# Capterra Review Battlecard Monitor (`geneius/capterra-review-battlecard-monitor`) Actor Convert Capterra review records into battlecard-ready complaint themes, praised features, sales talk tracks, risk scores, and recommended actions. - **URL**: https://apify.com/geneius/capterra-review-battlecard-monitor.md - **Developed by:** [Gene](https://apify.com/geneius) (community) - **Categories:** SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $6.00 / 1,000 capterra review analyzeds 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ### Capterra Review Battlecard Monitor Convert Capterra review records into **field-aware objection themes, praised features, evidence quotes, sales talk tracks, and battlecard row candidates**. This Actor analyzes Capterra review records collected by another Apify Actor or supplied inline; it does not scrape Capterra directly. This Actor is built for product marketing, sales enablement, competitor intelligence, and agencies that already collect Capterra reviews and need a repeatable way to turn buyer language into sales-ready competitive intelligence. ### Workflow Hub See the public [review intelligence workflow](https://geneiusk.github.io/apify-actor-powerhouse-hub/review-intelligence.html) for the scraper dataset -> analyzer path and links across the review-intelligence Actors. For the first run, use the [Capterra battlecard demo script](https://geneiusk.github.io/apify-actor-powerhouse-hub/capterra-review-battlecard-demo.html) or the [competitor review battlecards use case](https://geneiusk.github.io/apify-actor-powerhouse-hub/competitor-review-battlecards.html). The [proof GIF](https://geneiusk.github.io/apify-actor-powerhouse-hub/assets/capterra-review-battlecard-proof.gif) shows reviews becoming a battlecard row. ### What You Learn - Which Capterra reviews mention pricing, support, reliability, integrations, implementation, usability, reporting, or missing features - Which negative reviews should become objection rows in a competitor battlecard - Which positive reviews reveal competitor strengths your sales team must counter - Which snippets are worth saving for enablement and win/loss research - Which proof asset is needed to answer each objection - Which aggregate battlecard row candidates deserve PMM review - What action to take for each Capterra review ### Use Cases - Monthly Capterra-based battlecard refreshes - Competitive messaging research for software categories - Pricing, renewal, and implementation objection mining - Product marketing proof-point planning - Agency reporting for competitive intelligence retainers ### Input Provide Capterra reviews inline or pass an Apify `datasetId` from another Actor. The analyzer recognizes common Capterra-style fields such as `softwareName`, `productName`, `rating`, `summary`, `pros`, `cons`, `positive`, `negative`, `industry`, `reviewDate`, and `reviewUrl`. ```json { "defaultCompetitor": "Acme CRM", "productCategory": "CRM", "reviews": [ { "softwareName": "Acme CRM", "rating": 2, "summary": "Good CRM, hard renewal", "pros": "The Salesforce integration is useful.", "cons": "The pricing got expensive at renewal and support took days to respond.", "industry": "Marketing and Advertising" } ], "maxReviews": 100 } ```` ### Output Each dataset item is one analyzed Capterra review: ```json { "status": "succeeded", "recordIndex": 1, "billingEventName": "capterra-review-analyzed", "competitorName": "Acme CRM", "productCategory": "CRM", "sourceName": "Capterra", "rating": 2, "sentimentLabel": "negative", "complaintThemes": ["pricing", "support"], "praisedFeatures": ["integrations"], "objectionType": "pricing_objection", "riskScore": 85, "priority": "high", "battlecardSnippet": "Good CRM, hard renewal The Salesforce integration is useful. The pricing got expensive at renewal and support took days to respond.", "proofAssetNeeded": "ROI calculator, transparent packaging page, and renewal-risk proof.", "battlecardRow": { "objection": "pricing_objection", "buyerQuote": "The pricing got expensive at renewal and support took days to respond.", "competitorWeakness": "pricing objection backed by pricing, support language.", "repTalkTrack": "When Capterra buyers question Acme CRM pricing, lead with transparent packaging, ROI proof, and renewal-risk controls.", "priority": "high" }, "salesTalkTrack": "When Capterra buyers question Acme CRM pricing, lead with transparent packaging, ROI proof, and renewal-risk controls.", "recommendedAction": "Add a high-priority pricing_objection row to the Acme CRM battlecard and alert sales enablement." } ``` The run also writes a `SUMMARY` key-value-store record with analyzed review counts, top objections, top themes, top praised features, battlecard row candidates, summary bullets, and the charge event name. ### FAQ #### Does this scrape Capterra? No. It analyzes Capterra-style review records you provide inline or through a dataset from another Actor. It is built for analysis and battlecard generation. #### What input do I need for the first run? Use the Store example with a `defaultCompetitor`, `productCategory`, and Capterra-style fields such as `summary`, `pros`, `cons`, `rating`, and `industry`. #### What do I get back? One dataset item per analyzed review with complaint themes, praised features, objection type, proof asset, sales talk track, and battlecard row candidate. The run also writes a `SUMMARY` record. #### Why use this instead of a raw Capterra scraper? Raw review exports tell you what people said. This Actor turns the review into sales enablement and product marketing actions. #### How much does it cost? The configured paid event is `capterra-review-analyzed` at `$0.006` per successfully analyzed Capterra review. ### Pricing Default monetization model: pay per event. Chargeable event: - Event name: `capterra-review-analyzed` - Event meaning: one successfully analyzed Capterra review - Store price: `$0.006` per analyzed review - Pricing status: active from `2026-05-13T19:04:25Z`; verified by private and public paid smokes Successful rows are pushed only after the charge path allows the event. If `Actor.charge` fails, the Actor fails closed before returning paid output. ### Limitations - This MVP analyzes supplied Capterra review records; it does not scrape Capterra directly. - Source schemas vary. The Actor recognizes common fields such as `summary`, `pros`, `cons`, `positive`, `negative`, `reviewText`, `rating`, `softwareName`, `productName`, `competitorName`, `industry`, `reviewUrl`, and `reviewDate`. `pros` and `cons` are classified separately so positive integration language does not become a complaint theme. - The taxonomy is deterministic and explainable. It does not call external AI APIs. ### Automation And Agent Use - Run a Capterra review scraper first, then pass its dataset ID to this Actor. - Schedule monthly battlecard refreshes by competitor. - Send high-risk pricing, support, or reliability objections to sales enablement. - Export `battlecardSnippet`, `salesTalkTrack`, and `recommendedAction` to Google Sheets or a battlecard document. ### Local Development ```bash python3 -m pip install -r requirements.txt ACTOR_TEST_PAY_PER_EVENT=true apify run --purge --input-file examples/smoke-input.json ``` # Actor input Schema ## `defaultCompetitor` (type: `string`): Fallback software or competitor name when a Capterra record does not include one. ## `productCategory` (type: `string`): Category used in the output, such as CRM, project management, accounting, HRIS, or help desk software. ## `reviews` (type: `array`): Optional Capterra review records. Each record should include review text, pros/cons, or Capterra-like fields. Use this or datasetId; if both are supplied, inline records are analyzed first and dataset records are appended. ## `datasetId` (type: `string`): Optional Apify dataset ID containing Capterra review records from another Actor. Provide this or inline reviews. ## `maxReviews` (type: `integer`): Safety cap for analyzed Capterra reviews in one run. ## `datasetItemLimit` (type: `integer`): Maximum number of records to load from the source dataset. ## `includeRawReview` (type: `boolean`): When enabled, dataset items include the normalized Capterra review text assembled from summary, pros, cons, and body fields. ## `failOnInvalidRecord` (type: `boolean`): When enabled, the Actor fails on the first invalid Capterra review record instead of returning partial error records. ## Actor input object example ```json { "defaultCompetitor": "Acme CRM", "productCategory": "CRM", "reviews": [ { "softwareName": "Acme CRM", "rating": 2, "summary": "Good CRM, hard renewal", "pros": "The Salesforce integration is useful.", "cons": "The pricing got expensive at renewal and support took days to respond.", "industry": "Marketing and Advertising" } ], "maxReviews": 100, "datasetItemLimit": 100, "includeRawReview": true, "failOnInvalidRecord": false } ``` # Actor output Schema ## `results` (type: `string`): Dataset items with one row per analyzed Capterra review, including complaint themes, praised features, objection type, talk track, risk score, and recommended action. ## `summary` (type: `string`): Run-level summary with analyzed Capterra review count, failures, charge event name, top themes, top objections, praised feature counts, battlecard row candidates, and summary bullets. # 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 = { "defaultCompetitor": "Acme CRM", "productCategory": "CRM", "reviews": [ { "softwareName": "Acme CRM", "rating": 2, "summary": "Good CRM, hard renewal", "pros": "The Salesforce integration is useful.", "cons": "The pricing got expensive at renewal and support took days to respond.", "industry": "Marketing and Advertising" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("geneius/capterra-review-battlecard-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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "defaultCompetitor": "Acme CRM", "productCategory": "CRM", "reviews": [{ "softwareName": "Acme CRM", "rating": 2, "summary": "Good CRM, hard renewal", "pros": "The Salesforce integration is useful.", "cons": "The pricing got expensive at renewal and support took days to respond.", "industry": "Marketing and Advertising", }], } # Run the Actor and wait for it to finish run = client.actor("geneius/capterra-review-battlecard-monitor").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 '{ "defaultCompetitor": "Acme CRM", "productCategory": "CRM", "reviews": [ { "softwareName": "Acme CRM", "rating": 2, "summary": "Good CRM, hard renewal", "pros": "The Salesforce integration is useful.", "cons": "The pricing got expensive at renewal and support took days to respond.", "industry": "Marketing and Advertising" } ] }' | apify call geneius/capterra-review-battlecard-monitor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=geneius/capterra-review-battlecard-monitor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Capterra Review Battlecard Monitor", "description": "Convert Capterra review records into battlecard-ready complaint themes, praised features, sales talk tracks, risk scores, and recommended actions.", "version": "0.1", "x-build-id": "c1s7MPzRzGxEcqFNi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/geneius~capterra-review-battlecard-monitor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-geneius-capterra-review-battlecard-monitor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/geneius~capterra-review-battlecard-monitor/runs": { "post": { "operationId": "runs-sync-geneius-capterra-review-battlecard-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/geneius~capterra-review-battlecard-monitor/run-sync": { "post": { "operationId": "run-sync-geneius-capterra-review-battlecard-monitor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "defaultCompetitor": { "title": "Default Capterra competitor", "type": "string", "description": "Fallback software or competitor name when a Capterra record does not include one." }, "productCategory": { "title": "Product category", "type": "string", "description": "Category used in the output, such as CRM, project management, accounting, HRIS, or help desk software.", "default": "B2B software" }, "reviews": { "title": "Inline review records", "minItems": 1, "type": "array", "description": "Optional Capterra review records. Each record should include review text, pros/cons, or Capterra-like fields. Use this or datasetId; if both are supplied, inline records are analyzed first and dataset records are appended." }, "datasetId": { "title": "Source dataset ID", "minLength": 1, "type": "string", "description": "Optional Apify dataset ID containing Capterra review records from another Actor. Provide this or inline reviews." }, "maxReviews": { "title": "Maximum reviews", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Safety cap for analyzed Capterra reviews in one run.", "default": 100 }, "datasetItemLimit": { "title": "Dataset item limit", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Maximum number of records to load from the source dataset.", "default": 100 }, "includeRawReview": { "title": "Include raw review text", "type": "boolean", "description": "When enabled, dataset items include the normalized Capterra review text assembled from summary, pros, cons, and body fields.", "default": true }, "failOnInvalidRecord": { "title": "Fail on invalid record", "type": "boolean", "description": "When enabled, the Actor fails on the first invalid Capterra review record instead of returning partial error records.", "default": 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 } } } } } } } } } } ```