Target Product Reviews Scraper

Export public Target customer reviews from product URLs or TCINs. Get review text, star ratings, authors, dates, verified-purchase signals, recommendations, helpfulness, customer media, secondary scores, and product rating totals. No Target login or private API key is required.

Use the results in JSON, CSV, Excel, XML, RSS, or HTML, or connect the Actor to a scheduled review-monitoring workflow through the Apify API.

What does Target Product Reviews Scraper do?

Target Product Reviews Scraper turns Target product review feeds into one clean dataset row per review. Add a product URL such as https://www.target.com/p/-/A-13521110, or provide its numeric TCIN directly. The Actor follows Target's public paginated review data rather than stopping at the few reviews visible in initial page state.

For each review, it can extract:

• ⭐ Review rating, title, text, and submission date
• 👤 Reviewer nickname and public external reviewer ID
• ✅ Verified-purchase, recommended, incentivized, syndicated, and ratings-only signals
• 👍 Helpful, unhelpful, and inappropriate-feedback counts
• 📷 Public customer photo and video URLs when exposed
• 📊 Secondary dimensions such as quality, value, comfort, and fit
• 🧮 Aggregate average, rating count, written-review count, recommendation percentage, and 1–5 star distribution
• 🛍️ Product TCIN, title, brand, canonical URL, image, and category when available

The primary route is HTTP-based and runs with 256 MB of memory, keeping scheduled review exports lighter than a browser-only workflow.

Who is it for?

Ecommerce brands and marketplace sellers

• Monitor feedback on owned products sold at Target.
• Detect recurring quality, fit, packaging, or value complaints.
• Compare launch sentiment before and after merchandising changes.

Consumer-insights and research teams

• Export review text for sentiment, topic, and language analysis.
• Compare rating distributions across competing product cohorts.
• Study verified-purchase feedback separately from other reviews.

Product and quality teams

• Route one-star reviews into issue-tracking or alert workflows.
• Track secondary ratings such as comfort, quality, or value.
• Build evidence for product iteration and supplier discussions.

Data engineers and agencies

• Schedule repeatable review collection through Apify.
• Deliver typed JSON or CSV to warehouses and dashboards.
• Trigger webhooks whenever a monitoring run finishes.

Why use this Target reviews extractor?

• Full pagination: collect beyond the small initial set embedded in a product page.
• No login: use only anonymous public Target product-review surfaces.
• Flexible product input: mix normal Target URLs, short /A-TCIN links, and TCINs.
• Useful filters: select star rating, verified purchases, photo reviews, and sort order.
• Monitoring-ready: every row includes stable review and product identifiers plus timestamps.
• CSV-friendly: changing secondary dimensions are flattened into readable text.
• Resilient HTTP: requests run directly first, with bounded US residential fallback only after blocking.
• Apify-native: schedule runs, export datasets, call an API, and connect integrations without managing servers.

What Target review data can you extract?

Category	Fields
Product	tcin, productTitle, productBrand, productUrl, productImageUrl, productCategory
Review identity	reviewId, externalReviewId, sourceChannel
Review content	reviewTitle, reviewText, rating, ratingRange, submittedAt, modifiedAt
Reviewer	authorNickname, authorId
Trust signals	isVerifiedPurchase, isRecommended, isIncentivized, isSyndicated, isRatingsOnly
Engagement	helpfulCount, unhelpfulCount, inappropriateCount
Media and dimensions	photoUrls, videoUrls, badges, secondaryRatings
Product totals	aggregate average, rating count, written-review count, recommendation count/percentage, 1–5 star counts
Monitoring	scrapedAt

Fields that Target does not expose for a particular product or review are omitted. Boolean and count fields remain typed for filtering and downstream analysis.

How much does it cost to scrape Target product reviews?

This Actor uses pay-per-event pricing. A run has a $0.005 start charge, then charges per review saved. Platform compute and proxy use are included.

Apify plan	Price per review	1,000 reviews plus start
Free	$0.000047483	about $0.05
Starter / Bronze	$0.00004129	about $0.05
Scale / Silver	$0.000032206	about $0.04
Business / Gold	$0.000024774	about $0.03
Platinum	$0.000016516	about $0.02
Diamond	$0.000011561	about $0.02

A small 10-review test costs about $0.01 on the Free tier. Apify's free platform credit can cover many small validation runs before you scale. Always start with one product and a low review limit.

How to scrape Target product reviews

1. Open Target Product Reviews Scraper in the Apify Store.
2. Paste one or more Target product URLs, or enter numeric TCINs.
3. Set Maximum reviews per product to a small value for the first run.
4. Optionally choose a star rating, verified-only, or photo-only filter.
5. Click Start and wait for the run to finish.
6. Open the dataset and export JSON, CSV, Excel, XML, RSS, or HTML.
7. Save the task and add a schedule if you want recurring monitoring.

A minimal input:

{
  "productUrls": ["https://www.target.com/p/-/A-13521110"],
  "maxReviewsPerProduct": 10,
  "sort": "most_recent"
}

A verified-purchase export by TCIN:

{
  "tcins": ["13521110"],
  "maxReviewsPerProduct": 100,
  "verifiedOnly": true,
  "sort": "most_recent"
}

An incremental low-rating monitor that only saves reviews newer than its previous collection window:

{
  "productUrls": [
    "https://www.target.com/p/-/A-13521110",
    "https://www.target.com/p/-/A-54551690"
  ],
  "maxReviewsPerProduct": 50,
  "ratingFilter": 1,
  "sort": "most_recent",
  "submittedAfter": "2026-09-15T00:00:00Z"
}

Input parameters

Parameter	Type	Default	Description
productUrls	string[]	prefilled sample	Target product URLs containing a TCIN
tcins	string[]	empty	Numeric Target product IDs
maxReviewsPerProduct	integer	100	Maximum matching rows saved per unique product; range 1–5,000
sort	string	most_recent	most_recent, highest_rating, lowest_rating, or helpfulness_desc
submittedAfter	ISO-8601 string	unset	With most_recent, save only reviews strictly newer than this timestamp and stop after reaching older history
ratingFilter	integer	unset	Return only 1-, 2-, 3-, 4-, or 5-star reviews
verifiedOnly	boolean	false	Return only Target-verified purchases
reviewsWithPhotosOnly	boolean	false	Return only reviews with public customer photos
includeRatingsOnly	boolean	true	Keep star-only records when exposed
maxRequestRetries	integer	3	Bounded request attempts; range 1–6

At least one valid product URL or TCIN is needed to produce data. Duplicates are processed once.

Output example

{
  "tcin": "13521110",
  "productTitle": "Microfiber Sheet Set - Room Essentials™",
  "productBrand": "Room Essentials",
  "productUrl": "https://www.target.com/p/microfiber-sheet-set-room-essentials/-/A-13521110",
  "reviewId": "195a9b24-f415-4640-9c1e-2373401bac37",
  "reviewTitle": "fits great",
  "reviewText": "purchased for college apt off campus. Fits great on the full bed.",
  "authorNickname": "just perfect",
  "rating": 5,
  "ratingRange": 5,
  "submittedAt": "2026-07-11T19:45:14.000+00:00",
  "isRecommended": true,
  "isVerifiedPurchase": true,
  "isIncentivized": false,
  "isSyndicated": false,
  "isRatingsOnly": false,
  "helpfulCount": 0,
  "unhelpfulCount": 0,
  "photoUrls": [],
  "videoUrls": [],
  "secondaryRatings": "comfort: 5/5; fit: true to size; quality: 5/5; value: 5/5",
  "aggregateAverageRating": 4.53,
  "aggregateRatingCount": 15582,
  "aggregateWrittenReviewCount": 4398,
  "aggregateRecommendedPercentage": 82,
  "scrapedAt": "2026-07-14T03:30:00.000Z"
}

The exact fields vary because reviewers may omit a nickname, title, text, recommendation, secondary rating, or media.

Sort and filter Target reviews

Use most_recent for launch monitoring and incremental feedback collection. Set submittedAfter to the end of your last successful collection window to return only newer reviews. The cutoff is strict (a review at the exact timestamp is excluded) and cannot be combined with another sort order. If Target returns a malformed review timestamp, that row is skipped with a warning and pagination continues rather than stopping early. Use helpfulness_desc when researching the feedback other shoppers found most useful. Use highest_rating or lowest_rating to inspect product strengths and weaknesses.

The star, verified-purchase, and photo filters are applied by Target's public review service. This reduces transferred records and keeps the output focused. The Actor continues pagination until it saves the requested number of matching rows or Target reports the last available page.

Tips for reliable review exports

• 🧪 Begin with 10 reviews from one known product.
• 🔢 Prefer TCINs when your catalog already stores Target product IDs.
• 🕐 Schedule recent-review runs daily or weekly rather than repeatedly collecting full history.
• ⭐ Use a star filter for complaint or advocacy workflows.
• ✅ Use verifiedOnly when your analysis requires stronger purchase evidence.
• 📷 Use photo-only filtering for user-generated-content research.
• 📉 Split very large product cohorts into tasks for easier retries and monitoring.
• 🔗 Keep reviewId as the deduplication key in your destination system.

Review monitoring workflow

For recurring monitoring, save an Actor task with sort: "most_recent". After each successful run, set submittedAfter to the end of that collection window for the next daily or weekly run. The Actor then saves only strictly newer reviews and stops paging after it reaches older history. Keep reviewId as a destination-side deduplication key for retries and overlapping collection windows.

For complaint alerts, use ratingFilter: 1 or 2. A downstream automation can send product title, review text, verification status, and canonical Target URL to Slack or an issue tracker.

Integrations

Target reviews → Google Sheets

Export a product cohort into a worksheet for category managers and research teams. Use reviewId as a stable key and refresh the sheet on a schedule.

Target reviews → Slack or Discord

Run a one-star monitor and post newly observed complaints with product context.

Target reviews → Make or Zapier

Trigger a sentiment-analysis step after each run, then route negative topics to product or supplier owners.

Target reviews → data warehouse

Use a webhook after successful runs, fetch dataset items through the API, and load typed review rows into BigQuery, Snowflake, or PostgreSQL.

API usage

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/target-product-reviews-scraper').call({
    tcins: ['13521110'],
    maxReviewsPerProduct: 25,
    sort: 'most_recent',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Using the Apify API with Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('automation-lab/target-product-reviews-scraper').call(run_input={
    'productUrls': ['https://www.target.com/p/-/A-13521110'],
    'maxReviewsPerProduct': 25,
    'verifiedOnly': True,
})

items = client.dataset(run['defaultDatasetId']).list_items().items
print(items)

Using the Apify API with cURL

curl -X POST \
  "https://api.apify.com/v2/acts/automation-lab~target-product-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "tcins": ["13521110"],
    "maxReviewsPerProduct": 25,
    "sort": "most_recent"
  }'

After the run succeeds, use its defaultDatasetId to download items from the dataset API.

Use Target reviews with AI agents via MCP

Target Product Reviews Scraper can be called by AI assistants through Apify's Model Context Protocol integration.

For Claude Code:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/target-product-reviews-scraper"

For Claude Desktop, Cursor, or VS Code, add:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=automation-lab/target-product-reviews-scraper"
    }
  }
}

Example prompts:

• "Use automation-lab/target-product-reviews-scraper to export the 50 newest reviews for Target TCIN 13521110."
• "Collect verified one-star reviews from these Target product URLs and summarize recurring quality complaints."
• "Monitor this Target product cohort weekly and prepare a table of new reviews, recommendation status, and helpful votes."

Your MCP client authenticates with Apify and receives normal Actor run and dataset links.

Legality

This Actor extracts information publicly visible without a Target login. Public availability does not remove every legal or contractual obligation. Use the data for legitimate purposes, respect Target's terms and applicable rate limits, and avoid collecting more personal data than your workflow needs.

Review text may be user-generated content. Do not republish it in a misleading way, profile reviewers, or use public reviewer identifiers for unsolicited contact. If your organization handles personal data, apply relevant privacy, retention, and data-subject procedures such as GDPR or CCPA requirements. Seek legal advice for your specific use case.

Data handling, retention, and AI

The Actor processes supplied Target URLs or TCINs and public review data only during the run. It creates no external cache or account: datasets, key-value-store records, and logs follow your Apify storage and retention settings and can be deleted through Apify. A bounded Apify US residential proxy may receive Target request metadata only when a direct request fails. The Actor itself does not call an AI model or send input or extracted data to an AI provider; MCP clients may process returned data under their own configured terms. Target is identified only as the public data source, and this Actor is not affiliated with or endorsed by Target.

Support

Use the Actor's Issues tab in Apify Console to report a reproducible problem. Include a sanitized input, run link, expected result, and observed result, but never post credentials or private data.

Limitations

• Target can change its product page or public review service without notice.
• Some reviews do not contain a title, text, nickname, recommendation, or secondary scores.
• Product context enrichment can be absent while complete review rows still succeed.
• Ratings-only records may not include written feedback.
• Target may syndicate reviews from another source; inspect isSyndicated when provenance matters.
• Deleted, moderated, or newly submitted reviews can change totals between runs.
• The Actor accepts product URLs and TCINs, not Target search queries. Use Target Scraper to discover products first.

Troubleshooting

Why did my run return no reviews?

Check that the URL contains a numeric A- TCIN or provide the TCIN directly. The chosen star, verified, or photo filter may also have no matching reviews. Test the same product without filters and with a limit of 10.

Why is productTitle missing?

Target's product-page context request can occasionally be blocked while the review endpoint remains available. The row still includes the TCIN and stable Target URL, and review extraction continues by design.

Why are there fewer rows than maxReviewsPerProduct?

The limit is a maximum, not a promise. Target may expose fewer matching public records after filters, or the review feed may reach its last page.

FAQ

Does the Actor require a Target account or API key?

No. It uses anonymous public Target product and review surfaces.

How fast is it?

Small one-product exports usually finish quickly because the primary route is direct HTTP. Runtime grows with products, pages, filters, and temporary retry needs.

Can it retrieve all historical reviews?

Set maxReviewsPerProduct up to 5,000. The Actor paginates until that maximum or Target's last available matching page.

How are duplicate reviews handled?

The Actor deduplicates on Target's public review ID across every input in the same run. For scheduled runs, use submittedAfter to limit results to the new collection window and keep reviewId as a retry-safe destination key.

Is this the same as Target Scraper?

No. Target Scraper discovers products and returns search-result product details and aggregate counts. This Actor returns the individual customer-review records for known products.

Can I export to CSV or Excel?

Yes. Open the run dataset and select CSV or Excel, or request the format through the Apify dataset API.

Related actors

• Target Scraper — discover Target products, prices, images, ratings, and counts.
• Walmart Reviews Scraper — collect individual Walmart customer reviews.
• Walmart Scraper — discover Walmart product records.
• Amazon Reviews Scraper — export Amazon product feedback.
• Best Buy Reviews Scraper — collect electronics retailer reviews.
• Ulta Product Reviews Scraper — analyze beauty-product feedback.
• Google Maps Reviews Scraper — export location and business reviews.

Combine Target Scraper for product discovery with Target Product Reviews Scraper for review depth.