Target Reviews Scraper — Export to CSV

Export public Target product reviews to CSV, Excel, JSON, or the Apify API. Enter Target product URLs or TCIN item IDs to collect review text, star ratings, dates, recommendation signals, and product-level rating summaries.

No Target login or cookies are required.

Each dataset row is one review, with its product context attached. Use the data to investigate customer complaints, compare product feedback, or prepare a review dataset for analysis.

Before you start: maxReviews is a total limit across the run—not a limit per product. Sorting by lowest rating does not filter exclusively for one-star reviews. Optional fields can be null when Target does not supply them.

Try a small Target review export

Open the Actor, switch to JSON input, and start with one product and an explicit review limit:

{
  "tcins": ["85978622"],
  "maxReviews": 20,
  "sortBy": "most_recent",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Replace the TCIN with your product's item ID. You can find it in a Target product URL after A-. Product availability and review counts can change.

After the run, open the dataset and export your results as CSV, Excel, or JSON. For a partial or failed run, also inspect RUN_SUMMARY in key-value storage.

What one review looks like

This is an illustrative excerpt, not a fresh review or a live product-rating claim. The full field list appears below.

{
  "tcin": "85978622",
  "productUrl": "https://www.target.com/p/-/A-85978622",
  "productTitle": "Example product",
  "reviewId": "example-review-id",
  "reviewTitle": "Comfortable for everyday use",
  "reviewText": "The fit worked well for me.",
  "rating": 4,
  "submittedAt": "2026-07-01",
  "verifiedPurchaser": null,
  "averageRating": 4.3,
  "reviewCount": 120,
  "scrapedAt": "2026-08-31T09:00:00.000Z"
}

rating describes this review. averageRating and reviewCount describe the product's public summary; they are not calculated from the sample you export.

Ready-to-run examples

These public examples let you inspect and reuse a saved input:

• Monitor Target Product Feedback — start with recent reviews for repeat monitoring.
• Find Lowest Rated Target Product Reviews — investigate complaints before filtering the exported ratings.
• Find Highest Rated Target Product Reviews — examine positive feedback.
• Scrape Target Reviews By Tcin — use item IDs instead of full URLs.
• Scrape Recent Target Airpods Reviews — inspect a single-product example.
• Build Ai Dataset From Target Reviews — collect review text for downstream analysis.

View all ready-to-run examples.

What can you do with Target review data?

• Investigate product complaints: request lowest_rating, then group review text by recurring issues in your own analysis tool.
• Monitor recent feedback: schedule runs with most_recent and compare tcin plus reviewId across datasets to identify newly collected reviews.
• Compare competing products: keep product IDs and product summary fields beside each review. For equal-sized samples, run each product separately with the same limit and sort.
• Prepare AI analysis: pass review text, ratings, and product IDs to your analysis workflow. The Actor exports data; it does not perform sentiment analysis or generate summaries itself.

Input settings

Provide at least one product URL or TCIN. If both are supplied, duplicate TCINs are combined.

Setting / JSON key	Accepted input	What to know
Target product URLs / startUrls	Array of objects with url	Public HTTPS Target product URLs containing A- followed by the 6–12 digit TCIN.
Target item IDs / tcins	Array of strings	TCINs only, for example ["85978622"]. Do not put product URLs here.
Maximum reviews / maxReviews	Integer, 1–10,000	Total across all products. Set it explicitly in automated runs. The input form suggests 20.
Review order / sortBy	most_recent, highest_rating, lowest_rating	Controls ordering, not an exact star-rating filter. Default: most_recent.
Connection settings / proxyConfiguration	Apify proxy configuration object	The input form defaults to residential Apify Proxy. The example above includes it explicitly.

URL input works too:

{
  "startUrls": [{"url": "https://www.target.com/p/-/A-85978622"}],
  "maxReviews": 20,
  "sortBy": "lowest_rating",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Products are processed in sequence. An earlier product can use the entire review limit before later products are reached.

Output fields

Missing optional values are null; missing is not the same as zero or false.

Review-level data

Field	Meaning
reviewId	Review identifier; combine with tcin when comparing exports.
reviewTitle, reviewText	Review heading and body text, when supplied.
rating	Star rating attached to the individual review.
authorNickname	Public reviewer display name, not contact information.
submittedAt	Review submission date supplied by Target.
isRecommended	Whether the reviewer recommends the product, when supplied.
helpfulVotes, notHelpfulVotes	Counts of helpful and unhelpful feedback votes.
verifiedPurchaser	Target's verified-purchaser flag, when available.
isSyndicated	Whether Target identifies the review as syndicated.
dataSourceName	Review source name supplied by Target.
sortBy	Sort order requested for this run.
scrapedAt	Extraction timestamp for the run, not the review's posting date.

Product context repeated on each review

Field	Meaning
productUrl, tcin	Target product URL and item identifier.
productTitle, brand	Product name and brand when available.
averageRating	Product's aggregate rating supplied by Target.
ratingCount	Count of ratings in the product summary.
reviewCount	Count of reviews in the product summary; can differ from the ratings count.
recommendedCount, notRecommendedCount	Product-level counts of recommendation responses.
recommendedPercentage	Product-level recommendation percentage supplied by Target.

There are no separate product-summary dataset rows. Avoid summing repeated product totals across review rows.

Pricing

The Actor uses pay-per-event pricing:

• start: one charge when a run passes the product-input checks and begins processing. A valid run can incur this charge even if no reviews are returned.
• item: charged per Target review extracted. Product summaries are included on review rows, not sold as separate result rows.

See the live Pricing tab for current rates and any discounts available to your account. Check the run's cost details for the charges applied to that run.

Control the size of an export with maxReviews. A scheduled run is a new extraction; the Actor does not maintain a history of reviews already collected by previous runs.

Tips, limitations, and partial results

• Public reviews only: no Target account credentials are required. This does not provide private customer records or purchase histories.
• Fewer results are possible: the requested limit is a ceiling, not a guarantee. Availability, Target access restrictions, and the run deadline can limit collection.
• Not a star filter: for one-star reviews, use lowest_rating and then filter exported rows where rating equals 1. This is not a guarantee of collecting every one-star review.
• Check failed products: RUN_SUMMARY can identify skipped TCINs and failure reasons. A run with no extracted reviews is marked failed; previously saved data can still be useful after a partial interruption.
• Continue carefully: when processing stops near the deadline, PENDING_TCINS may contain products to retry. Supply those IDs in a new run; retries can repeat reviews from a partially processed product.
• Do not infer missing data: a null verified-purchaser flag does not prove an unverified purchase. A missing recommendation percentage does not mean zero recommendations.

Run through the Apify API

Install apify-client for Node.js or apify-client for Python, and set APIFY_TOKEN securely in your environment. These examples start a paid Actor run under your account's pricing.

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/target-reviews-scraper').call({
  tcins: ['85978622'],
  maxReviews: 20,
  sortBy: 'most_recent',
  proxyConfiguration: {
    useApifyProxy: true,
    apifyProxyGroups: ['RESIDENTIAL']
  }
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/target-reviews-scraper').call(run_input={
    'tcins': ['85978622'],
    'maxReviews': 20,
    'sortBy': 'most_recent',
    'proxyConfiguration': {
        'useApifyProxy': True,
        'apifyProxyGroups': ['RESIDENTIAL']
    }
})
print(client.dataset(run['defaultDatasetId']).list_items().items)

cURL

curl --fail-with-body -X POST \
  'https://api.apify.com/v2/acts/fetch_cat~target-reviews-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"tcins":["85978622"],"maxReviews":20,"sortBy":"most_recent","proxyConfiguration":{"useApifyProxy":true,"apifyProxyGroups":["RESIDENTIAL"]}}'

The cURL request starts an asynchronous run and returns run metadata, not review rows. Wait for completion before reading its default dataset. For larger exports, paginate dataset retrieval. See the Actor API tab.

Use with AI agents through MCP

Use the official hosted Apify MCP server; this Actor does not run its own MCP server.

For Claude Code:

claude mcp add --transport http apify-target \
  "https://mcp.apify.com?tools=fetch_cat/target-reviews-scraper"

For a client that accepts remote HTTP servers in mcpServers configuration:

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

Authorize access to your Apify account when prompted. Client setup formats differ; use the official Apify MCP setup guide for your client.

The focused URL exposes this Actor. The general server at https://mcp.apify.com also provides Actor discovery tools. Connecting a tool is separate from having its page appear in an AI search answer.

Example prompts:

• "Collect up to 20 recent reviews for Target TCIN 85978622. Group complaints by theme and include the source review IDs."
• "Fetch up to 30 reviews sorted by lowest rating, then show only rows rated one star. Tell me if the sample is smaller than requested."
• "Compare these two saved datasets by TCIN and review ID. Separate newly collected reviews from reviews seen in both exports."

FAQ

Can I download Target reviews to CSV or Excel?

Yes. Export the dataset after the run, or retrieve rows through the Apify API.

Is this a Target reviews API alternative?

It provides API access to extracted public Target reviews through Apify. It is not Target's official API and does not grant access to private or partner-only data.

Can I get 20 reviews from each of several products?

maxReviews applies to the whole run. Use a separate run per product with maxReviews: 20 when you need separate per-product limits; each run has its own start charge.

Can I track new reviews automatically?

Schedule the Actor in Apify and compare successive exports in your own workflow. Use tcin and reviewId to identify repeats; scheduling alone does not create an incremental-only export or an alerting service.

Does it perform sentiment analysis?

No. It exports review data. You can analyze the text downstream, preserving product and review identifiers for traceability.

Related Actors

• AliExpress Reviews Scraper — collect customer feedback from another retail marketplace.
• Walmart Products Scraper — add product research from a neighboring retail channel.
• Shopify Products Scraper — export public storefront catalogs for product comparisons.
• Ecommerce Product Page Scraper — collect product-page details for a broader research workflow.
• Google Shopping Results Scraper — complement review research with shopping-result data.

Support

Report failed runs, wrong output, or missing fields through the Issues tab. Include the run ID or run URL, input JSON with secrets removed, expected output, actual output, and one reproducible public product URL or TCIN. Add the relevant RUN_SUMMARY details if a product was skipped.

Privacy and data handling

This Actor runs with Apify limited permissions and only processes data needed for the documented run. It uses review lookup inputs and public review results to produce the output dataset and sends requests to public Target Reviews pages/endpoints; results are stored in Apify run storage for your account. FetchCat does not use your inputs or outputs for advertising, does not use them for model training, and does not retain them outside the Apify run except for transient support debugging when you explicitly share run details. You are responsible for using the Actor lawfully, respecting the target site's terms, and avoiding unnecessary personal or sensitive data in inputs.