Platform · Just Eat Takeaway

Just Eat Takeaway Data Scraping

One group, several national brands. Collecting them separately is not a choice — a group-level rate averages markets that are priced independently.

Just Eat Takeaway data scraping covers the group's consumer brands across European and other markets — including the Just Eat, Takeaway.com, Lieferando and Menulog names. They share a group and a technology base and operate as separate national businesses, so brand and country are dimensions and a group-level figure describes no market.

Four rows on your hub list are the same company. That is worth knowing before you commission four engagements.

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jet.jsonl LIVE FEED

{"brand":"brand-nl","country":"NL", "merchant_id":"jt-44120","city":"Example city", "fulfilment_model":"restaurant_delivery", "item_name_local":"as published","language_dominant":"nl", "item_price":12.50,"currency":"EUR", "price_setter":"merchant", "min_realisable_price":14.00, "delivery_fee":2.50} {"brand":"brand-de","country":"DE", "fulfilment_model":"platform_courier", "delivery_fee":1.90,"promise_minutes":32, "note":"same group. different model — promise means a different thing here"} {"group_rollup":true, "brands_included":4,"countries_included":9, "caution":"a computed rollup. no market is priced at this"}

3 of 5,884,220 merchant-item rows · multi-brandbrand + country are dimensions · never a group feed · schema v1.0

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

How we handle Just Eat Takeaway specifically

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

Group: Just Eat Takeaway — several consumer brands
The brands: Just Eat, Takeaway.com, Lieferando, Menulog and others by market
What they share: A group and a technology base
What they do not: Pricing, commission and market position
So: brand and country are dimensions on every record
Fulfilment: Mixed — platform couriers in some markets, restaurant delivery in others
Price setter: Merchant, as on any marketplace
Refresh: Daily standard; sub-daily where promotional intensity is the question

Platform specifics

One group, several markets, one schema

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

The brands are national businesses, not skins

The group operates under different consumer brands in different countries. They share infrastructure and they do not share market position.

Market share differs enormously by country, and so does competitive pressure.
Commission structures differ, which feeds into what merchants list at.
Promotional intensity differs, driven by local competition rather than group strategy.
Fulfilment models differ — platform couriers in some markets, restaurant-own delivery in others, sometimes both in one market.

brand and country are dimensions on every record, and group-level figures are computed rollups with the detail retained — the same discipline our Expedia page applies to OTA brands and our Loblaw page to retail banners.

Why this matters commercially before it matters analytically

Four of the rows on a typical delivery-platform list are this one group. Commissioning them as four separate engagements buys the same schema four times. One engagement with brand as a dimension is cheaper and produces a better dataset, because the records are comparable by construction.

Fulfilment model changes what availability means

In markets where restaurants deliver their own orders, the platform is a listing and ordering layer rather than a logistics operator. Where the platform runs couriers, it is both.

That changes the data:

Delivery fees behave differently. Restaurant-delivered orders frequently carry the restaurant's own fee structure.
Availability reflects restaurant capacity rather than courier supply, or both.
Delivery radius is the restaurant's in one model and the platform's in the other.
A promise time means different things in each.

fulfilment_model is recorded where the platform exposes it, and flagged undetermined where it does not. An availability series that pools both models is measuring two different constraints.

Merchant sets the price

price_setter is merchant, as on any marketplace. Menu prices frequently sit above a restaurant's dine-in prices because the merchant is covering commission — the same point our Uber Eats page makes, and we do not infer the markup from one side.

Menu mechanics and language, across a lot of markets

Modifiers

Required selections make a headline item price not the entry price. We deliver modifier groups structured with is_required and option prices, and compute min_realisable_price with the basis recorded.

Languages

The footprint spans Dutch, German, English, French, Italian, Polish and several others. Item names are retained exactly as published, with the dominant language recorded.

No machine translation into the name field. Matching runs on structure, price and position rather than title similarity, which performs badly across a language set this wide.

What we do not collect

Sales, order volumes or merchant revenue. Not published.
Customer, courier or reviewer identity. Review counts and ratings only.
Merchant contact details. Restaurant name is a commercial entity.

Scope

What we collect on Just Eat Takeaway, 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

brand and country as dimensions on every record
Group figures as computed rollups, with brand and country detail retained
fulfilment_model where exposed, flagged undetermined where not
price_setter recorded as merchant
Modifier groups with required flags and minimum realisable price
Delivery and service fees as separate fields
Item names in original language, with the dominant language recorded
One engagement covering several brands on one schema
Review counts and ratings, without reviewer identity

❌ What we do not, and why

A group-level figure presented as a market rate
An availability series pooling different fulfilment models
A markup inferred from one side
Machine translation written into name fields
Sales, order volumes, customer, courier or merchant contact data

Core Just Eat Takeaway fields

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

Field	What it is on this platform
brand / country	Dimensions. Not a single group feed
merchant_id / merchant_name / city	A commercial entity, and where
fulfilment_model	platform_courier, restaurant_delivery or undetermined
item_id / item_name_local / language_dominant	Original text and its language
item_price / currency / price_setter	Price, and that the merchant set it
modifier_groups / min_realisable_price / price_basis	The number a customer can pay
delivery_fee / service_fee / min_order_value	Each separately
promise_minutes	Means different things per fulfilment model
merchant_open / item_available	Two distinct states
review_count / rating	Values only
observed_at	Timestamp

Use cases

What teams do with Just Eat Takeaway data

Multi-market European panel on one schema

Brand and country as dimensions, so several of the group's national businesses are covered in one engagement with records comparable by construction rather than reconciled afterwards.

Fulfilment model comparison

Platform-courier and restaurant-delivered markets separated, so an availability series measures one constraint rather than pooling two.

Menu pricing at realisable level

Modifier groups with minimum realisable price across markets, so merchants listing identically are not treated as equivalent.

Competitive position by market

Promotional intensity and fee structures per brand and country, which differ for local competitive reasons rather than group strategy.

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

Send us a Just Eat Takeaway 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.

Just Eat Takeaway is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Just Eat Takeaway 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 & restaurant data covers, and a Just Eat Takeaway-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

Just Eat Takeaway data scraping: frequently asked questions

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

Same group, different national consumer brands. They share infrastructure and do not share market share, commission structure, promotional intensity or fulfilment model.

Which is worth knowing before you commission four engagements — four rows on a typical hub list are this one company, and one engagement with brand as a dimension is cheaper and produces a better dataset.

As a computed rollup with brand and country detail retained, if you ask. Not as a primary figure.

The brands operate as separate national businesses in markets with very different competitive pressure, so a group average describes none of them.

Because in markets where restaurants deliver their own orders the platform is a listing layer rather than a logistics operator, and availability reflects restaurant capacity rather than courier supply.

Delivery fees, radius and promise times all mean different things in each model. An availability series pooling both is measuring two different constraints.

No. The merchant sets the platform price and it frequently sits above dine-in, because the merchant is covering commission.

We record price_setter as merchant and do not infer the markup from one side — the same position as on our Uber Eats page.

Item names retained exactly as published with the dominant language recorded. No machine translation into the name field.

Matching runs on structure, price and position rather than title similarity, which performs badly across Dutch, German, English, French, Italian and Polish in one dataset.

We quote individually. Country and brand count is the main driver rather than merchant volume, because each market needs its own language, currency and fee handling.

Covering several of the group's brands in one engagement costs materially less than commissioning them separately. One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Just Eat Takeaway 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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