Platform · Intermarché

Intermarché Data Scraping

Independent owners who also manufacture. Own label here is made rather than sourced, and that changes what its price means.

Intermarché data scraping collects pricing, promotions and availability across this French grocery network. Two structural features. The stores are run by independent owner-operators, so pricing is store-level. And the group manufactures a substantial part of its own-label range rather than buying it from third parties — which means an own-label price reflects production economics, not a supply negotiation.

Most own-label pricing tracks what a retailer paid a manufacturer. Here a large part of it tracks what the retailer's own factories cost to run.

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intermarche_2026-08-25.jsonl LIVE FEED

{"retailer":"intermarche","store_id":"st-4412", "surface":"in_store", "is_own_label":true,"own_label_tier":"standard", "own_production_stated":true, "price":1.89,"currency":"EUR"} {"is_own_label":true, "own_production_stated":"not_stated", "caution":"we do NOT infer. which facility makes which line is not on a product page"} {"matchable_share_category":0.46, "production_volume":"not_collected", "own_label_margin":"not_produced", "note":"build the French cross-retailer index on BRANDED lines"}

3 of 1,884,110 store-product rows · Franceown production only where STATED · never inferred · schema v1.0

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

How we handle Intermarché specifically

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

Retailer: Intermarché — France
Structure one: Independent owner-operators. Store-level pricing
Structure two: The group manufactures own label
Consequence: Own-label price reflects production, not procurement
So: Own label behaves differently from sourced own label
Branded: Prices normally. The divergence is on own label
Drive: Click-and-collect, as a separate surface
Refresh: Daily. Weekly promotional cycles

Platform specifics

Vertical integration, and what it does to own label

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

Made own label does not move like sourced own label

Most retailer own label is manufactured by third parties to a specification, and its price moves with what the retailer negotiated. This group produces a substantial share of its own-label range in its own facilities.

That changes the behaviour:

Input cost pass-through differs. A vertically integrated line responds to commodity movement on a different timeline from a negotiated supply contract.
Promotional flexibility differs. Margin on a made product is structured differently from margin on a bought one.
Range decisions differ. Adding a line means production capacity, not a new supplier.
So an own-label index pooling this retailer with sourced-own-label competitors is pooling two different economics.

We flag is_own_label and own_label_tier as everywhere, and where the group publicly identifies a line as its own production we record own_production_stated. Where it does not, we do not infer it — which factories make which lines is a fact about the business rather than something visible on a product page.

Independent owners, so store level again

As with the other French networks in this set, stores are run by independent owner-operators who price locally.

Store-level variation is real and deliberate.
A national figure is a rollup, with the store count stated.
Range varies by owner, so assortment is a store-level field.
A fixed versioned panel is required or the series moves with the panel.

store_id is mandatory. The panel discipline is on our panel design page.

The branded comparison is the clean one

Branded products price normally here and match cleanly across retailers where identifiers are published. If you want a clean French cross-retailer index, build it on branded lines and treat own label as a separate series with its own argument. We report matchable_share_category so you can see what that leaves.

French conventions, and what we do not collect

Conventions

Displayed unit pricing, VAT by product class, French-language names retained exactly, and drive as a separate surface. All four are handled as on our E.Leclerc page and set out in full on the European grocery page.

What we do not produce

Production volumes, factory output or sourcing. None of it is published, and the vertical integration argument on this page is about how prices behave, not about inferring manufacturing data.
Which facility makes which line, unless the group states it.
Margin on own label. Requires cost data nobody publishes.
Loyalty account data, sales, customer or employee data.

That first point is worth emphasising. It would be easy to let a page about a manufacturer-retailer imply we can see into the manufacturing. We cannot, and nothing on this page should be read that way.

Scope

What we collect on Intermarché, 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
is_own_label and own_label_tier from range naming
own_production_stated only where the group publicly identifies it
matchable_share_category reported, so the branded comparison is sized
surface distinguishing in-store from drive
Unit price computed by us alongside the displayed one
French names retained exactly, translation additive only
A fixed versioned store panel
Promotional mechanics as displayed

❌ What we do not, and why

Own production inferred where the group does not state it
Production volumes, factory output or sourcing data
Own-label margin, which needs cost data nobody publishes
A national average presented as the price
In-store and drive prices blended

Core Intermarché fields

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

Field	What it is on this platform
retailer / store_id / region / surface	Store mandatory. Surface separates drive
product_id / ean	Identifiers where published
price / price_vat_basis	As displayed. Never adjusted
is_own_label / own_label_tier	From range naming
own_production_stated	Only where the group publicly identifies it
matchable_share_category	So the branded comparison is sized
pack_size / price_per_unit / unit_basis	Computed by us
displayed_unit_price / unit_price_matches	Theirs, and whether it agrees
promo_mechanic / promo_ends	As displayed
store_count_observed	Stated on any rollup
observed_at	Timestamp

Use cases

What teams do with Intermarché data

Own-label price behaviour under vertical integration

Own label tracked as its own series, since a made line responds to input costs on a different timeline from a negotiated supply contract.

Clean French branded index

Branded lines matched across retailers with the matchable share reported, which is the comparison that holds when own-label economics differ by retailer.

Store-level French variation

Store as the mandatory unit in a network of independent owners who price locally and range locally.

Drive channel analysis

Click-and-collect as its own surface, since French grocery runs a large share of volume through it.

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

Send us a Intermarché 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.

Intermarché is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Intermarché 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 grocery data scraping covers, and a Intermarché-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

Intermarché data scraping: frequently asked questions

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

Because the price reflects production economics rather than a supply negotiation. Input cost pass-through runs on a different timeline, promotional flexibility differs, and range decisions mean production capacity rather than a new supplier.

So an own-label index pooling this retailer with sourced-own-label competitors is pooling two different economics.

Only where the group publicly identifies a line as its own production. Where it does not, we do not infer it.

Which facilities make which lines is a fact about the business, not something visible on a product page.

No, and this is worth emphasising because a page about a manufacturer-retailer could easily imply otherwise. Production volumes, factory output and sourcing are not published.

The vertical integration argument here is about how prices behave, not about seeing into the manufacturing.

Branded lines. They price normally and match cleanly across retailers where identifiers are published.

If you want a French cross-retailer index, build it on branded and treat own label as a separate series with its own argument. We report the matchable share so you can see what that leaves.

Yes. Stores are run by independent owner-operators who price and range locally, so a national figure is a rollup rather than a price.

The panel also has to be fixed and versioned, or the series moves when the panel does.

We quote individually, with store count as the dominant driver since store-level collection is not optional here.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Intermarché 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.

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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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