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Platform · MediaMarkt

MediaMarkt Data Scraping Services

Two banners, one group, and sometimes two different prices for the same product in the same city.

MediaMarkt data scraping is the automated collection of publicly visible MediaMarktSaturn data with banner as a mandatory dimension — MediaMarkt and Saturn treated as separate price surfaces — alongside country, VAT basis, store pickup availability and marketplace seller resolution.

MediaMarkt and Saturn belong to the same group and frequently operate in the same cities. They are not the same price surface, and a dataset that treats them as one banner throws away a genuinely unusual competitive signal.

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

mms_offers.jsonl LIVE FEED
{"product_id":"288* redacted","model_number":"KD-65* redacted", "banner":"mediamarkt", "country":"DE","currency":"EUR", "vat_basis":"gross_incl_19pct", "price":1099.00, "seller_type":"retailer_own","is_default_offer":true, "store_id":"DE-0271", "pickup_available":true,"pickup_window":"today", "delivery_available":true,"delivery_window":"4-6 days"} {"product_id":"288* redacted", "banner":"saturn", "price":1149.00, "store_id":"DE-0448", "pickup_available":false, "seller_type":"unknown"}
2 of 1,912,600 product-banner-store rowsbanner mandatory · VAT basis recorded · schema v2.4

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to MediaMarkt or its owners. MediaMarkt and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
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blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall
MediaMarkt at a glance

How we handle MediaMarkt specifically

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

Platform
MediaMarkt and Saturn, across country storefronts on your list
Mandatory dimension
Banner — the two are separate price surfaces
Country
Per-country pricing with VAT basis recorded
Stores
Pickup availability per store across a store panel
Marketplace
Own stock separated from marketplace sellers
Bundles
Contents captured as displayed, kept out of the price
Refresh
Daily standard; sub-daily on launch and Black Friday windows
Region
Europe, per country storefront
Platform specifics

What makes MediaMarktSaturn data different

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

Two banners in one group is a dimension, not a footnote

MediaMarktSaturn runs both banners in several markets, often with overlapping catchments. The same product can carry different prices, different promotions and different stock at the two fascias in one city.

  • For a brand, this is two retail relationships that happen to share an owner, and price integrity has to be assessed at each.
  • For a competitor, the gap between banners is a live signal about how the group is positioning them.
  • For a price index, blending them produces a series that moves with banner mix rather than with pricing.

banner is mandatory on every record. Where a client wants a group-level view we aggregate to it, but the underlying rows stay per banner so the aggregation is inspectable and the banner gap is never lost.

Country is a separate price surface again

Both banners trade across multiple European countries with different currencies, VAT rates and promotional calendars. The same model routinely differs in price between Germany, Spain and Poland.

Every record carries country, the price in local currency and the VAT basis as displayed. In European electronics, displayed prices are gross of local VAT at local rates, so a raw cross-country comparison is invalid before normalisation and we do not perform that normalisation silently.

Where a converted or net-of-VAT view is needed, the rate used is supplied as its own field. Baking it in is how a cross-market comparison ends up measuring tax policy.

Store pickup is the competitive edge, so it needs measuring

A large store estate is the group's structural advantage over pure-play electronics retailers, and that shows up as pickup availability rather than as price.

We capture pickup_available and the stated pickup window per store, kept separate from delivery availability and its window. A product deliverable next week but collectable this afternoon is a materially different proposition, and a single in-stock flag makes them identical.

Pickup availability also differs between the two banners' stores in the same city, which is one of the more practical uses of banner as a dimension.

The EU energy label is a published, comparable attribute

EU rules require energy-efficiency labelling and a product information sheet for large appliances, televisions, lighting and several other categories, with a registered entry in the EU product database. That makes energy class one of very few technical attributes that is standardised, published and directly comparable across retailers and countries.

  • It is a real purchase driver in European appliance retail, and it moves independently of price.
  • It is standardised, unlike marketing specification text, so it can be compared without normalisation guesswork.
  • A rescaled class matters commercially. Two products can carry the same letter under different scale versions and not be equivalent, so the scale version has to travel with the value.

We capture energy_class, the scale version where indicated and the product-information-sheet reference where published. For appliance categories this frequently explains a price gap that a specification-free dataset presents as an unexplained anomaly.

A shared currency makes intra-market gaps unusually visible

Across the eurozone markets the group trades in, prices are quoted in the same currency. There is no exchange rate hiding a cross-border difference.

That makes intra-eurozone price gaps directly readable in a way they are not for a retailer spanning several currencies, and it makes them commercially sensitive: a visible, unexplained gap between two euro markets invites cross-border sourcing and grey-market flow on high-value items.

Because the comparison needs no FX step, the only remaining normalisation is VAT, which differs by country and is captured as a basis on every record. Where a net-of-VAT view is wanted the rate is supplied separately, so a euro-to-euro comparison can be made either gross or net and the choice stated.

For non-eurozone markets in the estate, currency is on every record and no conversion is performed silently.

Marketplace sellers sit alongside own stock

Both banners operate marketplace layers, so some offers on a product page come from third-party sellers rather than from the retailer.

The retailer's own price reflects its buying and trading decision, which is what a supplier negotiates against. A marketplace seller's price is an independent third party's decision, and for a brand that is a channel-integrity question rather than a wholesale one.

We capture seller_type and the seller name where displayed, plus which offer held the default position. Where the page gives no indication, the field is recorded as unknown rather than assumed to be the retailer.

Scope

What we collect on MediaMarkt, 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

  • banner on every record, MediaMarkt and Saturn kept separate
  • country, local currency and VAT basis as displayed
  • The rate supplied as its own field where a converted or net view is needed
  • pickup_available and pickup window per store, across a store panel
  • delivery_available and delivery window, kept separate from pickup
  • seller_type separating own stock from marketplace sellers
  • Unknown recorded as unknown where the page gives no seller indication
  • is_default_offer, so which offer holds the page is tracked over time
  • Bundle contents as displayed, kept out of the product price
  • EU energy class with the scale version, so two identical letters are not conflated
  • Product information sheet reference where published
  • is_eurozone, so euro-to-euro gaps are readable without an FX step
  • Product identifier, brand model number and EAN retained together
  • Rating, review count and review velocity

❌ What we do not, and why

  • Customer identities or any personal data
  • Anything behind a login, including account or trade pricing
  • Silent VAT normalisation or currency conversion inside a price field
  • Seller identity where the page does not indicate one
  • Extended warranty or installation quotes configured per order
  • Internal cost, margin or vendor terms

Core MediaMarkt fields

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

Field What it is on this platform
product_id / model_number / ean Identifiers retained together for joins
banner mediamarkt or saturn — mandatory on every row
country / currency / vat_basis Market, currency and how VAT is displayed
price Price for this banner, country and offer
store_id Which store the pickup record resolves to
pickup_available / pickup_window Store collection state and stated timing
delivery_available / delivery_window Delivery state and timing, kept separate
seller_type / seller_name Own stock or marketplace, unknown where not shown
is_default_offer Whether this offer held the page at capture
bundle_contents What is included, as displayed
energy_class / energy_scale_version EU energy label and which scale version it uses
energy_info_sheet_ref Product information sheet reference where published
is_eurozone Whether this market quotes in euro, so gaps need no FX step
fx_or_vat_rate Supplied separately where a normalised view is required
captured_at Timestamp at minute precision with store timezone
Use cases

What teams do with MediaMarkt data

Banner-gap analysis

MediaMarkt and Saturn prices held separately reveal how the group positions its two fascias against each other, which a blended series destroys.

Cross-market pricing without tax artefacts

Country, currency and VAT basis captured with the rate supplied separately means a multi-market comparison measures pricing rather than tax policy.

Pickup-versus-delivery positioning

Store pickup availability per banner and store shows where the group's estate wins on immediacy, which is its structural advantage over pure-play competitors.

Marketplace channel integrity

Seller type on every offer separates the retailer's own pricing decision from a third party's, which need different responses.

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

Send us a MediaMarkt 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.

MediaMarkt is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. MediaMarkt 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 consumer electronics data covers, and a MediaMarkt-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

MediaMarkt data scraping: frequently asked questions

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

No, as two. Banner is mandatory on every record, because the same product can carry different prices, promotions and stock at the two fascias in the same city.

Where a group-level view is wanted we aggregate to it, but the rows stay per banner so the aggregation is inspectable and the banner gap is never lost.

Displayed prices are captured as displayed, gross of local VAT, with the VAT basis recorded. We do not normalise silently.

Where a net-of-VAT or converted view is needed, the rate used is supplied as its own field. Baking it into the price is how a cross-market comparison ends up measuring tax policy rather than commercial decisions.

Yes, per store across a store panel, with the stated pickup window, kept separate from delivery.

The store estate is the group's structural advantage, and it shows up as pickup availability rather than as price. A single in-stock flag cannot express it.

Yes, with seller type on every offer and the seller name where displayed. Where the page gives no indication we record unknown rather than assuming the retailer.

Assuming is how a third party's pricing quietly enters a retailer benchmark, and it is a mistake that is very hard to detect afterwards.

Yes, with the energy class, the scale version where indicated and the product information sheet reference where published.

The scale version matters. Two products can carry the same letter under different scale versions and not be equivalent, so the version travels with the value rather than being dropped. For appliance categories this frequently explains a price gap that a specification-free dataset shows as an unexplained anomaly.

Any on your list where the banners trade. Country is mandatory on every record and cross-country analysis is done after explicit normalisation rather than by blending raw displayed prices.

See real MediaMarkt 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.

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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