A client needing a monthly view of menus and prices across three delivery platforms.

Use Case

Restaurant and menu-level market data

Region

Europe [Confirm countries / cities]

Sources

Uber Eats, Deliveroo, DoorDash

Number of sources

Restaurant Listings

Platform by Platform

Menu Items & Prices

Month by Month

Contact Sales

Key Takeaways

Actowiz Solutions delivers monthly food delivery menu data to a Germany-based restaurant-tech company. Each month we capture restaurant listings and full menus from Uber Eats, Deliveroo and DoorDash, harmonise them into one schema and deliver a dated snapshot, so the client can compare menus, prices and fees across platforms and track changes over time.

What did the client need?

The client needed restaurant-level and menu-level data from several platforms in one consistent format, refreshed every calendar month. Their product team wanted to compare how restaurants list items, prices and fees on each app, and what changes month to month.

The inquiry arrived on 1 September 2026 and work began on 10 September 2026. Coverage is [Confirm countries / cities], at roughly [Add restaurant count] restaurants per monthly cycle.

What made this hard?

Three platforms, several markets and a monthly rhythm create problems that a one-off scrape never meets. The core difficulties were keeping one stable schema, matching the same restaurant across apps and making each monthly snapshot directly comparable with the previous one.

Shifting platform landscape: DoorDash completed its acquisition of Deliveroo on 2 October 2025 and operates across many European markets through Wolt, which it acquired in 2022. Market coverage is defined separately for each country to account for differences in platform availability and operations.
Different menu structures: each app groups categories and add-ons differently.
Location-dependent results: listings and fees depend on the delivery address.
Multiple currencies: markets priced in different currencies need a currency code on every row.
Month-on-month comparability: IDs and field names must stay fixed so snapshots line up.

How did Actowiz solve it?

We built one monthly pipeline that collects each platform separately, then maps everything into a shared schema.

Approach

Address-based discovery: fixed delivery points per city keep monthly restaurant lists comparable.
Full menu capture: categories, items, prices and modifiers for every restaurant, using our Uber Eats data scraping workflows and their equivalents.
Platform harmonisation: fields map to one schema, keeping the source platform on every row.
Cross-platform matching: restaurants are linked across apps by name and address; weak matches are flagged.
Monthly QA: checks for missing prices, duplicates and menu-size drops.
Public data only: publicly displayed business data only, site terms respected, legal review recommended.

Data Attributes / Fields

Every row carries its platform, market and snapshot month, so the client can filter by app, compare cities and stack monthly files into a time series. The table lists the core fields delivered in each cycle, all captured as publicly displayed.

Field	Description
platform / snapshot_month	Source app and month of capture
country / city	Market and city of the delivery address
restaurant_id / restaurant_name	Platform ID and display name
cuisine	Cuisine labels shown on the listing
rating / rating_count	Displayed rating and number of ratings
delivery_fee_shown	Delivery fee displayed at capture time
menu_category	Menu section, e.g. Pizza or Drinks
item_name / item_description	Item title and description
item_price / currency	Displayed price and currency code, e.g. EUR or GBP
modifier_group / modifier_option / modifier_price	Add-on group, option and price add-on

What were the results?

The client now receives one dated file of food delivery menu data each month instead of stitching together three platform exports, which removes manual cleaning before analysis. Quantified metrics will be added once the delivery team has reviewed the first monthly cycles.

One schema, three platforms: menus from all three platforms follow a consistent structure, reducing manual data cleaning before client integration.
Month-on-month tracking: stable identifiers support tracking changes in menu items, prices and delivery fees over time.
Cross-platform comparison: matched restaurants enable side-by-side price comparisons across delivery apps and help identify pricing differences.
Coverage: the dataset includes restaurant menus, item details, prices and applicable fees for the agreed platform coverage in each collection cycle.

Project at a Glance

Metric	Value
Use Case	Menu-level data for a restaurant-tech product
Region	Europe – [Confirm countries / cities]
Sources	Uber Eats, Deliveroo, DoorDash
Metrics	Restaurants, ratings, fees, menu items, prices, modifiers
Reconciliation / Method	Fixed addresses, shared schema, restaurant matching
Cadence	Monthly (kickoff 10 Sep 2026)
Output	[Confirm delivery format, e.g. CSV / JSON / API]

Client Feedback

“Actowiz Solutions helped us establish a structured approach to collecting food delivery menu and pricing data across Uber Eats, Deliveroo, and DoorDash. The recurring monthly feed gives our team better visibility into menu updates, price changes, and platform-level differences, helping us strengthen our restaurant market analysis and deliver more valuable insights to our customers.”

— Product Manager, Restaurant Technology Company

Frequently Asked Questions

Q: What food delivery menu data can be collected?

A: Public restaurant details, ratings, fees, menu items, prices and add-ons, as in our menu pricing extraction work.

Q: Why refresh food delivery menu data monthly?

A: Monthly snapshots suit menu analysis. Promotions can run weekly or daily, as in our Lieferando and Wolt menu pricing project.

Q: Does DoorDash coverage include Wolt?

A: In many European markets DoorDash operates as Wolt, so markets are confirmed per project.

Q: Can other platforms be added?

A: Yes, into the same schema, as in our Uber Eats and DoorDash data extraction project.

Q: Is personal data included?

A: No. Only public business data such as restaurants, menus and prices; reviewer identities are excluded.

Need Monthly Food Delivery Menu Data?

See our food datasets or request a custom feed. Visit actowizsolutions.com to discuss your data requirement.

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