We extract software profiles, user reviews, category grids, pricing data, and competitor matrices from G2. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Profiles objects from g2.com. All fields typed and schema-versioned.
"product_id": "salesforce-sales-cloud", "product_name": "Salesforce Sales Cloud", "vendor_name": "Salesforce", "primary_category": "CRM", "overall_rating": 4.3, "review_count": 18492, "starting_price": 25.0, "free_trial_available": true
| # | product_id | product_name | vendor_name | primary_category | overall_rating | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for User Reviews objects from g2.com. All fields typed and schema-versioned.
"review_id": "rev_89324792", "product_id": "salesforce-sales-cloud", "reviewer_title": "Enterprise Account Executive", "company_size": "1001-5000 employees", "industry": "Information Technology", "star_rating": 4.5, "verified_current_user": true, "review_date": "2026-03-12"
| # | review_id | product_id | reviewer_name | reviewer_title | company_size | industry |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Grids objects from g2.com. All fields typed and schema-versioned.
"category_name": "CRM Software", "product_id": "salesforce-sales-cloud", "grid_quadrant": "Leader", "satisfaction_score": 88, "market_presence_score": 99, "g2_score": 94, "rank_position": 1, "report_season": "Spring", "report_year": 2026
| # | category_id | category_name | product_id | grid_quadrant | satisfaction_score | market_presence_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Alternatives & Competitors objects from g2.com. All fields typed and schema-versioned.
"source_product_id": "salesforce-sales-cloud", "alternative_product_id": "hubspot-sales-hub", "similarity_score": 92, "satisfaction_diff": -4.2, "ease_of_use_diff": -12.5, "support_quality_diff": -3.1, "setup_time_diff": 14.0
| # | source_product_id | alternative_product_id | similarity_score | common_features_count | price_difference_pct | satisfaction_diff |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Granular Ratings objects from g2.com. All fields typed and schema-versioned.
"product_id": "salesforce-sales-cloud", "ease_of_use": 8.1, "quality_of_support": 8.3, "ease_of_setup": 7.4, "meets_requirements": 8.9, "ease_of_admin": 7.6, "net_promoter_score": 42
| # | product_id | ease_of_use | quality_of_support | ease_of_setup | meets_requirements | ease_of_admin |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our G2 scraper handles the platform's anti-bot protections, dynamic pagination, and complex Grid rendering to deliver structured software data — from top-level category rankings down to individual user reviews.
Extract vendor data, descriptions, target markets, deployment options, and aggregated rating scores across thousands of software categories.
Capture full text for 'What do you like best?', 'What do you dislike?', and 'Problems solved' alongside star ratings and helpful votes.
Track quadrant positioning (Leaders, High Performers, Contenders, Niche) and underlying satisfaction vs market presence scores.
Map 'Alternatives to X' lists to build relational graphs of software competitors and feature overlap matrices.
Extract specific scores for ease of use, quality of support, ease of setup, and product direction sentiment.
Capture reviewer job titles, company size brackets, and industry verticals to normalise sentiment by user persona.
Extract public pricing models, starting prices, free trial availability, and billing cycle options where published.
Extract supported features, native integrations, API availability, and compliance certifications listed on product profiles.
Run one-off category bulk exports or configure continuous pipelines to track new reviews and rating changes over time.
Brief in. Clean data out.
Provide category URLs, competitor lists, or specific software products. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for g2.com.
Schema validation, null-rate checks, and sample review data verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
G2 protects its proprietary Grid data and review corpus with strict anti-scraping measures. Here is how we maintain reliable extraction.
G2 relies heavily on Cloudflare for bot mitigation. Our crawlers use US-based residential ISP proxies with realistic TLS fingerprints, randomised request timing, and full cookie session management to bypass interstitial challenges.
G2 product pages and review sections load dynamically via React. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering to capture paginated reviews and hidden pricing details.
G2 frequently updates its Grid reports and product page DOM structures. Our selector strategy uses multiple fallback chains — CSS selectors, XPath, and JSON state extraction — to ensure data continuity when layouts change.
For high-volume software profiles, we maintain a hash index of existing review IDs. Subsequent runs only push new or modified reviews — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes in rating fields, missing Grid data, and coverage drops to maintain strict SLA uptime.
Product marketing teams track competitor feature gaps, pricing changes, and negative review sentiment to refine positioning.
Sales teams use alternative matrices and satisfaction scores to build battle cards against entrenched market leaders.
Product managers aggregate 'What do you dislike?' feedback across categories to prioritise roadmap features based on market demand.
Machine learning teams use the structured review corpus to train B2B sentiment analysis models and intent classifiers.
Procurement and IT teams ingest granular rating data to evaluate software vendors on support quality and ease of administration.
Private equity firms track momentum scores and review velocity to identify high-growth SaaS companies and category disruptors.
"G2 holds the definitive dataset for B2B software sentiment and market positioning — but mapping it requires infrastructure built for dynamic, heavily protected DOMs."
Most teams underestimate the investment required: reliable G2 scraping requires bypassing strict Cloudflare protections, handling React-based dynamic pagination, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our g2.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About g2.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from G2 is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated product profiles, category grids, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should review G2's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour. Our systems monitor for challenge loops and trigger automated solver queues when necessary.
We can extract the current visible Grid data and any historical seasonal reports that G2 exposes publicly on the category pages. We also maintain a time-series of Grid movements from the date your pipeline starts.
Pipelines can be configured to run daily or weekly to capture new reviews. Our change-detection system ensures we only process and deliver net-new reviews, keeping latency low and reducing data duplication.
Yes. Every review record includes the reviewer's job title, company size, and industry, provided the user disclosed this information on their public review profile.
Our smallest packages start at a defined list of software categories or specific product profiles with weekly delivery. For full-site extraction or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 50 software profiles or 5 category grids as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category dump or a continuous competitor-monitoring feed — we scope, build, and operate the pipeline. Tell us what you need.
