Upload your product list, scrape the competitor shops, and Scrapewise links each competitor row to your product. A shared EAN or ID wins first. Rows without one are matched on text columns such as title, brand and size, then on product pictures. Unsure pairs wait for your review.
Many shops do not publish a barcode, and some publish the code of a different pack. Matching on EAN alone finds the easy part of the catalogue and quietly misses the rest.
"Kaerahelbed 500g", "Oat flakes whole grain 0.5 kg" and "Rolled oats 500 g bag" are the same product. A VLOOKUP on the title finds none of them.
A 500 g bag and a 1 kg bag share the brand, the title and often the picture. Compared as one product, your price gap is wrong before anyone reads it.
A spreadsheet formula gives a match or nothing. It never says "I am not sure", so bad pairs reach the pricing decision without a second look.
Scrape, clean, match and a ready competitor comparison, in one place
Ready data APIs for marketplaces and search, plus AI scrapers for any other shop
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Matching is step 3 of 4. It only works well when the rows are scraped and cleaned first, and it is only useful when it ends in a comparison you can act on.
Point Scrapewise at the competitor shops: a ready data API for marketplaces and search, or an AI scraper for any other site. Scrape a whole category, a link list or a sitemap, on demand or on a schedule.
Group rules turn raw text into comparable numbers: the price as a number, the pack size in grams, price per kg or per litre, and other currencies converted to EUR. Duplicate product pages in a run are kept once.
Upload your product list as the master file and connect the columns that mean the same thing. Same EAN or ID matches first, then text columns such as title, brand and size, then product pictures. Unsure pairs go to review.
You get your product next to each matched competitor row, with price, price per unit and the reason it matched. Export it as CSV or Excel, read it by REST API, or hand it to an AI agent over MCP.
Each signal has a blind spot on its own. The matcher uses the strongest one a row actually has, and every matched row shows which one it was.
When both rows carry the same EAN or ID, that match wins over any text match. It is the most reliable signal, but most shops do not publish it, so it never covers the whole list.
You choose which of your columns connect to which competitor columns. The matcher scores the pair on those columns and decides: matched, to review or not matched. Size and variant in the text keep a 500 g bag apart from a 1 kg bag.
For rows where the text is not enough, the product pictures are compared. Pictures catch translated or rewritten titles. They cannot tell two pack sizes with the same photo apart, so a size check in the text still matters.
The matcher does not hide its doubt. Each competitor row lands in one of three groups, and you can see why.
The pair is linked to your product. A "Why matched" label shows the basis: ID, code, text, picture or approved in review. A pair you approved by hand can be unmatched again, and it goes back to review.
The evidence points one way but is not strong enough. The two rows are shown side by side, and a person approves or rejects the pair before it reaches a price decision.
Nothing is close enough. These rows stay visible in their own tab with a count, so nothing disappears from the comparison without you seeing it.
You bring your own product list and the shops to compare against. You get competitor rows linked to your products.
CSV or Excel upload: your SKU, title, brand, size and any EAN or model code you have.
OAT-500, Rolled oat flakes 500 g, 4740000000017Any shop or marketplace: a category page, a search, a link list or a sitemap.
shop-a.exampleshop-b.examplemarketplace.exampleUsed first when both sides have it. Matching still works without it.
4740000000017How often the competitor shops are scraped again.
One row per matched competitor product, 8 columns each
CSV or Excel export, REST API, or MCP for AI agents.
Example rows in the style of a grocery price comparison (illustrative data, not a customer's). Before: what the shops show. After: what you get once the rows are cleaned and matched to your list.
| Your product (master) | Competitor row as scraped (before) | Matched and cleaned (after) | Why matched |
|---|---|---|---|
| OAT-500 · Rolled oat flakes 500 g · EAN 4740000000017 | Shop A: "KAERAHELBED 500G -20%!" · 1,29 € · EAN 4740000000017 | Matched · EUR 1.29 · EUR 2.58 per kg | ID · EAN |
| OAT-500 · Rolled oat flakes 500 g | Shop B: "Oat flakes whole grain 0.5 kg" · 1.19 EUR · no EAN | Matched · EUR 1.19 · EUR 2.38 per kg | Text |
| OAT-500 · Rolled oat flakes 500 g | Shop C: "Oat flakes" · 2.09 EUR · no size, no EAN | To review · size unknown, no per-kg price | Text, unsure |
| YOG-400 · Greek yogurt 10% 400 g | Shop A: "Kreeka jogurt 10% 400g" · 2,15 € | Matched · EUR 2.15 · EUR 5.38 per kg | Text + picture |
| OAT-500 · Rolled oat flakes 500 g | Shop B: "Oat flakes 1 kg family pack" · 2.19 EUR | Not matched · other pack size · EUR 2.19 per kg | – |
Competitor rows arrive linked to your products, cleaned to the same units, ready for a price gap per product.
Unsure pairs wait in review and every matched row says why it matched, so a wrong pair is easy to find.
You upload your own product file once. Competitor rows are linked to it, so the comparison always follows your catalogue.
Upload your product list as the master file, scrape or upload the second list, and connect the columns that mean the same thing, such as EAN to EAN and title to title. Scrapewise links each row to your product, shows why it matched, and sends unsure pairs to review.
Connect your EAN, UPC or SKU column to the same code on the competitor side. When both rows carry the same code, that match wins over any text match. Rows without a code are not dropped: they are matched on title, brand and size, then on product pictures.
Yes. Most shops publish no barcode, so the matcher scores text columns you choose, such as title, brand, size and variant, and compares product pictures where the text is not enough. Each pair ends as matched, to review or not matched, so you check the unsure ones yourself.
Upload the CSV with your titles and SKUs as the master file, then schedule the competitor scrapers. Each run is cleaned by the same group rules and matched to the same master, so the mapping is refreshed without manual work. Export the result as CSV, Excel or by REST API.
Start with your own product list and one competitor shop. Every new account gets 5 free requests, and after that you pay as you go from a prepaid balance. The same scrape, clean and match flow runs bebo.ee, a grocery price comparison built on Scrapewise. bebo.ee compares 12 grocery e-shops: more than 130,000 products, and about 36,000 of them are matched across shops so their prices can be compared.
How matching works in Scrapewise, what you need to start, and what it does not do.
Product matching software finds the rows in two product lists that are the same physical product, even when the title, SKU and pictures differ. It is the step that makes a competitor price comparison possible. Scrapewise matches on the same EAN or ID first, then on the text columns you choose, then on product pictures, and keeps unsure pairs for review.