This shop owns its warehouse, so the stock number means something. On a marketplace it is a guess; here it is inventory.
ScrapeIt runs the collection as a managed service. You name the categories or brands; we build the pipeline with unit prices computed, variable-weight items flagged, regular and promotional prices separated, and hand back CSV, JSON, Excel or a push into your warehouse.
Where stockouts or promotions matter we run repeat collection and keep every observation, because an item that came back into stock leaves no record of having been out.
We collect public catalogue data only, honour the crawl rules and pace requests. No customer data, no order information, no account access. Terms restrict commercial reuse, so the dataset is for analysis rather than republication, and your counsel should see the use case before the project starts.
Product records carry the product name, brand, category path, pack size and unit, price in koruna, unit price per kilogram or litre, availability state, promotional price where running, and the address.
Unit price is computed rather than trusted or omitted. Grocery pack sizes vary constantly and comparing pack prices compares packaging, so price per kilogram or litre travels on every row alongside the raw price.
Variable weight items are flagged as such. A piece of cheese sold at an approximate weight is a different kind of row from a sealed 500 gram pack, and treating them identically produces unit prices that look precise and are not.
Promotional and regular price are separate fields with the promotion type where stated, since a series that records only the price paid cannot distinguish a discount from a price change.
Every row carries the collection timestamp and the currency.
Stockout series are the strongest output here and need repeat collection with every observation kept. Which products go unavailable, how often and for how long is a supplier-facing metric that cannot be reconstructed from a snapshot.
Own-label share by category is a straightforward read on retailer strategy once brand is collected properly, and it is one of the questions brand manufacturers most often bring to grocery data.
Promotion analysis needs regular and promotional price kept apart, plus enough collection frequency to catch promotions that run for days rather than weeks.
Assortment change tracking shows products entering and leaving the range, which for a supplier is an early signal about a listing decision and cannot be seen from a current catalogue at all.
Rohlik is the largest online grocer in the Czech Republic, running its own warehouses and delivery fleet rather than acting as a marketplace over existing shops.
That distinction changes what the data means. On a delivery marketplace, availability reflects whatever a partner shop told the platform, with a lag and an error rate. Here the operator holds the stock, so availability and stock indications are its own inventory position rather than a third party's estimate. For anyone studying assortment or out-of-stock behaviour that is a materially better signal.
The second structural feature is fresh produce. A grocer selling loose fruit, vegetables, meat and cheese has items priced per kilogram and sold by approximate piece, and the relationship between the shelf price, the pack size and what a customer actually pays is not a single number.
Category pages respond directly with substantial content. Crawl rules are published and disallow a single path, leaving the catalogue open.
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Out-of-stock analysis is one of the most requested things in grocery data and one of the least reliable, because on most sources the availability signal is second hand.
A marketplace reports what a partner shop's system said, at whatever refresh interval that integration runs. Errors are common, the lag is invisible, and an analysis of stockouts built on it is measuring integration quality as much as inventory.
An own-inventory retailer publishes its own position. When it says an item is unavailable, that is the operator's own state, which makes availability a field worth building a series on. For suppliers watching whether their products are actually on virtual shelves, that distinction is the whole point.
The second reason is assortment analysis. A retailer that chooses its own range makes visible decisions - which brands it carries, how deep each category goes, where own-label sits against branded - and those are strategy rather than marketplace accident.
The third is the Czech market specifically, which is well developed in online grocery relative to its size and is frequently used as a reference point for the region. It is also one country: regional conclusions need the neighbouring markets too.
The fourth is fresh produce pricing, which is where naive pipelines produce their most confident errors.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it as category structures change, and repairs it before a stockout series develops a gap that cannot be backfilled.
You see a sample first, in your format, over the categories you actually track, with unit prices computed and variable-weight items flagged so you can judge the handling on real produce.
Because the operator holds the stock. A marketplace reports what a partner shop's system said, with an invisible lag and a real error rate - an analysis built on that measures integration quality as much as inventory. Here the signal is the retailer's own position.
Variable-weight items are flagged as such. A piece of cheese sold at approximate weight is a different row from a sealed 500 gram pack, and treating them identically produces unit prices that look precise and are not.
Because pack sizes vary constantly and comparing pack prices compares packaging. Price per kilogram or litre travels on every row next to the raw price, so the comparison is about the product rather than the box.
Yes, because regular and promotional price are separate fields with the promotion type where stated. A series recording only the price paid cannot distinguish the two, which makes any promotional analysis built on it unreliable.
It represents the largest online grocer in a market that is well developed online for its size. Physical retail is a different picture, and regional conclusions need the neighbouring markets collected too.
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.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
