Every unieuro.it address ends in a pid, every record carries an EAN-13, and the crossed out figure beside the price is the lowest Unieuro charged in the previous 30 days.
ScrapeIt runs this as a managed service. We build the Unieuro scraper, host it, watch it and repair it when the site shifts, and you receive files instead of code. Anti-bot handling, proxy rotation and CAPTCHA solving are part of the service, and so is scheduling that respects the narrow overnight window the Unieuro robots file asks crawlers to use and the paths it puts off limits.
Delivery is CSV, JSON, XLSX, a Google Sheet, a direct database load or a Unieuro API endpoint you call yourself. Runs go weekly for catalogue sweeps, daily for pricing, hourly for a watchlist. We stay on public catalogue pages, never touch cart, checkout or account areas, and leave review text and reviewer names out by default because those are personal data; the aggregate rating and the review count come through as ordinary fields.
Every unieuro.it address ends the same way: a name slug, then the literal string pid, then the product code, as in pidWHIFFSP85IT. That code is not a serial number: a short supplier prefix plus the manufacturer's own model reference, so WHI, SAM, SNG or BKO in front of a model string names the vendor before the record is opened. It is the join key for the whole dataset, and a Unieuro scraper keys on it rather than on the slug, which is rewritten when the marketing copy changes.
One record hands over:
Fields appear only where they apply. A washing machine brings the energy fiche and an installation service; a handset brings a trade-in flag and damage cover instead. None of it arrives as schema markup: the pages carry breadcrumb and site level structured data only, so anyone who wants to extract Unieuro product data reads the rendered application state instead of a tidy Product block.
Unieuro is an omnichannel retailer and the data shows it. Beside the home delivery window every record carries a second window for Clicca e Ritira (click and collect) with its own earliest and latest date and its own cost line, plus flags marking an item as home delivery only or pickup only. What the site labels Disponibile (available) refers to the central warehouse: the wording says the item is held there and can be bought immediately. Presence in a named shop is checked further into the basket, so a standard feed carries national availability and per store checks are a separate job.
The listing layer holds fields the detail record does not. Category pages run on a hosted search index whose facets change by category: washing machines expose classe energetica (energy class), drum capacity, load type and cabinet dimensions, while smartphones expose storage, RAM, screen size and operating system version. Each card also carries inVolantino, the flag saying the item appears in the printed volantino (flyer), a bestseller rank, an availability facet and the promotion badge. A standing outlet section holds several hundred discounted units, and results mix in placements marked Sponsorizzato (sponsored), which a clean feed labels rather than merges.
Two quirks matter before you match anything. Carrier badged handsets appear under codes prefixed TIM, Wind Tre or Vodafone while the manufacturer field still reads Samsung, so brand counts need care. And enriched brand content can quote dimensions in centimetres that disagree with the millimetre spec block on the same record, so we return both and label the source.
Unieuro is the Italian market leader in consumer electronics and home appliances, and it sells through a store network and through unieuro.it at the same time. The company works from Forli, keeps a central logistics platform at Piacenza with support hubs at Carini in Sicily and Colleferro, and counts 512 direct and affiliated outlets and roughly two million active Unieuro Club members.
Ownership matters to anyone buying Unieuro data. The chain grew inside the Expert buying group and traded under the Marco Polo Expert banner, then left it and folded every store into a single Unieuro sign, listed on the Italian exchange, and has since been taken into the Fnac Darty group. The legal line at the bottom of every unieuro.it page now states that Unieuro S.p.A. is under the direction and coordination of Fnac Darty S.A. The practical consequence for a buyer of data is that Italian shelf prices now sit in the same group as the Fnac and Darty catalogues, and because Unieuro publishes EAN codes you can put an Italian price beside a French one on the same identifier instead of guessing at model names.
The storefront is a single Italian site. There is no country switcher, no second language and no third party marketplace: everything listed is sold and shipped by Unieuro itself, so one code means one seller and one price. That makes Unieuro scraping cleaner than a marketplace crawl, and it makes the price line worth trusting.
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Brands and distributors selling into Italy use Unieuro price data to see how their list price survives contact with the shelf. The gap between Prezzo consigliato and the live figure regularly runs to forty or fifty percent on appliances and it moves week by week, so a scheduled run tells a category manager what the street price actually is before the next negotiation rather than after it.
Competing retailers buy it for the plainer reason: to price against the largest Italian chain. Because unieuro.it publishes an EAN-13 on both the record and the listing card, the feed joins straight onto an internal catalogue and onto any other European retailer using the same identifier.
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If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.
E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.
Amazon provides valuable information gathered in one place: products, reviews, ratings, exclusive offers, news, etc. So scraping data from Amazon will help solve the problems of the time-consuming process of extracting data from e-commerce.
ScrapeIt is a managed web scraping agency, not a toolkit you have to operate. We already run retail crawls across Europe, MediaMarkt in Germany, Currys in the United Kingdom and Fnac in France among them, so an Italian feed lands in the same shape as the rest of your price monitoring stack and joins on the same EAN keys.
You get a named engineer, a sample file before anything is signed and a fixed monthly figure. Tell us the categories, the fields and the cadence, and we will send a sample drawn from unieuro.it so you can test the mapping against your own catalogue first.
No. There is no published developer programme, no partner product feed and no documented Unieuro API for outside use. The storefront talks to an internal commerce service that requires a token issued to the site itself, and the robots file puts that path off limits to crawlers. What you can have is an API we run for you: we crawl the public catalogue, normalise the fields and expose them as a REST endpoint or a scheduled file drop, so your systems call one stable interface even when Unieuro changes its own.
Prices carry their own start and end dates, and promotions typically run in weekly blocks that close late on a Sunday, so a daily run captures every change with room to spare. For a watchlist of a few thousand codes we go hourly. Unieuro also states that online prices can differ from the prices in its shops and can move up or down daily, so a nightly full sweep plus an hourly watchlist is the usual arrangement for competitor price tracking.
Yes. An EAN-13 sits in the product record and is repeated on every listing card, alongside the Unieuro code, the manufacturer name and the manufacturer's own article reference. That gives you three keys for EAN matching. Two cautions: carrier badged phones are listed under codes prefixed with an operator name even though the manufacturer field names the maker, and a product can sit under more than one category node, so we deduplicate on the code before delivery.
Either. The public product sitemap listed about 20,800 items in September 2026, spread over roughly 400 category nodes three levels deep. Paging inside any one node stops after a thousand results, so a broad node holding fifteen hundred or two thousand items cannot be walked in one pass. We cover a category by descending to its leaf nodes and, where a leaf is still too large, by slicing on brand and price band, then reconciling the result against the sitemap.
We collect public catalogue pages only. No account is created, no login is used, and the cart, checkout and account paths the robots file excludes are never touched. We gather factual product data such as codes, prices, specifications and availability rather than reproducing editorial copy or images wholesale. Customer review text and reviewer names are personal data and stay out by default; if you need sentiment we deliver the aggregate rating and count instead.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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