Sitejabber Reviews Scraper

Scrape public Sitejabber / SmartCustomer business reviews, ratings, review text, reviewer names, review dates, and business-level rating totals from review pages or plain domains.

What does Sitejabber Reviews Scraper do?

Sitejabber Reviews Scraper extracts public review data from Sitejabber business pages.

It is built for recurring reputation monitoring, competitor research, marketplace analysis, and customer sentiment tracking.

You can enter Sitejabber review URLs such as https://www.sitejabber.com/reviews/amazon.com or plain domains such as amazon.com.

The actor normalizes those inputs, paginates the public review pages, and saves one dataset row per review.

Who is it for?

🧑‍💼 Reputation management agencies can monitor new complaints and star changes for client brands.

🛒 Ecommerce operators can compare their Sitejabber feedback against marketplace competitors.

📊 Analysts can collect review text for sentiment, support, logistics, or product-quality research.

🏢 Brand teams can build recurring review archives outside Trustpilot and Google Maps.

Why use this actor?

Sitejabber is a review source that many broad reputation stacks miss.

This actor is HTTP-first and lightweight, so small monitoring runs are fast and inexpensive.

It returns a flat review dataset that is easy to export to CSV, Excel, Google Sheets, a database, or an LLM pipeline.

What data can you extract?

Field	Description
businessDomain	Domain from the Sitejabber review page
businessName	Public business display name
businessWebsite	Website listed in structured data
aggregateRating	Overall business rating
totalReviewCount	Total reviews reported by the page
reviewId	Review identifier when derivable from the review URL
reviewUrl	Public review URL
rating	Individual review star rating
title	Review headline
text	Review body text
reviewerName	Public reviewer name
reviewerProfileUrl	Public reviewer profile URL when available
reviewDate	Review publication date
pageNumber	Page number where the review was found
scrapedAt	Timestamp of the scrape

How much does it cost to scrape Sitejabber reviews?

The actor uses pay-per-event pricing.

There is a small run-start charge and a per-review charge for each dataset item saved.

A prefilled run is intentionally small so you can test the output before scaling up.

For large recurring jobs, increase maxReviewsPerBusiness after verifying the first result sample.

How to scrape Sitejabber reviews

1. Open the actor on Apify.
2. Add one or more Sitejabber review URLs or company domains.
3. Choose the maximum reviews per business.
4. Pick a sort order if needed.
5. Start the run.
6. Download results from the default dataset.

Input configuration

Sitejabber review URLs

Use startUrls for full review page URLs.

Example:

[
  { "url": "https://www.sitejabber.com/reviews/amazon.com" }
]

Company domains

Use domains when you only have company domains.

Example:

["amazon.com", "ebay.com"]

Maximum reviews per business

maxReviewsPerBusiness controls the cap for each business.

Use a low value for smoke tests and a higher value for production exports.

Sort order

The actor supports the public Sitejabber sort query values exposed by the website.

Use the default published sort for monitoring new reviews.

Example input

{
  "startUrls": [
    { "url": "https://www.sitejabber.com/reviews/amazon.com" }
  ],
  "domains": [],
  "maxReviewsPerBusiness": 40,
  "sort": "published"
}

Example output

{
  "businessDomain": "amazon.com",
  "businessName": "Amazon",
  "aggregateRating": 2.5,
  "totalReviewCount": 11109,
  "rating": 1,
  "title": "Order problem",
  "text": "The review text appears here...",
  "reviewerName": "Jane D.",
  "reviewDate": "2026-05-14",
  "reviewUrl": "https://www.smartcustomer.com/reviews/amazon.com#review-123456",
  "pageNumber": 1,
  "scrapedAt": "2026-05-27T00:00:00.000Z"
}

Tips for better results

✅ Start with one domain and 20 to 40 reviews.

✅ Use published sort for monitoring workflows.

✅ Use domains when your source list comes from a CRM or spreadsheet.

✅ Store scrapedAt with your exports so you can compare snapshots over time.

Common use cases

• Daily brand reputation monitoring
• Ecommerce competitor review tracking
• Customer complaint clustering
• Negative-review alerting
• Marketplace trust research
• LLM sentiment analysis datasets
• Support and fulfillment issue discovery

Integrations

Connect the dataset to Google Sheets for daily reporting.

Send output to a webhook and alert Slack when low-star reviews appear.

Export JSON to a data warehouse and join it with sales, returns, or support metrics.

Feed review text into an LLM to classify complaint categories and urgency.

API usage

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('automation-lab/sitejabber-reviews-scraper').call({
  domains: ['amazon.com'],
  maxReviewsPerBusiness: 20,
  sort: 'published'
});
console.log(run.defaultDatasetId);

Python

from apify_client import ApifyClient

client = ApifyClient('MY-APIFY-TOKEN')
run = client.actor('automation-lab/sitejabber-reviews-scraper').call(run_input={
    'domains': ['amazon.com'],
    'maxReviewsPerBusiness': 20,
    'sort': 'published',
})
print(run['defaultDatasetId'])

cURL

curl -X POST "https://api.apify.com/v2/acts/automation-lab~sitejabber-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"domains":["amazon.com"],"maxReviewsPerBusiness":20,"sort":"published"}'

MCP usage

Use the Apify MCP server with Claude Code, Claude Desktop, Cursor, or VS Code to call this actor from your AI workspace.

MCP URL:

https://mcp.apify.com/?tools=automation-lab/sitejabber-reviews-scraper

Claude Code setup:

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

Claude Desktop JSON config:

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

Cursor setup:

Add the same MCP URL in Cursor's MCP server settings as an HTTP server named apify-sitejabber.

VS Code setup:

Add the same MCP URL in your VS Code MCP configuration as an HTTP server named apify-sitejabber.

Example prompts:

• "Scrape 20 newest Sitejabber reviews for amazon.com and summarize the top complaints."
• "Compare recent Sitejabber ratings for these ecommerce domains."
• "Find one-star review themes from the Sitejabber dataset."

Data quality notes

The actor extracts structured data that Sitejabber publishes in the initial HTML.

Some optional fields may be null if the public page does not expose them in parseable structured data.

Review availability and pagination depend on the public website.

Limitations

The actor does not log in.

It does not bypass private pages or account-only content.

It focuses on business review pages, not category discovery.

FAQ

Why did I get fewer reviews than requested?

The page may have fewer public reviews for that sort/filter, or Sitejabber may stop pagination for that business.

Try a lower cap first, confirm the business page exists, then increase the limit.

Can I enter only a domain?

Yes. Enter example.com in domains and the actor converts it to a Sitejabber review page.

Why are helpful counts null?

The v0.1 scraper uses public structured HTML. Helpful counts are only emitted when they are available in parseable public data.

Legality and responsible use

This actor extracts publicly available information.

You are responsible for using the data in compliance with applicable laws, platform terms, and privacy requirements.

Avoid collecting data that you do not need, and respect deletion or compliance requests in your downstream systems.

Related scrapers

• https://apify.com/automation-lab/trustpilot-scraper
• https://apify.com/automation-lab/trustpilot-reviews-scraper
• https://apify.com/automation-lab/google-maps-reviews-scraper
• https://apify.com/automation-lab/yelp-scraper

Changelog

0.1

Initial Sitejabber review extraction from public review pages.

Support

If a public Sitejabber review page no longer parses correctly, open an Apify issue with the input URL and a recent run link.

Output schema stability

The actor keeps reserved nullable fields for reviewer location, helpful count, and business reply metadata so future improvements can fill them without breaking downstream exports.

Performance

The scraper is HTTP-only and does not launch a browser.

This keeps memory low and makes the actor suitable for frequent monitoring jobs.

Best practices for recurring monitoring

Run the actor daily or weekly with published sort.

Store review URLs or review IDs in your database.

Deduplicate downstream by reviewUrl or by business domain plus review date and text.

Feedback workflows

Use the dataset to route low-star reviews to support, product, logistics, or marketplace teams.

Combine rating, text, and reviewDate for alert rules.

Keep the raw dataset for auditability before applying sentiment labels.