# G2 Reviews Scraper & Switching-Signal Monitor (`herazur/g2-reviews-scraper-monitor`) Actor

Monitor G2 reviews for new complaints, pricing issues, support problems, feature gaps, competitor mentions, and customer switching signals. Stateful tracking emits only new reviews and changes.

- **URL**: https://apify.com/herazur/g2-reviews-scraper-monitor.md
- **Developed by:** [Furkan Toluç](https://apify.com/herazur) (community)
- **Categories:** Business, Marketing, Lead generation
- **Security:** Limited permissions
- **Stats:** 2 total users, 1 monthly users, 89.3% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.0028 / g2 review

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## G2 Reviews Scraper & 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

```json
{
  "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. Plain URL strings, `http://`, missing `www.`, localized paths (`/es/products/...`), review permalinks and other product tabs are normalized to the canonical review listing, and duplicates are removed. Entries that are not G2 product URLs are skipped and reported in `OUTPUT.productErrors` with status `INVALID_INPUT`; the run fails with a clear message only when no usable product remains.

`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` (an ISO date-time is truncated to its date). 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. Apify storage names may contain only letters, digits and hyphens, so other characters (for example dots, underscores or spaces) are replaced with hyphens. If the state store cannot be opened, the run still returns reviews as a baseline and records a warning in `OUTPUT.warnings`.

### Review output

```json
{
  "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",
  "sourceLocale": "en",
  "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 2 stars or lower (G2 uses half stars and also shows 0-star reviews).
- `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.

```json
{
  "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 status in `OUTPUT` is `PARTIAL`, successful products are retained, and the failure is listed in `OUTPUT.productErrors` with an actionable message. Errors are not written as Dataset rows, so you are never charged a review event for a failed product. If a later results page cannot be retrieved, the reviews from earlier pages are still saved and the product is marked `complete: false` with a `stopReason` in `OUTPUT.productStats`. The Actor run fails only when no product could be monitored at all.

### Pricing

This Actor uses pay-per-event pricing:

- **G2 review saved:** $0.004 per review row written to the Dataset on the Apify Free plan, with lower prices on paid Apify plans (down to $0.0028).
- **Actor start:** $0.01 per GB of run memory, charged once when the run starts.

Proxy (including Apify Unblocker) and compute usage are included in these prices. Failed products, skipped products and diagnostics are not written to the Dataset and are not charged. When the run's maximum total charge is reached, the Actor stops saving reviews, skips the remaining products, and lists them in `OUTPUT.productErrors`.

### Performance and reliability

With the default Unblocker strategy, the Actor requests public G2 review HTML with Apify's recommended `got-scraping` client and sends one document request per review page (no billable warm-up). Each attempt is bounded to 55 seconds. A page gets up to six attempts with exponential backoff across newest-first full, localized and review-fragment surfaces, within a 150-second retry budget per page. The surface that worked for the previous page is tried first on the next page. Authentication errors stop immediately, a missing product (repeated HTTP 404) stops after two attempts, and normal-looking pages without parseable reviews stop after three attempts; the first such page is saved to the `DEBUG_UNPARSEABLE_PAGE` record for diagnostics.

The remaining run time is shared fairly between pending products, and the Actor always keeps time to save the Dataset, monitoring state and `OUTPUT` before the run timeout. Deep crawls that reach the time budget return the reviews collected so far instead of failing; increase the run timeout for very large `maxReviewsPerProduct` values. Pagination also stops when a page only repeats earlier reviews. For custom or non-Unblocker proxies, a resource-restricted Chrome fallback remains available, but G2 usually blocks those routes.

Apify Unblocker is enabled in the Store input default because G2 commonly rejects ordinary datacenter and residential sessions. No private proxy credentials are hard-coded and no third-party API keys are required.

### 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. A product fails with an explicit error when its first page reports reviews but none can be parsed; it is never reported as an empty success.
- 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.
- English review pages are always tried first. When only G2's Spanish or Portuguese page is reachable, review text is G2's translation of the review and `sourceLocale` is `es` or `pt`; English-only signal rules then detect fewer signals.
- 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

```bash
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.

# Actor input Schema

## `products` (type: `array`):

One or more public G2 product review URLs. URLs are used as stable monitoring identifiers; names are optional labels.

## `maxReviewsPerProduct` (type: `integer`):

Maximum number of recent reviews inspected per product during this run. Monitoring stops early after a streak of already-known reviews.

## `onlyNewReviews` (type: `boolean`):

When enabled, reviews seen during a previous successful run are not returned again. The first run establishes a clearly marked baseline.

## `detectSignals` (type: `boolean`):

Detect low ratings, pricing/support/reliability complaints, feature gaps, competitor mentions, and switching intent without an AI API.

## `competitors` (type: `array`):

Competitor product names to match as complete names or word-safe phrases in review content.

## `minimumRating` (type: `number`):

Return reviews at or above this 1–5 star rating. The default 1 also keeps reviews rated below 1 star (G2 shows 0 and 0.5 star reviews).

## `maximumRating` (type: `number`):

Return reviews at or below this 1–5 star rating.

## `includeReviewText` (type: `boolean`):

Include public written review content in Dataset rows. Disable to minimize personal/content data after signal detection.

## `dateCutoff` (type: `string`):

Optional YYYY-MM-DD date. Older reviews are ignored and newest-first pagination stops after reaching it.

## `emitReviewsOnBaseline` (type: `boolean`):

Return current reviews during the first run with isNew=false and baselineRun=true. Disable to establish state without Dataset review rows.

## `stateStoreName` (type: `string`):

Named Key-Value Store used to preserve review IDs and aggregate values across runs. Use a different name to isolate independent monitors.

## `proxyConfiguration` (type: `object`):

Apify Proxy settings. UNBLOCKER is the production default for G2 and is billed by Apify per successful request. Country is intentionally omitted so Unblocker can choose the best route.

## Actor input object example

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

# Actor output Schema

## `reviews` (type: `string`):

No description

## `summary` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "products": [
        {
            "name": "Slack",
            "url": "https://www.g2.com/products/slack/reviews"
        }
    ],
    "maxReviewsPerProduct": 1,
    "onlyNewReviews": false,
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "UNBLOCKER"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("herazur/g2-reviews-scraper-monitor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "products": [{
            "name": "Slack",
            "url": "https://www.g2.com/products/slack/reviews",
        }],
    "maxReviewsPerProduct": 1,
    "onlyNewReviews": False,
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["UNBLOCKER"],
    },
}

# Run the Actor and wait for it to finish
run = client.actor("herazur/g2-reviews-scraper-monitor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "products": [
    {
      "name": "Slack",
      "url": "https://www.g2.com/products/slack/reviews"
    }
  ],
  "maxReviewsPerProduct": 1,
  "onlyNewReviews": false,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "UNBLOCKER"
    ]
  }
}' |
apify call herazur/g2-reviews-scraper-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,herazur/g2-reviews-scraper-monitor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/dXf4oV7wg1XVTzq9R/builds/gQp0uDbtLwG7kYrhk/openapi.json