaged SKUs with small pack sizes and high markup over shelf price.
Grocery is packaged SKUs with larger packs, unit pricing and retailer-set markup.

Merged, modifier trees are null on every retail row and pack sizes are null on every restaurant row. We collect each with its own schema, joining on zone where you want more than one.

Convenience and grocery also carry the markup-over-shelf-price structure we handle on Instacart. Where we collect the underlying retailer too, that markup becomes measurable rather than assumed.

Fee stack and membership waivers move independently of price

What a customer pays is item prices plus delivery fee, service fee, possible small-order fee, taxes and a tip. Membership waives or reduces some of those.

Fee components move independently of menu prices, so a fee-only change is competitive activity price monitoring misses.
Membership waivers change basket economics materially, particularly on small orders.
Fees differ by zone and by merchant, so a single national fee assumption ages fast and is wrong locally.
Minimum order thresholds interact with small-order fees.

We capture each component at every observation, plus membership waiver terms where publicly displayed. We do not fold them into one total, since fee structure changes are their own signal and merging hides both. And we do not assume membership: whether a given shopper holds it is not observable, so the fields let you model either.

Scope

What we collect on DoorDash, 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

Order path separating marketplace from network-powered merchant-site ordering where identifiable
Menu item matching across the two paths with confidence, low confidence reported separately
Restaurant, convenience and grocery collected with separate schemas
Delivery, service and small-order fees captured at every observation
Membership fee waiver terms where publicly displayed
Delivery zone per city with a serves-zone flag
Item and modifier pricing for restaurants; pack and unit pricing for retail
Markup over retailer shelf price where we also collect the retailer
Ratings and review text without reviewer profiles

❌ What we do not, and why

Confirmed disclosure that a merchant site is network-powered, since that is not published
Commission rates or platform economics
Order volumes, courier data or tips
Prices requiring a signed-in or membership session
Reviewer names, profiles or review histories

Core DoorDash fields

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

Field	What it is on this platform
doordash_store_id	Merchant storefront identifier, the record key
order_path	marketplace or merchant_site where identifiable as network-powered
path_link_confidence	Confidence that the merchant site is network-powered
vertical	restaurant, convenience or grocery, kept separate
city / delivery_zone / serves_zone	Geography and whether this zone is served
menu_item_id / base_price / modifier_price	Restaurant item and modifier pricing
sku / pack_size / unit_price_computed	Retail item, pack and unit price
price_vs_retailer_shelf	Markup where we also collect the underlying retailer
delivery_fee / service_fee / small_order_fee	Fee components at every observation
membership_waiver_terms	Membership fee waiver terms where publicly displayed
min_order_value	Minimum basket, which interacts with small-order fees

Use cases

What teams do with DoorDash data

Channel price consistency for chains

Marketplace and merchant-site prices are captured separately with item matching, revealing where the same menu is priced differently across the two paths.

Markup measurement on convenience and grocery

Where the underlying retailer is also collected, the markup over shelf price becomes measurable rather than assumed.

Fee structure monitoring

Fee components captured at every observation surface fee-only competitive changes that price monitoring misses entirely.

Zone coverage and competitive density

Serves-zone flags per record show where customers can order from you and where only competitors reach.

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

Send us a DoorDash 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 inside two business days
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.

DoorDash is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. DoorDash 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 food delivery data covers, and a DoorDash-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

DoorDash data scraping: frequently asked questions

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

DoorDash runs a consumer marketplace and also powers ordering on merchants' own websites. The same restaurant can be reached both ways, and prices are set independently because commission structures differ.

A merchant driving traffic to its own site may price lower there, which marketplace-only collection cannot see. For a chain this is a channel-consistency question they frequently cannot answer across franchisees.

It is inference from publicly observable signals, not disclosure. We deliver path_link_confidence and, where confidence is low, report the two paths separately with the possible link flagged rather than merging them.

A wrong merge would produce a price comparison between two things that are not the same channel, which is worse than reporting them apart.

Because they share an app and nothing that matters to a schema. Restaurant is menu items with modifier trees; convenience and grocery are packaged SKUs with pack sizes and unit pricing.

Merged, modifier trees are null on every retail row and pack sizes are null on every restaurant row — a table that looks large and is mostly empty.

On convenience and grocery, yes, where we also collect the underlying retailer directly. The structure is the same as our Instacart service.

Where the cross-channel item match is not confident, markup is null with a reason rather than estimated. A false match produces a markup figure that is wrong and looks plausible.

Yes, each component at every observation, plus membership waiver terms where publicly displayed. We do not fold them into one total.

Fee components move independently of menu prices, so a fee-only change is competitive activity that price monitoring misses. And we do not assume membership, since whether a shopper holds it is not observable.

We quote individually. Drivers are merchant count, zone coverage, how many verticals, whether full modifier trees are needed, and whether merchant-site path identification is included.

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

See real DoorDash 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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