Platform · McDonald's

McDonald's Data Scraping

The same item, the same city, two stores, two prices. Franchise density makes that wider here than at any chain in this set.

McDonald's data scraping collects menus, prices, app offers and availability from the chain's own channels. The single most important handling: franchise density is very high, and franchisees set prices within a framework — so store-level variation is the widest of any major chain we cover, and a national figure is a rollup rather than a price.

Every chain page in this set says store matters. Here it matters more than anywhere else, and it is measurable.

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mcdonalds.jsonl LIVE FEED

{"chain":"mcdonalds","store_id":"st-4412", "city":"Example city","country":"GB", "franchise_status":"not_published","channel":"own_app", "item_price":4.29,"currency":"GBP"} {"store_id":"st-8812","city":"Example city", "item_price":5.19, "note":"SAME city, same item, 21% apart. that is franchise latitude"} {"price_spread_pct":24.8,"store_count_observed":1180, "gated_offer_share":0.41, "caution":"a national mean here describes very few actual transactions"}

3 of 5,884,110 store-item rows · multi-marketspread is the finding, not the caveat · schema v1.0

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

How we handle McDonald's specifically

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

Chain: McDonald's — global QSR
The point: Very high franchise density
Consequence: Widest store-level price variation in our chain set
So: store_id is mandatory. National is a rollup
App offers: Heavy, and frequently gated. Share reported
Channel: Own app and site versus delivery platforms
Markets: Many, priced and ranged independently
Refresh: Weekly for base menu; daily where app offers matter

Platform specifics

Franchise density, and what it does to a price series

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

Store-level variation is the measurement

Franchisees operate the large majority of restaurants and set prices within a framework. The result is variation that is deliberate, material and largest in this chain.

Two stores in one city can differ on the same item, by more than most people expect.
Airport, motorway and transport-hub sites price differently again.
A national average is a figure very few customers actually face.
Company-operated sites price differently from franchised ones, where the chain distinguishes them.

store_id is mandatory. National and market figures are computed rollups with store_count_observed stated, and we report price_spread_pct per item per market — which on this chain is the finding rather than the caveat.

Franchise status

Recorded where the chain publishes it, unstated where it does not. That is a fact about the business arrangement, not something a menu page establishes.

App offers, channels and markets

App offers

App-exclusive pricing is heavy here and frequently requires a signed-in account. Where it is publicly visible we collect it; where gated, the field is null with a reason and gated_offer_share is reported.

We do not create accounts — the boundary on our access page.

Channel

Own channel prices sit below delivery-platform prices for the same item at the same store, because the platform price covers commission. channel is on every record and the gap is computed from paired records — the argument our chain-direct page sets out in full.

Markets

Menus, ranges and prices differ substantially by country, and local items are a meaningful share of several markets. country is a dimension, names are retained in local languages, and a global average is a computed rollup rather than a primary figure.

What we do not collect

App account data, loyalty balances, sales, volumes, customer or employee data. Nutrition and allergen information is collected where published.

Scope

What we collect on McDonald's, 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

store_id mandatory, with national figures as computed rollups
price_spread_pct per item per market, since spread is the finding here
franchise_status where published, unstated where not
channel distinguishing own app and site from delivery platforms
App offers where publicly visible, with gated_offer_share reported
country as a dimension, with local items captured
Combos with components where the chain exposes them
Nutrition and allergen data where published
Item availability distinct from a store being closed

❌ What we do not, and why

A national price presented as the price
A franchise status assumed where not published
An account created to reach app-exclusive offers
A channel gap inferred from one side
App account data, loyalty balances, sales or customer data

Core McDonald's fields

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

Field	What it is on this platform
chain / store_id / city / country	Store is mandatory here
franchise_status	Where published. Not assumed
channel	own_app, own_web or platform
item_id / item_name_local	As the chain presents it
item_price / currency	Store-level
price_spread_pct	Per item per market. The finding, not the caveat
is_combo / combo_components	Where exposed
app_offer / app_offer_gated / gated_offer_share	Offers, gating and the share
min_realisable_price	Where required selections apply
nutrition / allergens	Where published
store_open / item_available	Two distinct states

Use cases

What teams do with McDonald's data

Franchise price variation analysis

Store-level records with spread reported per item and market, which on this chain is the widest of any we cover and is the thing competitors and suppliers actually want measured.

Channel gap measurement

Own-channel and platform prices for the same item at the same store, so the commission gap is computed from paired records rather than assumed.

Market-level menu comparison

Country as a dimension with local items captured, since ranges differ substantially and local lines are a meaningful share in several markets.

App promotional intensity

Offers where publicly visible with the gated share stated, so promotional depth is measured on a basis that says what it could not see.

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

Send us a McDonald's 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.

McDonald's is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. McDonald's 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 McDonald's-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

McDonald's data scraping: frequently asked questions

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

Because franchise density is very high and franchisees set prices within a framework. Two stores in one city can differ on the same item by more than most people expect, and transport-hub sites differ again.

A national average is a figure very few customers face. We report price spread per item per market — on this chain that is the finding rather than the caveat.

Where the chain publishes it, yes. Where it does not, the field is unstated rather than assumed.

That is a fact about the business arrangement, not something a menu page establishes.

Only where publicly visible without signing in. We do not create accounts in any market.

Where an offer is gated the field is null with a reason and we report the gated share, so promotional analysis states what it could not see.

Because the platform price covers commission — the chain's decision, not the platform's. Channel is on every record and the gap is computed from paired records at the same store.

Substantially, and local items are a meaningful share in several markets. Country is a dimension, names are retained in local languages, and a global average is a computed rollup rather than a primary figure.

We quote individually. Store count is the dominant driver here, more than at any other chain, because store-level collection is where the value is.

One scoping call, a free pilot within 24 hours including price spread on your markets, then a fixed monthly quote. Request a quote.

See real McDonald's 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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