Give it a target.com product URL - or just the TCIN - and get that item's reviews back as rows: the author, the rating, the headline, the full review, the date, whether it is marked verified, how many people found it helpful, and the reviewer's location. Sort by most recent, highest or lowest rating, or most helpful.
The input is the item, not the review - you name the products you want feedback on and the job collects what their buyers wrote.
One row per review, each tagged with the query it came from - so a run across a long product list still reconciles back to your input.
You do not have to build URLs. A target.com product link works, and so does the TCIN on its own, one per line - and either way every row carries tcin as a column, so the export lines up against a product list you already keep by item number.
Most review exports stop at who wrote it and what they said. This one carries a location column as well, which is what lets a complaint pattern be read regionally rather than as one undifferentiated pile of text.
Most recent, highest rating, lowest rating or most helpful - plus a toggle to start from the oldest review instead. Combined with a per-query limit, that is what decides which slice of a long review list you spend credits on.
Each row carries the review as Target displays it: who wrote it, the rating, the headline, the full text, the date, the verified mark, the helpful count and where the reviewer was.
Review text comes back verbatim, with no preprocessing applied, so downstream sentiment work reads the shopper's own wording rather than ours. The column list below is the header row of real run exports rather than a specification - and it is worth reading the note under it, because those runs are the reason this page describes the schema and not the value formats.
Taken from the header row of real run exports, identical across every one of them and identical to the column list the platform ships. Read the note underneath before you write a parser: these names are verified, and the value formats are not.
One thing to know before you write code against this. Target sits behind Akamai, and the free demo pool has not returned rows through it - the platform notes that the reviews API may need a residential connection. An export header row still names its columns, which is why the ten column names above are trustworthy. What we have not seen is the shape of the values, so this page deliberately claims no date pattern, no rating scale, no form for the verified mark, no unit for the helpful column and no granularity for location - whether that is a city, a state or something looser. Pull a free run and read the first rows before you assume any of them.
A per-query limit decides what a run costs and how much lands in the file. The sort order and the direction are chosen alongside it, before the job starts.
A few examples of how teams use retail review data to answer a question they actually have.
Sort a product lowest-rated first and read the comments in order rather than as a score. Recurring language - arrived damaged, missing part, not as pictured - names the failure long before it shows up as a return rate.
The location column turns a general grumble into a regional one. A shipping complaint concentrated in a few places is a logistics question; the same complaint spread evenly is a packaging question.
Feed the TCINs you keep in your own product master and get the reviews back tagged with the same number. There is no name matching to do, which is usually where this kind of join falls apart.
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, every sort order, 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 product, the other big-box retailer. Running both on an item you sell is the quickest way to see whether a complaint is about the product or about one channel.
The marketplace with the deepest review history. Useful when a Target listing is too new to have said much yet.
Short answer: yes for the public review content - and this export carries nothing about the purchase behind it.
Reviews on Target are published to be read. The rating, the headline, the review text, the date, the verified mark, the helpful count and the reviewer's location are shown to anyone who opens the product page, signed in or not. Collecting publicly visible feedback for research is long-established practice, and nothing here touches a login, a cart or a checkout.
The author field carries what the site itself publishes, which is a display name rather than a full identity - there is no email and no address in this output. The location column is the coarse place Target shows next to a review, not a street. Nothing about the purchase behind a verified review is collected either: not what was paid, not when, not where it shipped. If you are processing the review text in the EU, the usual rules still apply to what you do with it downstream.
Target's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a commercial relationship with them, 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 Target Reviews Scraper turns a list of products into review data. You submit target.com product URLs or bare TCINs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - choose a sort order, cap the rows per query if you want to, and download what shoppers wrote as a clean CSV, Excel or JSON file.
Each row carries the review as Target displays it: the author's display name, the rating, the headline, the full review text, the posting date, whether the review is marked verified, how many people found it helpful, and the reviewer's location. That last column is what makes this dataset different from most review exports - a complaint can be read regionally rather than as one undifferentiated pile of text.
Product teams sort an item lowest-rated first and read the complaints as a queue, where recurring language names a defect long before it shows up as a return rate. Operations teams split those complaints by location to tell a logistics problem from a packaging one. Data teams feed the TCINs they already keep and get reviews back tagged with the same number, so there is no name matching to do.
One practical note up front: Target sits behind Akamai, and the platform notes that its reviews API may need a residential connection - routing and rate limiting are handled on our side, so from your end it is a form or an API call. This page describes the ten columns and their order, which the archived exports and the platform's published list agree on exactly, but claims no value formats, because no populated Target export exists to base them on. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.