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ScrapingBuddy
Restaurant food prepared for takeaway and delivery
All use cases

RESTAURANT INTELLIGENCE / MANAGED DATA

DoorDash
restaurant data.

Menus, locations and feedback in context.

Our DoorDash collection work spans 42 restaurant brands, including McDonald's, KFC and Jimmy John's. Scope menus, local prices, store listings, ratings and reviews around your research.

Discuss your project Explore data points

OUR DOORDASH COLLECTION WORK

42 restaurant brands.

One delivery channel. A broader view of the restaurant market.

  • McDonald's
  • KFC
  • Jimmy John's
  • Pizza Hut
  • Arby's
  • Applebee's

Selected examples, not the full brand list. Brands, stores and available data streams are agreed for each project.

FROM MENU DETAIL TO MARKET INSIGHT

Connect menu intelligence with customer feedback.

ScrapingBuddy collects multiple kinds of restaurant data from DoorDash. Keep menus, store records, restaurant ratings, item ratings and reviews as distinct streams, then connect them using validated source context.

01

Benchmark delivery-channel menus

Compare products and displayed menu prices across selected brands and stores. Keep direct-channel and DoorDash observations separate.

02

Monitor restaurant presence

Track listed stores, addresses, coordinates and published ordering-state signals where available.

03

Research customer feedback

Analyze restaurant ratings, item-level feedback and review text at their own levels, with counts and observation context.

WHAT WE COLLECT

Distinct data streams. A connected market view.

Choose the DoorDash data points your research needs. Each observation retains its source and location context; the final fields are agreed around your workflow.

01

Stores & ordering state

Restaurant locations, public store links, contact information and published ordering-state signals where available.

View field names
  • brand_name
  • store_number
  • store_url
  • address
  • city
  • state
  • postal_code
  • country
  • phone
  • latitude
  • longitude
  • ordering_available
  • inactive_flag
02

Menu items & sizes

Menu products, category labels and size options, associated with the restaurant where they appear.

View field names
  • item_id
  • item_name
  • category
  • subcategory
  • size_label
03

Menu prices

Displayed item prices with ordering channel and collection time. These are not complete delivery-cart totals.

View field names
  • price
  • channel
  • observed_at
04

Restaurant ratings

Store-level star ratings and rating counts, kept separate from item feedback and individual reviews.

View field names
  • restaurant_stars
  • restaurant_rating_count
05

Item ratings

Published feedback scores and counts attached to individual product names and restaurant context.

View field names
  • rated_item_name
  • item_rating
  • item_rating_count
06

Customer reviews

Published review text, review score and review date for location-level feedback research. Reviewer identities are not part of the default scope.

View field names
  • review_text
  • review_stars
  • review_date

Representative delivery field names, not a fixed API response. Availability varies by source, location and collection scope.

Define your schema

CONTEXT IS PART OF THE DATA

Different signals. Different meanings.

Restaurant stars, item ratings and individual review scores measure different things. We preserve their separate scales and counts instead of blending them into one metric.

Keep rating levels separate
Restaurant stars, product scores and individual reviews retain their own scales and counts. Ratings are not sales or order volume.
Keep location and channel attached
A local menu price is not a national price. Direct ordering and delivery-platform observations stay distinguishable.
Separate observation from inference
Ordering unavailable does not establish permanent closure. Records are connected only where the available identifiers support a reliable match.

BUILT AROUND YOUR RESEARCH BRIEF

From target locations
to a maintained feed.

We handle the source-specific collection and prepare data for your workflow. Scope, validation and delivery come before a scale commitment.

  1. 01 / DEFINE

    Choose your market

    Agree brands, locations, fields and refresh frequency. Define the ordering channel, market and any collection-window requirements.

  2. 02 / VALIDATE

    Review the data together

    Check representative records, price context, identifiers and missing fields. Agree matching rules and how collection issues are reported.

  3. 03 / DELIVER

    Connect your pipeline

    Receive an agreed CSV, JSON or Parquet feed, or a scoped API integration, with monitoring and maintenance defined for the project.

Need content from a few public pages?

Our self-service Scraping API collects accessible public content with optional AI analysis. It does not bypass CAPTCHA or login, or automatically return these restaurant datasets.

Explore Scraping API

BEFORE YOU BUILD ON THE DATA

The details that
shape your feed.

Which restaurant brands can you collect?

Our existing DoorDash collection work spans 42 brands. Examples include McDonald's, KFC, Jimmy John's, Pizza Hut, Arby's and Applebee's. We agree the brands, stores and data streams for your project; that does not imply every field is available at every location.

Can every menu item be joined to an item rating?

Only after validating the identifiers available in each stream. Store and item keys are not always exposed consistently. We scope matching and leave unresolved records separate rather than joining by brand name alone.

Are restaurant stars and item ratings the same scale?

No. Store-level stars, item feedback scores and individual review scores must retain their source scale and their own counts. We do not convert them into one rating or use rating counts as order volume.

Do displayed prices include taxes, delivery fees and promotions?

Not necessarily. We preserve the observed menu price and its location and channel. Taxes, service fees, delivery fees, coupons and checkout totals are different fields and require a separately agreed scope. Currency and price units are confirmed in the delivery schema.

How are refresh frequency and project pricing decided?

We scope the brands, markets, locations, data streams and collection frequency together. After validating a sample, we agree the schema, delivery method and recurring price. A wider store set or more frequent collection changes the work involved.

What happens when a collection fails or a field disappears?

We agree freshness and completeness checks for the feed. Failed or incomplete collection is reported separately from a genuine menu change. Missing values are not silently turned into zero prices, closed restaurants or discontinued products.

Can the feed connect to our existing data pipeline?

Yes. We can scope CSV, JSON or Parquet delivery, or a project-specific API integration. We agree field names, identifiers, timestamp conventions and missing-value handling before delivery. This managed dataset is separate from the self-service Scraping API.

EXTEND YOUR RESTAURANT RESEARCH

YOUR MARKET. YOUR DATA REQUIREMENTS.

Build your
DoorDash
data feed.

Share the brands, locations and fields you need. We will review access and scope a sample before agreeing delivery.

  • Brands, locations and ordering channels
  • Menu, price and other required fields
  • Refresh schedule and delivery format
Talk through your project

Up to 5,000 characters.

Projects are accepted individually, subject to feasibility and available capacity. An enquiry does not reserve a solution or confirm delivery. Pricing and terms are agreed directly with our team.

A real person reviews your brief and follows up about scope and delivery.

ScrapingBuddy is an independent data service, not affiliated with or endorsed by DoorDash or the restaurant brands mentioned. Access, coverage and delivery commitments are agreed per project.

Food imagery is illustrative.