Platform · Restaurant chain own channels

Restaurant Chain Direct Data

The same burger, from the same restaurant, at three prices: in store, on the chain's own app, and on a delivery platform.

Restaurant chain direct data covers menus, prices and availability from major chains' own apps and websites rather than from delivery marketplaces. The reason it is a separate service: own-channel prices are frequently below the same chain's delivery-platform prices, because the chain is not covering platform commission. A chain's price is not one number.

This is one page rather than seven because the argument is identical for every major chain. The mechanics do not change between them; only the brand does.

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chain_direct.jsonl LIVE FEED

{"chain":"chain-a","store_id":"st-44120","city":"Example city", "channel":"own_app", "franchise_status":"not_published", "item_name":"Example item", "item_price":5.49,"currency":"GBP", "min_realisable_price":5.49, "app_offer":"as displayed","app_offer_gated":false} {"chain":"chain-a","store_id":"st-44120", "channel":"platform","item_price":6.99, "note":"same item, same store, same day. the gap is commission, not the chain position"} {"store_id":"st-88120","city":"Example city", "channel":"own_app","item_price":5.89, "gated_offer_share":0.31, "caution":"same brand, same city, different store. franchise autonomy is deliberate"}

3 of 4,404,880 store-item rows · multi-chainchannel on every row · store is the unit · schema v1.0

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

How we handle Restaurant chain own channels specifically

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

Scope: Major chains' own apps and sites — QSR, coffee, pizza and casual dining
Why one page: The argument is identical across chains. Only the brand changes
The finding: Own channel is frequently cheaper than the same chain on a platform
Price setter: The chain, or its franchisee — recorded where distinguishable
Store-level: Prices vary by store, including between franchised and company-operated
Offers: App-exclusive offers are common. Gated ones reported as a share
Menu structure: Combos and required modifiers, as on any menu source
Refresh: Weekly for base menu; daily where promotional cycles matter

Platform specifics

Why own channel is a different dataset

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

Three prices for the same item, and the chain sets two of them

A major chain's item can carry three different prices at once.

In store — the counter price, which we do not collect because it is not published.
On the chain's own app or site — set by the chain or its franchisee.
On a delivery platform — set by the merchant, frequently above own channel to cover commission.

So a chain's "price" depends entirely on which channel you looked at, and a competitive analysis using platform prices is measuring the platform's economics as much as the chain's.

channel is on every record. Where you also collect the chain on delivery platforms, the gap between channels is an observation you compute from two records — we do not infer it from one side.

Which makes this the more useful side for some questions

If you are benchmarking a chain's own pricing strategy, the own channel is where the chain's decisions are visible. Platform prices carry a merchant markup that varies and is not the chain's positioning.

Store-level pricing, and the franchise question

Major chains price by store, not nationally, and the reasons include local costs, local competition and — critically — whether the store is company-operated or franchised.

Franchisees frequently set their own prices within a framework.
So two stores of the same brand in one city can differ, and that is a deliberate arrangement rather than an error.
A national price for a chain averages company-operated and franchised stores with different pricing autonomy.

store_id is mandatory and national figures are computed rollups with the store detail retained — the same discipline our Edeka page applies to a retail cooperative.

Where a chain publishes whether a store is franchised we record it. Where it does not, we do not assert it — that is a fact about the business, not something visible on a menu page.

App-exclusive offers

Chains use app-only pricing heavily. Where an offer requires a signed-in account we do not sign in; the field is null with a reason and we report gated_offer_share per chain, the same position as member pricing on our Whole Foods page.

Why this is one page and not seven

Your hub list carries rows for several individual chains plus a general "restaurant chain sites and apps" entry. We have built one page for all of them, and the reason is worth stating.

The data mechanics are identical across chains. Store-level pricing, franchise variation, combos, required modifiers, app-exclusive offers, channel gap against platforms — every one of those applies to every major chain in the same way.

Seven pages would be one page written seven times with the brand swapped, which is the thing we have declined to do throughout this project. A reader arriving from any chain's name gets the same answer, and it is a better answer for being written once properly.

What differs between chains, and does not need a page

Menu structure depth — a pizza chain's builder is deeper than a coffee chain's, which changes record counts rather than mechanics.
Franchise share — higher in some brands, which changes how much store-level variation you see.
App offer intensity — which changes the gated share.

All three are scoping variables. None of them is a different argument.

Scope

What we collect on Restaurant chain own channels, 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

channel on every record, distinguishing own channel from platform
store_id mandatory, with national figures as computed rollups
Franchise status where the chain publishes it, never asserted where not
Combos and required modifiers with minimum realisable price
App-exclusive offers where publicly visible, null with a reason where gated
gated_offer_share reported per chain
Menu category structure as the chain presents it
Nutrition and allergen information where published
Item availability, distinct from a store being closed

❌ What we do not, and why

A national chain price presented as the price
A franchise status asserted where the chain does not publish it
A channel gap inferred from one side
An account created to obtain app-exclusive pricing
In-store counter prices, which are not published

Core Restaurant chain own channels 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	The brand, the store, the geography
channel	own_app, own_web or platform. On every record
franchise_status	Where published. Not asserted where not
item_id / item_name / menu_category	As the chain presents them
item_price / currency / price_setter	Price, and who set it
modifier_groups / min_realisable_price	Required selections make headline not entry
is_combo / combo_components	Where the chain exposes composition
app_offer / app_offer_gated / gated_offer_share	Offers, gating, and the share
nutrition / allergens	Where published
store_open / item_available	Two distinct states
observed_at	Timestamp

Use cases

What teams do with Restaurant chain own channels data

Channel gap analysis

Own-channel prices alongside the same chain on delivery platforms, showing the gap as an observation from two records — which is the chain's commission economics made visible.

Chain pricing strategy benchmarking

Own-channel prices at store level, which is where a chain's own decisions are visible rather than obscured by a merchant markup that varies by platform.

Franchise price variation

Store-level records with franchise status where published, showing how much pricing autonomy is exercised within one brand.

Promotional and app-offer intensity

App 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 Restaurant chain own channels 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.

Restaurant chain own channels is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Restaurant chain own channels 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 Restaurant chain own channels-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

Restaurant chain own channels data scraping: frequently asked questions

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

Because the data mechanics are identical. Store-level pricing, franchise variation, combos, required modifiers, app-exclusive offers and the channel gap against platforms apply to every major chain in the same way.

Seven pages would be one page written seven times with the brand swapped. A reader arriving from any chain's name gets the same answer, and it is a better answer for being written once properly.

Because a chain's own prices are frequently below its platform prices, since on a platform the merchant is covering commission.

If you are benchmarking a chain's pricing strategy, the own channel is where the chain's decisions are visible. Platform prices carry a markup that varies and is not the chain's positioning.

Because chains price by store rather than nationally, driven by local costs, local competition and whether the store is company-operated or franchised.

Franchisees frequently set their own prices within a framework, so two stores of one brand in a city can differ — deliberately. A national figure averages stores with different pricing autonomy.

Where the chain publishes it, yes. Where it does not, we do not assert it — that is a fact about the business, not something visible on a menu page.

Only where they are 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 gated_offer_share per chain, so promotional analysis states what it could not see.

We quote individually. Store count and menu depth are the drivers — a pizza chain's builder produces far more records per store than a coffee chain's menu.

One scoping call, a free pilot within 24 hours including the gated offer share, then a fixed monthly quote. Request a quote.

See real Restaurant chain own channels 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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