Platform · Argos

Argos Data Scraping Services

Where store-level stock is not a supporting field. It is the product the retailer actually sells.

Argos data scraping is the automated collection of publicly visible Argos data organised around per-store availability — the stock-check result, collection point type and reservation state — with catalogue numbers retained as the identifier the retailer and its customers actually use.

Most retailers sell a product and mention where you can get it. Argos built its business the other way round: the question the shopper asks first is whether the local store has it right now.

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argos_store_stock.jsonl LIVE FEED

{"catalogue_number":"801/* redacted", "model_number":"HD-2200* redacted","ean":"505* redacted", "store_id":"0412","location_type":"standalone", "is_ranged_at_location":true, "store_stock_state":"in_stock", "stock_held_locally":true, "reservation_state":"reservable_now", "home_delivery_available":true, "price":59.99,"was_price":74.99} {"catalogue_number":"801/* redacted", "store_id":"0887","location_type":"in_sainsburys", "is_ranged_at_location":false, "store_stock_state":"not_ranged", "home_delivery_available":true}

2 of 5,220,800 product-store rowscatalogue number as primary key · schema v2.6

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

How we handle Argos specifically

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

Platform: Argos UK, online with per-store stock check
Primary field: Per-store availability, not price
Collection points: Standalone stores and in-Sainsbury's points distinguished
Identifiers: Catalogue number retained as the primary key
Store panel: Built to your list, including collection-point types
Reservation: Reserve and collect state where displayed
Refresh: Daily standard; sub-daily on stock-sensitive lines
Region: United Kingdom

Platform specifics

What makes Argos data different from general UK retail

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

Store stock is the primary field, and it has to be collected that way

An Argos dataset organised around products with an availability attribute has the structure backwards. The useful record is product-by-store, because that is the unit the shopper transacts on.

Nationally in stock is close to meaningless if the shopper's three nearest stores are out.
Available for home delivery and available to collect today are different products commercially, even though they are the same item.
Stock moves within the day on popular lines, so daily capture on a stock-sensitive category records a level that has already changed.

Every row is product-by-store-by-capture. A national roll-up can be produced from those rows, but the rows stay at store level so the roll-up is auditable and the local picture is never lost.

Collection points are not all the same thing

Argos operates standalone stores and collection points inside Sainsbury's supermarkets. From a data perspective these are not interchangeable.

Range differs: a collection point inside a supermarket typically carries a narrower assortment than a standalone store. Fulfilment differs: some locations hold stock, others receive it for collection. Opening hours differ, which affects whether same-day collection is realistic.

We capture location_type and, where displayed, whether stock is held locally or arriving for collection. Treating every collection point as a store overstates the network's effective range coverage, sometimes considerably.

The catalogue number is the identifier that matters

Argos has used catalogue numbers for decades, and they remain the identifier customers quote, staff look up and the retailer organises around.

A dataset keyed only on a URL or an internal web ID will not join to anything a client's category team recognises, and will break when the site structure changes.

We retain the catalogue number as the primary key alongside brand model number and EAN where visible, so the data joins both to Argos's own world and to a client's product master.

The service promise is measured in minutes, not days

Most retail datasets carry a delivery window in days. Argos built its proposition on collection measured in minutes, and same-day delivery slots measured in hours.

That changes what the field has to hold. A promise of "ready in 5 minutes" and one of "ready tomorrow" are not two points on the same scale for a shopper deciding where to buy a replacement kettle — they are different products. Storing both as a coarse day count throws the distinction away.

We capture the collection promise at the granularity the platform states it, with promise_unit recording whether the figure is in minutes, hours or days. Tracked over time and across locations this becomes a service-level series in the unit the retailer actually competes on, which is the only version worth benchmarking.

Catalogue numbers get reissued, and a naive series will join two products

The catalogue number is the right key for this retailer, but it is not permanently unique. Numbers are retired and reissued across ranges and seasons, so a time series keyed on the number alone can silently splice two unrelated products into one line.

The failure is quiet. Nothing errors, the series simply develops a step change that looks like a repricing and is actually a different product.

We hold a catalogue_number_epoch alongside the number, incremented when the title, brand and category behind a number change together beyond a threshold, and we retain the brand model number and EAN so a break can be confirmed rather than guessed. Series that cross an epoch boundary are flagged rather than silently continued.

Reserve-and-collect state is a genuine demand signal

Where Argos exposes reservation or collection state, it carries information a plain stock flag does not: an item reservable now is different from one showing as in stock but only orderable for later.

We capture reservation state as displayed and label it as displayed. Combined with per-store stock tracked over time, the pattern of a line going out across a cluster of nearby stores within a day is one of the stronger public sell-through signals in UK general merchandise.

It is a signal, not a sales figure, and it is presented that way.

Scope

What we collect on Argos, 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

One row per product per store per capture
Catalogue number retained as the primary identifier
location_type distinguishing standalone stores from in-Sainsbury's points
Whether stock is held locally or arriving for collection, where displayed
Reservation and collect state, as displayed
Collection promise at the granularity stated, with the unit recorded
catalogue_number_epoch, so a reissued number does not splice two products
Series flagged rather than silently continued where they cross a reissue
Home delivery availability kept separate from store collection
Price and was-price with the displayed basis
Assortment per location, so range differences by location type are visible
Brand model number and EAN where visible, alongside the catalogue number
Rating, review count and review velocity
Store panel built to your list rather than to a national default

❌ What we do not, and why

Customer identities or any personal data
Anything behind a login, including account-specific pricing
Actual unit counts where only a stock state is shown
Inferred sales volume presented as fact; reservation patterns are signals
Argos internal cost, margin or vendor terms
Full review text at scale; structured attributes and counts instead

Core Argos fields

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

Field	What it is on this platform
catalogue_number	The primary identifier, and the one customers and staff use
model_number / ean	Retained alongside, for joining to a client product master
store_id / location_type	Which location, and whether standalone or in-Sainsbury's
store_stock_state	Availability at this location at capture time
stock_held_locally	Whether stock is at the location or arriving for collection
reservation_state	Reserve-and-collect state as displayed
collection_promise / promise_unit	The promise, and whether it is in minutes, hours or days
catalogue_number_epoch	Incremented when a number is reissued for a different product
epoch_break_flag	Raised where a series crosses a reissue boundary
home_delivery_available	Delivery availability, kept separate from collection
price / was_price / was_price_basis	Price, reference price and its stated basis
is_ranged_at_location	Whether the product appears in this location's assortment at all
captured_at	Timestamp at minute precision

Use cases

What teams do with Argos data

True local availability measurement

Product-by-store rows give the availability picture a shopper actually faces, which a national in-stock flag cannot express.

Network range analysis

Location type and per-location assortment reveal how much narrower in-supermarket collection points are, and where that leaves a brand under-ranged.

Sell-through signal from stock movement

A line going out across a cluster of nearby stores within a day is one of the stronger public demand signals in UK general merchandise.

Catalogue-number joins that survive

Keying on the identifier the retailer itself organises around means the dataset joins to a client's category world and does not break with site changes.

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

Send us a Argos 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 inside two business days
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.

Argos is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Argos 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 ecommerce data scraping covers, and a Argos-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

Argos data scraping: frequently asked questions

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

Because that is the unit the shopper transacts on. Nationally in stock is close to meaningless if the three nearest stores are out.

Rows are product-by-store-by-capture. A national roll-up can be produced from them, but the underlying rows stay at store level so the roll-up stays auditable and the local picture is not lost.

No, and treating them the same overstates the network's effective range coverage. In-supermarket collection points typically carry a narrower assortment, and some hold stock while others receive it for collection.

We capture location type and, where displayed, whether stock is held locally.

Only where a count is displayed, which is uncommon. Otherwise we record the stock state as shown.

We do not infer unit counts from stock states. That would be modelling presented as measurement, and it is exactly the sort of number that falls apart when someone checks it.

It is considerably more stable than a URL or a web ID, which is why we key on it. We retain brand model number and EAN alongside so a join can be made against your product master even where a catalogue number is reissued.

No. They are retired and reissued across ranges and seasons, which means a time series keyed on the number alone can splice two unrelated products into one line.

The failure is silent — nothing errors, the series just develops a step change that looks like a repricing and is a different product. We hold an epoch alongside the number and flag series that cross a reissue boundary rather than continuing them.

More often than daily. On popular lines stock moves within the day, so a daily capture records a level that has already changed by the time it is read.

We set frequency per category rather than applying one schedule across the catalogue.

See real Argos 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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