Platform · Mango

Mango Data Scraping

In many markets the brand is operated under franchise. So who sets the price is a country question, not a brand one.

Mango data scraping collects product listings, prices, size availability and range across markets. The feature that shapes multi-market analysis: a substantial share of international markets are operated under franchise or distribution arrangements rather than directly. So price and range are set by the market operator, and a brand-level view attributes to one company decisions taken by several.

Our Europcar page makes this argument in car rental. In fashion it cuts deeper, because range differs as well as price.

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mango.jsonl LIVE FEED

{"brand":"mango","country":"ES", "operating_model":"direct","surface":"own_channel", "price":49.99,"currency":"EUR", "size_label":"as published","size_system":"EU", "range_coverage_vs_core":1.00} {"country":"XX","operating_model":"not_published", "range_coverage_vs_core":0.41, "note":"41% of the core range. that is more telling than any price gap"} {"surface":"third_party","price_setter":"marketplace_seller", "size_converted":false, "caution":"a brand price series ingesting this measures SELLERS, not the brand"}

3 of 984,220 market-sku rows · multi-marketthe MARKET OPERATOR prices · sizes never converted · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Mango or its owners. Mango 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

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NYKAA

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Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Mango at a glance

How we handle Mango specifically

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

Brand: Mango — Spanish origin, international
The point: Many markets franchised or distributed
Consequence: Market operator sets price and range
So: country is a decision-making dimension
Range: Differs by market, not only price
Operating model: Recorded where published, unstated where not
Own channel vs partners: Separate surfaces
Refresh: Daily for core markets; weekly elsewhere

Platform specifics

Who decides, by market

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

Franchise markets set their own price and range

Where a market is operated under franchise or distribution, the local operator makes commercial decisions within brand standards.

Price levels differ by market beyond what cost base and currency explain.
Range differs too — a franchise market carries a selection, not the full collection.
Sale timing differs, since local operators set it.
So a brand-level figure attributes to one company decisions taken by several.

country is a decision-making dimension, operating_model is recorded where published and unstated where not, and brand figures are computed rollups with country_mix stated.

Range coverage is the useful measure

range_coverage_vs_core — what share of the core market's range a given market carries — is more informative than a price comparison, because it shows what the brand actually offers where.

It requires a core market in scope to compare against, so engagements here usually include one.

Surfaces, sizes and Spanish conventions

Own channel and partner listings

The brand sells through its own site and through third-party retailers and marketplaces, at prices the third party sets. surface distinguishes them — the argument on our surface separation page.

A brand price series that ingests marketplace listings is measuring third-party sellers, not the brand.

Size and fit

Size labelling differs by market, and the same garment carries different size systems. size_system travels with every size record, and we do not convert between systems — conversion tables are approximate and a converted size in a data field looks exact.

Sale periods

Sale timing is regulated in some markets and set locally in others. on_sale with sale_period_market, so a cross-market discount comparison does not compare a market mid-sale against one outside it.

What we do not collect

Franchise agreements, operator identities, stock quantities, or a size converted between systems.

Scope

What we collect on Mango, 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

country as a decision-making dimension
operating_model where published, unstated where not
Brand figures as computed rollups with country_mix stated
range_coverage_vs_core where a core market is in scope
surface distinguishing own channel from third-party listings
size_system on every size record, never converted
on_sale with sale_period_market
size_availability per size, with counts
SKU lifespan with introduction and delisting rates

❌ What we do not, and why

A brand average without its country mix
An operating model assumed where not published
Third-party marketplace listings ingested into a brand price series
A size converted between labelling systems
Franchise agreements, operator identities or stock quantities

Core Mango fields

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

Field	What it is on this platform
brand / country / operating_model	Country is where decisions are made
surface	own_channel or third_party
sku / product_name / colourway	Identity as published
price / currency / fx_observed_at	Price, with FX stamped
range_coverage_vs_core	Where a core market is in scope
size_label / size_system	Never converted
size_availability / sizes_available_count	Buyable, not a quantity
on_sale / sale_period_market	So cross-market discount comparison holds
sku_first_seen / sku_lifespan_days	The range cycle
country_mix	Stated on any brand rollup
observed_at	Timestamp

Use cases

What teams do with Mango data

Multi-market range coverage

What share of the core range each market carries, which shows what the brand actually offers where — more informative than price in a franchised network.

Correct price attribution

Country as a decision-making dimension, so a price difference between markets is not attributed to the brand when it reflects a local operator's decision.

Brand versus third-party pricing

Surface on every record, so a brand price series measures the brand rather than third-party sellers listing its products.

Cross-market sale timing

Sale periods per market, so a discount comparison does not put a market mid-sale against one outside it.

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

Send us a Mango 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.

Mango is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Mango 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 fashion & apparel data covers, and a Mango-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

Mango data scraping: frequently asked questions

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

Because the local operator sets price and range within brand standards. Price levels differ beyond what cost base and currency explain, and range differs too — a franchise market carries a selection rather than the full collection.

So a brand-level figure attributes to one company decisions taken by several.

Range coverage against a core market — what share of the core range a given market carries. It shows what the brand actually offers where, which a price comparison does not.

It needs a core market in scope to compare against, so engagements usually include one.

Not in a brand price series. The third party sets that price, so a series ingesting them measures third-party sellers rather than the brand.

Surface is on every record so the two are separable.

No. Conversion tables are approximate, and a converted size sitting in a data field looks exact.

We record the size label with its system and leave the conversion to you, where you can state the assumption.

Where the brand publishes it, yes. Where it does not, the field is unstated rather than assumed.

We quote individually. Market count is the main driver since each needs its own currency, size system and sale calendar handling.

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

See real Mango 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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