Paste a Feedback Company reviews URL and get the profile behind it back as a row: the company name and its id, the average score and the scale it sits on, how many reviews it has, the website and domain, and the contact details and address the profile publishes. One row per company.
The input is the review page you would otherwise read. Find the company on Feedback Company, copy the address, hand it over.
One row per company, tagged with the URL it came from and numbered in the order the run returned it.
Feedback Company rates out of ten, and the export says so: rating comes with best_rating beside it. You never have to guess whether an 8.7 is excellent or mediocre, and a mixed table of review platforms stays honest.
Website, bare domain, email, telephone and a split address arrive as their own columns rather than as a block of text to regex. The domain column in particular is what you join on when you are matching this against a CRM.
Paste a column of profile URLs or upload the file you already have, and every one comes back as a row in the same shape. Re-running the same list later is how you watch an average score move.
What the profile publishes about the company: who they are, how they score, and how to reach them.
One thing about that number belongs before the table rather than after it. Ten of the twenty-six columns duplicate another column - the profile URL comes back three times, and the company name, its id, the review count, the phone number and all four address parts each arrive twice under a second name. And one column, description, is empty in every export we have checked. Counting honestly, that is fifteen columns carrying distinct information, not twenty-six.
The duplication has a visible cause. The second block of columns is a schema.org Organization projection: type is literally Organization, address_address_locality is address.addressLocality flattened, address_postal_code is address.postalCode. It sits alongside the fields already parsed from the page, which is why so much of it repeats. Four of those columns do carry something new - domain, email, type and the perpetually empty description.
The column set, the duplicate pairs and the ten-point scale are properties of the export and hold regardless of what you submit. Whether an optional field like telephone carries a value is a different matter - that depends on what each company chose to publish on its own profile.
The export's header row, in order. Where a field is marked optional below, that describes what a company chose to publish on its profile - not the scrape.
best_rating and never against a five-star scale.address is not dependable.website, without the scheme or a trailing slash. This is the column to join on.Twenty-six columns, but ten are copies and one never fills. profile_url, listing_url, name, profile_id, reviews, phone, address_address_country, address_address_locality, address_postal_code and address_street_address each repeat an earlier column and carried the same value as it, and description came back empty throughout - which leaves fifteen carrying distinct information. Everything is a string except position, including rating, review_count and both ids, so cast before you sort or a 9 will sit above a 10. And treat the address block with care: country is not always the country of the address - a profile whose address is in Manchester came back as NL - and on that same profile the UK postcode arrived split, the outward half left at the end of address and only the inward half in postcode.
The input is a Feedback Company reviews URL. Paste them one per line, or hand over the file you already keep the list in - either way you get the same twenty-six columns back, one row per company.
A few examples of what a review profile is worth once it is a row rather than a page.
Take the profile URLs of everyone you compete with in the Benelux and pull them in one run. Average score, the scale it sits on and the review count land side by side, so the comparison is a sort rather than an afternoon of tabs.
The website, bare domain, email and telephone come parsed. Join on domain against the CRM and you learn which of these companies you already know - and which you do not.
Re-run the same list of URLs on a schedule and keep the rows. The average score and review count are the two numbers that drift, and a stored history is the only way to see a slide while it is still small.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Per-query limits, file upload and every export format included.
Roughly $2 per 1,000 companies. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for tracking a large list of companies on a schedule. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.One platform's average is a number without a second opinion. These supply it.
The same companies on the platform most of Europe checks first. A profile that scores well on one and poorly on the other is the interesting case, and you only see it with both.
The other Dutch review source worth holding next to this one. Different companies, same question - what the customers actually said.
The reviews a customer sees before they ever reach a review platform. Pair it with this when you want the public picture rather than the curated one.
Short answer: these profile pages are public - but the export contains business contact details, and in the EU that carries obligations you should know about.
Everything collected here is shown on a public Feedback Company profile to any visitor, signed in or not: the company name, its average score and the scale, the review count, the website, and the address and contact details the company itself chose to publish there. No login, no paywall, nothing behind an account.
Be deliberate about the contact columns. email and telephone are published by the company as business contact points, not scraped from individuals - but Feedback Company is a Dutch platform and the companies on it are largely EU-based, so if you load them into a CRM or use them for outreach you are processing data under the GDPR and the Dutch implementation of it. Have a lawful basis, say where the data came from, and honour opt-outs. That is a requirement about what you do next, not about the collection.
There are no reviewer names, review texts or personal details in this export - those belong to a different service and are not collected here. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
best_rating alongside rating for exactly this reason - read the two together, and if you are merging this with a five-star source, normalise before you average or the Feedback Company rows will dominate.Your first 500 rows are free - no card, no subscription. After that it is $0.002 per row, flat.
Feedback Company is a Dutch review platform, and every company on it has a public profile page carrying an average score, a review count and the contact details that company chose to publish. The Feedback Company Profiles Scraper turns that page into a row. You submit reviews URLs - one per line, or as a CSV, XLSX, TXT or Parquet upload - and each one comes back as a record with the company name, Feedback Company's own id for it, the average score and the scale that score sits on, the review count, the website and its bare domain, and an email, telephone and split address where the profile publishes them.
The score is the part most people get wrong on the way in. Feedback Company rates out of ten rather than five, so a rating of 8.7 is a strong result and not a broken one. The export gives you best_rating next to rating so the scale travels with the number - which matters the moment you put these rows in the same table as a five-star source and take an average.
Two honest notes before you start, because a column count is not the same as a field count. Ten of the twenty-six columns duplicate another column exactly - the profile URL appears three times, and the company name, its id, the review count, the phone number and all four address parts each appear twice - and description came back empty throughout, which leaves fifteen carrying distinct information. That duplication is a schema.org Organization projection sitting alongside the fields parsed from the page, and the field names show it plainly. And treat the address block with care: country is not always the country of the address, and on a UK profile the postcode arrived split between two columns.
Everything arrives as a string except position, so cast rating and review_count before you sort or compare them. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.