Same operator, same platform, two countries. That is a controlled comparison, and controlled comparisons are rare in retail data.
ScrapeIt runs the collection as a managed service. You name the categories or brands and the markets; we build the pipeline with matched category paths, market and currency as fields, unit prices computed, and hand back CSV, JSON, Excel or a push into your warehouse.
Where both markets are in scope we deliver them on one schema with the structural alignment preserved, which is the entire reason to collect the pair together.
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 forint, unit price per kilogram or litre, availability, promotional price where running, and the address.
Market is a field on every row, so a client collecting both countries can aggregate deliberately rather than by accident. Forint and koruna are different currencies moving independently and nothing should be averaged across them without a decision.
Category path is preserved as published, which is what makes the two markets align. Normalising it to some internal taxonomy destroys the property that made the pair worth collecting together.
Variable weight items are flagged and unit prices computed, exactly as on the sister site, because a comparison is only as good as its least careful side.
Every row carries the collection timestamp and the currency.
Two-market price comparison is the distinctive product and it should be built on matched categories with currency kept separate, leaving conversion to the client so the raw local prices stay checkable.
Local assortment analysis is the other half and is often more interesting: which products exist in one market and not the other, and how deep local brands run against international ones. That is a set difference once both sides are collected on the same schema.
Stockout and promotion series work exactly as on the sister site and need the same repeat collection to exist at all.
Limits: this is one retailer in each market, not the market. Hungarian grocery is mostly physical retail with different pricing, and we describe the dataset as online grocery rather than as national prices.
Kifli is the Hungarian online grocer of the Rohlik Group, running the same own-warehouse model and, visibly, the same platform as its Czech sister site - the category address structure and the crawl rules are identical.
That shared platform is what makes this source worth a page of its own rather than a footnote. Comparing grocery prices between countries normally means reconciling two retailers with different category trees, different product identifiers and different conventions, and most of the budget goes into the reconciliation.
Here the structures match. A client collecting both markets gets data that lines up on category path and field shape, which turns cross-border comparison from a project into a query.
The catalogue is Hungarian and its assortment is not a translation of the Czech one - local brands, local products and local pricing differ, which is exactly what makes the comparison interesting rather than redundant.
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Cross-border retail comparison is a standard brief and it usually delivers less than it promised, because the matching eats the value.
Two independent retailers organise their categories differently, name products differently and use different pack conventions. Building a comparison means building a mapping, the mapping is probabilistic, and every conclusion inherits its error rate. Clients rarely see how much of the finding is really the mapping.
A shared platform removes that layer for the structural fields. Category paths correspond, field shapes match, and the remaining differences are genuine market differences rather than artefacts of two different retail systems.
The product level still needs care - a Hungarian local brand has no Czech counterpart and should not be forced to have one - but the comparison rests on a much smaller inference than usual, and the places where inference is needed are visible rather than buried.
The second reason is that Hungary is under-covered in retail data generally. Consistent structured grocery pricing for the market is not easy to assemble, and this is one of the cleaner routes to it.
The third is the same own-inventory point as the sister site: availability is the retailer's own position rather than a partner's estimate.
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 whether the two markets' structures stay aligned as the platform evolves, and tells you when they diverge rather than quietly patching over it.
You see a sample first, in your format, over the categories you actually track, with both markets on one schema so you can test the comparison on real products.
Because the shared platform means category paths and field shapes correspond, so cross-border comparison rests on a much smaller inference than comparing two independent retailers. That mapping layer is normally where most of the budget and most of the error go.
No, and that is what makes the comparison worth having. Local brands, local products and local pricing differ. The structures match; the contents do not, and the difference is the finding.
No - currency and market stay on the row and conversion is left to you. A converted price with the original lost is a number nobody downstream can check, and these are two independently moving currencies.
No. Category path is preserved as published, because that is precisely the property that makes the two markets align. Normalising it to an internal taxonomy destroys the reason to collect the pair together.
It is one online grocer. Hungarian grocery is mostly physical retail with different pricing, so we describe the dataset as online grocery rather than as national prices - though for online it is one of the cleaner sources available.
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.
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