Give it a Feedback Company reviews URL - or just the numeric customer id - and get that merchant's reviews back as rows: the score and the scale it was given on, whether the customer would recommend the shop, the review text, a real timestamp, and the merchant's reply where there is one. Sorted newest-first by default, or however you choose.
The input is the shop, not the review - you name the merchants you want feedback on 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.
Every row carries the denominator alongside the number, so a score is never ambiguous once it leaves the page. That matters the moment you put this dataset next to a five-star platform and need the two to mean the same thing.
The publication date comes back as a full date and time, so a review can be placed on a timeline without arithmetic against your run date. Sorting, windowing and cohort work all follow directly from it.
Where the merchant replied, the reply and its own timestamp come back in the same row as the review. Whether a shop answers its critics, and how quickly, is visible without a second pass.
Each row carries the review as Feedback Company publishes it: who wrote it, the score and the scale, whether they would recommend the shop, the text, when it was published, and the merchant's response where one exists.
Text comes back verbatim, 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 it carries one parsing note worth reading before you join anything on the author column.
Taken from the header row of real runs, identical across them and identical to the column list the platform ships.
score was given on, carried on the row rather than left implied. Every row seen used a ten-point scale.yes and no. No other value appeared, and the field was never blank.YYYY-MM-DD HH:MM:SS. A real timestamp - no arithmetic against your run date required.response is.Three things to know before you write code against this. author is the one that will bite: some values carry trailing whitespace, so trim before you group, join or deduplicate on it. score is meaningless without max_score - the pair is what makes the number portable, and reading the first without the second is how a ten-point score quietly becomes a five-star one. And position exists only in the JSON, so a CSV or XLSX export has ten columns rather than eleven. The merchant response and its date are optional and usually absent; treat their presence as the signal, not their absence as a failure.
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 merchant review data to answer a question they actually have.
Pull your merchant's history and read the one- and two-out-of-ten text together rather than one at a time. Recurring complaints surface as repeated language, which is far more actionable than an average that moved a tenth of a point.
The response column and its timestamp sit next to the review they answer, so you can see which complaints got a reply, how long it took, and which were left alone. That is a service-quality metric you cannot get from a star average.
Export a set of merchants in one category and compare scores knowing the denominator travels with each row. The recommendation column adds a second, blunter signal alongside the number.
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.Another verified-review platform with the same shape of job - merchant in, customer reviews out. Useful when a brand collects feedback in more than one place.
The largest of the review platforms, and the one most likely to hold the same merchants. Title, full text, star rating, date and the author's reviewing history.
Short answer: yes for the public review content - with one field worth thinking about before you scale.
Reviews on Feedback Company are published to be read. The score, the recommendation, the text, the publication date and the merchant's reply are shown to anyone who opens the merchant's reviews page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login or a paywall.
The field to think about is author, which carries the display name the reviewer chose. That is personal data even when it is only a first name, and Feedback Company operates largely in the Netherlands and the wider EU - so the GDPR applies to what you do with it downstream, not to the fact that it was published. If your analysis does not need the name, dropping the column at ingest is the cleanest way to stay out of that question entirely.
Feedback Company'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 Feedback Company Reviews Scraper turns a list of merchants into review data. You submit Feedback Company reviews URLs or bare numeric customer ids - 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 customers wrote as a clean CSV, Excel or JSON file.
Each row carries the review as the platform publishes it: the reviewer's display name, the score and the scale that score was given on, whether the customer would recommend the shop, the text verbatim, a full publication timestamp, and the merchant's reply with its own date where one exists. Carrying the scale on the row is what keeps a score portable - the number means nothing on its own once it sits in a table beside a five-star platform.
CX teams pull their own merchant's history and read the low-scoring text as a corpus, where recurring complaints appear as repeated language instead of as an average that drifted. Support teams use the response column and its timestamp to audit which criticisms got a reply and how quickly. Strategy teams export a set of merchants in one category and compare them knowing every score arrives with its denominator.
One parsing note is worth carrying into your first run: the author column can contain trailing whitespace, so trim it before grouping, joining or deduplicating on the name. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.