# Review Intelligence Analyzer - Google Reviews Sales Signals (`santamaria-automations/review-intelligence-analyzer`) Actor Analyze a business's Google Maps reviews with an LLM and return structured intelligence a sales team can act on: overall sentiment, complaint themes, praise themes, owner-response tone, red flags and opportunity signals. Bring your own LLM key (Gemini, Groq, OpenRouter). Pay-per-result. - **URL**: https://apify.com/santamaria-automations/review-intelligence-analyzer.md - **Developed by:** [NanoScrape](https://apify.com/santamaria-automations) (community) - **Categories:** Lead generation, AI, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 1,000 analyzed places 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 ## Review Intelligence Analyzer Turn a business's Google Maps reviews into structured intelligence a sales team can act on. Feed in place records with `user_reviews[]` populated and get back overall sentiment, complaint themes, praise themes, owner-response tone, red flags and opportunity signals. Bring your own LLM key (Gemini has a generous free tier). Pair with the [Google Maps Scraper](https://apify.com/santamaria-automations/google-maps-scraper) run with `includeReviews: true`, and get one intelligence row per business ready to prioritize outbound. ### What It Does Given a Google Maps place record with `user_reviews[]` populated, the actor asks an LLM to analyze the reviews and return: - `overall_sentiment` - one of `strongly_positive`, `positive`, `mixed`, `negative`, `strongly_negative` - `sentiment_score` - a number between -1.0 and +1.0 - `complaint_themes[]` - up to 5 objects `{theme, frequency, sample_quote}` when reviews contain recurring complaints - `praise_themes[]` - up to 5 objects with the same shape when reviews contain recurring praise - `owner_tone` - one of `professional`, `defensive`, `dismissive`, `absent`, `mixed` - `red_flags[]` - subset of `fake_review_pattern`, `recent_1_star_cluster`, `no_owner_response`, `hostile_owner_response`, `rating_drop_recent`, `language_barrier` - `opportunity_signals[]` - subset of `ready_for_promotion`, `high_repeat_customer`, `asks_for_specific_service`, `seasonal_demand`, `underrated_gem` The LLM only references what actually appears in the reviews. Sample quotes are verbatim snippets, never paraphrased. Themes below 2 occurrences are dropped. ### When To Use - Prioritize outbound: skip businesses with `strongly_positive` sentiment and empty `red_flags`, prioritize those with `no_owner_response` or `recent_1_star_cluster`. - Sharper cold email: fold `complaint_themes[0].theme` or an `opportunity_signals` value into your opener. - Reputation risk audits: flag agency clients whose review streams show `hostile_owner_response` or a `rating_drop_recent`. - Discovery for the "underrated gem" ICP. ### Input Two shapes are accepted and can be mixed in one run. Flat places list (natural shape, pipe the google-maps-scraper output directly): ```json { "places": [ { "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4", "title": "Example Coffee House", "category": "Coffee shop", "rating": 4.6, "review_count": 1240, "complete_address": { "city": "Berlin", "country": "Germany" }, "user_reviews": [ { "rating": 5, "text": "...", "published_at": "2026-05-01", "response_from_owner": null }, { "rating": 2, "text": "...", "published_at": "2026-04-15", "response_from_owner": "..." } ] } ], "geminiApiKey": "AIza..." } ```` Companies shape (with a `company_id` passthrough for join-back to your CRM): ```json { "companies": [ { "company_id": "acme-42", "place": { "place_id": "ChIJ...", "title": "Example Company", "user_reviews": [] } } ], "geminiApiKey": "AIza..." } ``` `maxReviewsSampled` (default 30, min 5, max 60) caps how many reviews per place go into the LLM prompt. The sampler blends most-recent, highest-rated and lowest-rated reviews so both praise and complaint edges are represented. ### LLM Configuration You must supply at least one LLM key. All three providers are supported: | Provider | Free tier | Get a key | |----------|-----------|-----------| | Gemini | Yes (generous) | https://aistudio.google.com/app/apikey | | Groq | Yes | https://console.groq.com/keys | | OpenRouter | Pay-as-you-go, cheap | https://openrouter.ai/keys | Set `llmProvider` to pick the primary, `fallbackProvider` and `fallback2Provider` for the chain. Same-provider transient errors (5xx, timeouts) get one retry. Auth and quota errors fall through to the next provider. ### Output One row per input place: ```json { "company_id": "acme-42", "place_id": "ChIJN1t_tDeuEmsRUsoyG83frY4", "name": "Example Coffee House", "analyzed_at": "2026-07-21T10:30:00Z", "overall_sentiment": "positive", "sentiment_score": 0.62, "complaint_themes": [ { "theme": "slow service on weekends", "frequency": 3, "sample_quote": "waited 20 minutes just to order" } ], "praise_themes": [ { "theme": "friendly staff", "frequency": 6, "sample_quote": "the team remembers our order" }, { "theme": "great pastries", "frequency": 4, "sample_quote": "chocolate croissant was fantastic" } ], "owner_tone": "professional", "red_flags": [], "opportunity_signals": ["high_repeat_customer"], "reviews_analyzed": 10, "llm_provider": "gemini", "llm_model": "gemini-2.0-flash", "llm_tokens_input": 1240, "llm_tokens_output": 340 } ``` Places with no reviews or no LLM key emit a row with `skip_reason` set (`no_reviews`, `no_llm_key`, or `llm_failed`) and no billing charge. Themes and enum arrays default to `[]`, sentiment fields to `null`. ### How To Use It 1. Run [Google Maps Scraper](https://apify.com/santamaria-automations/google-maps-scraper) with `includeReviews: true` to collect places and their reviews. 2. Feed the dataset into this actor as `places` (or wrap each in `companies` for join-back). 3. Set your Gemini key. Optionally add Groq and OpenRouter as fallbacks. 4. Feed the output into your prioritization rules or your outbound sequencer. ### Pricing Pay-per-result via Apify Pay-Per-Event billing: | Event | Price | Description | |-------|-------|-------------| | Actor start | $0.001 | Charged once per run when there is at least one analyzable place | | Result | $0.005 | Charged once per analyzed place. Skipped places (no reviews, no key, LLM failure) are NOT billed | Analyzing 1,000 places costs about $5. LLM API usage is billed by your provider directly, not by this actor. ### Related Actors Discovery: - [Google Maps Scraper](https://apify.com/santamaria-automations/google-maps-scraper) - collect places and their reviews in bulk Enrichment: - [AI Icebreaker Generator](https://apify.com/santamaria-automations/ai-icebreaker) - one hyper-personalized cold-outreach line per place - [GBP Completeness Audit](https://apify.com/santamaria-automations/gbp-completeness-audit) - score Google Business Profiles for lead prioritization - [Website Contact Extractor](https://apify.com/santamaria-automations/website-contact-extractor) - LLM-powered people extraction from company sites ### Notes - Empty input is free. `enrichment-start` is only billed when there is at least one place with analyzable reviews. - The proxy configuration field is accepted for schema consistency but is not used by this actor (LLM-only). - Language: set `outputLanguage: "auto"` to have theme labels and quotes translated back to the business's country language. ### Issues & Feature Requests If the model keeps under- or over-flagging a class of business, open an issue on the actor's Issues tab and we will look into it. # Actor input Schema ## `places` (type: `array`): Array of Google Maps place records. Each element is a full place object as emitted by the google-maps-scraper actor with `user_reviews[]` populated (run gmaps with `includeReviews: true`). ## `companies` (type: `array`): Alternative to `places`. Each entry carries a company\_id (echoed on the output row) plus the place record. ## `maxReviewsSampled` (type: `integer`): Cap on the number of reviews sent to the LLM per place. Higher = sharper signals but higher per-row token cost. The sampler blends most-recent, highest-rated and lowest-rated reviews so both praise and complaint angles are represented. ## `llmProvider` (type: `string`): Primary AI provider. Gemini Flash is fastest and has a generous free tier. ## `fallbackProvider` (type: `string`): Second-level fallback if the primary provider fails (rate limit, auth error, etc). ## `fallback2Provider` (type: `string`): Third-level fallback if both primary and first fallback fail. ## `llmModel` (type: `string`): Override the default model. Defaults: Gemini -> gemini-2.0-flash, Groq -> llama-3.3-70b-versatile, OpenRouter -> google/gemini-2.0-flash-001. If your OpenRouter key does not have access to the default paid Gemini model, try a free model like meta-llama/llama-3.1-8b-instruct. ## `geminiApiKey` (type: `string`): Google Gemini Flash. Best quality/cost ratio, generous free tier. Get a key at https://aistudio.google.com/app/apikey ## `groqApiKey` (type: `string`): Groq. Ultra-fast inference. Get a key at https://console.groq.com/keys ## `openrouterApiKey` (type: `string`): OpenRouter. Access to 100+ models. Get a key at https://openrouter.ai/keys ## `outputLanguage` (type: `string`): Language for the theme labels and sample quotes translated back. Uses standard ISO 639-1 codes: en, de, fr, ja, es, pt, it, ko, zh-CN, ar, nl, pl, sv, da, fi, no, cs, hu, ro, el, tr, th, vi, id, and more. Set to `auto` to match the business's country/language. ## `proxyConfiguration` (type: `object`): Not used by this actor (LLM calls only). Accepted for schema consistency with sibling enrichment actors. ## Actor input object example ```json { "places": [ { "place_id": "ChIJExamplePositive1", "title": "Example Coffee House", "category": "Coffee shop", "rating": 4.8, "review_count": 1240, "complete_address": { "city": "Berlin", "country": "Germany" }, "user_reviews": [ { "name": "Anna", "rating": 5, "text": "Best flat white in the neighborhood. Staff remember my name.", "published_at": "2026-05-01", "response_from_owner": null }, { "name": "Ben", "rating": 5, "text": "Great pastries, always fresh. Cozy atmosphere too.", "published_at": "2026-04-15", "response_from_owner": null }, { "name": "Chris", "rating": 4, "text": "Excellent coffee, gets crowded on Saturdays but worth the wait.", "published_at": "2026-03-20", "response_from_owner": null } ] } ], "maxReviewsSampled": 30, "llmProvider": "gemini", "outputLanguage": "en", "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `reviewIntelligence` (type: `string`): Dataset of review intelligence rows # 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 = { "places": [ { "place_id": "ChIJExamplePositive1", "title": "Example Coffee House", "category": "Coffee shop", "rating": 4.8, "review_count": 1240, "complete_address": { "city": "Berlin", "country": "Germany" }, "user_reviews": [ { "name": "Anna", "rating": 5, "text": "Best flat white in the neighborhood. Staff remember my name.", "published_at": "2026-05-01", "response_from_owner": null }, { "name": "Ben", "rating": 5, "text": "Great pastries, always fresh. Cozy atmosphere too.", "published_at": "2026-04-15", "response_from_owner": null }, { "name": "Chris", "rating": 4, "text": "Excellent coffee, gets crowded on Saturdays but worth the wait.", "published_at": "2026-03-20", "response_from_owner": null } ] } ] }; // Run the Actor and wait for it to finish const run = await client.actor("santamaria-automations/review-intelligence-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 = { "places": [{ "place_id": "ChIJExamplePositive1", "title": "Example Coffee House", "category": "Coffee shop", "rating": 4.8, "review_count": 1240, "complete_address": { "city": "Berlin", "country": "Germany", }, "user_reviews": [ { "name": "Anna", "rating": 5, "text": "Best flat white in the neighborhood. Staff remember my name.", "published_at": "2026-05-01", "response_from_owner": None, }, { "name": "Ben", "rating": 5, "text": "Great pastries, always fresh. Cozy atmosphere too.", "published_at": "2026-04-15", "response_from_owner": None, }, { "name": "Chris", "rating": 4, "text": "Excellent coffee, gets crowded on Saturdays but worth the wait.", "published_at": "2026-03-20", "response_from_owner": None, }, ], }] } # Run the Actor and wait for it to finish run = client.actor("santamaria-automations/review-intelligence-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 '{ "places": [ { "place_id": "ChIJExamplePositive1", "title": "Example Coffee House", "category": "Coffee shop", "rating": 4.8, "review_count": 1240, "complete_address": { "city": "Berlin", "country": "Germany" }, "user_reviews": [ { "name": "Anna", "rating": 5, "text": "Best flat white in the neighborhood. Staff remember my name.", "published_at": "2026-05-01", "response_from_owner": null }, { "name": "Ben", "rating": 5, "text": "Great pastries, always fresh. Cozy atmosphere too.", "published_at": "2026-04-15", "response_from_owner": null }, { "name": "Chris", "rating": 4, "text": "Excellent coffee, gets crowded on Saturdays but worth the wait.", "published_at": "2026-03-20", "response_from_owner": null } ] } ] }' | apify call santamaria-automations/review-intelligence-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=santamaria-automations/review-intelligence-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Review Intelligence Analyzer - Google Reviews Sales Signals", "description": "Analyze a business's Google Maps reviews with an LLM and return structured intelligence a sales team can act on: overall sentiment, complaint themes, praise themes, owner-response tone, red flags and opportunity signals. Bring your own LLM key (Gemini, Groq, OpenRouter). Pay-per-result.", "version": "1.0", "x-build-id": "bVgKCD8swpH5UPwff" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/santamaria-automations~review-intelligence-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-santamaria-automations-review-intelligence-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/santamaria-automations~review-intelligence-analyzer/runs": { "post": { "operationId": "runs-sync-santamaria-automations-review-intelligence-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/santamaria-automations~review-intelligence-analyzer/run-sync": { "post": { "operationId": "run-sync-santamaria-automations-review-intelligence-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "places": { "title": "Places", "type": "array", "description": "Array of Google Maps place records. Each element is a full place object as emitted by the google-maps-scraper actor with `user_reviews[]` populated (run gmaps with `includeReviews: true`).", "items": { "type": "object" } }, "companies": { "title": "Companies (with company_id passthrough)", "type": "array", "description": "Alternative to `places`. Each entry carries a company_id (echoed on the output row) plus the place record.", "items": { "type": "object", "required": [ "place" ], "properties": { "company_id": { "title": "Company ID", "type": "string", "description": "Your internal identifier, echoed on the output row so you can join intelligence back to your source data." }, "place": { "title": "Place", "type": "object", "description": "The Google Maps place record including `user_reviews[]`." } } } }, "maxReviewsSampled": { "title": "Max Reviews Sampled per Place", "minimum": 5, "maximum": 60, "type": "integer", "description": "Cap on the number of reviews sent to the LLM per place. Higher = sharper signals but higher per-row token cost. The sampler blends most-recent, highest-rated and lowest-rated reviews so both praise and complaint angles are represented.", "default": 30 }, "llmProvider": { "title": "LLM Provider", "enum": [ "gemini", "groq", "openrouter" ], "type": "string", "description": "Primary AI provider. Gemini Flash is fastest and has a generous free tier.", "default": "gemini" }, "fallbackProvider": { "title": "Fallback LLM Provider", "enum": [ "gemini", "groq", "openrouter" ], "type": "string", "description": "Second-level fallback if the primary provider fails (rate limit, auth error, etc)." }, "fallback2Provider": { "title": "Third-Level Fallback", "enum": [ "gemini", "groq", "openrouter" ], "type": "string", "description": "Third-level fallback if both primary and first fallback fail." }, "llmModel": { "title": "LLM Model (optional override)", "type": "string", "description": "Override the default model. Defaults: Gemini -> gemini-2.0-flash, Groq -> llama-3.3-70b-versatile, OpenRouter -> google/gemini-2.0-flash-001. If your OpenRouter key does not have access to the default paid Gemini model, try a free model like meta-llama/llama-3.1-8b-instruct." }, "geminiApiKey": { "title": "Gemini API Key (recommended)", "type": "string", "description": "Google Gemini Flash. Best quality/cost ratio, generous free tier. Get a key at https://aistudio.google.com/app/apikey" }, "groqApiKey": { "title": "Groq API Key (optional)", "type": "string", "description": "Groq. Ultra-fast inference. Get a key at https://console.groq.com/keys" }, "openrouterApiKey": { "title": "OpenRouter API Key (optional)", "type": "string", "description": "OpenRouter. Access to 100+ models. Get a key at https://openrouter.ai/keys" }, "outputLanguage": { "title": "Output Language", "type": "string", "description": "Language for the theme labels and sample quotes translated back. Uses standard ISO 639-1 codes: en, de, fr, ja, es, pt, it, ko, zh-CN, ar, nl, pl, sv, da, fi, no, cs, hu, ro, el, tr, th, vi, id, and more. Set to `auto` to match the business's country/language.", "default": "en" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Not used by this actor (LLM calls only). Accepted for schema consistency with sibling enrichment actors.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```