# Product Opportunity Analyzer (`muhdarifx/product-opportunity-analyzer`) Actor Analyzes product opportunities by combining Amazon product data, Reddit discussions, and Google Trends with Claude AI analysis. - **URL**: https://apify.com/muhdarifx/product-opportunity-analyzer.md - **Developed by:** [Muhammad Arif](https://apify.com/muhdarifx) (community) - **Categories:** AI, E-commerce, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / result 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 ## 🔍 Product Opportunity Analyzer **Find winning products before your competitors do.** Stop guessing which products to sell. This tool cross-references live marketplace data, consumer sentiment, and search demand trends — then uses advanced AI to surface the exact opportunities worth pursuing, complete with differentiation strategies your competitors haven't figured out yet. Enter a product category. Get back a ranked list of opportunities with scores, market gaps, and specific angles to exploit. --- ### Why This Exists Every day, thousands of sellers launch products based on gut feeling. Most fail. The ones who win do their homework — they study the competition, read the complaints, track the trends, and find the gaps. This tool does that homework in under 60 seconds. It pulls real-time data from multiple sources, identifies where demand is rising but competition is weak, pinpoints what customers are actually complaining about, and tells you exactly how to position a product to capture that gap. --- ### What You Get Each run produces a comprehensive intelligence report with: #### 🏆 Ranked Opportunities (AI-Scored) Every opportunity is scored 1–100 based on demand signals, competition density, consumer pain points, and trend momentum. Each one includes: - **Opportunity Score** — How strong the opportunity is overall - **Demand Trend** — Rising, stable, or falling - **Competition Density** — Low, medium, or high - **Average Review Score** — How satisfied (or dissatisfied) customers are - **Top Complaints** — The exact pain points customers mention most - **Price Range** — Min, max, and average across the market - **Recommended Angle** — A specific product differentiation strategy (e.g., *"sell a portable blender with a leakproof lid — competitors average 3.1 stars on seal quality"*) - **Related Trending Searches** — What people are actively searching for right now #### 📊 Competitive Snapshot - Average price and full price range across the category - Average rating and total reviews analyzed - Competition level assessment - Top brands dominating the space #### 📈 Demand Analysis - 12-month trend direction with data points - Related rising search queries - Consumer buzz score from community discussions - Key consumer pain points extracted from real conversations #### 📋 Raw Data - Top marketplace products with prices, ratings, and review counts - Top community discussions with engagement metrics - Full 12-month trend timeline --- ### How to Use It #### Input | Field | Required | Default | Description | |-------|----------|---------|-------------| | **Search Query** | ✅ | — | Product category or keyword (e.g., `portable blenders`, `yoga mat`, `wireless earbuds`) | | **Marketplace** | No | Amazon US | Which marketplace to analyze (`Amazon US`, `Amazon UK`, `Amazon DE`) | | **Max Products** | No | 20 | Number of marketplace products to analyze (5–50) | | **Max Community Posts** | No | 25 | Number of community discussions to analyze (5–100) | | **Top Opportunities** | No | 5 | How many opportunities to return (1–20) | #### Minimal Example ```json { "searchQuery": "portable blenders" } ```` That's it. One field. The defaults handle the rest. #### Advanced Example ```json { "searchQuery": "wireless earbuds", "marketplace": "amazon_us", "maxProducts": 30, "maxRedditPosts": 50, "topOpportunities": 10 } ``` *** ### Sample Output ```json { "searchQuery": "portable blenders", "marketplace": "amazon_us", "analyzedAt": "2026-03-27T10:42:08.000Z", "executionTimeSeconds": 10, "competitiveSnapshot": { "avgPrice": 24.99, "priceRange": { "min": 9.99, "max": 59.99 }, "avgRating": 4.2, "totalProductsAnalyzed": 16, "totalReviewsAnalyzed": 48500, "competitionLevel": "medium", "topBrands": ["BlendJet", "PopBabies", "Hamilton Beach"] }, "demandAnalysis": { "trendDirection": "rising", "trendDataPoints": 53, "relatedSearches": ["portable blender for smoothies", "travel blender usb"], "consumerPainPoints": ["broke", "cheap", "stopped working"] }, "opportunities": [ { "rank": 1, "productCategory": "Leakproof Portable Blenders", "opportunityScore": 87, "demandTrend": "rising", "competitionDensity": "low", "avgReviewScore": 3.1, "topComplaints": ["leaks during blending", "lid doesn't seal", "spills in bag"], "recommendedAngle": "Launch a portable blender with a patented triple-seal lid system. Competitors average 3.1 stars specifically on leak complaints — a leakproof design at $29.99 would dominate.", "reasoning": "High search demand with rising trend. Current products have a clear weakness in seal quality that no major brand has addressed." } ] } ``` *** ### Who This Is For | Audience | Use Case | |----------|----------| | **E-commerce Sellers** | Find the next product to source before the market gets saturated | | **Amazon FBA Sellers** | Identify product gaps with weak competition and bad reviews to exploit | | **Dropshippers** | Spot trending products with rising demand and low competition | | **SEO & Content Marketers** | Discover content gaps and affiliate angles in rising niches | | **Product Researchers** | Get data-driven validation before investing in inventory | | **Agencies** | Resell monthly opportunity reports to e-commerce clients | *** ### Pricing Each run costs based on Apify platform compute usage. A typical run: - Analyzes 20 products, 25 community posts, and 12 months of trend data - Completes in under 60 seconds - Uses approximately 0.05 compute units Run it once for a quick check, or schedule it weekly to track how opportunities evolve over time. *** ### Scheduling & Automation Set up a recurring schedule to monitor opportunities in your niche: 1. Go to the actor's **Schedules** tab 2. Set frequency (daily, weekly, monthly) 3. Configure your search query 4. Results are stored in the dataset — export to CSV, JSON, or connect via API Perfect for agencies delivering monthly product intelligence reports to clients. *** ### API Access Integrate directly into your workflow: ```bash curl https://api.apify.com/v2/acts/YOUR_ACTOR_ID/runs \ -d '{"searchQuery": "wireless earbuds"}' \ -H 'Content-Type: application/json' \ -H 'Authorization: Bearer YOUR_API_TOKEN' ``` Results are available via the Apify API as JSON — plug into dashboards, spreadsheets, or your own applications. *** ### FAQ **How long does a run take?** Typically 10–60 seconds depending on the number of products and posts analyzed. **How accurate is the opportunity scoring?** The scoring combines real marketplace data, consumer sentiment analysis, and trend signals. It's designed to surface directional opportunities — always validate with your own research before making sourcing decisions. **Can I analyze multiple categories?** Yes. Run the actor once per category, or set up multiple schedules for different niches. **What marketplaces are supported?** Amazon US, Amazon UK, and Amazon DE. More marketplaces coming soon. **Does it work for any product category?** Yes. Enter any product keyword — from `yoga mats` to `mechanical keyboards` to `dog harnesses`. The broader the category, the more opportunities it finds. Specific long-tail queries work too. **Can I get more than 5 opportunities?** Yes. Set `topOpportunities` up to 20. *** ### Support Found an issue or have a feature request? Reach out through the Apify platform or open an issue on the actor's page. # Actor input Schema ## `searchQuery` (type: `string`): Product category or keyword to analyze (e.g., 'wireless earbuds', 'yoga mat'). ## `marketplace` (type: `string`): Amazon marketplace to search. ## `maxProducts` (type: `integer`): Maximum number of Amazon products to analyze. ## `maxRedditPosts` (type: `integer`): Maximum number of Reddit posts to analyze. ## `topOpportunities` (type: `integer`): Number of top product opportunities to return. ## Actor input object example ```json { "searchQuery": "wireless earbuds", "marketplace": "amazon_us", "maxProducts": 20, "maxRedditPosts": 25, "topOpportunities": 5 } ``` # Actor output Schema ## `analysisReport` (type: `string`): Full opportunity analysis with ranked product gaps, competitive snapshot, demand trends, and AI-generated differentiation strategies. ## `fullReport` (type: `string`): Complete analysis report stored as a single JSON document in the key-value store. # 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 = { "searchQuery": "wireless earbuds" }; // Run the Actor and wait for it to finish const run = await client.actor("muhdarifx/product-opportunity-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 = { "searchQuery": "wireless earbuds" } # Run the Actor and wait for it to finish run = client.actor("muhdarifx/product-opportunity-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 '{ "searchQuery": "wireless earbuds" }' | apify call muhdarifx/product-opportunity-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=muhdarifx/product-opportunity-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Product Opportunity Analyzer", "description": "Analyzes product opportunities by combining Amazon product data, Reddit discussions, and Google Trends with Claude AI analysis.", "version": "0.1", "x-build-id": "fKH367TjxcGbGGJRB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/muhdarifx~product-opportunity-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-muhdarifx-product-opportunity-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/muhdarifx~product-opportunity-analyzer/runs": { "post": { "operationId": "runs-sync-muhdarifx-product-opportunity-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/muhdarifx~product-opportunity-analyzer/run-sync": { "post": { "operationId": "run-sync-muhdarifx-product-opportunity-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Product category or keyword to analyze (e.g., 'wireless earbuds', 'yoga mat')." }, "marketplace": { "title": "Marketplace", "enum": [ "amazon_us", "amazon_uk", "amazon_de" ], "type": "string", "description": "Amazon marketplace to search.", "default": "amazon_us" }, "maxProducts": { "title": "Max Products", "minimum": 5, "maximum": 50, "type": "integer", "description": "Maximum number of Amazon products to analyze.", "default": 20 }, "maxRedditPosts": { "title": "Max Reddit Posts", "minimum": 5, "maximum": 100, "type": "integer", "description": "Maximum number of Reddit posts to analyze.", "default": 25 }, "topOpportunities": { "title": "Top Opportunities", "minimum": 1, "maximum": 20, "type": "integer", "description": "Number of top product opportunities to return.", "default": 5 } } }, "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 } } } } } } } } } } ```