# Changelog of Inditex Product Scraper — 7 Brands, One Dataset (`sian.agency/inditex-product-scraper`) Actor

- **URL**: https://apify.com/sian.agency/inditex-product-scraper/changelog.md
- **Full Actor documentation**: https://apify.com/sian.agency/inditex-product-scraper.md

## Changelog

All notable changes to the Inditex product scrapers — Zara, Zara Home, Bershka, Massimo Dutti,
Stradivarius, Oysho, Pull\&Bear, and the combined all-brands Actor — are documented in this file.

### \[1.0.1] — 2026-09-22

#### 🐛 Fixed

- **A single category no longer comes back empty** — the brands list the same category under more than one internal number, and only one of them actually holds the products. Runs that named a valid category could be told it was "a container category" and stop with nothing. Every number behind a category is now tried, so the one with the products is the one you get.
- **Paste the category URL** — the `category` field now accepts the address straight from the brand site (`https://www.bershka.com/gb/women/clothes/skirts-n3864.html`), as well as the slug or the number. Previously only some of those spellings were recognised.
- **Britain** — choosing `UK` as the market now resolves to the same store as `GB` instead of reporting that the brand has no store there.
- **Sweep All Categories reaches more of the catalogue** — the sweep addresses each category by its own number, so categories that were previously skipped over are now included.

### \[1.0.0] — 2026-08-13

#### 🎉 First build

- **Seven brands, one schema** — Zara, Zara Home, Bershka, Massimo Dutti, Stradivarius, Oysho and Pull\&Bear return the same 58 columns, so a Bershka row and a Zara row can sit in the same table and mean the same thing
- **200+ country stores** — every market the brands operate, each priced in its own currency with the market's own divisor applied, so a Japanese run returns usable yen rather than raw numbers
- **Per-SKU sizes, stock and GTIN barcodes** — colour by size with buyable and back-soon flags, barcodes on the six shared-catalogue brands, and the physical dimensions, gram weight and country of origin of each SKU
- **Promotion windows** — per-SKU price start and end dates, so a price row tells you the date a markdown ends rather than only that one is running
- **Drop detection on Zara** — `first_visible_date` records when a product entered the catalogue; diff two runs and you have a new-arrivals feed
- **Three ways in** — sweep every category, sweep the sitemap for catalogue items no category lists, or paste your own product URLs and ids
- **Filters that match the site** — facet filters by colour, size, category and discount, plus price floor and ceiling in the market's own major units
- **A bill you can audit** — the `source` column on every row names the call that produced it, so an invoice reconciles line by line against the dataset

#### 💎 User benefits

- Sizes and per-SKU stock arrive in the cheap category sweep on six of the seven brands, with no second request and no second charge
- Prices come back with a currency and a correct divisor, so a multi-market export is comparable without out-of-band knowledge
- GTIN barcodes give you a join key to any other retail dataset instead of fuzzy name matching
- Duplicates are removed before anything is charged, so the product count you set is the product count you pay for
- The run summary separates products that no longer exist from fetches that genuinely failed, so you know whether a short dataset needs a re-run

#### 🎯 Use cases

- Pricing analysts comparing the same garment across six markets in local currency
- Retail analysts tracking markdown depth and the exact date each promotion ends
- Resellers checking which sizes are genuinely in stock before sourcing
- Marketplace teams matching Inditex products to their own inventory on GTIN barcodes
- Trend researchers diffing weekly runs to catch new arrivals the day they appear
- Category managers measuring range width, carryover versus seasonal split, and price architecture across sibling brands

#### 📋 Known data limits, stated up front

- No Inditex brand publishes ratings or reviews through these catalogues, so no review field exists on any of the eight Actors
- None of these sites offers a server-side keyword search; scrape a category or the whole tree and filter the results yourself
- Massimo Dutti publishes no GTIN barcodes and fills descriptions on roughly 7% of products, which is why that Actor is priced below its sibling brands
- Bershka fills descriptions on roughly 30% of products
- `first_visible_date` is filled on Zara detail records only — an overview-only run returns it as null
- `traceability` and `certified_materials` are present in the schema and frequently empty