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.

Quick start

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, found after A- in a Target product URL. After the run, open the dataset to export the reviews.

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Choose an example to start with a preconfigured input. Review the product and limit before running it.

• Scrape Target Reviews By Tcin — collect recent reviews using item IDs.
• Find Lowest Rated Target Product Reviews — examine complaints before filtering exported ratings.
• Find Highest Rated Target Product Reviews — examine positive feedback.

What you get

• Review content: titles, text, star ratings, public reviewer names and submission dates when available.
• Feedback signals: helpful votes, recommendations and nullable verification or syndication flags.
• Product context: TCIN, product URL, name, brand and public rating summaries repeated beside each review.

Sorting changes order, not the star-rating filter. The Actor exports data rather than performing sentiment analysis.

Who is it for?

• Product teams: investigate recurring complaints and positive feedback.
• Retail researchers: compare product feedback using separate, equally bounded samples.
• Analysts and AI builders: prepare review datasets for downstream analysis with source identifiers.

Input settings

Provide at least one product URL or TCIN. Duplicate TCINs are combined when both are supplied.

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.

Products are processed in sequence. maxReviews is the total run limit, not a per-product limit. The form defaults to 20. The entrypoint falls back to 50 if that field is absent.

Output data

This illustrative excerpt shows the output shape. It is not a fresh review or a live product-rating claim.

{
  "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.

Missing optional values are null, not 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.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

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 discounts. 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 for best results

• Start small. Try one product with an explicit limit before collecting more.
• Compare like with like. Use separate runs per product for equal-sized samples.
• Filter after collection. Use lowest-rating order and filter the exported rating column for one-star reviews.
• Track identifiers. Compare TCIN and review ID across exports to identify repeats.

Limits and caveats

Only public Target reviews are supported. No Target login or cookies are needed. This is not Target’s official API.

The requested limit is a ceiling, not a guarantee. Target restrictions, available reviews and the run deadline can limit collection. A run with no extracted reviews is currently marked failed. That does not prove a product is private or deleted.

Inspect RUN_SUMMARY for skipped products and failure reasons. Previously saved reviews can remain useful after a partial interruption. PENDING_TCINS may list products to retry after a deadline. Retrying can repeat reviews from a partly processed product.

Optional fields may be null when Target does not supply them. There are no separate product-summary rows. Do not sum repeated product totals across reviews.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

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"]}}'

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)

MCP and AI agents

You can ask your AI assistant to collect data with Target Reviews Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Target Reviews Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

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

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Target Reviews Scraper by Fetch Cat, or give it the identifier fetch_cat/target-reviews-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Get the 20 most recent reviews for this Target product and summarize common complaints, keeping source review IDs.”
• “Collect up to 30 reviews for this Target product, starting with the lowest ratings, then show the one-star reviews.”
• “Collect recent reviews for these two Target products and help me compare recurring positive feedback.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

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 and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review from the Actor page when the review form is available. Reviews help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥