Platform · Rewe

Rewe Data Scraping

Q: How is Pfand handled?

As separate deposit_amount and deposit_type fields, never folded into the price. It is refundable and it differs by container type. Comparing a deposit market against a non-deposit one without separating them produces a systematic error across the whole beverage category, in one direction.

Q: Can you tell which stores are merchant-operated?

Where the group publishes it, yes. Where it does not, store_type is unstated rather than assumed. That is a fact about the business arrangement, not something a product page establishes.

Company stores, independent merchants and a discount banner. Three pricing logics, one group name.

Rewe data scraping collects pricing, promotions and availability across this German group. The structure is the analytical point: company-operated stores, independently operated merchant stores, and a separate discount banner all sit under one group. Those price on different logics, so store type and banner are dimensions rather than metadata.

A German group figure here averages a full-range supermarket, an independently run store and a discounter. Three different businesses.

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rewe_2026-08-25.jsonl LIVE FEED

{"group":"rewe","banner":"banner-full-range", "store_type":"company_operated","region":"Example region", "price":4.99,"currency":"EUR", "deposit_amount":1.50,"deposit_type":"einweg_6x", "displayed_unit_price":2.52,"price_per_unit":2.52, "unit_basis":"per litre, EXCL deposit"} {"store_type":"merchant_operated","price":5.29, "note":"same fascia, independent latitude. shoppers cannot tell. datasets must"} {"banner":"banner-discount","price":3.79, "store_type":"unstated", "caution":"a different FORMAT, not a cheaper version. never averaged with the parent"}

3 of 2,404,110 banner-store-sku rows · Germanystore_type + banner · Pfand never folded in · schema v1.0

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

How we handle Rewe specifically

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

Group: Rewe — Germany
Structure one: Company-operated stores
Structure two: Independent merchant stores under the same fascia
Structure three: A separate discount banner
So: store_type and banner on every record
Pfand: Deposit is a separate field, never folded in
Grundpreis: Displayed unit price captured, ours computed alongside
Refresh: Daily. Weekly promotional cycles

Platform specifics

Three structures under one group

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

Company, merchant and discount price differently

The group operates through more than one arrangement, and they do not share a pricing logic.

Company-operated stores price centrally within a regional structure.
Independent merchant stores trade under the same fascia with their own commercial latitude, so prices and range vary.
The discount banner is a different retail format entirely, with a narrower range and a discount price position.

A shopper does not distinguish the first two — the fascia is the same. A dataset that does not distinguish them is averaging a centrally priced store and an independently priced one, and reporting the result as one retailer's pricing.

store_type and banner are on every record. Where the group does not expose whether a store is merchant-operated, store_type is unstated rather than assumed — the same position our Edeka page takes.

The discount banner is not a cheaper version

It is a different format with a different range, competing against other discounters rather than against the parent fascia. Pooling the two produces a group average describing neither, and it moves with banner mix.

German conventions: Pfand and Grundpreis

Pfand

Container deposits are refundable and differ by container type. A beverage at €4.99 plus €1.50 deposit is not a €6.49 product.

deposit_amount and deposit_type are separate fields and never folded into the price. Comparing a deposit market against a non-deposit one without separating them produces a systematic error across the whole beverage category, in one direction.

Grundpreis

German rules require a displayed unit price. We compute our own alongside it and flag unit_price_matches where they disagree — usually a listing error rather than a parsing failure, and for a retailer that is a finding worth having.

Own label

Several own-label ranges operate across tiers, including a discount-positioned range. Flagged with tier from range naming, matched within the group, unmatched across retailers.

All three conventions are set out in full on our European grocery page, which is the cross-border layer.

Delivery, collection and what we do not collect

Channels

The group runs online delivery and click-and-collect alongside stores, and prices can differ between them. That is a surface distinction, recorded as one — see our surface separation page.

Regional structure

Pricing runs through regional structures, so region is recorded alongside store. A national figure is a computed rollup with store_count_observed stated.

What we do not collect

Whether a specific store is merchant-operated, where the group does not publish it.
Loyalty or app account data. No accounts created, in any market.
Sales, volumes or store performance. Not published.
Customer or employee data. German data protection is strict and our exclusion predates it, as everywhere.

Scope

What we collect on Rewe, 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_type and banner on every record
store_type unstated where the group does not expose it
deposit_amount and deposit_type as separate fields, never folded in
Unit price computed by us alongside the displayed Grundpreis
unit_price_matches flagged where they disagree
Own label with tier, unmatched across retailers
surface distinguishing store, delivery and collection
region recorded, with national figures as rollups
German names retained exactly, translation additive only

❌ What we do not, and why

Company and merchant stores pooled as one retailer
The discount banner averaged with the parent fascia
Deposit folded into the product price
A store type assumed where not published
Loyalty account data, sales, customer or employee data

Core Rewe fields

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

Field	What it is on this platform
group / banner / store_type / store_id	Banner and store type are the dimensions
region	Pricing runs through regional structures
surface	store, delivery or collection
product_id / ean	Identifiers where published
price / price_vat_basis	As displayed. Never adjusted
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	Grundpreis, and whether it agrees
is_own_label / own_label_tier	From range naming
store_count_observed	Stated on any rollup
observed_at	Timestamp

Use cases

What teams do with Rewe data

Store-type aware German pricing

Company and merchant stores separated, so a retailer's pricing is not an average of centrally set and independently set prices reported as one.

Banner-level competitive position

The discount banner analysed against other discounters rather than averaged with the parent fascia, since it is a different format with a different range.

Deposit-correct beverage comparison

Pfand as its own field, without which a German beverage comparison against a non-deposit market is systematically wrong in one direction.

Grundpreis accuracy monitoring

Computed unit price against the displayed one with disagreements flagged, which for a retailer is a finding it cannot easily get itself.

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

Send us a Rewe 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
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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.

Rewe is usually collected alongside its competitors

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

Rewe data scraping: frequently asked questions

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

Because they price on different logics. Company stores price centrally within a regional structure; independent merchant stores trade under the same fascia with their own commercial latitude.

A shopper does not distinguish them — the fascia is the same. A dataset that does not is averaging a centrally priced store and an independently priced one, and reporting it as one retailer's pricing.

No. It is a different retail format with a narrower range, competing against other discounters rather than against the parent fascia.

Pooling the two produces a group average describing neither, and it moves with banner mix rather than with pricing.

As separate deposit_amount and deposit_type fields, never folded into the price. It is refundable and it differs by container type.

Comparing a deposit market against a non-deposit one without separating them produces a systematic error across the whole beverage category, in one direction.

We capture it and compute our own alongside, flagging where they disagree. A disagreement is usually a listing error rather than a parsing failure.

For a retailer that is a finding worth having, which is why we keep both rather than trusting one.

Where the group publishes it, yes. Where it does not, store_type is unstated rather than assumed.

That is a fact about the business arrangement, not something a product page establishes.

We quote individually on banners, store count, category scope and refresh. Whether the discount banner is in scope matters, since it is effectively a second retailer.

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

See real Rewe 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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