Platform · Mercadona

Mercadona Data Scraping

In several categories there is no branded line to compare against, because Mercadona chose not to stock one.

Mercadona data scraping collects product listings, pricing and availability from Mercadona's Spanish and Portuguese online range. The distinguishing constraint goes further than a discounter's: in a number of categories Mercadona carries own brands and nothing else, so there is not merely a matching problem — there is no branded comparison set in that category at all.

Aldi's problem is that most lines cannot be matched. Mercadona's is that in some categories there is nothing to match to, because the branded alternative was delisted years ago.

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mercadona_2026-08-25.jsonl LIVE FEED

{"country":"ES","product_id":"mer-4471", "name_local":"Example producto 500g", "is_own_brand":true,"own_brand_tier":"core", "category_has_branded_lines":false, "matchable_share_category":0.00, "price":1.85,"currency":"EUR", "caution":"no branded line exists in this category — not a thin comparison, NO comparison"} {"country":"PT","product_id":"mer-pt-8812", "price":2.05, "note":"separate market — different range and pricing, never pooled with ES"} {"product_id":"mer-9902", "first_seen":"2026-06-11","last_seen":"2026-08-20", "archive_limited":false, "note":"delisted. on a deliberately narrow range that is a category decision, not noise"}

3 of 204,110 product rows · ES + PTcategory_has_branded_lines reported · schema v1.0

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

How we handle Mercadona specifically

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

Retailer: Mercadona — Spain and Portugal
Own brands: Dominant, and in some categories exclusive
Consequence: No branded comparison set exists in those categories
So the question changes: Not 'how do we match' but 'what does this category tell us'
Online range: Narrower than the store range. Stated, not implied
Delivery model: Hub-based, so availability and slots are area-level
Portugal: A separate market, with its own range and pricing
Refresh: Weekly usually right. Price movement is slower than a promotional retailer

Platform specifics

What makes Mercadona different from a discounter

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

Category exclusivity is a stronger version of the own-label problem

The Aldi and Lidl pages describe own-label lines that cannot be matched to another retailer. Mercadona takes that further: in a number of categories it has removed branded alternatives entirely and stocks only its own brands.

There is no branded line in the category to compare against, at Mercadona or through Mercadona.
A shopper's only choice in that category is Mercadona's own product, at Mercadona's price.
So a matched-product index cannot populate that category at all — not thinly, not at all.

What we report

category_has_branded_lines as a boolean per category, alongside matchable_share_category. The first tells you whether a comparison is possible in principle; the second tells you how much of it you could populate. Those are different questions and most feeds answer neither.

What that leaves you with

Category-level unit pricing, own-brand tier structure, and range composition. All real, none of them a matched price index. We would rather scope an engagement to those than build an index with empty columns.

Two markets, not one, and an online range that is not the store range

Spain and Portugal are separate

Mercadona operates in both, and they are different markets with different ranges, pricing and supplier bases. country is a dimension on every record and the two are never pooled — a cross-market average would describe neither.

Online is narrower than store

Mercadona's online range is smaller than what a store carries, and its online operation runs from dedicated hubs rather than picking from shop floors in most areas.

We collect the online range and say so on the page rather than letting a coverage figure imply store range.
Availability and delivery slots are hub-area level, so serviceability is recorded as its own state.
We do not infer store assortment from online listings. The relationship is not stable enough.

If store-level range is what you need, that is a different exercise and extraction will not answer it.

What a Mercadona feed is genuinely good for

Given no branded comparison in several categories, the useful outputs are structural rather than competitive.

Category price levels as a Spanish market reference point, on unit price with a stated basis.
Own-brand tier structure where a category carries entry and premium own-brand lines, and the spread between them.
Range composition — how many lines a category carries and how that changes, which on a deliberately-narrow retailer is a strategic signal rather than merchandising noise.
Delisting and introduction, which on a narrow range is a much stronger signal than on a wide one.

The last point is worth drawing out. On a superstore carrying tens of thousands of lines, one delisting is noise. On a retailer that carries a deliberately small range, a delisting is a decision someone made about the category. We record first_seen and last_seen so that is queryable, with the same archive_limited caveat that applies everywhere: first seen means first seen by us, not launched.

Scope

What we collect on Mercadona, 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

Product listings, price and availability across the online range
country as a dimension — Spain and Portugal never pooled
category_has_branded_lines, so you know if comparison is possible at all
matchable_share_category, so you know how much could populate
Own brand flagged, with tier where a category carries more than one
Pack parsed, with unit price on a stated basis
first_seen and last_seen, with archive_limited flagged
Serviceability as a distinct state from out of stock
Spanish and Portuguese names retained exactly as published

❌ What we do not, and why

Own-brand lines matched to another retailer on name similarity
A category comparison where no branded line exists to compare
Store assortment inferred from the online range
A pooled Spain-Portugal average
Sales, volumes or category share

Core Mercadona fields

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

Field	What it is on this platform
country / city / hub_area	Two markets, and hub-level geography
product_id / ean	Identifiers where published
name_local	Spanish or Portuguese, retained exactly
is_own_brand / own_brand_tier	The dominant case, with tier where one exists
category_has_branded_lines	Whether comparison is possible in principle
matchable_share_category	How much of it could actually populate
price / currency	Displayed price
pack_size / pack_unit / price_per_unit / unit_basis	Parsed, with the basis named
first_seen / last_seen / archive_limited	Lifecycle, with the honest caveat
serviceable / in_stock	Two distinct states
observed_at	Timestamp

Use cases

What teams do with Mercadona data

Spanish category price reference

Unit price at category level as a market reference point, which works without matched products and is the realistic output where no branded set exists.

Own-brand tier spread

Entry and premium own-brand lines within a category and the gap between them, readable without any cross-retailer join.

Range composition as strategy

How many lines a category carries and how that changes, which on a deliberately narrow retailer is a decision rather than merchandising noise.

Delisting as a category signal

On a small range, a delisting is a decision someone made about the category — a far stronger signal than the same event at a superstore.

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

Send us a Mercadona 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.

Mercadona is usually collected alongside its competitors

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

Mercadona data scraping: frequently asked questions

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

Aldi's own-label lines mostly cannot be matched to another retailer. Mercadona goes further: in a number of categories it stocks own brands and nothing else, so there is no branded line to compare against at all.

That is not a thin comparison, it is no comparison. We report category_has_branded_lines so you know which categories are in that position before scoping.

Category price levels on unit price, own-brand tier spreads, range composition, and delisting or introduction events. All structural rather than competitive, and all real.

We would rather scope to those than build a matched index with categories that cannot populate.

Separate. Different ranges, pricing and supplier bases, with country on every record and no pooling. A cross-market average would describe neither.

No, and online is narrower. We collect the online range and say so rather than letting a coverage figure imply store range.

We do not infer store assortment from online listings — the relationship is not stable enough to sell as a number.

Because the range is deliberately narrow. At a superstore carrying tens of thousands of lines, one delisting is noise. On a small range it is a decision someone made about the category.

first_seen and last_seen make it queryable, with the usual caveat that first seen means first seen by us rather than launched — flagged as archive_limited.

We quote individually, and this is at the lighter end — a narrow range with slow price movement means weekly refresh is usually right.

One scoping call, a free pilot within 24 hours including which categories carry branded lines at all, then a fixed monthly quote. Request a quote.

See real Mercadona 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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