# G2 Software Review Intelligence Scraper (`glowing_glove/g2-software-review-intelligence-scraper`) Actor Analyze G2 review and software-category result signals for competitor pages, buyer pain themes, and SaaS positioning research. - **URL**: https://apify.com/glowing\_glove/g2-software-review-intelligence-scraper.md - **Developed by:** [Ushba Khan](https://apify.com/glowing_glove) (community) - **Categories:** Lead generation, Automation, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $25.00 / 1,000 analyzed g2 software queries This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 Software Review Intelligence Scraper Analyze G2 review and software-category result signals for competitor pages, buyer pain themes, and SaaS positioning research. ### Why use this actor? Use G2 result signals to identify competitor review pages, buyer pain themes, and comparison-page opportunities. It is designed for SaaS marketers, competitive intelligence teams, sales teams, and SEO strategists. Each input becomes one compact, reviewable dataset row so buyers can scan the result quickly, export it to a spreadsheet, or send it into a CRM, research board, or automation workflow. Unlike a generic web-search export, this actor is configured around a specific source strategy and a specific output contract. The run checks the source surface that fits this actor first, then uses a targeted fallback only when the public source limits direct access. The final dataset keeps the useful signals and leaves out raw page noise. ### Best for - Research SaaS competitors - Find review-page opportunities - Plan alternative pages - Extract buyer pain themes ### Input Use `softwareQueries` with focused values that match this actor's purpose. Good example inputs: - `crm software` - `project management` For larger monitoring runs, add up to 20 values at a time. Keep each value short and specific. For example, use a product category, software category, creator niche, role keyword, local permit query, or video URL depending on the actor. ### Output you get The dataset is intentionally compact. It avoids raw HTML dumps and noisy scrape artifacts, and highlights the fields that matter for this actor: - `buyerPainLevel` - `reviewSource` - `reviewPagesFound` - `buyerPainSignals` - `competitorPages` - `positioningAngle` The table is designed for review and export. It focuses on the buyer-facing signals, short lead/product lists, useful classifications, and a next action. Internal fetch details are not shown as normal output fields. ### Practical workflow 1. Start with 2-5 focused inputs to check result quality. 2. Review the opportunity level, signal fields, and short lead/product lists in the dataset table. 3. Export useful rows to your spreadsheet, CRM, prospecting workflow, content brief, product research board, or monitoring system. 4. Schedule repeat runs for niches, categories, keywords, or lead segments that produce useful rows. 5. Use Apify Proxy if a source becomes restrictive in cloud runs. ### Notes - Uses G2 software search and product review pages. - Keeps arrays short so exported data stays readable. - Filters obvious ad/tracking redirects and low-value navigation links. - Supports optional Apify Proxy for cloud runs where public sources limit traffic. # Actor input Schema ## `softwareQueries` (type: `array`): G2 Software Review Intelligence Scraper accepts focused inputs for this actor's exact job. Add up to 20 values. Good examples: crm software, project management. ## `maxItems` (type: `integer`): Maximum top links/items included inside each result row. ## `requestTimeoutSecs` (type: `integer`): Timeout for each public source request in seconds. ## `maxConcurrency` (type: `integer`): How many inputs to process at the same time. ## `proxyConfiguration` (type: `object`): Optional Apify Proxy for websites that limit cloud traffic. ## Actor input object example ```json { "softwareQueries": [ "crm software", "project management" ], "maxItems": 10, "requestTimeoutSecs": 20, "maxConcurrency": 4, "proxyConfiguration": { "useApifyProxy": false } } ```` # Actor output Schema ## `results` (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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "softwareQueries": [ "crm software", "project management" ] }; // Run the Actor and wait for it to finish const run = await client.actor("glowing_glove/g2-software-review-intelligence-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "softwareQueries": [ "crm software", "project management", ] } # Run the Actor and wait for it to finish run = client.actor("glowing_glove/g2-software-review-intelligence-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "softwareQueries": [ "crm software", "project management" ] }' | apify call glowing_glove/g2-software-review-intelligence-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=glowing_glove/g2-software-review-intelligence-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "G2 Software Review Intelligence Scraper", "description": "Analyze G2 review and software-category result signals for competitor pages, buyer pain themes, and SaaS positioning research.", "version": "1.0", "x-build-id": "Jjz8vkXg94k6GHyEu" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/glowing_glove~g2-software-review-intelligence-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-glowing_glove-g2-software-review-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/glowing_glove~g2-software-review-intelligence-scraper/runs": { "post": { "operationId": "runs-sync-glowing_glove-g2-software-review-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/glowing_glove~g2-software-review-intelligence-scraper/run-sync": { "post": { "operationId": "run-sync-glowing_glove-g2-software-review-intelligence-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "softwareQueries" ], "properties": { "softwareQueries": { "title": "Software categories or products", "type": "array", "description": "G2 Software Review Intelligence Scraper accepts focused inputs for this actor's exact job. Add up to 20 values. Good examples: crm software, project management.", "items": { "type": "string" } }, "maxItems": { "title": "Max items per row", "minimum": 1, "maximum": 30, "type": "integer", "description": "Maximum top links/items included inside each result row.", "default": 10 }, "requestTimeoutSecs": { "title": "Request timeout", "minimum": 5, "maximum": 90, "type": "integer", "description": "Timeout for each public source request in seconds.", "default": 20 }, "maxConcurrency": { "title": "Concurrency", "minimum": 1, "maximum": 8, "type": "integer", "description": "How many inputs to process at the same time.", "default": 4 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional Apify Proxy for websites that limit cloud traffic.", "default": { "useApifyProxy": false } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```