Give it a Feefo review URL - or just the merchant slug that sits at the end of it - and get that merchant's service reviews back as rows: the star rating, the headline, the full review text, who wrote it, where they say they are, the date and a permalink. Newest first by default, or oldest, or most helpful. No Feefo account, no residential proxy, no parser to keep alive.
The input is the merchant, not the review - you name the companies whose feedback you want and the job collects what their customers wrote.
One row per review, each tagged with the query it came from - so a run across a dozen merchants still reconciles back to your input list.
Paste the full feefo.com review URL, or just the merchant identifier that ends it. Both accept a file - CSV, XLSX, TXT or Parquet - and both come back the same: in a run that submitted one merchant each way, the two blocks of rows matched field for field, permalink included.
This one reads Feefo's public reviews API through our own rotating pool, so it runs on the free pool - unlike the scrapers that insist on a residential IP. Your address is never used, and there is no proxy bill to add to the row cost.
Newest, oldest or most helpful, plus a cap on the rows per query - all chosen before the job starts. Credits go on the slice you actually want rather than on an export you delete most of.
Each row carries the review as Feefo publishes it: the star rating, the headline, the full text, the reviewer's name and stated location, the posting timestamp, the helpful-vote count, and a link straight back to the review it came from.
Text comes back verbatim - line breaks and all, with no preprocessing applied - so downstream sentiment work reads the customer's own wording rather than ours. The column list below is the header row of real runs rather than a specification, and the honest part of it is which columns are often empty, because a Feefo reviewer can leave a rating and a paragraph and skip the headline, the name and the location entirely.
Taken from the header row of real runs. Our testing covered a single merchant, so read the optional-field notes as what that company's reviewers chose to fill in, not as a rate the scraper achieves.
/reviews/ in the URL, such as m-c-fire-protection. A slug, not a trading name: it is lower-case and hyphenated, and it is what you would paste back in as a query.GBR, London, CORNWALL, Cheltenham. and Mullion Cove, Cornwall side by side, so treat it as free text rather than as a place. Often blank.2026-01-23T11:11:33.205Z, not a plain date. Filled on every row.Three things to know before you write code against this. Every value is a string, including rating and helpful_votes - parse before you sort, or 10 will come before 2. The permalink is not built from the query you sent: review_url writes the Feefo locale with an underscore (/en_GB/) where the input URL uses a hyphen (/en-GB/), and where title is empty the URL simply ends customer-review-, so match on the review id rather than on the whole string. And there is no rank column in any format - the JSON objects carry the same ten keys as the workbook header, in the same order the reviews were returned under your chosen sort.
Sort order and a per-query limit decide what a run costs and what lands in the file. Both are chosen before the job starts, alongside how you hand over the list of merchants.
A few examples of how teams use verified service reviews to answer a question they actually have.
Pull your merchant page's full history and read the one- and two-star text together rather than one at a time. Recurring complaints surface as repeated language - far more actionable for an operations team than a score that moved a tenth of a point.
Take the Feefo pages of everyone you compete with and export the lot in one run. Ratings, dates and verbatim text in a single file let you see where a rival is genuinely better and where they are simply louder.
Feefo reviews are collected from confirmed customers, which makes them a useful second opinion on a contractor or supplier. The timestamps let you weight the last twelve months over a good year in 2016.
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. Sorting, per-query limits, file upload and every export format included.
Roughly $2 per 1,000 reviews. 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 continuous monitoring or very large historical pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.The same company's reputation on the other big European review platform. Title, full text, star rating, date and the author's reviewing history - worth having next to the Feefo rows before you conclude anything from one score.
What people wrote where most of them actually look. Reviews per location rather than per merchant, so a chain's Feefo score can finally be read against the branch that earned it.
Short answer: yes for the public review content - but this export does carry reviewer names, and that is worth planning for.
Reviews on Feefo are published to be read. The rating, the headline, the text, the date and the reviewer's display name are shown to anyone who opens a merchant's review page, signed in or not, and this scraper reads them through Feefo's own public reviews endpoint. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login or a paywall.
One point deserves stating plainly rather than burying. Unlike an employer-review export, this one does identify people: customer carries a display name on most rows and location carries whatever place the reviewer typed. That makes the file personal data under the GDPR and comparable regimes. Reading the corpus in aggregate, or quoting a review the way anyone reading the page could, is ordinary use. Building a profile of a named individual across merchants is not, and it is not what this is for.
Feefo's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the platform, check it. 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.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Feefo Reviews Scraper turns a list of merchants into review data. You submit Feefo review URLs or bare merchant identifiers - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - choose how the reviews should be sorted, cap the rows per query if you want to, and download what verified customers wrote as a clean CSV, Excel or JSON file.
Each row carries the review as Feefo publishes it: the star rating from one to five, the headline, the full review body, the reviewer's display name and stated location, the posting time as an ISO 8601 UTC timestamp, the helpful-vote count, and a permalink back to the review itself. Text is preserved verbatim, line breaks included, which matters if the next step is a sentiment model - the analysis then reads the customer's wording rather than ours.
Operations and CX teams pull their own merchant history and read the low-star text as a corpus, where recurring complaints appear as repeated language instead of a score that drifted. Marketing teams export a set of competitors in one run and compare ratings and verbatim comments in a single file. Procurement teams check a supplier's recent reviews before signing, using the timestamps to weight the last twelve months over a good year a decade ago.
Two properties are worth knowing before you start. Every value comes back as a string, including the rating and the helpful-vote count, so parse before you sort. And unlike an employer-review export, this one identifies people - the reviewer's name is present on most rows - which makes it personal data under the GDPR and comparable regimes. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.