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Posted on Mar 25

Best G2 Scrapers in 2026: Software Reviews, Ratings, and Competitor Data

#python #datascience #webdev #tutorial

G2.com is the largest B2B software review platform — over 2 million verified reviews across 100K+ products. If you work in SaaS, sales, or market research, G2 data is gold: star ratings, detailed user reviews, competitor grids, pricing intel, and buyer intent signals.

But G2 doesn't offer a public API. So if you need this data at scale, you need a scraper.

In this guide, I'll cover what G2 data is available, the best tools to extract it, and how to automate the whole pipeline.

What Data Can You Get From G2?

G2 pages are structured around products, categories, and reviews. Here's what's scrapable:

Product profiles: Name, description, category, pricing tier, vendor info
Star ratings: Overall score, individual dimension ratings (ease of use, support, etc.)
User reviews: Full text, pros/cons, reviewer role and company size, date posted
Competitor grids: G2's own quadrant data showing market leaders vs. niche players
Alternatives & comparisons: Which products users evaluated together

This data feeds real business workflows — competitive analysis, sales enablement, product positioning, and investment research.

Top Use Cases

1. SaaS Competitor Intelligence

Track how competitors' ratings change over time. Set up weekly scrapes to monitor new reviews, catch emerging complaints, and spot when a rival's score drops.

2. Sales Prospecting

G2 reviews reveal which companies use which tools. If someone leaves a negative review of your competitor, that's a warm lead. Extract reviewer metadata (role, company size) to build targeted outreach lists.

3. Market Research

Building a new product? Scrape an entire G2 category to understand:

What users love about existing solutions
Common complaints and unmet needs
Price sensitivity signals
Feature gaps

4. Content Marketing

Aggregate review data to publish reports like "State of CRM in 2026" or "Top-Rated Project Management Tools." Data-driven content ranks well and attracts backlinks.

The Best G2 Scraper: Apify G2 Reviews Scraper

I built G2 Reviews Scraper on Apify specifically for this. It runs in the cloud, handles pagination and anti-bot measures, and outputs clean JSON.

Three modes:

Mode	Input	Output
`search`	Search query (e.g., "CRM software")	List of matching G2 products
`product`	G2 product URL	Full product profile with ratings
`reviews`	G2 product URL	All reviews with text, ratings, metadata

Quick Start with the Apify API

You can trigger a scrape programmatically:

import requests
import json

API_TOKEN = "your_apify_token"
ACTOR_ID = "eMYEbySW7UEyOMZdi"

# Start a scraping run
run = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR_ID}/runs",
    headers={"Authorization": f"Bearer {API_TOKEN}"},
    json={
        "mode": "reviews",
        "url": "https://www.g2.com/products/slack/reviews"
    }
)

run_id = run.json()["data"]["id"]
print(f"Run started: {run_id}")

# Fetch results (after run completes)
results = requests.get(
    f"https://api.apify.com/v2/actor-runs/{run_id}/dataset/items",
    headers={"Authorization": f"Bearer {API_TOKEN}"}
)

for review in results.json():
    print(f"{review['rating']}/5 - {review['title']}")
    print(f"  Pros: {review['pros']}")
    print(f"  Cons: {review['cons']}")
    print()

Sample Output

{
  "title": "Great for team communication, but notifications are overwhelming",
  "rating": 4,
  "date": "2026-02-15",
  "reviewer": {
    "role": "Product Manager",
    "company_size": "201-500 employees"
  },
  "pros": "Channel organization, integrations with other tools, search functionality",
  "cons": "Too many notifications, can be distracting, threading UX could improve"
}

Monitoring Your Scraping Pipeline

When you're running scrapers at scale — especially against sites with anti-bot protections — you need visibility into what's working and what's failing.

ScrapeOps gives you a monitoring dashboard purpose-built for web scraping. Track success rates, response times, and costs across all your scrapers. It integrates with Scrapy, Selenium, Playwright, and API-based scrapers.

Key features:

Real-time success/failure rates per scraper
Alert when success rates drop below thresholds
Cost tracking per run
Proxy usage analytics

If you're running multiple scrapers across G2, LinkedIn, Glassdoor, etc., centralized monitoring saves hours of debugging.

Other Approaches

DIY with Playwright/Puppeteer

You can build your own G2 scraper using headless browsers. The main challenges:

G2 uses Cloudflare bot protection
Reviews load dynamically (JavaScript rendering required)
Rate limiting kicks in fast
Pagination requires careful session management

Expect to spend 10-20 hours getting a reliable scraper working, plus ongoing maintenance.

Manual Export

G2 offers limited CSV exports for vendors who pay for their analytics platform. This is expensive ($10K+/year) and only covers your own product's data — not competitors.

Best Practices

Respect rate limits. Don't hammer G2 with thousands of requests per minute. Space your scrapes out.
Cache aggressively. Reviews don't change once posted. Store what you've already scraped.
Schedule weekly runs. New reviews trickle in steadily. Weekly scrapes catch everything without being excessive.
Validate output. Check that review counts match what G2 shows. Missing data usually means pagination issues.
Store raw data. Keep the full JSON output even if you only need ratings today. Future analysis might need review text.

Conclusion

G2 data is some of the most valuable B2B intelligence available — real users sharing real opinions about the software they use daily. With the right scraper, you can turn this into a competitive advantage.

Try the G2 Reviews Scraper on Apify — it handles the hard parts so you can focus on the analysis.

What G2 data are you most interested in? Drop a comment below.

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