G2 Reviews Scraper - Software Reviews, Ratings & Profiles

G2 Reviews Scraper — Software Reviews, Ratings & Reviewer Profiles

Extract B2B software reviews from G2.com without fighting complex anti-bot walls. This high-performance G2 reviews scraper extracts star ratings, full review titles, pros ("what do you like best?"), cons ("what do you dislike?"), use cases ("what problems does it solve?"), and verified reviewer profiles (role, industry, company size) across any software product on G2.

Whether auditing negative feedback for sales battlecards, tracking competitor sentiment shifts, or bulk-exporting 1,000+ reviews across an entire SaaS category, everything exports cleanly into JSON, CSV, Excel, or programmatic API.

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⚡ At a glance

Feature	Specification
Target Website	G2.com (g2.com/products/{slug}/reviews)
Data Extraction Method	High-speed HTTP syndication feeds (bypasses browser overhead)
Capacity Per Product	Up to ~125 unique reviews per product across 5 star ratings
Bulk Capacity	Fully unlocked for 1,000+ reviews across multi-product portfolios
Data Fields	Product, rating, title, pros, cons, useCase, reviewerType, role, industry, companySize, date, URL
Star Rating Filters	Selectable 1 to 5 stars (filters[nps_score][]={1-5})
Deduplication	Automatic reviewId collision detection with 0 duplicate records
Throughput & Speed	Pure HTTP (under 30s for ~125–250 reviews across products)
Memory Footprint	Lightweight (256 MB to 512 MB default)
Proxy Requirement	Standard Apify datacenter proxies (no costly residential required)
Export Formats	Dataset JSON, flat CSV, spreadsheet Excel, XML, HTML, Webhook

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🔎 Why scrape G2?

G2.com is the definitive global authority for B2B software ratings and enterprise technology evaluation. With over 2.5 million verified user reviews, G2 represents an irreplaceable source of voice-of-customer (VoC) feedback, competitive intelligence, and software market trends.

However, extracting review text from G2 poses significant challenges:

1. Aggressive anti-bot defenses: G2's HTML front-end relies on Cloudflare and DataDome challenges that block standard datacenter scrapers with HTTP 403 errors.
2. Browser automation overhead: Using headless browser clusters (Playwright, Puppeteer) to click through pagination is prohibitively slow, fragile, and memory-intensive.
3. Fragmented review text: G2 splits review responses across structured prompts ("What do you like best", "What do you dislike", "Recommendations for others", and "Problems solved").

This G2 data extractor circumvents these roadblocks entirely. By querying G2's public review syndication endpoints directly over high-speed HTTP, it captures clean, unblocked review data in seconds with zero browser overhead and zero proxy friction.

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⚖️ Feature comparison: Why choose this Actor?

Feature	Browser-Based G2 Scrapers	Manual Research	This Apify Actor
Anti-Bot Resistance	Frequently 403-blocked	High labor cost	100% Unblocked HTTP Access
Run Speed	3–5 minutes per product	Hours per batch	10–25 seconds per product
Star-Level Targeting	Manual page navigation	Error-prone	Direct 1–5 Star Rating Filter
Reviewer Metadata	Parsing HTML DOM	Copy-pasting text	Structured role, industry, size
Negative Review Mining	Must scroll all pages	Manual sorting	Instant 1-Star & 2-Star isolation
Bulk Scaling	High memory cost	Unscalable	1,000+ reviews across SaaS lists
Reliability	Breaks on CSS redesigns	Fatigue-prone	Standardized feed schema

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✨ Core capabilities

• ⭐ Multi-star coverage: Queries all 5 individual star levels (1 to 5) to collect up to ~125 fresh, unique reviews per target product.
• 💬 Complete Q&A breakdown: Separates each review into distinct, actionable fields: pros (strengths), cons (weaknesses), and useCase (business impact).
• 👤 Verified reviewer profiles: Extracts reviewer verification status, job title/role, target industry, and company headcount size.
• 🎯 Negative review isolation: Narrow starFilters to ["1", "2"] to extract churn risks, technical grievances, and competitor vulnerabilities instantly.
• 📦 Bulk category scaling: When requesting maxItems: 1000+, the actor automatically expands review collection across leading category leaders.
• 🧹 Automatic deduplication: Merges review records across feeds using unique reviewId identifiers to guarantee 0 duplicated items.
• ⚡ Lightweight & fast: Consumes only 256–512 MB of RAM, executing in seconds without expensive residential proxy bandwidth.

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🎯 Use cases & industry applications

1. Competitive intelligence & battlecards

• Identify competitor flaws: Mine 1-star and 2-star reviews of rivals to discover recurring bugs, pricing complaints, or customer support breakdowns.
• Arm sales teams: Build competitive battlecards with direct verbatim quotes explaining why customers switched away from legacy solutions.

2. Product management & roadmap prioritization

• Voice-of-customer (VoC) analysis: Feed hundreds of pros and cons into an LLM or clustering model to detect emergent feature requests and UI friction points.
• Benchmark satisfaction: Compare average star ratings and sentiment across direct market competitors.

3. Sales enablement & marketing copy

• Social proof mining: Pull 5-star positive reviews to uncover the exact terminology and value propositions real buyers use.
• Customer testimonial research: Find enthusiastic users within specific industries (e.g., Healthcare, FinTech) or company sizes (Enterprise vs. SMB).

4. SaaS investment & market diligence

• Alternative data for VCs & hedge funds: Assess product adoption velocity, brand loyalty, and sentiment health before investment rounds.
• Category market mapping: Benchmark market leaders across CRM, ERP, project management, and developer tooling.

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📋 Popular software product slugs on G2

You can use either full G2 URLs or short product slugs. Below is a reference list of popular software slugs ready for scraping:

Category	Popular Slugs
Collaboration & Chat	slack, zoom, microsoft-teams, loom, miro
Project & Task Management	asana, trello, monday-com, clickup, jira, basecamp
CRM & Sales	salesforce-sales-cloud, hubspot-sales-hub, pipedrive, zoho-crm
Customer Support	zendesk-support-suite, freshdesk, intercom
Productivity & Docs	notion, airtable, coda-io, confluence
Developer Tools	github, gitlab, postman, datadog
Marketing & SEO	hubspot-marketing-hub, semrush, ahrefs, mailchimp
Design & Media	figma, canva, adobe-creative-cloud

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📥 Input parameters

The actor is designed to run instantly with zero configuration (defaulting to popular tools like slack), while providing fine-grained filters for specialized review mining.

Parameter	Type	Default	Description
productUrls	array	["slack"]	List of G2 product slugs (e.g. slack, asana, zoom) or full URLs (e.g. https://www.g2.com/products/zoom/reviews), one per line.
starFilters	array	["1","2","3","4","5"]	Star ratings to collect. Narrow to ["1","2"] for negative feedback, or ["4","5"] for testimonials.
maxReviewsPerProduct	integer	0	Maximum reviews to collect per product (0 = all available, up to ~125).
maxItems	integer	0	Total maximum reviews across all products. Set to 1000 to trigger broad multi-product category coverage.
maxConcurrency	integer	5	Number of simultaneous products to scrape in parallel (1 to 12).
proxyConfiguration	object	Apify Proxy	Apify datacenter proxy. Enabled by default to distribute requests across multiple IPs.

Sample JSON configuration

{
  "productUrls": [
    "slack",
    "https://www.g2.com/products/zoom/reviews",
    "asana",
    "monday-com",
    "clickup"
  ],
  "starFilters": ["1", "2", "3", "4", "5"],
  "maxReviewsPerProduct": 0,
  "maxItems": 500,
  "maxConcurrency": 5,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

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📊 Output data fields

Every record pushed to the Apify dataset represents a verified software review. All textual values are sanitized, decoded, and normalized.

Field	Coverage	Type	Description
product	🟢 High (100%)	String	G2 software product slug (e.g., "slack", "zoom").
reviewId	🟢 High (100%)	String	Unique numeric identifier for the review.
title	🟢 High (100%)	String	Headline title of the review written by the user.
rating	🟢 High (100%)	Number	Star rating awarded by the reviewer (1.0 to 5.0).
pros	🟢 High (98%)	String	Full text response to "What do you like best?".
cons	🟢 High (96%)	String	Full text response to "What do you dislike?".
useCase	🟢 High (92%)	String	Business problem solved and operational benefits gained.
reviewerType	🟢 High (100%)	String	"Verified User" or the verified full name of the author.
role	🟡 Conditional (70%)	String	Reviewer's professional title (e.g., "Software Engineer").
industry	🟡 Conditional (45%)	String	Reviewer's company industry (e.g., "Information Technology").
companySize	🟡 Conditional (65%)	String	Employee headcount bracket (e.g., "Mid-Market(51-1000 emp.)").
date	🟢 High (100%)	String	ISO 8601 timestamp when the review was published.
url	🟢 High (100%)	String	Direct canonical URL to the review on G2.com.
scrapedAt	🟢 High (100%)	String	ISO 8601 UTC timestamp of data collection.

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📋 Example JSON outputs

1. Positive review (5 stars)

{
  "product": "slack",
  "reviewId": "9482103",
  "title": "Essential communication hub for fast-paced distributed teams",
  "rating": 5,
  "pros": "The huddle feature has essentially replaced unscheduled Zoom meetings for our engineering team. Channels keep project discussions organized, and integrations with Jira and GitHub allow us to receive deployment notifications seamlessly.",
  "cons": "Search functionality can occasionally feel sluggish when querying across channels from several years ago. Notification settings require fine-tuning to prevent notification fatigue.",
  "useCase": "We use Slack as our company-wide operating system across 3 global offices. It has reduced internal email volume by over 80% and improved response times on production incidents.",
  "reviewerType": "Verified User",
  "role": "VP of Engineering",
  "industry": "Computer Software",
  "companySize": "Mid-Market (51-1000 emp.)",
  "date": "2026-10-18T14:32:00.000Z",
  "url": "https://www.g2.com/products/slack/reviews#review-9482103",
  "scrapedAt": "2026-11-10T14:22:15.823Z"
}

2. Critical review (1 star / competitor vulnerability)

{
  "product": "salesforce-sales-cloud",
  "reviewId": "9120481",
  "title": "Overly complex setup that requires a full-time dedicated administrator",
  "rating": 1,
  "pros": "Extremely customizable and has every conceivable enterprise feature if you have a developer team to build it.",
  "cons": "The user interface is dated and cluttered with redundant fields. Reporting is counter-intuitive, and basic changes require consulting hours. Annual pricing escalation is aggressive.",
  "useCase": "We attempted to deploy Salesforce across our 40-person sales team, but adoption was abysmal due to system friction. We ended up migrating to a lightweight modern CRM.",
  "reviewerType": "Verified User",
  "role": "Director of Revenue Operations",
  "industry": "FinTech",
  "companySize": "Small-Business (50 or fewer emp.)",
  "date": "2026-09-04T10:15:00.000Z",
  "url": "https://www.g2.com/products/salesforce-sales-cloud/reviews#review-9120481",
  "scrapedAt": "2026-11-10T14:22:18.104Z"
}

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💻 Programmatic usage examples

Call this Actor from your backend or data workflows using Apify's official client libraries.

Python integration

from apify_client import ApifyClient

# Initialize the ApifyClient with your API token
client = ApifyClient("YOUR_APIFY_TOKEN")

# Prepare actor input (target negative reviews for churn analysis)
run_input = {
    "productUrls": ["slack", "microsoft-teams", "zoom"],
    "starFilters": ["1", "2"],
    "maxReviewsPerProduct": 50,
    "maxItems": 150
}

# Run the actor
run = client.actor("haketa/g2-reviews-scraper").call(run_input=run_input)

# Stream results into your sentiment analysis pipeline
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"[{item['product']}] {item['rating']} Stars: {item['title']}")
    print(f"Cons: {item['cons']}\n")

Node.js integration

import { ApifyClient } from 'apify-client';

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

const input = {
    productUrls: ['hubspot-marketing-hub', 'salesforce-sales-cloud'],
    starFilters: ['4', '5'],
    maxReviewsPerProduct: 50,
};

const run = await client.actor('haketa/g2-reviews-scraper').call(input);
const { items } = await client.dataset(run.defaultDatasetId).listItems();

console.log(`Successfully scraped ${items.length} reviews from G2!`);
items.slice(0, 5).forEach(item => {
    console.log(`- ${item.product} (${item.rating}★): ${item.title}`);
});

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🤖 Feeding G2 review data into AI & LLM workflows

G2 reviews are structured mini-case studies containing authentic customer language. This makes them ideal inputs for Generative AI and LLM workflows:

Automated competitor SWOT analysis prompt

You are a senior product strategy director. Analyze the following 50 negative reviews (1-2 stars) and 50 positive reviews (5 stars) for our direct competitor:

Reviews Dataset:
{{g2_reviews_json}}

Deliver:
1. Top 3 recurring technical weaknesses customers complain about.
2. The primary churn catalysts leading customers to look for alternatives.
3. Feature gaps where we can position our solution as superior.
4. Two verbatim customer quotes to include in our competitive sales battlecards.

Voice of Customer (VoC) sentiment clustering

By passing the pros, cons, and useCase columns into an embedding pipeline (e.g. OpenAI text-embedding-3-small), you can cluster hundreds of reviews into quantitative thematic buckets:

• Usability & UX friction
• Pricing & contract renewal frustration
• Customer support responsiveness
• Integration & API reliability

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🚀 How to use this actor

1. Identify product slugs: Find your target software product on G2. The slug is the segment immediately following /products/ (e.g., g2.com/products/hubspot/reviews → hubspot).
2. Paste products: Enter one or more slugs or full URLs into the G2 products field.
3. Filter star ratings: Choose whether you need comprehensive 360-degree reviews (1–5 stars) or want to isolate negative feedback (1–2 stars).
4. Set review ceilings: Leave maxReviewsPerProduct and maxItems at 0 for complete coverage, or set specific numerical caps.
5. Start extraction: Click Start. The actor logs live progress showing products processed, valid reviews extracted, and running item counts.
6. Download dataset: Export your findings as CSV, Excel, or JSON, or query the Apify dataset directly via API.

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💡 Best practices & optimization tips

• Extracting 1,000+ reviews: Because G2 caps its syndication feed at 25 reviews per star rating (~125 reviews per product), pass a list of 8–10 related products or set maxItems: 1000 to automatically expand collection across industry category leaders.
• Isolate negative reviews for sales: To prepare competitive sales battlecards, set starFilters: ["1", "2"]. This strips out positive fluff and isolates real operational pain points.
• Handling missing company size: Note that industry and companySize are voluntarily provided by reviewers. In our validation runs, core review fields (rating, pros, cons, date) achieve ~100% coverage, while optional demographic metadata achieves 40–70% coverage.
• Automate weekly monitoring: Schedule this actor to run every Monday morning to capture newly posted reviews for your product and immediate rivals.

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❓ Frequently Asked Questions (FAQ)

Do I need a G2 account or login credentials?

No. The scraper queries publicly syndicated feeds that require no login, username, or subscription.

Does this Actor require expensive residential proxies?

No. Because G2 serves its syndication feeds without Cloudflare or DataDome bot challenges, standard Apify datacenter proxies are more than sufficient.

How many reviews can I collect per product?

G2's syndication endpoint returns 25 reviews per star rating tier, yielding up to ~125 unique reviews per product. To collect 1,000+ reviews, provide a list of multiple competitor products or leverage the Actor's automatic product expansion.

What happens if a product has changed its slug or redirects?

The scraper handles URL redirections gracefully. If a slug has changed (such as zoom transitioning to zoom-workplace), it resolves the canonical endpoint and continues scraping.

How can I export reviews directly to Google Sheets?

You can use Apify's native Google Sheets integration, connect via Zapier/Make webhooks, or import the dataset's direct CSV export link using Google Sheets =IMPORTDATA() function.

Can I filter by reviewer role or company size?

The feed returns the reviewer's role, industry, and company size as output columns. You can filter these downstream in pandas, Excel, or SQL to segment enterprise reviews from small-business reviews.

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🔗 Related review & market intelligence scrapers

• Trustpilot Reviews Scraper — Extract consumer product and service reviews.
• RepVue Sales Compensation Scraper — Sales compensation, quotas, and culture metrics.
• Google Trends Scraper — Historical search interest and breakout query monitoring.
• Wikidata Entity Resolver — Ground company and brand entities for automated research.

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📋 Changelog

Version 1.2 (2026-11)

• Unlocked 1,000+ deep review collection: Implemented automatic product list expansion across top software leaders when maxItems > 125 is requested.
• Accelerated prefill performance: Optimized HTTP connection pooling to finish single-product prefill audits in under 15 seconds.
• Refined Q&A parsing engine: Enhanced regular expression matching for nuanced G2 question formats ("What problems is it solving?").
• Strict schema validation: Updated schema specifications to 100% Apify CLI 2026 standards with full dataset view support.

Version 1.1 (2026-10)

• Added star rating filter support (filters[nps_score][]={1-5}).
• Implemented robust HTML entity decoding across pros, cons, and use case fields.
• Added reviewer role, industry, and company size extraction.

Version 1.0 (2026-10)

• Initial release featuring high-speed HTTP review syndication parsing.