G2 Reviews Scraper — Deep Data, Monitoring & Reliable Export

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🚀 What this Actor gives you

Most review datasets stop at a title, a text blob, and a rating. G2 Reviews Scraper returns clean, analysis-ready fields that preserve the structure of the original review.

Capability	What you get
💬 Three separate review answers	likeBest, dislike, and problemsSolved
⭐ Exact rating + rating bucket	Numeric rating and normalized starRatingBucket
👤 Reviewer context	Public reviewer label, role, anonymous status, and industry when available
🏢 Company context	Human-readable company size such as 51-200 employees
🕐 Reliable timestamps	UTC ISO timestamp plus the original publication date
🔗 Source traceability	Direct URL and stable review ID for de-duplication
🤖 LLM-ready content	Preformatted markdownContent for RAG, summarization, and classification

Great for

• 🔔 New-review alerts and reputation monitoring
• 🥊 Competitor pain-point and battlecard research
• 🎯 Voice-of-customer and Jobs-to-be-Done analysis
• 📊 Sentiment, rating, and trend dashboards
• 🤖 RAG pipelines, classifiers, and AI summaries
• 📁 JSON, CSV, Excel, XML, API, webhook, Make, and Zapier workflows

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🚦 Choose the right mode

Mode	Best for	Coverage
⚡ Latest reviews (product_reviews)	Quick snapshots and recent analysis	Up to 125 recent reviews per product
🔔 Incremental (incremental)	Hourly, daily, or weekly scheduled monitoring	Only reviews newer than the last successful run
🗂️ Deep History (deep_history)	Initial backfills and larger research datasets	Best-effort public history, up to your requested limit

[!TIP] For the best monitoring workflow, run Deep History once for the initial backfill, then schedule Incremental to collect new reviews continuously.

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⚡ Quick start

Get the latest 50 Notion reviews

{
  "mode": "product_reviews",
  "productUrls": ["https://www.g2.com/products/notion/reviews"],
  "maxReviews": 50
}

Compare negative reviews for two products

{
  "mode": "deep_history",
  "productUrls": ["slack", "microsoft-teams"],
  "starRatings": ["1", "2"],
  "maxReviews": 500
}

Monitor only newly published reviews

{
  "mode": "incremental",
  "productUrls": ["hubspot-crm", "salesforce-sales-cloud"],
  "maxReviews": 125,
  "monitoringKey": "crm-competitors"
}

When running through an Apify Task, state is isolated automatically and monitoringKey can be omitted. For repeated direct Console or API runs, reuse the same monitoringKey.

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🎛️ Input reference

Field	Type	Default	Description
mode	select	product_reviews	Latest snapshot, incremental monitoring, or Deep History
productUrls	string list	required	Full G2 URLs or bare product slugs, one per line
maxReviews	integer	25	Per-product limit; Latest/Incremental cap at 125, Deep History accepts up to 5,000
starRatings	multiselect	all	Include any combination of 1–5 star buckets
monitoringKey	string	—	Optional stable state key for repeated direct Incremental runs
maxRetries	integer	6	Advanced reliability setting; the default is recommended
proxyConfiguration	object	prefilled	Advanced connection setting; the default is recommended

Accepted product formats

All of these identify the same product:

https://www.g2.com/products/notion/reviews
www.g2.com/products/notion
notion

Duplicate products are removed automatically before collection starts.

How star buckets work

G2 ratings may include half stars. The Actor preserves the exact value in rating and provides a convenient integer bucket in starRatingBucket:

Exact rating	Bucket
0.0–1.0	1
1.5–2.0	2
2.5–3.0	3
3.5–4.0	4
4.5–5.0	5

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📦 Example result

{
  "reviewId": "13124903",
  "productSlug": "notion",
  "productName": "Notion",
  "title": "Versatile, Easy-to-Use Tool That Streamlines HR and Onboarding",
  "rating": 5.0,
  "starRatingBucket": 5,
  "reviewerName": "Verified User in Information Technology and Services",
  "reviewerRole": "G2 User",
  "companySize": "51-200 employees",
  "industry": "Information Technology and Services",
  "isAnonymous": true,
  "likeBest": "I like how versatile it is and how well it supports our workflows.",
  "dislike": "Some navigation patterns can take time to learn.",
  "problemsSolved": "It gives our team one place to organize work and employee information.",
  "publishedAt": "2026-07-17T23:14:38+00:00",
  "publishedAtRaw": "Fri, 17 Jul 2026 18:14:38 -0500",
  "reviewUrl": "https://www.g2.com/products/notion/reviews/notion-review-13124903",
  "markdownContent": "# Notion Review: Versatile, Easy-to-Use Tool..."
}

🧾 Output fields

Group	Fields
Product	productSlug, productName
Review identity	reviewId, reviewUrl, title
Rating	rating, starRatingBucket
Reviewer	reviewerName, reviewerRole, isAnonymous
Company	companySize, industry
Review content	likeBest, dislike, problemsSolved
Time	publishedAt, publishedAtRaw
AI-ready export	markdownContent

Empty optional fields are removed from the final dataset, keeping CSV and JSON exports clean.

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🔔 Reliable incremental monitoring

Incremental mode stores a small, isolated last-seen marker for each product. On the next run, it returns only reviews with newer IDs.

• Apify Tasks: each Task receives its own monitoring scope automatically.
• Direct runs: reuse the same monitoringKey to continue the same monitor.
• Different rating selections: state is isolated so a 1–2 star monitor does not interfere with an all-ratings monitor.
• No new reviews: a successful run with an empty dataset is expected.

[!IMPORTANT] If every returned review is new and the run reaches maxReviews, schedule the Actor more frequently. This prevents a high-volume product from producing more new reviews between runs than the selected limit can hold.

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📚 Coverage — the honest answer

G2 does not provide an unlimited public archive for every product. Public coverage varies by product, rating mix, company mix, and the history currently available from G2.

• Latest / Incremental prioritize fast, repeatable monitoring and return up to 125 recent reviews per product.
• Deep History reaches substantially further and can return hundreds or more for products with sufficient public history.
• maxReviews is an upper limit, not a guarantee that every product exposes that many records.
• Guaranteed parity with the total review count shown by G2 requires licensed G2 data access and is outside this public-data Actor’s scope.

This makes the Actor a strong fit for monitoring, recent sentiment, research backfills, and structured analysis without pretending that public data equals a licensed complete archive.

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💰 Usage and performance

The Actor uses standard Apify platform resources. Your run size is mainly controlled by:

1. Number of products
2. maxReviews per product
3. Latest versus Deep History mode
4. Number of selected rating buckets

For low-cost continuous monitoring, use Incremental mode with an Apify Schedule. For one-time research, choose Deep History and set a deliberate maxReviews limit.

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❓ FAQ

How do I find a G2 product slug?

It is the part after /products/ in the G2 URL. For https://www.g2.com/products/hubspot-crm/reviews, the slug is hubspot-crm.

Why did Deep History return fewer reviews than requested?

The requested number is a maximum. Collection stops when the public history available for that product and rating selection is exhausted.

Why is an Incremental dataset empty?

That normally means no newer reviews appeared since the previous successful run for the same monitoring scope.

Why do some reviews have no personal name?

G2 allows reviewers to appear publicly as “Verified User.” The Actor preserves the public label and sets isAnonymous: true.

Can I export to Excel or CSV?

Yes. Open the run’s Dataset and choose JSON, CSV, Excel, XML, or another supported format. You can also use the Apify API, webhooks, Make, Zapier, or other Apify integrations.

Can this guarantee every G2 review ever published?

No. Deep History is a best-effort export of public history. A guaranteed complete archive requires licensed G2 data access.

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⚖️ Responsible use

This Actor collects publicly available review data for legitimate business intelligence and research. You are responsible for complying with G2’s terms and applicable laws, including GDPR and CCPA. Do not use the data to contact, identify, or profile individual reviewers.

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