G2 Review & Switching-Signal Monitor

Turn G2 reviews into recurring competitor intelligence.

Monitor G2 product reviews and detect new negative reviews, pricing complaints, competitor mentions, feature gaps, reliability issues, and customer switching signals. Persistent state means scheduled runs can return only reviews discovered since the previous successful run.

What this Actor does

Raw review exports answer “what did reviewers write?” This Actor is designed to answer “what changed?”

• Monitors one or many public G2 product review URLs.
• Establishes a baseline on the first successful run.
• Remembers stable review IDs in a named Key-Value Store that persists across runs.
• Emits only unseen reviews on later runs when Only new reviews is enabled.
• Detects commercially useful signals without OpenAI, Anthropic, Gemini, or another paid AI API.
• Stops pagination after 20 consecutive known reviews, keeping scheduled runs small.
• Tracks public aggregate rating and review-count changes when G2 exposes them reliably.
• Writes detailed records to the default Dataset and a run-level summary to OUTPUT.

Common use cases

Competitive intelligence

Find public reviews that say customers switched from or are moving to another SaaS product.

Product research

Discover missing-feature and reliability complaints in recent competitor reviews.

Pricing research

Detect new complaints about price increases, billing, cost, and perceived value.

Customer intelligence

Identify public dissatisfaction with competing products without building a separate monitoring service.

Reputation monitoring

Detect newly published 1–2 star reviews during scheduled runs.

SaaS founder research

Turn repeated public pain points into evidence for product opportunities.

Input

{
  "products": [
    {
      "name": "Slack",
      "url": "https://www.g2.com/products/slack/reviews"
    },
    {
      "name": "Microsoft Teams",
      "url": "https://www.g2.com/products/microsoft-teams/reviews"
    }
  ],
  "maxReviewsPerProduct": 10,
  "onlyNewReviews": true,
  "detectSignals": true,
  "competitors": ["Microsoft Teams", "Slack", "Discord", "Zoom"],
  "minimumRating": 1,
  "maximumRating": 5,
  "includeReviewText": true,
  "dateCutoff": null,
  "emitReviewsOnBaseline": true,
  "stateStoreName": "g2-review-switching-signal-monitor-state",
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["UNBLOCKER"]
  }
}

products accepts 1–25 public URLs shaped like https://www.g2.com/products/<slug>/reviews. The URL, not the optional display name, is the monitoring identity.

maxReviewsPerProduct limits recent reviews inspected per product. The API default is 10. Increase it for deeper baselines; scheduled runs usually stop earlier after finding known reviews.

The Store prefilled example intentionally requests one Slack review and disables onlyNewReviews. Apify's automated Store QA requires a successful run with a non-empty Dataset within five minutes, while API and scheduled runs retain the stateful onlyNewReviews: true default.

dateCutoff is optional and uses YYYY-MM-DD. Pagination stops after older reviews are reached.

emitReviewsOnBaseline controls first-run output. When enabled, current reviews are returned with baselineRun: true and isNew: false. When disabled, the first run establishes state without emitting review rows.

Recurring monitoring

1. Run the Actor once to establish a baseline.
2. Confirm baselineRun: true in OUTPUT.
3. Create an Apify Schedule for the Actor or a saved Task.
4. Keep the same stateStoreName so the named Key-Value Store remains available across runs. Use a different name to isolate another monitor.
5. Later runs emit only unseen reviews when onlyNewReviews is enabled.

State is committed once, after product processing and Dataset writes finish. If the run is interrupted, it does not advance the stored watermark and silently lose reviews. A failed product does not erase its prior state. State retains up to 50,000 recent IDs per product.

G2 applies advanced traffic protection. The production default uses Apify Unblocker, which is billed separately per successful request. The Actor sends one document request per fetched review page, does not perform a billable warm-up through Unblocker, and never reports blocked or unparseable extraction as success. Remove any country selection unless geo-targeting is required; allowing Unblocker to select the route improves reliability.

Do not purge the named state store or change stateStoreName between scheduled runs. A new store name creates a new baseline.

Review output

{
  "recordType": "REVIEW",
  "reviewId": "987654",
  "productName": "Slack",
  "productSlug": "slack",
  "productUrl": "https://www.g2.com/products/slack/reviews",
  "reviewUrl": "https://www.g2.com/products/slack/reviews/slack-review-987654",
  "title": "Too expensive for our team",
  "text": "We switched from Slack to Microsoft Teams because the price increased.",
  "rating": 2,
  "reviewDate": "2026-08-28",
  "reviewerName": "A. Reviewer",
  "reviewerRole": null,
  "reviewerCompany": null,
  "reviewerCompanySize": "Mid-Market (51-1000 emp.)",
  "likes": null,
  "source": "g2",
  "scrapedAt": "2026-08-29T09:00:00.000Z",
  "isNew": true,
  "baselineRun": false,
  "signals": [
    {
      "type": "PRICING_COMPLAINT",
      "confidence": 0.86,
      "evidence": "We switched from Slack to Microsoft Teams because the price increased."
    },
    {
      "type": "SWITCHING_TO",
      "confidence": 0.96,
      "competitor": "Microsoft Teams",
      "evidence": "We switched from Slack to Microsoft Teams because the price increased."
    }
  ]
}

Unavailable public fields are null; they are never invented. When G2 does not expose a source ID, the Actor creates a deterministic SHA-256-based ID from the product slug, review URL, date, reviewer name, and title.

Signals

One review can contain multiple signals:

• NEGATIVE_REVIEW — rating of 1 or 2 stars.
• PRICING_COMPLAINT — negative pricing, billing, cost, or value phrases.
• SUPPORT_COMPLAINT — explicitly negative support/service context.
• BUG_OR_RELIABILITY_COMPLAINT — bugs, crashes, downtime, errors, or poor performance.
• FEATURE_GAP — missing capabilities and feature requests.
• SWITCHING_FROM — phrases such as “switched from X” or “previously used X.”
• SWITCHING_TO — phrases such as “moving to X” or “using X instead.”
• COMPETITOR_MENTION — case-insensitive, word-safe match of a configured competitor name.

Confidence values are deterministic heuristic scores, not machine-learning probabilities. Evidence is a short excerpt around the matching phrase. Full configured competitor names are matched: configuring Microsoft Teams does not make the generic word teams a match.

Run summary

The run's default Key-Value Store record OUTPUT includes run status, baseline state, succeeded/failed product counts, fetched/emitted/new review counts, signal totals, competitor mention counts, aggregate changes, pages fetched, timing, and per-product errors. Durable review watermarks are kept separately in the named stateStoreName store.

{
  "status": "SUCCEEDED",
  "baselineRun": false,
  "productsProcessed": 2,
  "productsSucceeded": 2,
  "productsFailed": 0,
  "reviewsFetched": 48,
  "reviewsEmitted": 3,
  "newReviews": 3,
  "signalsDetected": 5,
  "negativeReviews": 1,
  "pricingComplaints": 1,
  "supportComplaints": 0,
  "featureGaps": 1,
  "switchingFromSignals": 1,
  "switchingToSignals": 1,
  "competitorMentions": { "Microsoft Teams": 1 },
  "pagesFetched": 2
}

If one product fails, the run is PARTIAL, successful products are retained, and the Dataset receives a PRODUCT_ERROR row with an actionable message. If every product fails, the Actor run fails.

Cost and performance

With the default Unblocker strategy, the Actor requests public G2 review HTML with Apify's recommended got-scraping client. Each attempt is bounded to about 55 seconds and transient 403/429/5xx responses receive up to four independently routed attempts across the plain, localized, and review-fragment surfaces. It sorts newest-first when that surface is available, stops at the requested limit/date cutoff, and never turns a blocked or unparseable response into a successful product result. For custom or non-Unblocker proxies, a resource-restricted Chrome fallback remains available.

Apify Unblocker is enabled in the Store input default because G2 commonly rejects ordinary datacenter and residential sessions. Each successful page request consumes 10 Unblocker units in addition to Actor compute and storage usage. No private proxy credentials are hard-coded and no third-party API keys are required.

The logical commercial usage unit is review_processed: it is predictable and avoids charging once for a product check and again for every signal on the same review. The source is ready for a future Apify Pay Per Event configuration, but this repository intentionally does not invent a price or enable billing events without an Actor pricing configuration.

Privacy and public-data scope

This Actor collects only fields displayed on public G2 review pages and needed for review monitoring. It does not log in or access private profiles. Its default Apify Unblocker route may handle anti-bot/CAPTCHA challenges automatically; use it only when you are authorized to collect the target's public pages. Disable includeReviewText when downstream consumers only need metadata and detected signals.

Review text and reviewer display information can be personal data. Use the output only for a lawful purpose, minimize retention, and follow G2’s terms and applicable privacy rules.

Limitations

• G2 actively restricts automated traffic. Unblocker materially improves access but is not a guarantee and can add tens of seconds per page.
• Website structure and access conditions can change. The Actor fails loudly when a page reports reviews but none can be parsed.
• Public G2 markup does not expose every field on every review; unavailable values remain null.
• Keyword/regex signals are explainable but do not understand sarcasm, every linguistic nuance, or languages other than English.
• Switching entities that are not in the configured competitor list are best-effort phrase extractions.
• Aggregate rating/count changes are emitted only when both prior and current public values are available.
• First-run reviews are a baseline, not urgent new events, even if baseline rows are returned.

Development

npm install
npm run check
npm start

The project uses TypeScript, Crawlee/Playwright, Cheerio, Apify Key-Value Store, and Vitest. Tests cover parser behavior, missing/malformed fields, ratings/dates, signal combinations and false positives, stable IDs, state transitions, and transactional staging.