Platform · Kaufland

Kaufland Data Scraping

Q: Can you tell us which marketplace sellers are unauthorised?

No. authorised is a constant unknown_to_us. Authorisation is a contractual fact in your systems, not something visible on a listing. We supply seller identity, listing counts and price position; joining that to your authorised list is one operation on your side and the only correct place for the determination.

A hypermarket and a third-party marketplace under one domain. Blend them and you get a price series describing neither.

Kaufland data scraping collects both of Kaufland's surfaces: the hypermarket grocery range, priced by Kaufland, and the third-party marketplace, where independent sellers set their own prices. Those are entirely different datasets sharing a domain, and the single most important field is the one that says which is which.

Most German grocery feeds treat Kaufland as a hypermarket. It is also a marketplace, and a feed that does not separate the two produces a grocery price series contaminated with third-party seller pricing.

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kaufland_2026-08-25.jsonl LIVE FEED

{"surface":"kaufland_grocery","price_setter":"kaufland", "country":"DE","store_id":"kf-2210", "ean":"40123*** redacted", "price":1.99,"currency":"EUR", "deposit_amount":0.25, "grundpreis_displayed":3.98,"grundpreis_matches":true} {"surface":"marketplace","price_setter":"third_party_seller", "seller_id":"mk-88120","offer_count":7, "price":2.85,"price_min_across_offers":2.40, "shipping_cost":3.99, "authorised":"unknown_to_us", "note":"same domain, different kind of number entirely"} {"caution":"a German grocery index that ingested marketplace rows is measuring a third-party long tail"}

3 of 2,880,110 rows · both surfacessurface on every record · never blended · schema v1.0

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

How we handle Kaufland specifically

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

Retailer: Kaufland — hypermarket, Germany and Central Europe
The other surface: A third-party marketplace on the same domain
Price setter: Kaufland on grocery; independent sellers on the marketplace
The critical field: surface, on every record. Never blended
Marketplace mechanics: Multiple offers per product, seller identity, shipping separate
Grocery mechanics: Store-level, Pfand, Grundpreis — as any German grocer
Group context: Same parent as a major discounter. Related but priced separately
Refresh: Grocery weekly; marketplace faster, since seller pricing moves

Platform specifics

Two surfaces, and why the distinction is the whole page

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

A grocery price and a marketplace offer are not the same kind of number

On the hypermarket surface, Kaufland sets the price on its own inventory. On the marketplace, independent sellers list their own products at their own prices, with Kaufland as the platform.

Grocery prices reflect Kaufland's positioning. Marketplace prices reflect a seller's.
Marketplace listings carry multiple offers per product, so the record is an offer rather than a product — the same structure as our Idealo and Naver work.
Shipping is separate on the marketplace and frequently material to the total.
Assortment overlaps only partly, and where it does the two prices can differ substantially.

Every record carries surface and price_setter. A German grocery price index that has quietly ingested marketplace listings is measuring a mix of a hypermarket's pricing and a long tail of third-party sellers, and nothing in the output says so.

The marketplace surface needs marketplace discipline

Where the marketplace is in scope, it is handled as a marketplace rather than as more grocery rows.

One record per offer, with seller identity at shop level and the offer price.
Offer count and price spread per product, so the range is visible rather than collapsed to a lowest price.
Shipping cost captured separately from item price, since a lower item price with high shipping frequently loses.
Seller identity is a commercial entity — no individual seller personal data, in any market.

What we will not determine

Whether a marketplace seller is authorised to sell your brand. That is a contractual fact in your systems, not something visible on a listing. We supply seller identity, listing counts and price position; the determination stays with you — the same position as on our Shopee Taiwan page, where the same question arises for a distributor.

German grocery mechanics still apply on the hypermarket side

On the grocery surface, everything that applies to a German grocer applies here.

Pfand is captured as its own field and never folded into the shelf price.
Grundpreis — the mandated displayed unit price — is captured, and we compute our own from parsed pack data, flagging disagreement.
Own label is flagged and marked unmatched across retailers.
Store-level pricing where it varies, with a computed banner rollup rather than a banner-level primary.

Central Europe

Kaufland operates beyond Germany, and those are separate markets with different ranges and pricing. country is a dimension and they are never pooled. Cross-market comparison carries FX where currencies differ.

Group context, stated carefully

Kaufland sits in the same retail group as a major discounter. That is public and it is context worth knowing, but they are separately positioned and priced. We do not infer one's pricing from the other, and we do not treat them as a single commercial entity in the data.

Scope

What we collect on Kaufland, 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

surface and price_setter on every record, grocery and marketplace never blended
Grocery: store-level pricing, Pfand separate, Grundpreis captured and checked
Marketplace: one record per offer, with seller identity at shop level
Offer count and price spread per marketplace product
Shipping cost captured separately from marketplace item price
country as a dimension across Central European markets
Own label flagged and marked unmatched across retailers
German and local-language names retained exactly as published
Pack parsed, with unit price on a stated basis

❌ What we do not, and why

A blended price across the hypermarket and the marketplace
Any determination that a marketplace seller is authorised
Individual seller personal data
Pricing inferred from a related group brand
A deposit folded into the shelf price

Core Kaufland fields

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

Field	What it is on this platform
surface / price_setter	kaufland_grocery or marketplace, and who sets the price
country / store_id	Market and, on grocery, store
product_id / ean / ean_missing_reason	Identifiers, with nulls reasoned
price / currency	Grocery shelf price, or marketplace offer price
seller_id / seller_name	Marketplace only. Shop level, a commercial entity
offer_count / price_min_across_offers / price_spread_pct	Marketplace range, not collapsed
shipping_cost	Marketplace, separate from item price
deposit_amount / deposit_type	Grocery, as its own fields
grundpreis_displayed / grundpreis_matches	Theirs, and whether it agrees with ours
authorised	Constant unknown_to_us on marketplace sellers
in_stock / observed_at	Availability and timestamp

Use cases

What teams do with Kaufland data

German grocery benchmarking without marketplace contamination

surface on every record, so a grocery price index contains grocery prices rather than a mix of hypermarket pricing and a third-party long tail.

Marketplace seller monitoring for a brand

Offer-level records with seller identity and price position, showing who is listing your products and where they sit in the range. The authorisation call stays with you.

Cross-surface price gap

The same product on both surfaces where assortment overlaps, delivered as separate records, showing where marketplace sellers undercut or exceed the hypermarket.

Central European market comparison

country as a dimension with FX where currencies differ, so a German price and a Central European one are compared deliberately rather than pooled.

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

Send us a Kaufland 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.

Kaufland is usually collected alongside its competitors

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

Kaufland data scraping: frequently asked questions

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

Because Kaufland runs a hypermarket and a third-party marketplace on one domain, and they are different kinds of number. Kaufland sets grocery prices on its own inventory; independent sellers set marketplace prices on theirs.

A German grocery index that has quietly ingested marketplace listings is measuring a mix of hypermarket positioning and a third-party long tail, and nothing in the output says so.

As a marketplace. One record per offer rather than per product, seller identity at shop level, offer count and price spread so the range is visible, and shipping captured separately from item price.

Collapsing marketplace offers to a single lowest price discards the spread, which is the actual competitive picture.

No. authorised is a constant unknown_to_us. Authorisation is a contractual fact in your systems, not something visible on a listing.

We supply seller identity, listing counts and price position; joining that to your authorised list is one operation on your side and the only correct place for the determination.

It is public context worth knowing, and it does not let us infer one's pricing from the other. They are separately positioned and priced.

We do not treat them as a single commercial entity in the data, and we would not offer a discounter price as a proxy for a hypermarket one.

On the grocery surface, all of them. Pfand as its own field, the mandated Grundpreis captured with our own computed unit price alongside and disagreement flagged, own label flagged and unmatched across retailers.

We quote individually, and the main variable is whether the marketplace is in scope. Grocery alone is a conventional German engagement; adding the marketplace multiplies records through offer counts.

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

See real Kaufland 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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