Target Product Reviews Scraper - Ratings & Text

What is Target Product Reviews Scraper - Ratings & Text?

Target Product Reviews Scraper extracts customer reviews from Target product pages and returns one dataset row per review. Give it Target product URLs or TCINs and each row includes the star rating, review text, reviewer display name, submit and edit times, verified-purchase and recommendation flags, helpful votes, quality and value sub-ratings, customer photos and videos when present, and product-level context such as the average rating, rating distribution and recommended percentage. Brand managers, product teams, retail analysts and AI agents use it for review monitoring and voice-of-customer research.

Why product teams choose this Target review scraper

• Review plus product context on every row. productAverageRating, productRatingCount, productRatingDistribution and productRecommendedPercentage travel with each review.
• Filters that run before billing. ratings, verifiedOnly, hasOnlyPhotos and hasOnlyVideos narrow results upstream, so you pay only for matching reviews.
• Four sort orders. most_recent, highest_rating, lowest_rating and helpfulness_desc.
• Two spend caps. maxReviews caps the whole run and maxReviewsPerProduct caps each product.
• URLs or TCINs. Paste product URLs or raw 8–12 digit TCINs; duplicate products are processed once.
• Clear outcomes. OUTPUT and RUN_SUMMARY report COMPLETE, PARTIAL, VALID_EMPTY, INVALID_INPUT, UPSTREAM_FAILED or CONFIG_ERROR.

Best fit for this Actor

• Best fit for brand and category teams tracking how shoppers rate specific Target products.
• Designed for complaint triage, launch monitoring and competitive review comparisons across a short product list.
• When you start from a search term instead of known products, start with Target Product Search Scraper, then pass the product URLs or TCINs into productUrls here.
• To compare the same product's sentiment on Amazon, use Amazon Reviews Scraper alongside this dataset.

Example workflow: from a product launch to a fix list

A consumer-electronics brand launches new earbuds at Target. The product manager starts a weekly job with the product's TCIN in productUrls, ratings set to [1, 2], verifiedOnly on, sortBy at most_recent and maxReviewsPerProduct at 100. The dataset returns each low-star review's reviewText, submittedAt and secondaryRatings. The team tags recurring themes, compares the week's productAverageRating with the previous week, exports the rows to their issue tracker, and feeds the top complaints into the next firmware release plan.

Input

{
  "productUrls": ["https://www.target.com/p/-/A-89827259", "87830660"],
  "maxReviews": 100,
  "maxReviewsPerProduct": 50,
  "sortBy": "most_recent",
  "ratings": [1, 2],
  "verifiedOnly": true,
  "hasOnlyPhotos": false,
  "hasOnlyVideos": false
}

Field	Type	What it controls
productUrls	array of strings	Target product URLs or TCINs. Required.
maxReviews	integer	Reviews saved across the whole run. Default 50.
maxReviewsPerProduct	integer	Reviews saved per product. Default 50.
sortBy	string	most_recent (default), highest_rating, lowest_rating or helpfulness_desc.
ratings	array of integers	Exact star ratings such as [1] or [4, 5]; empty includes every rating.
verifiedOnly	boolean	Only reviews Target marks verified. Default false.
hasOnlyPhotos	boolean	Only reviews with customer photos. Default false.
hasOnlyVideos	boolean	Only reviews with customer videos. Default false.

Output

Field	What it contains
reviewId, productId, productUrl, productTitle	Review and product identity
rating, reviewTitle, reviewText	Star rating, headline when the shopper wrote one, and review body
reviewerName	Public display name
submittedAt, modifiedAt	Submit and edit times
isVerified, isRecommended, isIncentivized, isSyndicated, sourceChannel	Review flags and origin
helpfulVotes, unhelpfulVotes	Shopper votes
photos, videos, topics	Customer media and tagged topics when present
secondaryRatings	Sub-ratings such as quality and value
productAverageRating, productRatingCount, productReviewCount	Product-level rating totals
productRecommendedPercentage, productRatingDistribution, productSecondaryAverages	Product-level sentiment breakdown
availableReviews, paginationNote	Matching reviews available and a note when your cap trims them
sourceUrl, scrapedAt	Source page and collection time

{
  "reviewId": "31addde7-7e8b-4013-96bc-2e3bc159f62c",
  "productId": "89827259",
  "productUrl": "https://www.target.com/p/-/A-89827259",
  "productTitle": "Apple EarPods (USB-C)",
  "rating": 1,
  "reviewTitle": null,
  "reviewText": "Only one of my earphones work",
  "reviewerName": "Shopper_J",
  "submittedAt": "2026-09-30T11:15:15.000Z",
  "modifiedAt": "2026-09-30T12:30:00.000Z",
  "isVerified": true,
  "isRecommended": true,
  "isIncentivized": false,
  "isSyndicated": false,
  "sourceChannel": "TARGET",
  "helpfulVotes": 0,
  "unhelpfulVotes": 0,
  "photos": [],
  "videos": [],
  "secondaryRatings": { "quality": 1, "value": 1 },
  "topics": [],
  "productAverageRating": 4.35,
  "productRatingCount": 990,
  "productReviewCount": 219,
  "productRecommendedPercentage": 73,
  "productRatingDistribution": { "1": 122, "2": 18, "3": 33, "4": 40, "5": 777 },
  "productSecondaryAverages": { "quality": 3.92, "value": 4.02 },
  "availableReviews": 218,
  "paginationNote": "Results are capped at 3 reviews for this request.",
  "sourceUrl": "https://www.target.com/p/-/A-89827259",
  "scrapedAt": "2026-10-01T01:43:58.427Z"
}

Pricing

This Actor uses Pay per event plus Apify platform usage. The events are Actor Start (once per run) and Review scraped (one per complete review saved to the dataset). Rating and media filters apply before billing, so only matching reviews create events. For example, the default maxReviews cap of 50 means at most one start event plus one review event per saved review up to that cap, plus the platform usage of the job. The live Pricing tab on this page shows the current price of each event.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~target-product-reviews-scraper/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"productUrls": ["89827259"], "maxReviews": 20, "maxReviewsPerProduct": 20, "sortBy": "most_recent"}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("khadinakbar/target-product-reviews-scraper").call(run_input={
    "productUrls": ["https://www.target.com/p/-/A-89827259"],
    "ratings": [1, 2],
    "verifiedOnly": True,
    "maxReviews": 50,
})
for review in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(review["rating"], review["submittedAt"], review["reviewText"][:80])

Use with AI agents (MCP)

The Actor is available through Apify MCP for Claude, ChatGPT, Cursor and other MCP clients. Example prompt:

Pull the 30 most recent verified 1- and 2-star reviews for Target TCIN 89827259 and summarize the top three complaints with one quoted example each.

The agent sends productUrls, ratings, verifiedOnly and maxReviews, reads OUTPUT.outcome, then reads rows with get-dataset-items and clusters reviewText. Keep reviewId, sourceUrl and scrapedAt with each quote for provenance. For cost and scope, set maxReviews before starting; a VALID_EMPTY outcome means the filters matched no reviews.

Best results

Starting situation	Helpful action	Expected outcome
Complaint triage	ratings: [1, 2] with sortBy: "most_recent"	The newest negative feedback first
Proof points for marketing	ratings: [5] with sortBy: "helpfulness_desc"	The most useful positive reviews
Visual evidence	Turn on hasOnlyPhotos	Reviews with customer photos
Several products at once	Set both maxReviews and maxReviewsPerProduct	Balanced coverage across products
Trusted feedback only	Turn on verifiedOnly	Verified-purchase reviews

Read availableReviews and paginationNote to see how many matching reviews exist beyond your cap, then raise the caps if you want them all.

Builder's note

I designed every review row to carry the product's rating totals as well, because review analysis almost always compares one opinion to the overall picture. I put the filters upstream of billing so a lowest-rating pull costs only what it returns, and I collect only the reviewer's public display name so the dataset stays focused on product feedback.

FAQ

Can I paste TCINs instead of URLs?

Yes. productUrls accepts Target product URLs and raw 8–12 digit TCINs.

Why is the dataset empty for some filters?

Restrictive filters can match no reviews; the outcome is then VALID_EMPTY. Widen ratings or turn off the media filters.

Do I need a Target account?

No. The Actor reads public review data with no login or cookies.

Responsible use

Target's Terms & Conditions restrict commercial data extraction and automated access without approval. Use this Actor only with Target's written authorization or another valid legal basis, and follow applicable privacy, copyright, consumer-protection and database laws. Use review data for product insight, keep reviewer names out of profiling, and review the current site terms before production use. Target is a trademark of its owner; this independent Actor is not affiliated with or endorsed by Target.