Paste Etsy listing review URLs, one per line, and the review section comes back as a table - who wrote it, the rating, the headline, the review text and the date, alongside the listing's own overall rating and review count. Ten columns, one row per review, sorted the way you choose.
One required field and two controls. The sort is the one worth thinking about.
The limit is per query, not per run: five listings with a limit of a hundred is a hundred reviews from each, not a hundred altogether.
The sort opens on Relevancy, which is Etsy's own ordering rather than an unopinionated one. If you take the default and read the first hundred rows, you are reading what the marketplace chose to surface. Lowest Rating is the setting that answers the question most people arrive with, and switching it costs one click.
Small sellers change materials, printers and shipping partners, and a listing's review history outlives all of those. Most Recent reads the current state of the shop; Relevancy may hand you a beloved review from three suppliers ago. Which one you want depends on whether you are judging the product or the seller as they are today.
aggregate_rating and review_count describe the listing rather than the individual review - that is what the names say, and it is why they sit in a per-review row at all. Kept beside rating, they are what let you weigh one review against the average it sits in: a two-star among four hundred fives reads very differently from a two-star among nine. Whether they are filled in on every row or only where the export chooses to is the kind of thing to check on your first run; we have never seen a populated one.
These names are confirmed twice: they are the service's own declared list, shared with the other review scrapers on this pipeline, and they are the key order of every export we hold. What each column is for is below. What each will contain is not - the note explains why.
The names are solid. Everything past the names is your first run's job, and here that caveat is unusually strong. We hold three run exports for this service and none of them contains an Etsy review. Every review column - product, author, rating, title, review, date, aggregate_rating, review_count - was empty on all three rows; only query and status carried anything at all. Two of the three had been pointed at a placeholder address that is not Etsy at all, and came back http 404. The third used the form's own example URL, whose listing id we have no reason to think is a real listing, so it is not evidence about a real Etsy product either. Those runs tell you the export's shape and nothing whatsoever about its contents. What they do confirm is the column list: the key order is identical across all three exports and matches the service's declared set exactly. Everything else - what a rating looks like, what format a date arrives in, whether aggregate_rating is a number or a string - is deliberately absent rather than guessed. The form also warns that anti-bot sites return a blocked status on the free pool, which is residential-only; that is the platform's own warning about the shared pipeline and it is worth planning for. Run the free tier against one real listing and read the key order and the first few values before you build on them.
All of them start from a listing link you can copy out of the site.
Handmade and made-to-order goods fail in ways a spec sheet does not describe - a colour that photographs differently, a size that runs small, a delivery that took longer than the estimate. Sort by Lowest Rating and read the text, and the same two or three complaints usually surface again and again.
A competing listing's review section is the most candid description of it you will get, and it is public. Point the scraper at their listing and you get the same ten columns you get for your own, so the two files compare directly.
If you are about to resell, white-label or dropship from a shop, its listing reviews are the cheapest due diligence available. Most Recent tells you what buyers are experiencing now rather than what the listing earned its rating on.
No subscription, no minimum, no per-seat licence. Your first 500 rows are on us - after that it is pay-as-you-go.
For every new account, one time. No credit card. All scrapers unlocked. Given that no run we hold has returned a real review, this is the part that matters most here: spend a few rows establishing what this export actually contains before you plan around it.
Roughly $2 per 1,000 reviews, the same flat rate as every other scraper on the platform. The limit is per query, so ten listings with a limit of a hundred is a thousand rows - worth the arithmetic before you start, and a good reason to narrow the sort first.
Volume pricing, SLAs, dedicated workers and tailored onboarding for teams tracking a catalogue rather than a single listing. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Reviews are one view of a listing. These return the others in the same shape.
The listings themselves rather than what buyers said about them - title, price and the rest of the catalogue fields. Run it first to build the list of listing URLs you then feed to this one.
Many small sellers list on both marketplaces, and the two audiences complain about different things. Running both separates the product's faults from the channel's.
Reviews of the business rather than the item - refunds, dispatch, replying to complaints. A seller can make a lovely product and still be difficult to buy from, and buyers notice both.
Short answer: yes, when you only collect what is publicly visible on a listing page - and that is all this service reads.
A listing's review section is public. Anyone can open it without an account and read it, and collecting publicly visible information for product and market research is long-settled practice. As long as the data is publicly available and the process does not disrupt the service, there are no federal laws against it.
Reviews are written by people, though, and author is a display name. Publicly visible is not the same as free of obligation: if you store it, you are handling personal data, and the GDPR and similar regimes apply to you regardless of where you got it. Reading complaints to fix a product is an easy case; building a durable file on individual buyers is not, and this service is not intended for it.
Etsy's terms restrict automated access, so this remains a question of terms. We touch nothing behind a login, read only what an ordinary visitor sees, run no third-party trackers on the data layer, and your exports self-delete after 30 days.
The questions we hear most. Something else? Talk to us - humans write the answers, not bots.
Paste one listing URL, change the sort away from Relevancy, and see what buyers actually wrote. Your first 500 rows are free.
Etsy reviews are written by people who bought something made in small numbers, often to order, from a seller they will never meet. That makes the review section unusually informative and unusually hard to read in bulk, because it is a widget you can only scroll. This service turns it into a table: paste Etsy listing review URLs, one per line, and each review comes back as a row - the author, the rating, the headline, the body text and the date, alongside the listing's own overall rating and total review count. Ten columns, one row per review.
Two controls shape the run. The limit is per query rather than per run, so ten listings at a hundred each is a thousand rows; the field takes a minimum of 1 and defaults to 100, and leaving it empty takes everything. The sort has four settings - Relevancy, Most Recent, Highest Rating and Lowest Rating - and the first of those is the default. That is worth a moment's thought: Relevancy is the marketplace's own ordering, not a neutral one, so if you take the default you are reading what Etsy chose to surface. Lowest Rating is the setting that answers the question most people arrive with, and Most Recent is the one that tells you what a shop is like now rather than what it was like when it earned its average.
Keeping the review-level and listing-level scores in separate columns is what makes the export analysable. rating is this review; aggregate_rating and review_count belong to the listing the review sits on. A two-star review among four hundred five-stars is a different fact from a two-star among nine, and only the second pair of columns tells you which you are looking at. Exactly how those two are filled in across a listing's rows is worth a glance on your first run, since we have never seen a populated export to describe.
One limit is stated plainly because it changes what to expect from this page. We hold three run exports for this service and none of them contains an Etsy review: every review column was empty on all three. Two had been pointed at a placeholder address that is not Etsy at all and came back http 404; the third used the form's own example URL, whose listing id we have no reason to think is a real listing. What survives is the column list, and it survives well - the key order is identical across all three exports and matches the service's declared set. So the ten names above are dependable, and everything past them - what a rating looks like, what format a date arrives in, whether the aggregate is a number or a string - is deliberately absent rather than invented. The form also warns that anti-bot sites return a blocked status on the free pool, which is residential-only; that is the platform's own warning about the shared pipeline and it is worth planning for. Run one listing on the free tier and read the first few rows yourself; your first 500 cost nothing and need no credit card. See pricing for current rates.