# TikTok Shop Winning Products Finder (`davidbenittah/tiktok-shop-winning-products`) Actor Find TikTok Shop winning products by real sales velocity: sold/day measured between scheduled runs — with price range, rating, review count, and shop name. - **URL**: https://apify.com/davidbenittah/tiktok-shop-winning-products.md - **Developed by:** [David](https://apify.com/davidbenittah) (community) - **Categories:** E-commerce, Social media - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $12.00 / 1,000 results 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 ## TikTok Shop Winning Products Finder **TikTok Shop winning products finder** that scans any niche and ranks products by **sales velocity** — not the vanity total. Every product comes with its **7-day and 30-day sold counts**, 30-day GMV, and the **number of creators and videos pushing it**. A product with 20,000 lifetime sales at 159/week is fading; one at 6,000 growing 300/week is the wave to catch before everyone else sees it. ### Features - **Velocity, not totals**: ranked by `sold7d`, then `sold30d` — recent momentum beats lifetime volume. - **Creator signal**: `influencersCount`, `videosCount`, and `videosSales` show who is pushing each product and how hard — the TikTok-specific edge no generic product tool has. - **Cross-run acceleration**: schedule the Actor and every later run also measures `soldPerDay` since the last run, per product. - **Full product record**: title, category, price range, GMV 30d, rating, review count, shop name and type, hot flag, free-shipping flag, direct product URL, search rank. - **Multi-niche**: up to 5 keywords per run, 15 TikTok Shop markets (US, UK, EU, LATAM, Asia). - **Honest data**: metrics TikTok does not expose are reported as `null`, never guessed. ### Use cases - **Dropshippers & e-commerce sellers**: source rising products before saturation — velocity is the earliest public signal. - **TikTok Shop sellers**: track your niche's fastest movers weekly and adjust pricing/creative. - **Agencies & creators**: find products worth making content for, with the shops selling them. - **Market analysts**: watch category momentum across markets. ### Input / Output Input: ```json { "keywords": ["posture corrector"], "countryCode": "US", "productsPerKeyword": 20 } ```` Output — one dataset item per product, ranked by velocity: ```json { "title": "Adjustable posture corrector belt", "productUrl": "https://shop.tiktok.com/view/product/700001", "category": "Protective Gear", "priceMin": 17.84, "priceMax": 23.08, "soldCount": 6270, "sold7d": 300, "sold30d": 956, "gmv30d": 48000, "influencersCount": 257, "videosCount": 769, "rating": 4.6, "reviewCount": 1200, "isHot": true, "shopName": "Posture Aura", "rankInSearch": 1 } ``` A `SUMMARY` record reports products scanned, products with velocity data, and the current top product. ### Pricing Pay per event: a small fee per product analyzed. Under the hood the Actor runs the TikTok Shop search scraper (`pratikdani/tiktok-shop-search-scraper`, billed to your account at ~$15 per 1,000 results on the free tier — a 20-product niche scan costs ~$0.30 in scanning). ### FAQ **Why is `soldPerDay` null on my first run?** Velocity needs two measurements. The first run saves the baseline; schedule the Actor (daily or weekly) and every later run reports sold/day. That schedule IS the product — one-shot scrapers cannot measure velocity. **How is velocity computed?** `(current sold count − last run's sold count) / days between runs`, per product. Products that temporarily drop out of search results keep their baseline, so velocity survives result churn. **Which markets are supported?** US, UK, Germany, France, Spain, Italy, Mexico, Brazil, Japan, Singapore, Malaysia, Thailand, Philippines, Vietnam, Indonesia. **Is this legal?** The Actor reads public TikTok Shop product listings via an established Apify scraper. No personal data is collected. You are responsible for how you use the data. # Actor input Schema ## `keywords` (type: `array`): Niches to scan on TikTok Shop (e.g. 'posture corrector', 'led lamp'). Hard maximum: 5 keywords per run. ## `countryCode` (type: `string`): Target TikTok Shop country. ## `productsPerKeyword` (type: `integer`): How many search results to analyze per niche. Hard maximum: 50. ## `minSoldCount` (type: `integer`): Skip products with fewer total sales than this. Products whose sales TikTok does not expose are always kept. ## `monitorId` (type: `string`): Optional. Advanced: keep separate velocity baselines for the same keywords, or reset one by changing the ID. ## Actor input object example ```json { "keywords": [ "posture corrector" ], "countryCode": "US", "productsPerKeyword": 20, "minSoldCount": 0 } ``` # 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 = { "keywords": [ "posture corrector" ] }; // Run the Actor and wait for it to finish const run = await client.actor("davidbenittah/tiktok-shop-winning-products").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 = { "keywords": ["posture corrector"] } # Run the Actor and wait for it to finish run = client.actor("davidbenittah/tiktok-shop-winning-products").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 '{ "keywords": [ "posture corrector" ] }' | apify call davidbenittah/tiktok-shop-winning-products --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=davidbenittah/tiktok-shop-winning-products", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "TikTok Shop Winning Products Finder", "description": "Find TikTok Shop winning products by real sales velocity: sold/day measured between scheduled runs — with price range, rating, review count, and shop name.", "version": "1.0", "x-build-id": "JXg1RoqATt1zKh82S" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/davidbenittah~tiktok-shop-winning-products/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-davidbenittah-tiktok-shop-winning-products", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/davidbenittah~tiktok-shop-winning-products/runs": { "post": { "operationId": "runs-sync-davidbenittah-tiktok-shop-winning-products", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/davidbenittah~tiktok-shop-winning-products/run-sync": { "post": { "operationId": "run-sync-davidbenittah-tiktok-shop-winning-products", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords" ], "properties": { "keywords": { "title": "Product niche keywords", "type": "array", "description": "Niches to scan on TikTok Shop (e.g. 'posture corrector', 'led lamp'). Hard maximum: 5 keywords per run.", "items": { "type": "string" } }, "countryCode": { "title": "TikTok Shop market", "enum": [ "US", "GB", "DE", "FR", "ES", "IT", "MX", "BR", "JP", "SG", "MY", "TH", "PH", "VN", "ID" ], "type": "string", "description": "Target TikTok Shop country.", "default": "US" }, "productsPerKeyword": { "title": "Products scanned per keyword", "minimum": 1, "maximum": 50, "type": "integer", "description": "How many search results to analyze per niche. Hard maximum: 50.", "default": 20 }, "minSoldCount": { "title": "Minimum total sales", "minimum": 0, "type": "integer", "description": "Skip products with fewer total sales than this. Products whose sales TikTok does not expose are always kept.", "default": 0 }, "monitorId": { "title": "Custom monitor ID", "type": "string", "description": "Optional. Advanced: keep separate velocity baselines for the same keywords, or reset one by changing the ID." } } }, "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 } } } } } } } } } } ```