Platform · ICA

ICA Data Scraping

Individually owned stores across four formats. Price and range are decisions taken in the store, not at head office.

ICA data scraping collects pricing, promotions and availability across Sweden's largest grocery network. The structure: stores are individually owned and operated across four distinct formats, and the owner sets price and range. So a single national figure averages independent businesses running different formats.

Two variables at once here — ownership and format — and both move price. A dataset carrying neither is not describing much.

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ica_2026-08-25.jsonl LIVE FEED

{"network":"ica","store_id":"st-4412", "store_format":"large_format","region":"Example region", "name_local":"as published, Swedish", "price":24.90,"currency":"SEK", "deposit_amount":2.00,"deposit_type":"pant_can", "promo_participating":true} {"store_id":"st-8812","store_format":"small_local", "price":29.90,"promo_participating":false, "note":"different owner AND different format. a national average blends both"} {"online_range_note":"online range is STORE-specific, not national", "panel_version":2,"panel_entered_at":"2026-04-11"}

3 of 604,880 store-product rows · Swedenownership AND format both move price · schema v1.0

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

How we handle ICA specifically

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

Network: ICA — Sweden
Ownership: Individually owned stores, not company-operated
Formats: Four, from large hypermarket to small local
Both move price: Owner decisions and format position
So: store_id and store_format on every record
Pant: Deposit as a separate field, never folded in
Online: Available per store, with varying range
Refresh: Daily. Weekly promotional cycles

Platform specifics

Ownership and format, both moving price

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

Independently owned stores set their own prices

Swedish grocery here runs on individually owned stores within a shared brand and buying structure. The owner makes commercial decisions.

Prices differ store to store, deliberately, reflecting local competition.
Range differs, since owners make ranging decisions.
Local and regional lines appear in some stores and not others.
Promotional participation varies, so a national campaign is not uniformly executed.

store_id is mandatory and national figures are computed rollups with store_count_observed stated — the same discipline as our Edeka and E.Leclerc pages.

And the panel has to be fixed

In a network of independent stores, a panel that changes membership produces movement that looks like price change. panel_version and panel_entered_at travel with the data, as our panel design page requires.

Four formats, priced apart on purpose

The network operates several distinct store formats, from large out-of-town stores through to small local convenience-scale stores.

Format carries a price position, with smaller formats pricing above larger ones.
Range differs sharply by format — a small format carries a fraction of a large one's assortment.
So a national average across formats blends a hypermarket price and a convenience price.
And it moves with format mix, not only with pricing.

store_format is on every record. This is the same argument our Co-op page makes about convenience pricing in the UK, applied inside one network rather than across retailers.

The right comparison

Format-matched. A large format here against a large format elsewhere; a small format against other small formats. Anything else measures format.

Swedish conventions and what we do not collect

Pant

Container deposits apply and are refundable. deposit_amount and deposit_type are separate fields, never folded in — the argument our European grocery page makes in full.

Unit pricing and names

Displayed unit price captured with ours computed alongside and disagreement flagged. Swedish names retained exactly, translation additive only.

Online

Online ordering runs per store with range varying by store, which is unusual — in most markets online range is set centrally. online_range_note records that the online catalogue is store-specific rather than national.

What we do not collect

Store ownership details or owner identities. The store is a commercial entity; its owner is a person.
Loyalty or member account data. No accounts created.
Sales, volumes, customer or employee data.

Scope

What we collect on ICA, 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
store_format on every record, since format carries a price position
A fixed versioned panel with entry dates
deposit_amount and deposit_type as separate fields
Unit price computed alongside the displayed one, disagreement flagged
Swedish names retained exactly, translation additive only
online_range_note recording that online range is store-specific
Local and regional lines captured where a store ranges them
Promotional participation per store, not assumed national

❌ What we do not, and why

A national average blending formats
A format-mismatched comparison against another retailer
Deposit folded into the product price
Central online range assumed where it is store-specific
Store owner identities, loyalty account data or personal data

Core ICA fields

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

Field	What it is on this platform
network / store_id / store_format / region	Both ownership and format move price
product_id / ean	Identifiers where published
name_local	Swedish, retained exactly
price / currency	Store-level
deposit_amount / deposit_type	Separate. Never in the price
pack_size / price_per_unit / unit_basis	Computed by us
displayed_unit_price / unit_price_matches	Theirs, and whether it agrees
is_own_label / own_label_tier	From range naming
promo_participating	Campaign execution varies by store
online_range_note	Online range is store-specific here
panel_version / panel_entered_at	So the series is not moved by the panel

Use cases

What teams do with ICA data

Store-level Swedish price variation

Store as the mandatory unit in a network of independently owned businesses, where a national average is a figure no shopper faces.

Format-matched competitive comparison

Store format on every record, so a large format is compared against a large format rather than against a convenience-scale store.

Promotional execution tracking

Participation recorded per store, since a national campaign in a network of independent owners is not uniformly executed.

Deposit-correct Nordic comparison

Pant as its own field, without which a Swedish beverage comparison against a non-deposit market is systematically wrong.

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

Send us a ICA 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.

ICA is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. ICA 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 ICA-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

ICA data scraping: frequently asked questions

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

Because stores are individually owned and the owner sets price and range. Prices differ store to store deliberately, reflecting local competition, and local lines appear in some stores and not others.

A national average across the network is a figure no shopper faces.

Because format carries a price position — smaller formats price above larger ones, and range differs sharply.

A national average across formats blends a hypermarket price and a convenience price, and it moves with format mix rather than only with pricing. The right comparison is format-matched.

Not necessarily. Promotional participation varies because the owner decides, so we record promo_participating per store rather than assuming a campaign executed everywhere.

No, and this is unusual — online ordering runs per store with range varying by store, where in most markets online range is set centrally.

We record that the online catalogue is store-specific rather than national.

As separate deposit amount and type fields, never folded into the price. It is refundable and differs by container.

Without separating it, a Swedish beverage comparison against a non-deposit market is systematically wrong in one direction.

We quote individually, with store count as the dominant driver since store-level collection is not optional in a network of independently owned stores.

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

See real ICA 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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