# Temu Category Trend Report (`crw/temu-category-trend-report`) Actor Daily Temu category intelligence: top 200 products ranked by 7-day momentum. Spot Breakout products before they peak, track price trends, and get a curated Hot Picks list — all from a single run. Covers 16 categories including Fashion, Beauty, and Home. - **URL**: https://apify.com/crw/temu-category-trend-report.md - **Developed by:** [CRW](https://apify.com/crw) (community) - **Categories:** E-commerce, Automation, Developer tools - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $10.00 / 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 ## Temu Category Trend Report **Daily trend intelligence for Temu's top-selling categories.** Identify Breakout products before they peak, track price movements, and spot what's actually selling — updated every day. --- ### What You Get Each run delivers two outputs for a selected Temu category: - **Dataset (200 rows)** — One row per product currently ranked in the top 200 - **Key-Value Store: `REPORT_SUMMARY`** — Category-level summary, 7-day price trend, and curated Hot Picks list --- ### Why This Actor Temu moves fast. Products rocket from obscurity to top 10 in 48 hours, then disappear. By the time you notice a hot item manually, the margin window is already closing. This actor gives you: - **Breakout detection** — Products with rank climbing >5 positions AND sales up >200 units in 7 days - **7-day momentum scores** — Not just today's snapshot, but the trajectory - **Price trend history** — Category avg price over 8 days, so you see where pricing is heading - **Hot Picks list** — Pre-filtered shortlist of the highest-momentum products (up to 50) - **Market signal** — One-word category health: `Hot` / `Warm` / `Neutral` / `Cooling` --- ### Supported Categories | Category ID | Category Name | |-------------|---------------| | 36 | Home & Kitchen | | 28 | Women's Clothing | | 589 | Women's Curve Clothing | | 95 | Women's Shoes | | 1107 | Women's Lingerie & Lounge | | 67 | Men's Clothing | | 1536 | Men's Shoes | | 1232 | Men's Big & Tall | | 114 | Men's Underwear & Sleepwear | | 178 | Sports & Outdoors | | 352 | Jewelry Accessories | | 25 | Beauty & Health | | 204 | Toys & Games | | 580 | Automotive | | 218 | Kids Fashion | | 1553 | Kids Shoes | More categories added regularly. --- ### Input ```json { "category_id": 1107, "region": "US" } ```` | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `category_id` | integer | Yes | — | Category ID from the table above | | `region` | string | No | `"US"` | Market region. Currently US only | *** ### Output #### Dataset — Product Rows 200 rows, one per product ranked in the category today. ```json { "goods_id": "601099555477841", "title": "Women's Sexy Satin Lace Camisole and Panty Set...", "price": 6.03, "price_str": "$6.03", "original_price": 31.30, "discount_rate": 81, "sales_volume": 100000, "sales_tip_text": "100K+", "rating": 4.8, "review_count": 1803, "rank": 1, "rank_percentile": 0.5, "momentum_status": "Rising", "rank_7d_delta": -3, "sales_7d_delta": 0, "price_7d_delta": 0.0, "price_7d_min": 6.03, "price_7d_max": 6.03, "review_count_7d_delta": 16, "rating_7d_delta": 0.0, "appearances_7d": 6, "days_in_category": 9, "price_vs_top200_avg": 85.9, "first_seen_at": "20260429", "last_seen_at": "20260507", "is_on_sale": false, "is_low_stock": false, "is_new_product": false, "sale_ends_at": null, "has_video": true, "thumb_url": "https://img.kwcdn.com/product/fancy/...", "goods_url": "https://www.temu.com/goods.html?goods_id=601099555477841", "category_name": "Women's Lingerie & Lounge", "category_id": 1107, "region": "US", "collected_at": "20260507" } ``` ##### Product Field Reference | Field | Type | Description | |-------|------|-------------| | `goods_id` | string | Temu product ID | | `title` | string | Product title | | `price` | float | Current price (USD) | | `price_str` | string | Formatted price string, e.g. `"$6.03"` | | `original_price` | float | List price before discount (USD) | | `discount_rate` | integer | Discount percentage, e.g. `81` = 81% off | | `sales_volume` | integer | Cumulative units sold (normalized, e.g. `"94K+"` → `94000`) | | `sales_tip_text` | string | Original sales label from Temu, e.g. `"100K+"` | | `rating` | float | Product rating (0–5) | | `review_count` | integer | Total number of reviews | | `rank` | integer | Today's rank in the category (1 = best) | | `rank_percentile` | float | Rank as top-N%, e.g. `0.5` = top 0.5% | | `momentum_status` | string | See Momentum Status table below | | `rank_7d_delta` | integer | Rank change over 7 days. Negative = climbed (good). e.g. `-53` = moved up 53 spots | | `sales_7d_delta` | integer | Units sold increase over 7 days. May show `0` even for active products because Temu displays sales in rounded buckets (e.g. `"100K+"`), so the value only changes when the bucket threshold is crossed | | `price_7d_delta` | float | Price change over 7 days (USD). Positive = price increase | | `price_7d_min` | float | Lowest price in the past 7 days (USD) | | `price_7d_max` | float | Highest price in the past 7 days (USD) | | `review_count_7d_delta` | integer | New reviews added in the past 7 days | | `rating_7d_delta` | float | Rating change over 7 days | | `appearances_7d` | integer | Number of days this product appeared in the top 200 over the past 7 collection days (max 7). A value of 7 = consistently present. A value of 1–3 = unstable, may exit soon (`Fading`) | | `days_in_category` | integer | Days elapsed since `first_seen_at`, capped at 7. Reflects how long this product has been tracked within this category's top 200 over the 7-day window, not its age on Temu overall | | `price_vs_top200_avg` | float | This product's price as % of the category average. e.g. `85.9` = 14% cheaper than avg | | `first_seen_at` | string | Date first collected in this category (`YYYYMMDD`) | | `last_seen_at` | string | Most recent collection date (`YYYYMMDD`) | | `collected_at` | string | Date this row was collected (`YYYYMMDD`) | | `is_on_sale` | boolean | Whether a time-limited sale is active | | `is_low_stock` | boolean | Whether the low-stock badge is shown | | `is_new_product` | boolean | Whether Temu's "new product" tag is shown | | `sale_ends_at` | string | Sale end time in ISO 8601 UTC format (e.g. `"2026-05-19T06:59:59Z"`). Empty string if `is_on_sale` is false | | `has_video` | boolean | Whether the product has a video | | `thumb_url` | string | Thumbnail image URL | | `goods_url` | string | Clean product page URL (all tracking parameters removed) | | `category_name` | string | Human-readable category name | | `category_id` | integer | Category ID | | `region` | string | Region code, e.g. `"US"` | ##### Momentum Status | Value | Meaning | |-------|---------| | `New` | First seen in this category today | | `Breakout` | Rank climbed >5 spots AND sales up >200 units in 7 days — strongest buy signal | | `Rising` | Rank improving or sales growing steadily | | `Stable` | No significant movement in either direction | | `Declining` | Rank dropping or price rising | | `Fading` | Seen in top 200 on 3 or fewer of the last 7 days — at risk of exiting | Priority order when multiple conditions apply: `New` > `Breakout` > `Rising` > `Fading` > `Declining` > `Stable` *** #### Key-Value Store — `REPORT_SUMMARY` Retrieve via the Apify API using the run's `defaultKeyValueStoreId`: ``` GET /v2/key-value-stores/{kvStoreId}/records/REPORT_SUMMARY ``` ```json { "meta": { "category_id": 1107, "category_name": "Women's Lingerie & Lounge", "region": "US", "collected_at": "20260507", "generated_at": "2026-05-07T01:05:16Z", "data_freshness_hours": 1 }, "summary": { "total_products": 200, "avg_price": 7.02, "avg_discount_rate": 58.0, "avg_rating": 4.75, "new_entries_today": 7, "disappeared_today": 275, "on_sale_count": 96, "has_video_count": 149, "momentum_distribution": { "New": 7, "Breakout": 23, "Rising": 123, "Stable": 4, "Declining": 25, "Fading": 18 }, "market_signal": "Warm" }, "price_trend_7d": [ { "date": "20260429", "avg_price": 5.95, "avg_discount_rate": 59.7, "total_collected": 200, "new_entries": 200, "disappeared": 0, "momentum_distribution": { "New": 200 } } ], "hot_picks": [ { "goods_id": "601099584094697", "rank": 14, "momentum_status": "Rising", "rank_7d_delta": -28, "price": 5.70 } ] } ``` ##### REPORT\_SUMMARY Field Reference | Field | Type | Description | |-------|------|-------------| | `meta.collected_at` | string | Date the underlying data was collected (`YYYYMMDD`) | | `meta.generated_at` | string | ISO 8601 timestamp when the report file was generated | | `meta.data_freshness_hours` | integer | Hours elapsed since data was generated at time of this run | | `summary.market_signal` | string | `Hot` (≥75% Breakout+Rising), `Warm` (≥50%), `Cooling` (>40% Declining+Fading), `Neutral` | | `summary.new_entries_today` | integer | Number of products appearing in today's top 200 for the first time (i.e. not seen in any previous collection within the 7-day window) | | `summary.disappeared_today` | integer | Total number of products that were tracked at any point in the 7-day window but are absent from today's top 200. **This value can exceed 200** because it counts cumulative exits across all days, not just yesterday's departures. A high value indicates aggressive product churn in this category | | `summary.on_sale_count` | integer | Number of products currently running a time-limited sale | | `summary.has_video_count` | integer | Number of products with a video | | `summary.momentum_distribution` | object | Count of products per momentum status | | `price_trend_7d` | array | 8-day daily history of category avg price, avg discount rate, and momentum distribution. Each entry also includes `new_entries` (products first seen that day) and `disappeared` (cumulative products no longer in top 200 as of that day) | | `hot_picks` | array | Up to 50 highest-momentum products (all Breakout + top 20 Rising by rank delta), sorted by rank. Same fields as Dataset rows | *** ### Use Cases **E-commerce sellers & resellers** Filter `momentum_status = "Breakout"` to find products gaining traction before they peak. Use `price_vs_top200_avg` to find pricing gaps. **Amazon / Shopify merchants** Track which categories are heating up (`market_signal = "Hot"`) to guide sourcing before demand hits your platform. **Market researchers** Use `price_trend_7d` to analyze category pricing cycles. Track `disappeared_today` as a churn indicator — values consistently above 200 mean extremely high product turnover and aggressive competition in that category. **Dropshippers** `hot_picks` gives you a ready-made shortlist of the highest-momentum products updated daily — no manual filtering needed. *** ### Data Freshness Data is collected once per day. `meta.data_freshness_hours` tells you exactly how old the data is at run time. Each run always returns the most recent available snapshot. *** ### API Integration Example ```javascript const { ApifyClient } = require('apify-client'); const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const run = await client.actor('YOUR_ACTOR_ID').call({ category_id: 1107, region: 'US', date: 'latest', }); // Product rows — filter to Breakout items only const { items } = await client.dataset(run.defaultDatasetId).listItems(); const breakouts = items.filter(p => p.momentum_status === 'Breakout'); // Summary, price trend, hot picks const record = await client .keyValueStore(run.defaultKeyValueStoreId) .getRecord('REPORT_SUMMARY'); console.log('Market signal:', record.value.summary.market_signal); console.log('Hot picks count:', record.value.hot_picks.length); ``` *** ### Notes - Data covers the **US market only** at this time - Top 200 is determined by sales volume ranking within the category at time of daily collection - `sales_volume` values like `"94K+"` are normalized to integers (`94000`) - `goods_url` has all tracking parameters stripped — only `goods_id` is retained - Historical snapshots are available for dates since data collection began for each category # Actor input Schema ## `category_id` (type: `string`): Select the Temu category to analyze. When using the API, pass the numeric ID as a string: 36 = Home & Kitchen 28 = Women's Clothing 589 = Women's Curve Clothing 95 = Women's Shoes 1107 = Women's Lingerie & Lounge 67 = Men's Clothing 1536 = Men's Shoes 1232 = Men's Big & Tall 114 = Men's Underwear & Sleepwear 178 = Sports & Outdoors 352 = Jewelry Accessories 25 = Beauty & Health 204 = Toys & Games 580 = Automotive 218 = Kids Fashion 1553 = Kids Shoes ## `region` (type: `string`): Market region. Currently US only. ## Actor input object example ```json { "category_id": "1107", "region": "US" } ``` # Actor output Schema ## `dataset_api` (type: `string`): REST API endpoint to download the 200 product rows programmatically. ## `report_summary_api` (type: `string`): REST API endpoint to retrieve the REPORT\_SUMMARY record (meta, summary, price\_trend\_7d, hot\_picks). # 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 = { "category_id": "1107", "region": "US" }; // Run the Actor and wait for it to finish const run = await client.actor("crw/temu-category-trend-report").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 = { "category_id": "1107", "region": "US", } # Run the Actor and wait for it to finish run = client.actor("crw/temu-category-trend-report").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 '{ "category_id": "1107", "region": "US" }' | apify call crw/temu-category-trend-report --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crw/temu-category-trend-report", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Temu Category Trend Report", "description": "Daily Temu category intelligence: top 200 products ranked by 7-day momentum. Spot Breakout products before they peak, track price trends, and get a curated Hot Picks list — all from a single run. Covers 16 categories including Fashion, Beauty, and Home.", "version": "0.0", "x-build-id": "4UWffY9vDxodIf7DL" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crw~temu-category-trend-report/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crw-temu-category-trend-report", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/crw~temu-category-trend-report/runs": { "post": { "operationId": "runs-sync-crw-temu-category-trend-report", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/crw~temu-category-trend-report/run-sync": { "post": { "operationId": "run-sync-crw-temu-category-trend-report", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "category_id" ], "properties": { "category_id": { "title": "Category", "enum": [ "36", "28", "589", "95", "1107", "67", "1536", "1232", "114", "178", "352", "25", "204", "580", "218", "1553" ], "type": "string", "description": "Select the Temu category to analyze. When using the API, pass the numeric ID as a string:\n36 = Home & Kitchen\n28 = Women's Clothing\n589 = Women's Curve Clothing\n95 = Women's Shoes\n1107 = Women's Lingerie & Lounge\n67 = Men's Clothing\n1536 = Men's Shoes\n1232 = Men's Big & Tall\n114 = Men's Underwear & Sleepwear\n178 = Sports & Outdoors\n352 = Jewelry Accessories\n25 = Beauty & Health\n204 = Toys & Games\n580 = Automotive\n218 = Kids Fashion\n1553 = Kids Shoes" }, "region": { "title": "Region", "enum": [ "US" ], "type": "string", "description": "Market region. Currently US only.", "default": "US" } } }, "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 } } } } } } } } } } ```