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Platform · Namshi

Namshi Data Scraping Services

One platform, several GCC countries, and VAT rates that differ by a factor of three — so a cross-country price series that ignores tax is measuring tax policy.

Namshi data scraping is the automated collection of publicly visible Namshi data across GCC storefronts with the per-country VAT basis recorded on every row, size-level availability, and cash-on-delivery captured as an availability field rather than a payment detail.

Most multi-market datasets treat tax as a formatting concern. Across the GCC the rates differ enough that an untaxed comparison between two storefronts will attribute to pricing something that is entirely a matter of statute.

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

namshi_sizes.jsonl LIVE FEED
{"product_id":"NM-77* redacted", "country":"AE","vat_basis":"gross_incl_local_vat", "price":189.00,"currency":"AED", "minor_unit_digits":2, "size_label_raw":"EU 40","size_system":"EU", "size_normalised":"eu_40", "size_offered":true,"size_in_stock":false, "cod_available":true, "category_path_raw":"[as published]"} {"product_id":"NM-77* redacted", "country":"KW", "price":15.750,"currency":"KWD", "minor_unit_digits":3, "vat_rate_used":"supplied separately for net views", "cod_available":false}
2 of 2,840,500 product-size-country rowsgross prices retained · minor-unit precision recorded · schema v1.6

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

Our Data Powers
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Taxi Aggregator
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Tmall
Namshi at a glance

How we handle Namshi specifically

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

Platform
Namshi, GCC storefronts on your list
Mandatory dimension
Country, with the VAT basis recorded per row
Currencies
Multiple, retained without silent conversion
Size level
Per-size availability, with size systems normalised
Payment
Cash-on-delivery treated as an availability field
Categories
Modest-fashion structure captured as published
Refresh
Daily standard; sub-daily on regional sale events
Region
Gulf Cooperation Council
Platform specifics

What makes Namshi data different from other fashion platforms

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

VAT rates diverge sharply between storefronts on one platform

The GCC states apply materially different VAT rates. A displayed price in one storefront and a displayed price in another are gross of very different tax, so comparing them directly is not comparing pricing.

  • A raw cross-country comparison will show one market as systematically dearer when a large part of the gap is statutory.
  • A net-of-VAT comparison isolates the commercial decision, but requires the rate to be explicit and recorded per row.
  • Silent normalisation is the worst option, because the reader cannot tell whether a movement is a price change or a rate change.

country and vat_basis are mandatory on every record, prices are retained gross in local currency, and the rate used for any net view is supplied as its own field. Both gross and net series can then be produced and the basis stated.

Several currencies, and one of them has three decimal places

The platform trades in multiple GCC currencies, and they do not share a convention. Some are quoted to two decimal places and at least one to three, which breaks naive rounding and comparison logic written for a two-decimal world.

We retain the price exactly as displayed, with currency and the minor-unit precision recorded, and never convert inside the price field. Where a converted view is needed the daily rate is supplied separately.

This sounds like a formatting detail and is not. A pipeline that rounds a three-decimal currency to two introduces a systematic error into every figure derived from it, and the error is invisible in the output.

Size level, with several size systems in one catalogue

Fashion availability is meaningless above variant level, and this catalogue carries brands using EU, UK, US and numeric size systems side by side, sometimes within one category.

We collect one row per product per size with size_offered kept separate from size_in_stock, and normalise to one internal scale with the published mapping table shipped and the original label retained.

A style listed as available with only its extreme sizes remaining is unavailable to most of its buyers, and page-level data records it as available. That is the same discipline applied on our other fashion pages, and it matters more here because the size-system mix makes naive comparison worse.

Cash on delivery is an availability field, not a payment detail

Cash on delivery remains widely used across the region, and its availability varies by country, by order value and sometimes by category.

A listing without cash-on-delivery is effectively unavailable to a share of buyers regardless of price, which makes this an availability question wearing a payment label. We capture cod_available for the storefront and order context observed, where displayed.

For a brand assessing reach in the region, this field frequently explains a conversion gap that price and assortment data cannot.

Modest fashion is a category structure, not a filter

The catalogue carries modest-fashion categories with their own hierarchy, attributes and sizing conventions — abayas, kaftans, hijabs and related lines — that do not map onto Western fashion taxonomies.

We capture the category path as published, with translation supplied as a separate field rather than replacing the original, and we do not force these lines into a Western category tree. Where a client needs a mapping onto their own taxonomy it is built as an explicit layer with the raw path retained beneath.

Forcing the mapping is how a category that is commercially significant in this market ends up scattered across three unrelated buckets in the output.

Scope

What we collect on Namshi, 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 and vat_basis mandatory on every record
  • Gross prices retained in local currency, never silently normalised
  • The rate used for any net-of-VAT view supplied as its own field
  • Currency minor-unit precision recorded, including three-decimal currencies
  • One row per product per size, with size_offered separate from size_in_stock
  • Size systems normalised to one scale, with the mapping table shipped
  • Original size label retained alongside the normalised value
  • cod_available for the storefront and order context observed
  • Category path as published, with translation as a separate field
  • Brand and any own-label flag by a published rule
  • Rating, review count and review velocity where displayed

❌ What we do not, and why

  • Customer identities or any personal data
  • Anything behind a login, including account-specific pricing or vouchers
  • Silent VAT normalisation or currency conversion inside a price field
  • Rounding a three-decimal currency to two
  • Modest-fashion categories forced into a Western taxonomy
  • Full review text at scale; structured attributes and counts instead

Core Namshi 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 / sku Platform identifiers, retained together
country / vat_basis Storefront and how tax is shown, both mandatory
price / currency / minor_unit_digits Gross price, currency and its precision
size_label_raw / size_system The label as published and which system it uses
size_normalised Mapped to one internal scale, mapping table shipped
size_offered / size_in_stock Kept separate; never-offered is not sold-out
cod_available For the storefront and order context observed
category_path_raw / category_path_translated Published path, translation separate
brand / is_own_label By a published rule
vat_rate_used Supplied separately where a net view is produced
fx_rate_daily Supplied separately where a converted view is needed
captured_at Timestamp at minute precision
Use cases

What teams do with Namshi data

Cross-country pricing that measures pricing

Per-country VAT basis recorded and net views produced explicitly means a comparison isolates the commercial decision instead of the statutory one.

Size-level availability across mixed size systems

Per-size rows with a shipped mapping table make availability comparable across brands using EU, UK, US and numeric systems in one catalogue.

Regional reach assessment

Cash-on-delivery availability per storefront explains conversion gaps that price and assortment data alone cannot.

Modest-fashion category analysis

The published category structure retained rather than forced into a Western tree, so a commercially significant category stays analysable.

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

Send us a Namshi 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.

Namshi is usually collected alongside its competitors

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

Namshi data scraping: frequently asked questions

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

Because the rates differ by a factor of three between some states. A raw comparison of two displayed prices will show one market as systematically dearer when a large part of the gap is statutory rather than commercial.

Country and VAT basis are mandatory on every row, prices stay gross in local currency, and the rate used for any net view is supplied separately so both series can be produced and the basis stated.

The region's currencies do not share a convention, and at least one is quoted to three decimal places. A pipeline written for a two-decimal world will round it and introduce a systematic error into every derived figure.

We retain the price exactly as displayed with the minor-unit precision recorded. It sounds like formatting and it is not.

Normalised to one internal scale with the mapping table shipped, and the original label retained alongside. Brands using EU, UK, US and numeric systems sit side by side in this catalogue, sometimes within a category.

Without normalisation a cross-brand size comparison compares labels rather than fit.

Because a listing without it is effectively unavailable to a share of buyers regardless of price. Availability varies by country, order value and sometimes category, so it is captured for the context observed rather than as a listing-level constant.

Not by default. The published path is retained with translation as a separate field, because these lines have their own hierarchy, attributes and sizing conventions.

Forcing the mapping scatters a commercially significant category across unrelated buckets. Where a mapping onto your taxonomy is needed it is built as an explicit layer with the raw path beneath.

See real Namshi 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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