G2 Reviews Scraper

Turn G2's 5 million reviews into structured, actionable data. Ratings, NPS scores, pros/cons, reviewer demographics, and switching signals for any B2B software product. In seconds, not hours.

How it works

✨ Why teams choose this over other G2 scrapers

Spending hours copying reviews from G2 tabs? Running scrapers that fail half the time? Missing the data that actually matters for your analysis?

• ⚡ Get 1,000+ reviews per minute. Other G2 scrapers take 30x longer. Paste a URL, get structured data back before your coffee gets cold.
• 📊 38 fields per review, not 15. Star ratings, NPS scores, dimension ratings (ease of use, setup, support), each answer in its own field (likes, dislikes, problems solved, recommendations), reviewer country, company size, industry, and switching data. The fields competitors skip are often the ones you need most.
• 📅 Collect only what is new. Set publishedAfter to a date or a period like 7 days and a weekly schedule returns just the reviews published since the last run.
• 🤖 Ready for your LLM pipeline. Every review includes a markdownContent field you can feed directly into ChatGPT, Claude, or any RAG system. No preprocessing needed.
• 🔍 Search inside reviews. Filter by keyword like "slow", "integration", or "support" to find exactly the feedback you're looking for across thousands of reviews.
• 🏢 Scrape an entire vendor in one run. Paste a seller URL like /sellers/salesforce and get reviews across all 72 of their products at once.
• 💰 Pay per review, nothing else. No compute costs, nothing to set up, no monthly fees. You only pay for data you actually receive.

🎯 What you can do with G2 review data

Team	What they build
Product	Find the 3 features your competitor gets praised for that you don't have yet
Sales	Create battle cards with real quotes from verified G2 reviewers
AI / LLM	Feed thousands of structured reviews into your RAG pipeline or fine-tuning dataset
Research	Compare software adoption by company size and region across 220K+ products
Customer Success	Discover who switches away from your product and the exact reasons why
VCs	Run due diligence on any SaaS product using NPS trends and sentiment data
Content	Pull real testimonials and use cases mentioning specific keywords

📥 Input parameters

Product or Seller URLs

Parameter	Type	Description
productUrls	string[]	G2 product or seller URLs (e.g., https://www.g2.com/products/slack/reviews or https://www.g2.com/sellers/salesforce)

Search by Name

Parameter	Type	Description
productNames	string[]	Search for products by name (e.g., Slack, Notion, Salesforce)

Limits

Parameter	Type	Default	Description
maxReviews	integer	100	Maximum reviews to scrape per product (up to 50,000)

Sorting & Filtering

Parameter	Type	Default	Description
sort	select	Most recent	Sort by: most recent, highest rated, or lowest rated
publishedAfter	date	All dates	Only reviews published on or after this date (2026-08-01) or within a period (7 days, 2 weeks, 1 month). Maximum reviews still caps each product
starRatings	integer[]	All	Filter by star rating (1-5). Leave empty for all ratings
searchQuery	string		Filter reviews containing this keyword (e.g., "integration", "slow", "support")

Options

Parameter	Type	Default	Description
includeSummary	boolean	true	Add an analytics summary with rating distribution, avg NPS, the top pros and cons with their mention counts, top regions, and company segments

Advanced

Parameter	Type	Default	Description
debugMode	boolean	false	Enable detailed logging for troubleshooting

📊 Output data

Review example

{
    "reviewId": "12601006",
    "url": "https://www.g2.com/products/slack/reviews/12601006",
    "productName": "Slack",
    "productSlug": "slack",
    "productId": 3437,
    "reviewTitle": "Good tool for communication management",
    "pros": "Navigation is very easy, and I like having the option to organize different group chats.",
    "cons": "The only issue I have had was archiving old chats where there were people who already left.",
    "problemsSolved": "Communication with the team, all our project updates live in one place.",
    "recommendations": null,
    "reviewText": "Communication with the team, all our project updates live in one place.",
    "markdownContent": "# Good tool for communication management\n\n**Rating:** 5/5 | **NPS:** 9/10...",
    "starRating": 5,
    "npsScore": 9,
    "easeOfUse": 10,
    "easeOfSetup": 8.5,
    "qualityOfSupport": 7.1,
    "meetsRequirements": 8.5,
    "reviewerName": "Elis L.",
    "country": "Estonia",
    "region": "EMEA",
    "companySegment": "Mid-Market",
    "industry": "Information Technology and Services",
    "submittedAt": "2026-04-08T06:38:48.161-05:00",
    "publishedAt": "2026-04-08T14:02:11Z",
    "reviewSource": "g2",
    "switchedFrom": false,
    "responseType": "text",
    "dataType": "review"
}

Analytics summary example

{
    "productName": "Slack",
    "productSlug": "slack",
    "totalReviews": 21099,
    "ratingDistribution": {"1": 43, "2": 97, "3": 480, "4": 3788, "5": 16691},
    "prosSummary": [
        {"keyword": "Chat", "mentions": 3438},
        {"keyword": "Collaboration", "mentions": 2253},
        {"keyword": "Usability", "mentions": 1021}
    ],
    "consSummary": [
        {"keyword": "Chat", "mentions": 2473},
        {"keyword": "Notification", "mentions": 1942},
        {"keyword": "Platform", "mentions": 843}
    ],
    "avgNps": 9.2,
    "avgEaseOfUse": 9.3,
    "avgEaseOfSetup": 9.3,
    "avgQualityOfSupport": 9.0,
    "avgMeetsRequirements": 9.3,
    "topRegions": {"Americas": 12470, "Asia Pacific": 4537, "EMEA": 2405},
    "topCompanySegments": {"Small-Business": 7886, "Mid-Market": 7849, "Enterprise": 3394},
    "topSources": {"g2": 13617, "organic": 3981, "vendor": 450},
    "dataType": "summary"
}

🗂️ Data fields

Category	Fields
Review	reviewId, reviewTitle, pros, cons, problemsSolved, recommendations, reviewText, markdownContent, submittedAt, publishedAt
Ratings	starRating (1-5), npsScore (0-10), easeOfUse, easeOfSetup, easeOfAdmin, qualityOfSupport, meetsRequirements
Reviewer	reviewerName, companySegment, industry, country, region
Product	productName, productSlug, productId, url
Competitive	switchedFrom, switchedReason, loveTheme, hateTheme
Metadata	reviewSource, sourceType, isIncentivized, helpfulCount, responseType
Summary	totalReviews, ratingDistribution, avgNps, avgEaseOfUse, prosSummary, consSummary, topRegions, topCompanySegments, topSources

problemsSolved is the reviewer's answer to "What problems is the product solving and how is that benefiting you?". recommendations is the answer to "Recommendations to others considering the product", a question G2 asked until mid-2022, so it is filled on older reviews and null on recent ones. publishedAt is the date the review went live on G2.

🚀 Examples

Get the latest Slack reviews

{
    "productUrls": ["https://www.g2.com/products/slack/reviews"],
    "maxReviews": 100
}

Collect only last week's new reviews (weekly schedule)

{
    "productUrls": ["https://www.g2.com/products/slack/reviews"],
    "publishedAfter": "7 days",
    "maxReviews": 1000
}

Find reviews mentioning "integration"

{
    "productUrls": ["https://www.g2.com/products/notion/reviews"],
    "maxReviews": 500,
    "searchQuery": "integration"
}

Pull only negative reviews for competitive analysis

{
    "productUrls": ["https://www.g2.com/products/hubspot-sales-hub/reviews"],
    "maxReviews": 200,
    "sort": "lowest_rated",
    "starRatings": [1, 2]
}

Scrape all products from a vendor at once

{
    "productUrls": ["https://www.g2.com/sellers/salesforce"],
    "maxReviews": 50
}

Compare multiple products by name

{
    "productNames": ["Slack", "Notion", "Asana"],
    "maxReviews": 1000,
    "sort": "most_recent",
    "includeSummary": true
}

💻 Integrations

Python: Feed reviews into your LLM

from apify_client import ApifyClient

client = ApifyClient("<YOUR_API_TOKEN>")

run_input = {
    "productUrls": ["https://www.g2.com/products/slack/reviews"],
    "maxReviews": 1000,
    "sort": "most_recent",
}

run = client.actor("silentflow/g2-review-intelligence").call(run_input=run_input)

reviews = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item["dataType"] == "review":
        reviews.append(item)
        print(f"{item['starRating']}⭐ {item['reviewTitle']}")
        print(f"  Pros: {item['pros'][:100]}...")
        print(f"  Cons: {item['cons'][:100]}...")
    elif item["dataType"] == "summary":
        print(f"\nSummary: {item['totalReviews']} reviews, avg NPS: {item['avgNps']}/10")

# Use markdownContent for RAG pipelines
for review in reviews:
    # Feed review["markdownContent"] to your LLM
    pass

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_API_TOKEN>' });

const run = await client.actor('silentflow/g2-review-intelligence').call({
    productUrls: ['https://www.g2.com/products/slack/reviews'],
    maxReviews: 1000,
    sort: 'most_recent',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();

const reviews = items.filter(i => i.dataType === 'review');
const summaries = items.filter(i => i.dataType === 'summary');

reviews.forEach(item => {
    console.log(`${item.starRating}⭐ ${item.reviewTitle}`);
});

summaries.forEach(s => {
    console.log(`${s.productName}: ${s.totalReviews} reviews, NPS ${s.avgNps}/10`);
});

📈 Performance

Metric	Value
Speed	1,000+ reviews per minute
Max reviews per product	50,000
Total reviews accessible	5,300,000+
Total products	224,000+
Memory usage	256 MB (minimal)

💡 Tips for best results

1. Search inside reviews: searchQuery: "slow" finds every review mentioning performance issues across thousands of reviews. Great for competitive analysis.
2. Scrape entire vendors: Paste a seller URL like /sellers/salesforce to get reviews across all of their products in one run.
3. Filter by stars for sentiment analysis: starRatings: [1, 2] for negative feedback, [4, 5] for testimonials.
4. Use the summary: The analytics summary gives you rating distribution, avg NPS, the most mentioned pros and cons, and reviewer demographics without processing individual reviews.
5. Monitor new reviews: Schedule a weekly run with publishedAfter: "7 days" to receive only the reviews published since the last run.
6. Feed markdownContent to your LLM: Each review includes a pre-formatted markdown version ready for ChatGPT, Claude, or your vector database.

❓ FAQ

Q: Do I need a G2 account? A: No. The scraper extracts publicly available review data. No login, no API key, no account.

Q: Why is this so much faster than other G2 scrapers? A: We read G2's review data directly instead of rendering pages in a browser, so a run of thousands of reviews finishes in seconds.

Q: Can I get only the reviews published since my last run? A: Yes. Set publishedAfter to a date like 2026-08-01 or a period like 7 days, 2 weeks or 1 month. Pair it with a weekly or monthly schedule and each run delivers just the new reviews. Maximum reviews still caps each product, so keep it high enough for your window.

Q: Do I get the pros and cons summary? A: Yes. With the summary on, each product gets a summary item with the themes reviewers like and dislike most and how many reviews mention each, for example "Chat: 3,438 mentions".

Q: What is the markdownContent field? A: A pre-formatted markdown version of each review with structured headers (pros, cons, comments) and metadata. Drop it straight into your LLM prompt or vector database.

Q: Can I scrape all products from a single vendor? A: Yes. Paste a seller URL like https://www.g2.com/sellers/salesforce and the scraper finds and scrapes reviews from every product under that vendor.

Q: What are the dimension ratings (easeOfUse, etc.)? A: G2 asks reviewers to rate products on specific dimensions (ease of use, setup, admin, support, meets requirements). We normalize these to a 0-10 scale.

Q: How fresh is the data? A: Live data, every run. New reviews appear within minutes of being published on G2.

📬 Support

Need something this scraper doesn't do yet? We ship features fast.

• 💡 Feature requests go straight to our backlog
• ⚙️ Enterprise needs? We do custom integrations and high-volume setups

Response time: usually under 24 hours.

Check out our other scrapers: SilentFlow on Apify