Platform · Deliveroo

Deliveroo Data Scraping Services

With virtual brand detection, because one kitchen can run six storefronts and inflate your competitor count.

Deliveroo data scraping is the automated collection of publicly visible Deliveroo data — menus at item and modifier level, virtual brands linked to the kitchens operating them, grocery kept as a separate catalogue, fee stack decomposed and coverage tracked per delivery zone — across UK and European markets.

A single kitchen can operate several brand storefronts on the platform simultaneously. Counted separately, your local competitor set looks two or three times larger than the number of kitchens actually cooking.

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deliveroo_kitchens.jsonl LIVE FEED

{"deliveroo_entity_id":"dl-771204", "storefront_name":"Example Wings Co", "kitchen_group_id":"aw-kit-4481", "kitchen_link_confidence":0.88, "kitchen_link_evidence":["shared_address", "menu_overlap_82pct","availability_sync"], "storefronts_in_group":4, "vertical":"restaurant", "city":"Manchester","delivery_zone":"m1_central", "serves_zone":true, "base_price":9.50, "delivery_fee":2.49, "service_fee":1.20, "delivery_promise_min":29} {"deliveroo_entity_id":"dl-771990", "kitchen_group_id":"null", "kitchen_link_confidence":0.41, "possible_link_flagged":true, "note":"reported separately — link not confident"}

2 of 4,102,880 rowskitchen groups inferred · zones: 280 · schema v2.6

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Deliveroo or its owners. Deliveroo 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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Deliveroo at a glance

How we handle Deliveroo specifically

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

Platform: Deliveroo restaurant and grocery across UK and European markets
Distinctive field: Virtual brand linkage — storefronts grouped by operating kitchen
Verticals: Restaurant and grocery kept separate, each with its own schema
Granularity: Menu item and modifier for restaurants; SKU and pack for grocery
Fee stack: Delivery, service and small-order fees decomposed as displayed
Geography: Delivery zone per city, since coverage varies within cities
Refresh: Daily standard; sub-daily during peak hours
Region: United Kingdom and Europe

Platform specifics

What makes Deliveroo data different

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

Virtual brands inflate competitor counts

Delivery platforms host virtual brands: separate storefronts, separate menus, separate branding, all operated from the same physical kitchen. A single site can run several.

Why it distorts analysis

Competitor counts inflate. Six storefronts from one kitchen looks like six competitors.
Market share by storefront is wrong. Capacity is shared, so six storefronts do not represent six kitchens' worth of output.
Menu overlap looks like coincidence when it is the same kitchen using the same ingredients.
Pricing appears to converge across brands, because one operator is setting all of it.

How we detect it

Shared address where published, identical or near-identical menu items across storefronts, matching preparation times, and simultaneous availability and unavailability transitions. We deliver kitchen_group_id with kitchen_link_confidence.

The honest limit: this is inference, not disclosure. Deliveroo does not publish which storefronts share a kitchen, so a link is evidence-based and carries a confidence score. Where confidence is low we report the storefronts separately with the possible link flagged rather than merging them. A wrong merge collapses two genuine competitors into one, which is worse than a duplicate you can see.

Restaurant and grocery are different datasets

Deliveroo operates restaurant delivery alongside a grocery and convenience offer. As with every multi-vertical platform we collect, they share an app and share nothing that matters to a schema.

Restaurants are menu items with modifier trees and kitchen-driven availability.
Grocery is packaged SKUs with pack sizes, unit pricing and store-level ranging.

Merged, pack size is null on every restaurant row and modifier trees are null on every grocery row. We collect each with its own schema and its own refresh logic, joining on market and zone where you want both.

Grocery on delivery platforms also carries a markup over the retailer's own shelf price in many cases — the same structural fact as our Instacart service. Where we collect the underlying retailer too, that markup becomes measurable.

Zone coverage is the question chains actually ask

Delivery coverage varies within a city, not just between cities. A restaurant serving one zone may not serve the adjacent one, and fees differ by distance.

City-level collection produces an average across zones that describes no actual customer experience, and it hides coverage gaps — which is usually the thing a chain or brand most wants to know.

Where can customers order from us, and where can they only order from a competitor.
Which competitors reach zones we do not.
How fees and promise times vary across the zones we serve.
Where new competitors have entered a zone since last month.

We collect per zone with serves_zone on every record and design the zone set with you. As with pincode design in quick commerce, a well-chosen sample answers more than exhaustive coverage at a fraction of the cost, because adjacent zones frequently return near-identical results.

Scope

What we collect on Deliveroo, 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

Virtual brand linkage with kitchen group identity and link confidence
Storefronts reported separately where link confidence is low, with the possible link flagged
Menu items with modifier trees for restaurants
Grocery as a separate catalogue with pack size and unit pricing
Delivery, service and small-order fees captured at every observation
Delivery zone and promise time per city
serves_zone on every record, for coverage gap analysis
Item availability through the day
Ratings and review text without reviewer profiles

❌ What we do not, and why

Confirmed disclosure of which storefronts share a kitchen, since the platform does not publish it
Restaurant commission rates or platform economics
Order volumes or rider data
Prices requiring a signed-in or membership session
Reviewer names, profiles or review histories

Core Deliveroo fields

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

Field	What it is on this platform
deliveroo_entity_id	Storefront identifier, the record key
kitchen_group_id / kitchen_link_confidence	Inferred kitchen grouping and confidence in the link
kitchen_link_evidence	Which signals supported the link: address, menu overlap, availability sync
vertical	restaurant or grocery, kept separate
market / city / delivery_zone	Geographic context, with zone mandatory
serves_zone	Whether this storefront serves the zone, for coverage analysis
menu_item_id / base_price / modifier_price	Restaurant item and modifier pricing
sku / pack_size / unit_price_computed	Grocery item, pack and unit price
delivery_fee / service_fee / small_order_fee	Fee components at every observation
delivery_promise_min	Displayed delivery timing
item_available	Availability at observation time

Use cases

What teams do with Deliveroo data

Accurate competitor counts by zone

Virtual brands are linked to operating kitchens with confidence, so competitive density reflects kitchens actually cooking rather than storefront count.

Coverage gap analysis by delivery zone

serves_zone per record shows where customers can order from you and where only competitors reach, which city-level data conceals.

Fee structure monitoring

Fee components captured at every observation surface platform fee changes as their own signal rather than letting them drift into basket models.

Cross-platform menu price comparison

Item and modifier pricing joins to the same restaurant on other platforms with confidence-scored matching, making channel price gaps measurable.

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

Send us a Deliveroo 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.

Deliveroo is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Deliveroo 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 Deliveroo-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

Deliveroo data scraping: frequently asked questions

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

Through shared address where published, identical or near-identical menu items across storefronts, matching preparation times, and simultaneous availability transitions. We deliver kitchen_group_id with a confidence score and the evidence used.

The honest limit: this is inference, not disclosure. Deliveroo does not publish which storefronts share a kitchen. Where confidence is low we report the storefronts separately with the possible link flagged rather than merging — a wrong merge collapses two genuine competitors.

Because six storefronts from one kitchen looks like six competitors. Capacity is shared, so market share computed by storefront overstates that operator's presence and understates everyone else's.

It also explains menu overlap and price convergence that otherwise look like coincidence — one operator is setting all of it.

Yes, as a separate catalogue with pack sizes and unit pricing rather than merged into restaurant data.

Delivery-platform grocery also frequently carries a markup over the retailer's own shelf price. Where we collect the underlying retailer too, that markup becomes measurable — the same structure as our Instacart service.

Because coverage, fees and promise times all vary within a city. A restaurant serving one zone may not serve the adjacent one.

City-level collection averages across zones and describes no actual customer experience, and it hides coverage gaps — which is usually what a chain most wants to know.

No. Commission rates and platform economics are not published by any delivery platform.

We can show price differences where a restaurant appears on more than one platform. Why those differences exist is not observable, and any vendor supplying commission figures is estimating them.

We quote individually. Drivers are market count, zone coverage, entity count, whether full modifier trees are needed, and whether virtual brand detection is included — that inference is ongoing work rather than a one-time setup.

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

See real Deliveroo 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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