G2 Reviews Scraper — B2B Software Reviews, Ratings & Sentiment Data

Pull structured product reviews from G2.com — the world's largest B2B software review platform with 2 million+ verified reviews across 100,000+ products. Returns review text, star ratings, reviewer titles, pros and cons, recommendation status, and verified-buyer flags.

No login, no G2 API agreement required. Pay-per-result pricing means you only pay for reviews you actually receive.

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Why Scrape G2 Reviews?

G2 is the most cited source of B2B software reviews. Buyers consult G2 before making seven-figure software decisions. Vendors cite G2 ratings in their marketing. Investors use G2 sentiment as a leading indicator of product-market fit. But the data is locked inside G2's pages and is not available via a public free API.

The pain points:

• G2's commercial API requires a Vendor program subscription that starts at thousands per month
• Manually extracting reviews across multiple products and competitors is hours of work per category
• Comparing pros/cons across competing products requires structured data, not screenshots
• Reputation drift over weeks is invisible without regular snapshots
• G2's pages render with anti-bot protections; naive scrapers break within a week

This actor handles all of it. Submit one or more G2 product review-page URLs, and it returns clean structured records.

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What You Get Per Review

Every review record includes:

• productName — software product being reviewed
• productSlug — the G2 URL slug for the product
• productUrl — full G2 product page URL
• reviewId — G2's unique review identifier
• stars — star rating from 1 to 5
• title — reviewer-written review headline
• publishedAt — ISO timestamp of when the review was published
• reviewerName — public reviewer name (or "Verified User" if anonymous)
• reviewerTitle — reviewer's job title (e.g. "Marketing Manager")
• reviewerCompanySize — company-size bracket (e.g. "Small-Business (50 or fewer emp.)")
• reviewerIndustry — reviewer's listed industry
• likedBest — answer to "What do you like best about [product]?"
• likedLeast — answer to "What do you dislike about [product]?"
• problemsSolving — answer to "What problems is [product] solving?"
• isRecommended — boolean, whether the reviewer would recommend the product
• isVerified — boolean, whether the review is from a verified buyer
• helpfulCount — number of users who marked the review as helpful

Output is delivered as a structured Apify dataset — exportable to JSON, CSV, Excel, or XML, or fetchable via the Apify API.

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Use Cases

1. Competitive product analysis

Scrape your top three competitors' G2 review pages. Extract the most common complaints in likedLeast. Those complaints are your differentiation playbook — every gap that frustrates a competitor's user base is a feature you can win on. Update marketing copy, sales objection-handling, and product roadmap with this signal.

2. Vendor due diligence

Evaluating an enterprise software purchase? Pull every G2 review of the candidate vendors. Cluster the complaints, weight them by reviewer company size, and you get a structured picture of where each tool falls down — far more useful than vendor-curated case studies.

3. Voice-of-customer research

Marketing teams use review text to source landing-page copy, ad headlines, and email subject lines. Scrape your own product's reviews monthly, run sentiment analysis on likedBest, and you have a steady supply of authentic customer language.

4. Investment research

VC and PE firms use G2 review velocity, sentiment, and reviewer-company-size as a forward indicator of revenue trajectory. A product whose 1-star review velocity is accelerating is often six months ahead of a churn problem appearing in the financials.

5. Sales enablement

Pull recent positive reviews of your product, filtered by reviewer industry and company size, and route them to the SDRs targeting that segment. A 5-star review from a CMO at a 200-person SaaS company is the perfect quote for your outbound sequence to that ICP.

6. Market category mapping

Pull review data across an entire category (e.g. all CRM products on G2 above 100 reviews). Visualize average ratings, review counts, and segment penetration. The result is a category-level competitive landscape that you couldn't get any other way without paying a research firm.

7. Customer success and churn prevention

Schedule weekly runs on your own product. Slack-alert your CS team the moment a new 1- or 2-star review appears. Reach out to the reviewer (when contact info is public) within hours. The fastest CS responses save the most accounts.

8. M&A and acquisition intelligence

Before acquiring a SaaS company, cross-reference their G2 review trend, NPS-style scoring (recommendation rate), and reviewer-company-size mix against their financials. A premium-priced product whose reviews skew toward small-business and trend negative is a higher-risk acquisition.

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How to Use

1. Open the actor on Apify: apify.com/cryptosignals/g2-reviews-scraper
2. Click Try for free — no credit card required for small runs
3. Provide one or more product review-page URLs (e.g. https://www.g2.com/products/slack/reviews)
4. (Optional) Set maxReviews, sortBy, and filterStars to control what you pull
5. Click Start — small runs (50 reviews) typically complete in 1–3 minutes
6. Download results as JSON, CSV, Excel, or via the Apify API

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Input Parameters

Parameter	Type	Required	Default	Description
productUrls	array	One of productUrls/companyUrl	["https://www.g2.com/products/slack/reviews"]	List of G2 product review URLs
companyUrl	string	One of productUrls/companyUrl	—	Single G2 product review URL (alternative to productUrls)
maxReviews	integer	No	5	Maximum reviews per product (set 0 for all available)
sortBy	string	No	most_recent	Sort order: most_recent, most_helpful, highest_rated, lowest_rated
filterStars	integer	No	—	Only return reviews with this exact star rating (1–5). Leave empty for all.
proxyConfiguration	object	No	Apify residential	Proxy settings — residential IPs strongly recommended

Example — pull recent Slack reviews

{
  "productUrls": ["https://www.g2.com/products/slack/reviews"],
  "maxReviews": 50,
  "sortBy": "most_recent"
}

Example — compare three competitors

{
  "productUrls": [
    "https://www.g2.com/products/slack/reviews",
    "https://www.g2.com/products/microsoft-teams/reviews",
    "https://www.g2.com/products/discord/reviews"
  ],
  "maxReviews": 100,
  "sortBy": "most_recent"
}

Example — pull all 1-star reviews of a product

{
  "companyUrl": "https://www.g2.com/products/example-product/reviews",
  "filterStars": 1,
  "maxReviews": 200,
  "sortBy": "most_recent"
}

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Output Example

Each review is a JSON object:

{
  "productName": "Slack",
  "productSlug": "slack",
  "productUrl": "https://www.g2.com/products/slack/reviews",
  "reviewId": "9876543",
  "stars": 5,
  "title": "Best team communication tool we've used",
  "publishedAt": "2026-04-22T10:18:00Z",
  "reviewerName": "Verified User in Marketing",
  "reviewerTitle": "Marketing Manager",
  "reviewerCompanySize": "Mid-Market (51-1000 emp.)",
  "reviewerIndustry": "Marketing & Advertising",
  "likedBest": "The integrations with our other tools are seamless. Slack is the central nervous system of our company — every notification, every approval, every doc review flows through it.",
  "likedLeast": "Notification fatigue is real. We need better default settings for new hires.",
  "problemsSolving": "Replaces email threads, decouples async work, and makes cross-team coordination visible.",
  "isRecommended": true,
  "isVerified": true,
  "helpfulCount": 14
}

Datasets can be exported as JSON, CSV, XML, or Excel from the Apify console, or fetched programmatically via the dataset API.

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Calling the Actor Programmatically

Python — apify-client

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("cryptosignals/g2-reviews-scraper").call(run_input={
    "productUrls": ["https://www.g2.com/products/slack/reviews"],
    "maxReviews": 50,
    "sortBy": "most_recent",
})

for review in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{review['stars']}★] {review['title']} — {review['reviewerTitle']}")

Python — competitive analysis with pandas

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("cryptosignals/g2-reviews-scraper").call(run_input={
    "productUrls": [
        "https://www.g2.com/products/slack/reviews",
        "https://www.g2.com/products/microsoft-teams/reviews",
        "https://www.g2.com/products/discord/reviews"
    ],
    "maxReviews": 200,
    "sortBy": "most_recent",
})

reviews = list(client.dataset(run["defaultDatasetId"]).iterate_items())
df = pd.DataFrame(reviews)

# Average score by product
print(df.groupby("productName")["stars"].agg(["mean", "count"]))

# Common complaints
print(df[df["stars"] <= 3]["likedLeast"].value_counts().head(20))

Node.js — apify-client

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('cryptosignals/g2-reviews-scraper').call({
    productUrls: ['https://www.g2.com/products/zoom/reviews'],
    maxReviews: 100,
    sortBy: 'most_recent',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(r => console.log(`${r.stars}★ ${r.title}`));

cURL — direct API call

curl -X POST \
  "https://api.apify.com/v2/acts/cryptosignals~g2-reviews-scraper/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "productUrls": ["https://www.g2.com/products/notion/reviews"],
    "maxReviews": 50,
    "sortBy": "most_recent"
  }'

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Pricing

This actor uses Pay-per-event pricing — you only pay for reviews you actually receive. No charges for failed runs or compute time.

• Cost: per-review pricing as listed on the actor's pricing page
• Free tier: Apify's free plan includes $5/month of platform credits
• Cost examples:

Use case	Reviews	Approximate cost
Quick competitive check	25	< $1
Weekly competitor sweep	100/week	~$3/week
Voice-of-customer dataset	1,000	~$30
Full category audit	10,000	~$300

See Apify pricing →

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FAQ

Is scraping G2 legal?

Scraping publicly visible data — pages anyone can view without logging in — is generally considered lawful in many jurisdictions. The U.S. Ninth Circuit's hiQ v. LinkedIn (2022) decision held that scraping publicly accessible data does not violate the Computer Fraud and Abuse Act. This actor only collects data from G2's publicly visible review pages — no login, no private data. Always consult your own legal counsel for your specific use case.

Do I need a G2 account or API key?

No. The actor only accesses G2's publicly visible review pages — no login, no Vendor program subscription, no commercial API agreement.

What's the difference vs the G2 Vendor API?

G2's Vendor program API is restricted to paying G2 customers and provides commercial-grade data feeds. This scraper provides the same publicly visible review content without a subscription. For most independent use cases (research, competitive analysis, voice-of-customer mining), the public review pages are sufficient.

How fast is it?

A run of 50 reviews typically completes in 1–3 minutes. Larger runs (200+ reviews per product) finish within 10–15 minutes. Multiple runs can execute in parallel using Apify's concurrent-run feature.

What if G2 blocks the request?

The actor uses Apify residential proxies by default, which rotate IPs across thousands of geographies. This bypasses the standard anti-bot protections G2 uses. Failed requests are retried automatically.

Can I get reviewer email addresses?

No. The actor only returns publicly displayed reviewer information — typically a job title, company size bracket, and industry. Reviewer email addresses are not displayed on G2's public pages and are not collected.

Can I schedule recurring scrapes?

Yes. Apify has a built-in scheduler — set any cron expression (hourly, daily, weekly), and have results pushed to a webhook, Google Sheets, Airtable, Slack, or your own database. Combine with Apify's Zapier and Make integrations for downstream automation.

What output formats are supported?

JSON, CSV, Excel, and XML are all supported in the Apify console. You can also fetch results programmatically via the dataset API.

How does sortBy interact with filterStars?

sortBy controls the order reviews are pulled in (most recent, most helpful, etc.). filterStars filters the review pool down to a specific star rating. They work together — for example, sortBy: "most_recent" + filterStars: 1 gives you the freshest 1-star reviews.

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Why This Actor vs Alternatives?

• No G2 Vendor subscription required. Some competing tools require you to be a G2 paying customer. This actor doesn't.
• Pay-per-result. You only pay for reviews you actually receive. Failed runs cost zero.
• Multi-product input. Pass an array of product URLs in a single run — perfect for competitive sweeps.
• Filter by star rating in one input. Pull only 1-star reviews of a competitor or only 5-star reviews of your own product without any post-processing.
• Residential proxies built in. Anti-bot handling is automatic.
• Apify-native integrations. Pipe results into Zapier, Make, Google Sheets, Airtable, Slack, or any of Apify's 50+ integrations.
• Maintained. The actor is updated when G2 evolves its layout.

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• Software Advice Scraper — Reviews and product data from Software Advice

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About Web Data Labs

This actor is built and maintained by Web Data Labs — a team focused on production-grade web data extraction across jobs, e-commerce, social media, software reviews, and company data. We publish 100+ public actors on the Apify platform, all pay-per-result.

Need a custom build, enterprise SLA, or private actor for your team? Reach out via web-data-labs.com or the Apify contact form.