Target Reviews & Ratings Scraper

What does Target Reviews & Ratings Scraper do?

Target Reviews & Ratings Scraper extracts structured listing data from target.com — including full descriptions and seller ratings. It supports keyword search and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions) where the source provides them.

New to Apify? Sign up free and use the included $5 monthly platform credit to test this actor.

Key features

• 🔔 Notifications — Telegram, Slack, Discord, WhatsApp Cloud API, and generic webhook out of the box. Pair with incremental for daily new-listing alerts without pipeline glue.
• 📋 Detail enrichment — toggle two-stage scraping: first collect listings, then enrich each with full description + detail-page-only fields. Off by default to keep runs fast; flip on when you need the deep payload.
• 📧 Email + phone extraction — best-effort regex extraction of contact emails and phone numbers from descriptions — emitted as extractedEmails[] and extractedPhones[] on every record.
• 🔗 URL + social-profile extraction — every record carries extractedUrls[] plus a structured socialProfiles { linkedin, twitter, instagram, facebook, youtube, tiktok, github, xing } parsed from the description.
• 📦 Compact mode — AI-agent and MCP-friendly payloads with core fields only.
• ✂️ Description truncation — cap description length with descriptionMaxLength to control LLM prompt cost and dataset size — set 0 for full descriptions, or any char-limit to trim.
• 📌 Change classification — each record carries a changeType of NEW / UPDATED / UNCHANGED / REAPPEARED / EXPIRED. Default emits NEW + UPDATED + REAPPEARED; opt into the others with emitUnchanged / emitExpired.
• 🔌 MCP connectors — export your results into Notion via Apify's MCP connectors — a clean run-summary page, no glue code. Opt-in via the App connector field; deterministic field-mapping, no AI. Built on Apify's connector framework, so more destinations open up as their catalog grows.
• 📝 Description format selection — pick a single description representation — text, html, or markdown — and the unused variants are dropped from each record. Halves payload size when your pipeline only consumes one format.
• ♻️ Incremental mode — recurring runs emit and charge only for listings that are new or whose tracked content changed. First run builds the baseline; subsequent runs emit only NEW / UPDATED / REAPPEARED records (UNCHANGED + EXPIRED opt-in). Saves 80–95% on daily monitoring.
• 🧹 Empty-field stripping — drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards that already handle missing fields gracefully.

What data can you extract from target.com?

Each result includes Core listing fields (tcin, product_id, product_url, portalUrl, product_title, product_brand, product_image_url, and product_category, and more) and detail fields when enrichment is enabled (descriptionText, descriptionHtml, descriptionMarkdown, and detailFetched). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions where the source provides them.

Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — Find products by keyword (e.g. "laptops") and read their reviews. Use 🛒 Products instead when you already know the exact products. A product URL or TCIN pasted here also works.
• maxProducts — How many products each search term resolves to. Every product costs its own review requests, so this is the main lever on run size. Ignored when you list products directly. (default: 10)
• products — Target.com product URLs or bare TCINs, when you know exactly which products you want. Leave empty to search by keyword instead.
• maxResults — Maximum REVIEW records across all products (0 = as many as Target serves). Target paginates at most 2,550 reviews per product. Product summary records, when enabled, are additional and not counted against this. (default: 100)
• onlyVerified — Only reviews Target marks as a verified purchase. (default: false)
• onlyWithPhotos — Only reviews that include customer photos. (default: false)
• minRating — Drop reviews below this rating (1–5). Leave empty for all ratings. Ratings-only records (products with stars but no written review) are always kept.
• sortBy — Order of the delivered reviews. Target ignores sort requests, so this is applied to the reviews this run collected: with a Max Reviews cap the run reads the first N in Target's own order and sorts those. To rank across every review of a product, set Max Reviews to 0. (default: "source")
• includeDetails — Add the product title, brand, image and category to every review. Costs one extra request per product, not per review. (default: true)
• includeProductSummary — Add one analysis record per product: the star distribution as percentages, positive/negative split, verified and photo shares, review counts per year, a 12-month rating trend, the best- and worst-rated attributes, and the most helpful review. Computed from the reviews the run already fetched — no extra requests. Adds one record per product to the results. (default: true)
• descriptionMaxLength — Truncate the review body to N characters. 0 = no truncation. (default: 0)
• compact — Core fields only (for AI-agent/MCP workflows). (default: false)
• ...and 19 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "query": "laptops",
  "maxResults": 50
}

Incremental tracking — Only emit listings that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "query": "laptops",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "laptops-tracker"
}

Compact output for AI agents — Return only core fields for AI-agent and MCP workflows.

→ Small payload with the most important fields — ideal for piping into LLMs without token overhead.

{
  "query": "laptops",
  "maxResults": 50,
  "compact": true
}

Output

Each run produces a dataset of structured listing records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example listing record

{
  "tcin": "91122045",
  "product_id": "91122045",
  "product_url": "https://www.target.com/p/apple-macbook-neo-a18-pro-2026-laptop/-/A-95288385",
  "portalUrl": "https://www.target.com/p/apple-macbook-neo-a18-pro-2026-laptop/-/A-95288385",
  "product_title": "Apple MacBook Neo (A18 Pro, 2026) Laptop",
  "product_brand": "Apple",
  "product_image_url": "https://target.scene7.com/is/image/Target/GUEST_b9b8e7c7-e6c1-4d99-b5bc-fb167801174a",
  "product_category": "Traditional Laptops",
  "position": 1,
  "page": 1,
  "total_reviews_available": 114,
  "total_pages_available": 3,
  "review_id": "fecc22e2-72cf-41cb-a48d-1e50c4132eae",
  "external_review_id": "f6ce49c0-1e1f-5422-b7f4-1f5db0e8778e",
  "channel": "TARGET",
  "rating": 5,
  "rating_range": 5,
  "title": "Neo",
  "text": "So flexible for all my usage reasons . I dod noticed har dents on box though.",
  "is_recommended": true,
  "is_verified": true,
  "is_ratings_only": false,
  "is_syndicated": false,
  "is_incentivized": false,
  "status": "APPROVED",
  "source_client": "targetcom",
  "badges": {
    "verifiedPurchaser": {
      "Id": "verifiedPurchaser",
      "ContentType": "REVIEW",
      "BadgeType": "Custom"
    }
  },
  "badges_order": [
    "verifiedPurchaser"
  ],
  "client_response_count": 0,
  "secondary_ratings": [
    {
      "id": "Display",
      "label": "display",
      "value": 5,
      "range": 5
    },
    {
      "id": "Ease of use",
      "label": "ease of use",
      "value": 5,
      "range": 5
    },
    {
      "id": "Features",
      "label": "features",
      "value": 5,
      "range": 5
    },
    {
      "id": "Speed",
      "label": "speed",
      "value": 5,
      "range": 5
    },
    {
      "id": "Value",
      "label": "value",
      "value": 5,
      "range": 5
    }
  ],
  "secondary_ratings_order": [
    "Display",
    "Ease of use",
    "Features",
    "Speed",
    "Value"
  ],
  "author_external_id": "7987353824",
  "feedback_helpful": 0,
  "feedback_unhelpful": 0,
  "feedback_inappropriate": 0,
  "submitted_at": "2026-08-04T19:39:59.000+00:00",
  "modified_at": "2026-08-04T20:18:03.000+00:00",
  "statistics_average_rating": 3.99,
  "statistics_rating_count": 265,
  "statistics_review_count": 114,
  "statistics_rating_distribution": {
    "1": 55,
    "2": 7,
    "3": 10,
    "4": 8,
    "5": 185
  },
  "statistics_positive_percentage": 76,
  "statistics_recommended_count": 60,
  "statistics_not_recommended_count": 16,
  "statistics_recommended_percentage": 78,
  "statistics_reviews_with_images_count": 9,
  "statistics_reviews_with_videos_count": 0,
  "statistics_question_count": 90,
  "statistics_secondary_averages": [
    {
      "id": "display",
      "label": "display",
      "value": 4.42,
      "range": 5
    },
    {
      "id": "ease of use",
      "label": "ease of use",
      "value": 4.29,
      "range": 5
    },
    {
      "id": "features",
      "label": "features",
      "value": 4.36,
      "range": 5
    },
    {
      "id": "speed",
      "label": "speed",
      "value": 4.37,
      "range": 5
    },
    {
      "id": "value",
      "label": "value",
      "value": 4.3,
      "range": 5
    }
  ],
  "descriptionText": "So flexible for all my usage reasons . I dod noticed har dents on box though.",
  "descriptionHtml": "<p>So flexible for all my usage reasons . I dod noticed har dents on box though.</p>",
  "descriptionMarkdown": "So flexible for all my usage reasons . I dod noticed har dents on box though.",
  "scraped_at": "2026-08-05T12:56:02.936Z",
  "listingId": "7b3f5d991cd186934ea629688679024a03f20d8684f7cb1a1d1e1514e59f8fc6",
  "searchQuery": "91122045",
  "contentQuality": "full",
  "detailFetched": true,
  "scrapedAt": "2026-08-05T12:56:02.936Z",
  "source": "target.com",
  "contentHash": "83eada92c8ce146b75f5cec535fecd590b6b671a772ef8ba928e5f9e95f918fd"
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED. Default output covers NEW / UPDATED / REAPPEARED; set emitUnchanged: true or emitExpired: true to opt into the others.

How to scrape target.com

1. Go to Target Reviews & Ratings Scraper in Apify Console.
2. Enter a search keyword.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract listing data from target.com for market research and competitive analysis.
• Monitor new and changed listings on scheduled runs without processing the full dataset every time.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.
• Benchmark seller / dealer reputation using rating fields.

How much does it cost to scrape target.com?

Target Reviews & Ratings Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.0005 per listing record

Example costs:

• 10 results: $0.01
• 25 results: $0.018
• 100 results: $0.055
• 200 results: $0.11
• 500 results: $0.26

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of listings that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 listings per run, daily polling (30 runs/month). Costs scale linearly with the number of listings.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.13	$0.01	$0.12 (91%)	$0.34
15% — moderate broad query	$0.13	$0.02	$0.11 (82%)	$0.71
30% — high-volume aggregator	$0.13	$0.04	$0.09 (67%)	$1.27

Full re-scrape monthly cost at the same cadence: $3.90. First month with incremental costs $0.46 / $0.82 / $1.36 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

FAQ

How many results can I get from target.com?

The number of results depends on the search query and available listings on target.com. Use the maxResults parameter to control how many results are returned per run.

Does Target Reviews & Ratings Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Target Reviews & Ratings Scraper with other apps?

Yes. Target Reviews & Ratings Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Target Reviews & Ratings Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Target Reviews & Ratings Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, descriptionMaxLength, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape target.com?

This actor extracts publicly available data from target.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.

Disclaimer

This actor accesses only publicly available data on target.com. You are responsible for how you use the extracted data — in particular any personal information such as names, phone numbers, or email addresses — and for complying with Target Reviews & Ratings's terms of use, applicable data-protection law (including the GDPR where it applies), and the anti-spam rules of your jurisdiction.

This actor is not affiliated with, endorsed by, or connected to Target Reviews & Ratings.

Search keywords

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