# G2 Reviews Scraper — Reviews by Role & Company Size (`thenetaji/g2-reviews-scraper`) Actor

Export a software product's G2 reviews with the rating, headline, full text and dates, plus the reviewer's job title and the size of the company they work for. The three questions every reviewer answers arrive separated, so praise and complaints can be read apart.

- **URL**: https://apify.com/thenetaji/g2-reviews-scraper.md
- **Developed by:** [The Netaji](https://apify.com/thenetaji) (community)
- **Categories:** Business, Marketing, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.10 / 1,000 reviews

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?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## G2 Reviews Scraper

A software product's G2 reviews, exported as a spreadsheet. Each row carries the review's
rating, headline, full text and both dates, the product's own aggregate rating and category
list, and the two fields that turn a pile of opinions into something segmentable: the
reviewer's job title, and the size band of the company they work for.

Each review also arrives split back into the three questions every G2 reviewer answers —
what they like, what they dislike, and what problem the product solves. That split is what
makes a complaint findable.

### Accepted input

`productSlugs` is required and takes one or more product slugs, one per line, such as
`pipedrive`. A pasted product page link works too and is reduced to the slug inside it. A
slug is lowercase and carries no spaces and no query string; the page to start from is a
separate field rather than something appended to the slug.

Slugs are not derivable from product names — `Agentforce Sales` is
`agentforce-sales-formerly-salesforce-sales-cloud` — so they are worth copying rather than
typing. Every row [G2 Category Scraper](https://apify.com/thenetaji/g2-category-scraper)
produces carries the product's slug in its `slug` field.

`maxItems` bounds each product separately and defaults to `100`. Reviews arrive ten to a
page, so `100` is ten pages per product and `0` removes the bound. The cap is per product
rather than per run because a run given twenty products is asking for a comparable slice of
each.

`order` selects G2's own review ordering: relevance, most recent, most helpful, highest
rated or lowest rated. It defaults to relevance, which is what the site serves with no
ordering asked for.

`startPage` sets the page each product begins at and defaults to `1`.

```json
{
  "productSlugs": ["pipedrive"],
  "order": "most_recent",
  "maxItems": 100,
  "startPage": 1
}
```

### Response fields

```json
{
  "product_slug": "pipedrive",
  "product_name": "Pipedrive",
  "product_url": "https://www.g2.com/products/pipedrive/reviews",
  "product_categories": ["CRM", "Email Marketing", "Sales Analytics"],
  "product_rating": 8.7,
  "product_rating_best": 10,
  "product_review_count": 3118,
  "page": 1,
  "review_id": 13260876,
  "review_url": "https://www.g2.com/survey_responses/13260876",
  "title": "Visual Pipelines, Helpful AI Setup, and Great Value",
  "review_body": "The pipelines give a great visual representation of …",
  "sections": [
    {
      "question": "What do you like best about Pipedrive?",
      "answer": "The pipelines give a great visual representation of …"
    }
  ],
  "review_rating": 5,
  "review_rating_best": 5,
  "date_published": "2026-08-13T04:22:22-05:00",
  "date_modified": "2026-08-13T04:22:23-05:00",
  "reviewer_name": "Gavin A.",
  "reviewer_job_title": "Director",
  "reviewer_company_size": "Small-Business (50 or fewer emp.)",
  "reviewer_id": 9033304,
  "reviewer_profile_url": null,
  "product_id": 1619,
  "product_uuid": "4020c23b-cfa1-43a6-a5d4-17eb3494c795",
  "vendor_id": 1201,
  "video_review": false
}
```

Fields absent from a review are returned as null rather than omitted, so every row has the
same shape.

`review_body` and `sections` are the same prose twice, and the difference between them is
the point. G2 asks every reviewer three questions and the standard review-body field
concatenates all three answers together with nothing marking the joins, so a search of
`review_body` for a complaint also matches the praise beside it. `sections` is that prose
split back into the questions as they were put, each with the answer as it was written. The
three are not broken out into three named columns here, because the questions are worded per
product and matching them on keywords would mislabel an answer rather than fail to find one.

`reviewer_job_title` and `reviewer_company_size` are read from the review page rather than
from its structured data, and they are the fields that make a review set worth segmenting:
the same complaint from a fifty-person company and from an enterprise are different
findings.

`review_rating` is out of 5 and `product_rating` is out of 10, in the same row, and neither
is rescaled. Both are G2's own numbers on G2's own scales, which is why `review_rating_best`
and `product_rating_best` are published beside them rather than assumed.

`review_id` is the only stable key a review has, and it is what a repeated run should
de-duplicate on. `review_url` is its permalink.

`vendor_id` groups several products under one company, which is how a vendor's whole
portfolio is recognised across separate runs.

### Behaviour on partial results

A product slug that G2 serves nothing for is logged and skipped, and the run continues with
the next one. A product that exists and has no reviews returns a successful run with no rows
for it.

The walk over a product ends on G2's own next control rather than on a page coming back
short, because the last page of a three-thousand-review product is usually full and a full
page is not evidence that another one exists. A page request that runs past the end is
served as the last real page instead of as an error; the run notices that the page served
differs from the page asked for and stops there rather than collecting it again.

`review_id` is occasionally null across a whole page. That happens when the page's
structured data and its markup disagree about how many reviews are on it: rather than pair
each review with an identifier that might belong to a different one, the identifiers are
withheld and the reviews are published without them. The run log says so when it happens.
The prose, ratings, dates and reviewer fields are unaffected.

### Frequently asked questions

**Why did the first row take so much longer than the rest?**
Because the first request of a run is where G2 checks the connection before it will serve
anything, and that check takes time. Measured on 2026-08-22, a first read answered in 23.1
seconds and the one after it in 15.5. A run that appears to sit still for twenty seconds
before its first row is behaving normally.

**Which `order` should a repeated run use?**
`most_recent`. Relevance is G2's default and it is the right ordering for a single read, but
it is not a stable sequence to page through: a review that moves between requests can be
collected twice or missed entirely. Recency is fixed once a review is published, so a run
repeated weekly against `most_recent` sees the new reviews at the front and nothing shifts
underneath it.

**How many reviews does a product have in total?**
`product_review_count` states the figure G2 computes its aggregate from, across the whole
product. It is not the number of rows a run will return: `maxItems` bounds that, and G2
publishes no page count, so the walk asks for the next page until the site says there is
none.

**Do pages overlap, and does a walk need de-duplication?**
No, and not for that reason. Pages 1, 2 and 3 of one measured product shared no review
identifiers at all. A repeated identifier is dropped if one appears, but that guard is a
safety net rather than the load-bearing part. De-duplication is worth doing across separate
runs, on `review_id`, because the product will have gained reviews between them.

**Why is `reviewer_profile_url` empty on most rows?**
Because most reviewers do not have a public profile. `reviewer_id` is still populated, so
repeat reviewers can be recognised across a dataset without one.

**Where does the summary of what reviewers like and dislike come from?**
Not from here. G2 publishes a short list of praised and criticised traits per product on its
category listings, and nowhere on the product's own pages, so
[G2 Category Scraper](https://apify.com/thenetaji/g2-category-scraper) is where that comes
from. This Actor is the evidence underneath it.

**Is a G2 account, cookie or API key required?**
No. No account, session cookie or key of any kind is supplied to the Actor or needed by it.

### Related Actors

[G2 Category Scraper](https://apify.com/thenetaji/g2-category-scraper) exports a software
category in its ranked order, with each product's rating, review count, price band and the
traits reviewers praise and complain about. Every row carries the slug this Actor takes, so
it is where a run starts when the products are not already known.

# Actor input Schema

## `productSlugs` (type: `array`):

Product slugs, one per line — for example pipedrive. Every row the Category Scraper produces carries the slug in its `slug` field, so a category export feeds straight in. A pasted page link works too and is reduced to the slug inside it. Slugs are lowercase and can be long, so copy them rather than typing them.

## `order` (type: `string`):

Relevance is the ordering the site serves by default. Most recent is the one a repeated run usually wants, because relevance is not a stable sequence to page through — a review that moves between runs can be missed or collected twice.

## `startPage` (type: `integer`):

Which page each item in the list starts from. It resumes a walk that stopped, and it splits a long product across several runs.

## `maxItems` (type: `integer`):

Maximum reviews to save for each product in the list. Set 0 for no limit. Reviews come 10 to a page, so 100 is ten pages per product.

## Actor input object example

```json
{
  "productSlugs": [
    "pipedrive"
  ],
  "order": "g2_default",
  "startPage": 1,
  "maxItems": 20
}
```

# Actor output Schema

## `dataset` (type: `string`):

All records scraped by this run

# 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 = {
    "productSlugs": [
        "pipedrive"
    ],
    "order": "g2_default",
    "startPage": 1,
    "maxItems": 20
};

// Run the Actor and wait for it to finish
const run = await client.actor("thenetaji/g2-reviews-scraper").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 = {
    "productSlugs": ["pipedrive"],
    "order": "g2_default",
    "startPage": 1,
    "maxItems": 20,
}

# Run the Actor and wait for it to finish
run = client.actor("thenetaji/g2-reviews-scraper").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 '{
  "productSlugs": [
    "pipedrive"
  ],
  "order": "g2_default",
  "startPage": 1,
  "maxItems": 20
}' |
apify call thenetaji/g2-reviews-scraper --silent --output-dataset

```

## MCP server setup

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

```

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/vOpbOVge3TJ1hZTII/builds/Mb0tVYTOBUg3i0ZEA/openapi.json