# Google Business Profile Kpi Analyzer (`shahabuddin38/google-business-profile-kpi-analyzer`) Actor Google Business Profile KPI Analyzer helps businesses, agencies, and local SEO teams track profile visibility, customer actions, and conversion intent using clear KPI dashboards, insights, and optimization recommendations. - **URL**: https://apify.com/shahabuddin38/google-business-profile-kpi-analyzer.md - **Developed by:** [Shahab Uddin](https://apify.com/shahabuddin38) (community) - **Categories:** AI, Developer tools, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Google Business Profile KPI Analyzer Google Business Profile KPI Analyzer is now set up as a real Apify Actor for batch reporting, with a separate local browser dashboard for interactive review. It is built around this KPI framework: **Google Business Profile KPIs = Local Visibility + Customer Actions + Conversion Intent** ### What it does The project ingests Google Business Profile export data, normalizes it, calculates derived KPI rates, compares date ranges, explains what each KPI means, and generates business-ready reporting. It covers: - Local visibility - Customer actions - Conversion intent - Search query insights - Review and reputation analysis - Trend comparison - Opportunities and risks - Prioritized next actions - Final health scoring ### Runtime Modes #### 1. Apify Actor mode ```bash npm start ```` This runs [main.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/main.js:1), which: - reads Actor input - loads JSON bundles or uploaded CSV/JSON exports - builds the analysis report - pushes one output item to the default dataset - stores full report artifacts in the default key-value store - exits cleanly Stored records include: - `OUTPUT` - `REPORT` - `REPORT_MARKDOWN` - `INPUT_NORMALIZED` - `SUMMARY` #### 2. Local dashboard mode ```bash npm run web ``` Then open `http://localhost:3000`. This serves the interactive dashboard in [index.html](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/index.html:1) and [app.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/app.js:1). ### Why the Apify run was hanging before The earlier build used `npm start` to launch [server.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/server.js:1), which opened a local web server and waited forever on `localhost:3000`. That is correct for a local browser app, but it is the wrong runtime behavior for an Apify Actor because the process never completes and no result gets written to Output or Storage. That issue is now fixed by making `npm start` run the batch Actor entrypoint instead. ### Input Options The Actor accepts: - `inputJson` - a combined JSON bundle pasted directly into the Actor input - `sourceFiles` - uploaded CSV or JSON files using the Apify file upload field - `useSampleData` - a bundled sample dataset for test runs - date range overrides - `currentRangeStart` - `currentRangeEnd` - `previousRangeStart` - `previousRangeEnd` The local dashboard also supports: - pasted JSON bundle input - direct local CSV upload - direct local JSON upload ### Output Structure The report is organized into: 1. Business Profile Overview 2. Local Visibility Metrics 3. Customer Action Metrics 4. Conversion Intent Metrics 5. Search Query Insights 6. Review and Reputation Insights 7. Trend Comparison 8. Opportunities and Risks 9. Recommended Next Actions It also includes: - KPI summary - executive summary - actions breakdown - top search queries - branded vs discovery analysis when possible - recommendations prioritized by impact - final health score ### Test ```bash npm test ``` This runs: - the analytics engine test - the Actor integration test ### Files - [main.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/main.js:1) - [.actor/actor.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/.actor/actor.json) - [.actor/INPUT\_SCHEMA.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/.actor/INPUT_SCHEMA.json) - [.actor/output\_schema.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/.actor/output_schema.json) - [.actor/dataset\_schema.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/.actor/dataset_schema.json) - [.actor/key\_value\_store\_schema.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/.actor/key_value_store_schema.json) - [INPUT\_SCHEMA.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/INPUT_SCHEMA.json:1) - [src/analytics.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/src/analytics.js:1) - [index.html](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/index.html:1) - [app.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/app.js:1) - [server.js](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/server.js:1) - [sample-data/gbp-sample-bundle.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/sample-data/gbp-sample-bundle.json:1) - [schemas/input-schema.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/schemas/input-schema.json:1) - [schemas/output-schema.json](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/schemas/output-schema.json:1) - [apify-store-listing.md](/Users/apple/Documents/GitHub/google-business-profile-kpi-analyzer/apify-store-listing.md:1) # Actor input Schema ## `inputJson` (type: `string`): Paste the full input JSON bundle. This is the fastest way to run the analyzer. ## `sourceFiles` (type: `array`): Optional list of CSV or JSON files uploaded through Apify's file picker or provided as URLs. ## `useSampleData` (type: `boolean`): Enable this to run the Actor with a ready-made sample business profile dataset. ## `currentRangeStart` (type: `string`): Optional override for the current comparison range start date. ## `currentRangeEnd` (type: `string`): Optional override for the current comparison range end date. ## `previousRangeStart` (type: `string`): Optional override for the previous comparison range start date. ## `previousRangeEnd` (type: `string`): Optional override for the previous comparison range end date. ## `includeNormalizedData` (type: `boolean`): When enabled, the pushed dataset item will also include the normalized raw bundle used for analysis. ## Actor input object example ```json { "inputJson": "{\n \"business\": {\n \"name\": \"Northwind Dental\",\n \"category\": \"Dentist\",\n \"location\": \"Chicago, IL\"\n },\n \"dateRanges\": {\n \"current\": { \"start\": \"2026-03-15\", \"end\": \"2026-04-13\" },\n \"previous\": { \"start\": \"2026-02-14\", \"end\": \"2026-03-14\" }\n },\n \"timeline\": [\n {\n \"date\": \"2026-04-13\",\n \"profileViews\": 342,\n \"searchViews\": 188,\n \"mapsViews\": 154,\n \"websiteClicks\": 20,\n \"calls\": 15,\n \"directionRequests\": 18,\n \"bookings\": 5,\n \"messages\": 7,\n \"menuClicks\": 8,\n \"productViews\": 21,\n \"photoViews\": 96,\n \"postEngagement\": 14,\n \"impressions\": 371,\n \"brandedViews\": 118,\n \"discoveryViews\": 224\n }\n ],\n \"searchQueries\": [\n {\n \"query\": \"family dentist chicago\",\n \"impressions\": 380,\n \"clicks\": 41,\n \"branded\": false,\n \"avgRank\": 3.4,\n \"category\": \"Dentist\",\n \"location\": \"Chicago\"\n }\n ],\n \"reviews\": [\n { \"date\": \"2026-04-12\", \"rating\": 4, \"responded\": false }\n ],\n \"rankings\": [\n { \"date\": \"2026-04-10\", \"keyword\": \"family dentist chicago\", \"location\": \"Chicago\", \"rank\": 3 }\n ]\n}", "includeNormalizedData": false } ``` # Actor output Schema ## `results` (type: `string`): Structured report items stored in the default dataset. ## `output` (type: `string`): Primary JSON result stored in the default key-value store under OUTPUT. ## `report` (type: `string`): Detailed JSON report stored in the default key-value store under REPORT. ## `summary` (type: `string`): Compact summary stored in the default key-value store under SUMMARY. ## `reportMarkdown` (type: `string`): Human-readable Markdown report stored in the default key-value store under REPORT\_MARKDOWN. ## `normalizedInput` (type: `string`): Normalized bundle used during analysis, stored in the default key-value store under INPUT\_NORMALIZED. # 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 = { "inputJson": `{ "business": { "name": "Northwind Dental", "category": "Dentist", "location": "Chicago, IL" }, "dateRanges": { "current": { "start": "2026-03-15", "end": "2026-04-13" }, "previous": { "start": "2026-02-14", "end": "2026-03-14" } }, "timeline": [ { "date": "2026-04-13", "profileViews": 342, "searchViews": 188, "mapsViews": 154, "websiteClicks": 20, "calls": 15, "directionRequests": 18, "bookings": 5, "messages": 7, "menuClicks": 8, "productViews": 21, "photoViews": 96, "postEngagement": 14, "impressions": 371, "brandedViews": 118, "discoveryViews": 224 } ], "searchQueries": [ { "query": "family dentist chicago", "impressions": 380, "clicks": 41, "branded": false, "avgRank": 3.4, "category": "Dentist", "location": "Chicago" } ], "reviews": [ { "date": "2026-04-12", "rating": 4, "responded": false } ], "rankings": [ { "date": "2026-04-10", "keyword": "family dentist chicago", "location": "Chicago", "rank": 3 } ] }`, "useSampleData": false }; // Run the Actor and wait for it to finish const run = await client.actor("shahabuddin38/google-business-profile-kpi-analyzer").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 = { "inputJson": """{ \"business\": { \"name\": \"Northwind Dental\", \"category\": \"Dentist\", \"location\": \"Chicago, IL\" }, \"dateRanges\": { \"current\": { \"start\": \"2026-03-15\", \"end\": \"2026-04-13\" }, \"previous\": { \"start\": \"2026-02-14\", \"end\": \"2026-03-14\" } }, \"timeline\": [ { \"date\": \"2026-04-13\", \"profileViews\": 342, \"searchViews\": 188, \"mapsViews\": 154, \"websiteClicks\": 20, \"calls\": 15, \"directionRequests\": 18, \"bookings\": 5, \"messages\": 7, \"menuClicks\": 8, \"productViews\": 21, \"photoViews\": 96, \"postEngagement\": 14, \"impressions\": 371, \"brandedViews\": 118, \"discoveryViews\": 224 } ], \"searchQueries\": [ { \"query\": \"family dentist chicago\", \"impressions\": 380, \"clicks\": 41, \"branded\": false, \"avgRank\": 3.4, \"category\": \"Dentist\", \"location\": \"Chicago\" } ], \"reviews\": [ { \"date\": \"2026-04-12\", \"rating\": 4, \"responded\": false } ], \"rankings\": [ { \"date\": \"2026-04-10\", \"keyword\": \"family dentist chicago\", \"location\": \"Chicago\", \"rank\": 3 } ] }""", "useSampleData": False, } # Run the Actor and wait for it to finish run = client.actor("shahabuddin38/google-business-profile-kpi-analyzer").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 '{ "inputJson": "{\\n \\"business\\": {\\n \\"name\\": \\"Northwind Dental\\",\\n \\"category\\": \\"Dentist\\",\\n \\"location\\": \\"Chicago, IL\\"\\n },\\n \\"dateRanges\\": {\\n \\"current\\": { \\"start\\": \\"2026-03-15\\", \\"end\\": \\"2026-04-13\\" },\\n \\"previous\\": { \\"start\\": \\"2026-02-14\\", \\"end\\": \\"2026-03-14\\" }\\n },\\n \\"timeline\\": [\\n {\\n \\"date\\": \\"2026-04-13\\",\\n \\"profileViews\\": 342,\\n \\"searchViews\\": 188,\\n \\"mapsViews\\": 154,\\n \\"websiteClicks\\": 20,\\n \\"calls\\": 15,\\n \\"directionRequests\\": 18,\\n \\"bookings\\": 5,\\n \\"messages\\": 7,\\n \\"menuClicks\\": 8,\\n \\"productViews\\": 21,\\n \\"photoViews\\": 96,\\n \\"postEngagement\\": 14,\\n \\"impressions\\": 371,\\n \\"brandedViews\\": 118,\\n \\"discoveryViews\\": 224\\n }\\n ],\\n \\"searchQueries\\": [\\n {\\n \\"query\\": \\"family dentist chicago\\",\\n \\"impressions\\": 380,\\n \\"clicks\\": 41,\\n \\"branded\\": false,\\n \\"avgRank\\": 3.4,\\n \\"category\\": \\"Dentist\\",\\n \\"location\\": \\"Chicago\\"\\n }\\n ],\\n \\"reviews\\": [\\n { \\"date\\": \\"2026-04-12\\", \\"rating\\": 4, \\"responded\\": false }\\n ],\\n \\"rankings\\": [\\n { \\"date\\": \\"2026-04-10\\", \\"keyword\\": \\"family dentist chicago\\", \\"location\\": \\"Chicago\\", \\"rank\\": 3 }\\n ]\\n}", "useSampleData": false }' | apify call shahabuddin38/google-business-profile-kpi-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shahabuddin38/google-business-profile-kpi-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Business Profile Kpi Analyzer", "description": "Google Business Profile KPI Analyzer helps businesses, agencies, and local SEO teams track profile visibility, customer actions, and conversion intent using clear KPI dashboards, insights, and optimization recommendations.", "version": "0.5", "x-build-id": "GkxDRDCj4NltF8a9z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shahabuddin38~google-business-profile-kpi-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shahabuddin38-google-business-profile-kpi-analyzer", "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/shahabuddin38~google-business-profile-kpi-analyzer/runs": { "post": { "operationId": "runs-sync-shahabuddin38-google-business-profile-kpi-analyzer", "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/shahabuddin38~google-business-profile-kpi-analyzer/run-sync": { "post": { "operationId": "run-sync-shahabuddin38-google-business-profile-kpi-analyzer", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "inputJson": { "title": "Combined JSON bundle", "type": "string", "description": "Paste the full input JSON bundle. This is the fastest way to run the analyzer." }, "sourceFiles": { "title": "Uploaded export files", "type": "array", "description": "Optional list of CSV or JSON files uploaded through Apify's file picker or provided as URLs.", "items": { "type": "string" } }, "useSampleData": { "title": "Use bundled sample dataset", "type": "boolean", "description": "Enable this to run the Actor with a ready-made sample business profile dataset." }, "currentRangeStart": { "title": "Current range start", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "Optional override for the current comparison range start date." }, "currentRangeEnd": { "title": "Current range end", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "Optional override for the current comparison range end date." }, "previousRangeStart": { "title": "Previous range start", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "Optional override for the previous comparison range start date." }, "previousRangeEnd": { "title": "Previous range end", "pattern": "^(\\d{4})-(0[1-9]|1[0-2])-(0[1-9]|[12]\\d|3[01])$", "type": "string", "description": "Optional override for the previous comparison range end date." }, "includeNormalizedData": { "title": "Include normalized raw data in output", "type": "boolean", "description": "When enabled, the pushed dataset item will also include the normalized raw bundle used for analysis.", "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 } } } } } } } } } } ```