Target Reviews Scraper

Export public Target product reviews from product URLs or TCIN item IDs, including star ratings, review text, authors, recommendation signals, helpful votes, dates, and product summary fields.

Use this Actor when you need a repeatable CSV, JSON, Excel, API, or AI-agent export of Target customer feedback for ecommerce research, product quality analysis, brand monitoring, or review intelligence.

What Does Target Reviews Scraper Do?

Target Reviews Scraper turns public Target review pages into structured review rows.

• Scrape by product URL: paste one or more Target product URLs.
• Scrape by TCIN: use Target item IDs directly when you already have them.
• Collect review details: export rating, review title, review text, author nickname, date, recommendation status, helpful votes, and verified-purchaser signals.
• Include product context: repeat product URL, TCIN, title, brand, average rating, review count, and recommendation percentage on each row.
• Sort reviews: request most recent, highest rating, or lowest rating reviews.

Input Example

{
  "startUrls": [
    {
      "url": "https://www.target.com/p/apple-airpods-pro-2nd-generation-with-magsafe-case-usb-c/-/A-85978622"
    }
  ],
  "maxReviews": 25,
  "sortBy": "most_recent"
}

You can also run by TCIN:

{
  "tcins": ["85978622"],
  "maxReviews": 50,
  "sortBy": "lowest_rating"
}

Output Example

{
  "productUrl": "https://www.target.com/p/example/-/A-85978622",
  "tcin": "85978622",
  "productTitle": "Example Product",
  "brand": "Example Brand",
  "averageRating": 4.6,
  "ratingCount": 1240,
  "reviewCount": 1180,
  "recommendedPercentage": 91,
  "reviewId": "abc123",
  "reviewTitle": "Great sound quality",
  "reviewText": "Easy to use and good battery life.",
  "rating": 5,
  "authorNickname": "target_customer",
  "submittedAt": "2026-07-01",
  "isRecommended": true,
  "helpfulVotes": 12,
  "verifiedPurchaser": true,
  "isSyndicated": false,
  "dataSourceName": "Target",
  "sortBy": "most_recent",
  "scrapedAt": "2026-07-07T10:00:00.000Z"
}

Who Is It For?

Input settings

Tips For Better Results

• Use TCINs for stable inputs: product URLs are convenient, but TCINs are the cleanest identifier.
• Start with 20-50 reviews: test a product before increasing maxReviews.
• Use low-rating sort for issue discovery: lowest_rating is useful for product quality analysis.
• Use recent sort for monitoring: most_recent is best for recurring scheduled runs.
• Expect nulls: Target may omit author, verified status, brand, helpful votes, or recommendation fields for some reviews.

API usage

Run Target Reviews Scraper from your own code with the Apify API.

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const input = {
  "startUrls": [
    {
      "url": "https://www.target.com/p/apple-airpods-pro-2nd-generation-with-magsafe-case-usb-c/-/A-85978622"
    }
  ],
  "tcins": [],
  "maxReviews": 20,
  "sortBy": "most_recent",
  "proxyConfiguration": {
    "useApifyProxy": false
  }
};

const run = await client.actor('fetch_cat/target-reviews-scraper').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("fetch_cat/target-reviews-scraper").call(run_input={
  "startUrls": [
    {
      "url": "https://www.target.com/p/apple-airpods-pro-2nd-generation-with-magsafe-case-usb-c/-/A-85978622"
    }
  ],
  "tcins": [],
  "maxReviews": 20,
  "sortBy": "most_recent",
  "proxyConfiguration": {
    "useApifyProxy": false
  }
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~target-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.target.com/p/apple-airpods-pro-2nd-generation-with-magsafe-case-usb-c/-/A-85978622"}],"tcins":[],"maxReviews":20,"sortBy":"most_recent","proxyConfiguration":{"useApifyProxy":false}}'

Use with AI agents via MCP

Target Reviews Scraper can be used by AI assistants through the hosted Apify MCP server.

Claude Code setup

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

Claude Desktop, Cursor, or VS Code JSON config

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

Example prompts

• "Run Target Reviews Scraper with this input JSON and summarize the dataset."
• "Export the latest Target Reviews Scraper results to a table I can review."
• "Schedule this Actor for monitoring and tell me what changed between runs."

Limits And Notes

• The Actor extracts public Target review data; it does not access private customer information.
• Product summary fields are repeated on review rows for easier filtering and grouping.
• Target can return fewer reviews than requested if fewer public reviews are available for the selected sort.
• If Target blocks requests, try Apify Proxy settings and keep runs modest.

Support

Report bugs, wrong output, blocked runs, or missing fields from the Actor page. Include the Apify run ID or run URL, your input JSON, what you expected, what the Actor returned, and one reproducible public URL so the issue can be tested quickly.