# Kindle Niche Analyzer (`kuromaro/kindle-niche-analyzer`) Actor - **URL**: https://apify.com/kuromaro/kindle-niche-analyzer.md - **Developed by:** [Takuma](https://apify.com/kuromaro) (community) - **Categories:** Jobs, Automation, AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $500.00 / 1,000 category 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 ## Kindle Niche Analyzer Analyze Amazon Kindle categories and keywords to find profitable niches for KDP (Kindle Direct Publishing) authors and publishers. ### What It Does This Actor scrapes Amazon Kindle bestseller pages or keyword search results, then calculates two scores for each result: - **Competition Score** — `median(reviewCount) × mean(rating)`. Higher means the niche is dominated by well-established books. - **Niche Score** — `demand / competition`. Higher means a better opportunity: strong demand, weak competition. Use these scores to quickly identify low-competition, high-demand niches before publishing your next Kindle book. ### Features - Two analysis modes: **category bestsellers** or **keyword search** - Supports 9 Amazon marketplaces (US, UK, DE, FR, JP, IT, ES, CA, AU) - Random User-Agent rotation + exponential-backoff retry for reliability - Optional Apify proxy support to avoid IP blocks - Returns structured JSON ready for spreadsheet import or downstream automation ### Input Parameters | Parameter | Type | Description | |---|---|---| | `mode` | enum | `category` (bestseller page) or `keyword` (search) | | `categoryUrl` | string | Full URL of an Amazon Kindle bestseller category. Required when `mode=category` | | `keyword` | string | Search keyword for Kindle Store. Required when `mode=keyword`. Default: `python programming` | | `marketplace` | enum | Amazon marketplace: `com`, `co.uk`, `de`, `fr`, `co.jp`, `it`, `es`, `ca`, `com.au`. Default: `com` | | `maxResults` | integer | Max books to return (1–200). Default: `50` | | `proxyConfiguration` | proxy | Apify proxy settings. Recommended: Residential Proxies | ### Output Schema Each item in the output dataset has the following fields: ```json { "title": "Python Crash Course, 3rd Edition", "author": "Eric Matthes", "asin": "B09WJX7LYS", "url": "https://www.amazon.com/dp/B09WJX7LYS", "price": "$19.99", "rating": 4.8, "reviewCount": 3200, "categoryRank": 1, "salesRank": 1, "competitionScore": 14560.0, "nicheScore": 13.72 } ```` #### Field Descriptions | Field | Type | Description | |---|---|---| | `title` | string | Book title | | `author` | string | Author name | | `asin` | string | Amazon Standard Identification Number | | `url` | string | Direct link to the book's Amazon page | | `price` | string | Listed price (e.g. `$9.99`) | | `rating` | float | Average star rating (0–5) | | `reviewCount` | integer | Total number of customer reviews | | `categoryRank` | integer | Position in the bestseller category or search results | | `salesRank` | integer | Proxy for overall sales rank (lower = more popular) | | `competitionScore` | float | `median(reviewCount) × mean(rating)` across all results — higher = more competitive | | `nicheScore` | float | `demand / competition` — higher = better niche opportunity | ### Use Cases - **Niche research**: Identify keyword niches where demand is high but competition is low - **Category scouting**: Monitor bestseller categories to spot emerging trends - **Competitor analysis**: Track review counts and ratings of top books in your genre - **Portfolio planning**: Prioritize which books to write next based on niche scores - **Multi-market expansion**: Compare niches across Amazon US, UK, and other markets ### Example: Finding a Profitable Python Niche 1. Set `mode` to `keyword` 2. Set `keyword` to `python data science beginners` 3. Set `marketplace` to `com` 4. Run the Actor 5. Sort results by `nicheScore` descending — the top results are your best opportunities ### Pricing This Actor charges **per run** (1 event per Actor execution), regardless of the number of results returned. ### Notes - Amazon's HTML structure may change over time. If you notice missing fields, please open an issue. - Use residential proxies for best results, especially for high-volume scraping. - Respect Amazon's Terms of Service and robots.txt when using this Actor. # Actor input Schema ## `mode` (type: `string`): Choose 'category' to scrape a bestseller category page, or 'keyword' to search by keyword. ## `categoryUrl` (type: `string`): Full URL of an Amazon Kindle bestseller category page. Required when mode is 'category'. ## `keyword` (type: `string`): Keyword to search in the Amazon Kindle store. Required when mode is 'keyword'. ## `marketplace` (type: `string`): Amazon marketplace to scrape. ## `maxResults` (type: `integer`): Maximum number of books to return. ## `proxyConfiguration` (type: `object`): Proxy settings for scraping. Use Apify Residential Proxies to avoid blocks. ## Actor input object example ```json { "mode": "keyword", "categoryUrl": "https://www.amazon.com/Best-Sellers-Kindle-Store-Python-Programming/zgbs/digital-text/", "keyword": "python programming", "marketplace": "com", "maxResults": 50, "proxyConfiguration": { "useApifyProxy": true } } ``` # 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 = { "keyword": "python programming", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("kuromaro/kindle-niche-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 = { "keyword": "python programming", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("kuromaro/kindle-niche-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 '{ "keyword": "python programming", "proxyConfiguration": { "useApifyProxy": true } }' | apify call kuromaro/kindle-niche-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kuromaro/kindle-niche-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Kindle Niche Analyzer", "version": "0.1", "x-build-id": "mtMCpQcfJ8gRLK0kf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kuromaro~kindle-niche-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kuromaro-kindle-niche-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/kuromaro~kindle-niche-analyzer/runs": { "post": { "operationId": "runs-sync-kuromaro-kindle-niche-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/kuromaro~kindle-niche-analyzer/run-sync": { "post": { "operationId": "run-sync-kuromaro-kindle-niche-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Analysis Mode", "enum": [ "category", "keyword" ], "type": "string", "description": "Choose 'category' to scrape a bestseller category page, or 'keyword' to search by keyword.", "default": "keyword" }, "categoryUrl": { "title": "Category URL", "type": "string", "description": "Full URL of an Amazon Kindle bestseller category page. Required when mode is 'category'." }, "keyword": { "title": "Search Keyword", "type": "string", "description": "Keyword to search in the Amazon Kindle store. Required when mode is 'keyword'." }, "marketplace": { "title": "Amazon Marketplace", "enum": [ "com", "co.uk", "de", "fr", "co.jp", "it", "es", "ca", "com.au" ], "type": "string", "description": "Amazon marketplace to scrape.", "default": "com" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of books to return.", "default": 50 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for scraping. Use Apify Residential Proxies to avoid blocks." } } }, "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 } } } } } } } } } } ```