productName stringclasses 5
values | productSlug stringclasses 5
values | g2Category stringclasses 4
values | g2ReviewCount int64 13.7k 53.5k | g2AverageRating float64 4.3 4.5 | topCompetitors stringclasses 5
values | sampleReviews int64 100 100 | sampleAvgRating float64 4.62 4.86 | pctIncentivized int64 41 67 | pctSwitched int64 4 14 | topSwitchedFrom stringclasses 5
values |
|---|---|---|---|---|---|---|---|---|---|---|
Slack | slack | Knowledge Base | 37,395 | 4.5 | Microsoft Teams; Google Workspace; Rocket.Chat; Connecteam; Webex Suite; Trello | 100 | 4.85 | 41 | 9 | Microsoft Teams (3); Google Workspace (1); Rocket.Chat (1); Connecteam (1); Webex Suite (1) |
Microsoft Teams | microsoft-teams | Business Instant Messaging | 17,527 | 4.3 | Slack; Zoom Workplace; Google Workspace; Cisco Webex Support; Google Analytics; Google Classroom; Webex Suite; GoTo Meeting | 100 | 4.62 | 46 | 14 | Slack (4); Zoom Workplace (4); Google Workspace (2); Cisco Webex Support (1); Google Analytics (1) |
Zoom Workplace | zoom-workplace | AI Meeting Assistants | 53,476 | 4.5 | Google Workspace; Teams Manager for Microsoft Teams; Microsoft Teams; Aircall | 100 | 4.71 | 48 | 7 | Google Workspace (2); Teams Manager for Microsoft Teams (2); Microsoft Teams (2); Aircall (1) |
Google Workspace | google-workspace | Business Instant Messaging | 45,605 | 4.5 | Microsoft 365; Microsoft Teams | 100 | 4.86 | 67 | 4 | Microsoft 365 (3); Microsoft Teams (1) |
Trello | trello | Task Management | 13,653 | 4.3 | Asana; Jira; Notion; FogBugz; monday Work Management; ClickUp | 100 | 4.66 | 46 | 10 | Asana (5); Jira (1); Notion (1); FogBugz (1); monday Work Management (1) |
G2 Team Collaboration Software Reviews — Sample (2026)
A clean, ready-to-analyze sample of 500 public G2 reviews across five popular collaboration tools — Slack, Microsoft Teams, Zoom Workplace, Google Workspace, and Trello — with structured ratings, pros/cons, competitive switching data, and an LLM-ready markdown field per review.
Extracted with the G2 Reviews Scraper on Apify. This is a curated sample — run the actor for any product, at any scale.
Files
| File | Rows | What |
|---|---|---|
reviews.csv / reviews.json / reviews.jsonl |
500 | One row per review, 23 fields |
product_summary.csv / product_summary.json |
5 | Per-product G2 metadata + ranked competitors + sample aggregates |
.jsonl suits ML/RAG loading; .csv suits spreadsheets/Kaggle; .json for general use. Array fields (previousCompetitors) are kept as real arrays in JSON/JSONL and joined with ; in the CSV.
What makes this interesting: competitive switching
Every review records whether the reviewer switched from a competitor, and which one(s) — so you can see, from real customer voice, who is losing users to whom. Note the Slack ↔ Microsoft Teams two-way rivalry:
| Product | Sample avg (/5) | % switched from a rival | Top products they switched from |
|---|---|---|---|
| Slack | 4.85 | 9% | Microsoft Teams, Google Workspace, Rocket.Chat |
| Microsoft Teams | 4.62 | 14% | Slack, Zoom Workplace, Google Workspace |
| Zoom Workplace | 4.71 | 7% | Google Workspace, Microsoft Teams |
| Google Workspace | 4.86 | 4% | Microsoft 365, Microsoft Teams |
| Trello | 4.66 | 10% | Asana, Jira, Notion |
The previousCompetitors + whySwitched fields are the core of competitive battlecards. The companion product_summary also carries the actor's ranked topCompetitors array per product.
Field dictionary (reviews)
| Field | Type | Notes |
|---|---|---|
productName / productSlug |
string | Product and its G2 slug |
reviewId |
string | Unique review id |
reviewUrl |
string | Direct link to the review on G2 (provenance) |
submittedAt |
datetime | When the review was submitted |
overallRating |
int (1–5) | Star rating |
easeOfUse, easeOfSetup, meetsRequirements, qualityOfSupport |
int (1–7) | G2 sub-ratings; blank where the reviewer skipped (optional on G2's form) |
reviewTitle |
string | Review headline |
pros / cons / problemsSolved |
string | Structured voice-of-customer answers |
didSwitchFromCompetitor |
bool | Whether they switched from a rival |
previousCompetitors |
array<string> | Competitor(s) switched from — empty [] when they didn't switch |
whySwitched |
string | Why they switched (sparse — only when provided) |
reviewerRole |
string | Reviewer's role (~70% populated) |
reviewerIndustry |
string | Reviewer's industry |
companySize |
string | e.g. Small-Business, Mid-Market, Enterprise |
reviewerCountry |
string | Reviewer country |
isIncentivized |
bool | Whether G2 gave the reviewer an incentive (bias signal) |
markdownContent |
string | LLM-ready self-contained markdown block — drop straight into a RAG pipeline |
product_summary adds per product: g2Category, g2ReviewCount, g2AverageRating, topCompetitors (array), sampleReviews, sampleAvgRating, pctIncentivized, pctSwitched, topSwitchedFrom.
Fields intentionally omitted (kept clean)
From the actor's full 32-field schema, these were dropped for signal quality: mode/reviewFormat (constant), extractedAt (scrape timestamp), reviewText (sparse, redundant with markdownContent), recommendations (26% filled), and helpfulVotes (near-zero on recent reviews). The full schema is documented in the actor's repo.
Methodology
- Collected from publicly accessible G2 product-review pages via the G2 Reviews Scraper.
- Sort: newest. Up to 100 reviews per product. Sample date: 2026-06.
- Fields curated for signal (see above); no rows synthesized.
Source & usage
The reviews are public content authored by G2 users; all trademarks and content belong to their respective owners and to G2. This sample is shared for research, education, and demonstration of structured review extraction. It is not affiliated with or endorsed by G2. Verify any conclusions against the linked source reviewUrl, and review G2's Terms before redistribution or commercial use.
Get the full data
This is a 5-product, 500-row sample. To pull reviews for any G2 product — all 32 fields, sub-ratings, switching arrays, and LLM-ready markdown — run the actor:
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