Platform · Ocado

Ocado Data Scraping Services

A pure-play grocer with no store estate, which makes it the cleanest UK price benchmark available.

Ocado data scraping is the automated collection of publicly visible Ocado data — a single national catalogue with no store-level variation, the M&S range separated from Ocado own brand and third-party brands, price match commitments, computed unit pricing and delivery slot pricing.

Ocado has no shops. That single fact makes it structurally different from every other UK grocer in the dataset: one national catalogue, one price, no store-level variation to model. For benchmarking, that is a feature.

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Free pilot on your own Ocado list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

ocado_prices.jsonl LIVE FEED

{"ocado_product_id":"552104", "name":"Example Sourdough 800g", "price":3.25,"was_price":3.60, "range_type":"ms_range", "is_price_match":false, "unit_price_computed":0.41, "unit_basis":"per_100g", "pack_size":800,"pack_unit":"g", "in_stock":true, "substitution_shown":false, "store_id":"null", "store_note":"pure_play_no_store_estate"} {"ocado_product_id":"552988", "range_type":"ocado_own_brand", "is_price_match":true, "delivery_slot_price":3.99}

2 of 29,400 SKU rows · nationalno store dimension — none exists · schema v3.8

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

How we handle Ocado specifically

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

Platform: Ocado.com, a pure-play online grocer with no physical store estate
Structural difference: One national catalogue and price — no store-level variation
Range mix: M&S range, Ocado own brand and third-party brands, classified separately
Price match: Match commitments captured as flags where publicly stated
Delivery pricing: Slot pricing captured, since it varies by time and demand
Unit pricing: Computed by us per category, with the displayed value retained
Refresh: Daily standard; more frequent around promotional changes
Region: United Kingdom

Platform specifics

What makes Ocado data different from other UK grocers

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

No stores means no store-level noise

Every other major UK grocer has a physical estate, which introduces regional pricing, store-level availability and range variation that has to be modelled or explicitly ignored. Ocado has none of that.

One price nationally. There is no store-level price to reconcile, so a price index against Ocado is unambiguous.
One catalogue. Range is national, so assortment analysis is not confounded by store-level ranging decisions.
Availability is centralised. A stock-out is a genuine central availability event rather than one store's replenishment failure.

The practical consequence is that Ocado makes an unusually clean benchmark. When a client wants a stable reference point for UK grocery pricing without modelling store variation, this is the platform we recommend anchoring on — and we say so even though it is a smaller collection than a full-estate grocer.

Three distinct ranges on one site

Ocado carries the M&S food range, its own Ocado brand, and third-party branded products. Treating these as one catalogue makes private label analysis meaningless, because M&S is somebody else's private label being sold as a branded range here.

M&S range. Premium positioning, exclusive to Ocado among online grocers. It behaves like a branded range in competitive terms, not like Ocado's own label.
Ocado own brand. The genuine private label, and the correct population for own-label share analysis.
Third-party brands. The comparison population for both.

We classify range_type across all three so each analysis uses the right population. Datasets that classify M&S lines as Ocado own-label overstate Ocado's private label share substantially and mis-price the branded index.

Price match commitments and delivery slot pricing

Ocado publishes price match commitments against other grocers on qualifying lines. We capture these as flags where publicly stated, because which lines are matched — and which quietly leave the match list — reveals competitive positioning in the same way Aldi Price Match does at Sainsbury's.

Delivery slot pricing is genuinely part of the cost of an Ocado basket and varies by day, time and demand. We capture published slot pricing where exposed, which matters for total-basket comparison against grocers with different delivery fee structures.

A comparison that includes product prices but ignores delivery cost understates the difference between a pure-play and a grocer offering free click-and-collect, and for basket-level analysis that gap is material.

Scope

What we collect on Ocado, 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

Single national price and catalogue, with no false store-level granularity implied
Range type classified across M&S, Ocado own brand and third-party brands
Price match commitment flags where publicly stated
Unit price computed by us, with the displayed value retained
Promotional mechanics normalised across types
Availability as publicly exposed, with substitutions where shown
Delivery slot pricing where publicly displayed
Category and search placement with sponsored slots flagged
Ratings and review text without reviewer profiles

❌ What we do not, and why

Customer account data, saved baskets or personalised offers
Prices or offers visible only after signing in
Ocado Retail commercial or supplier systems
Availability revealed only by adding items to a basket
Reviewer names, profiles or review histories

Core Ocado fields

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

Field	What it is on this platform
ocado_product_id	The platform's own identifier, used as the join key
price	The national price, with no store dimension because none exists
was_price / promo_mechanic	Prior price and the normalised promotional mechanic
range_type	ms_range, ocado_own_brand or third_party_brand
is_price_match	Whether the line carries a published price match commitment
unit_price_computed	Computed on a consistent per-category basis
unit_price_displayed	The displayed figure, retained for comparison
in_stock / substitution_shown	Central availability and whether a substitute was offered
delivery_slot_price	Published slot pricing where exposed, for basket-level comparison
pack_size / pack_unit	Parsed pack architecture, for unit normalisation
category_path	Ocado's category structure as published

Use cases

What teams do with Ocado data

A clean national benchmark for UK grocery pricing

With no store-level variation to model, Ocado provides an unambiguous national reference price, which makes it the most stable anchor for a UK grocery index.

Correct private label analysis

Range type classification separates M&S lines from Ocado own brand, so own-label share and branded price indices are computed against the right populations.

Price match tracking as a competitive signal

Published price match commitments are tracked over time, so lines entering and leaving the match list indicate where Ocado is defending against specific competitors.

Total basket cost comparison

Delivery slot pricing captured alongside product prices supports basket-level comparison against grocers with different delivery fee structures.

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

Send us a Ocado 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.

Ocado is usually collected alongside its competitors

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

Ocado data scraping: frequently asked questions

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

Because it has no store estate. One national catalogue, one price, no store-level variation to model or ignore. A price index against Ocado is unambiguous in a way that an index against a full-estate grocer is not.

We recommend it as an anchor even though it is a smaller collection than a full-estate grocer, because for benchmarking purposes the absence of store variation is a genuine advantage.

As its own range type, separate from Ocado own brand. This matters more than it sounds: M&S is somebody else's private label being sold here as a premium branded range.

Datasets that classify M&S lines as Ocado own-label overstate Ocado's private label share substantially and mis-price the branded index. We classify range_type across M&S, Ocado own brand and third-party brands so each analysis uses the right population.

No, because there are no stores. This is the one UK grocer where a store dimension genuinely does not exist, and we do not manufacture one.

Some datasets add a nominal store or region field for schema consistency across retailers. We leave it absent, because a populated field implying granularity that does not exist is worse than a missing one.

Where publicly displayed, yes. Slot pricing varies by day, time and demand and is a genuine part of the cost of an Ocado basket.

It matters for total-basket comparison: an analysis including product prices but ignoring delivery cost understates the difference between a pure-play grocer and one offering free click-and-collect, which is material at basket level.

Structurally simpler and smaller. No store dimension, no loyalty-gated two-tier pricing of the Clubcard or Nectar kind, and a smaller catalogue.

That makes it cheaper to collect and cleaner to benchmark against, but it is not a substitute for the full-estate grocers if your question is about regional pricing or loyalty promotional intensity. Most clients collect Ocado alongside rather than instead.

Generally less than a full-estate grocer, because there is no store dimension multiplying volume. Drivers are category scope, SKU count and refresh frequency.

A full-catalogue daily collection here is comparable in cost to a partial-category collection at a store-level retailer. One scoping call, a free pilot on your own categories within 24 hours, then a fixed monthly quote. Request a quote.

See real Ocado 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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