# Temu Category Trend Report (`crw/temu-category-trend-report`) Actor Daily Temu category intelligence for brands, retailers, and agencies: top 100 products by sales volume, 7-day momentum tracking, price trend history, and a curated Hot Picks list — covering 30 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:** 9 total users, 4 monthly users, 97.9% 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 Temu category intelligence for brands, retailers, and agencies competing in the US market.** Track which products are gaining momentum, how category pricing is shifting, and where new competition is entering — updated every day. --- ### Who Uses This **Brands and retailers selling in categories where Temu competes** You sell on Amazon, your own site, or wholesale — and Temu operates in your category. You need to know when a new product is gaining traction on Temu, whether it's priced below your floor, and how fast it's climbing. Concrete example: you sell home cleaning tools on Amazon. A competing product appears in Temu's top 100 at $2.99 and climbs 30 ranks in a week. You see it in `hot_picks` with `rank_7d_delta: -30` before it starts showing up in your Amazon BSR. That's your window to respond — reprice, run a promotion, or flag it for your sourcing team. **Agencies providing market intelligence to retail and brand clients** Schedule a daily run per category, pull `market_signal` + `hot_picks` into your reporting pipeline, and generate client-ready output automatically. A 5-category daily monitor runs at a fraction of your retainer. **In-house e-commerce and category management teams** Pipe the data into your dashboard or Slack via Zapier or Make. When `market_signal` shifts to `"Hot"` in a category you own, alert the team. When a new product appears in `hot_picks` with `appearances_7d >= 5`, add it to the review queue. --- ### Supported Categories | Category ID | Category Name | |-------------|---------------| | 25 | Beauty & Health | | 28 | Women's Clothing | | 36 | Home & Kitchen | | 67 | Men's Clothing | | 95 | Women's Shoes | | 114 | Men's Underwear & Sleepwear | | 178 | Sports & Outdoors | | 202 | Home Gadgets & Office | | 204 | Toys & Games | | 218 | Kids Fashion | | 248 | Electronics & Accessories | | 259 | Business, Industry & Science | | 320 | Pet Supplies | | 352 | Jewelry Accessories | | 580 | Automotive | | 589 | Women's Curve Clothing | | 628 | Musical Instruments | | 731 | Men's Bags & Wallets | | 871 | Health & Household | | 1107 | Women's Lingerie & Lounge | | 1167 | Baby & Maternity | | 1232 | Men's Big & Tall | | 1422 | Smart Home | | 1536 | Men's Shoes | | 1553 | Kids Shoes | | 2640 | Cell Phones & Accessories | | 7084 | Food & Grocery | | 7085 | Books & Media | | 7166 | Beachwear | | 885 | Patio, Lawn & Garden | | 893 | Tools & Home Improvement | | 990 | Appliances | | 1493 | Arts, Crafts & Sewing | More categories added regularly. --- ### Input ```json { "category_id": "36", "region": "US" } ```` | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `category_id` | string | Yes | — | Category ID from the table above. Pass as a string (e.g. `"36"`) | | `region` | string | No | `"US"` | Market region. Currently US only | *** ### Output Each run delivers two outputs. **Dataset — 100 product rows**, one per product in today's top 100 ranked by sales volume. Key fields per row: | Field | Description | |-------|-------------| | `rank` | Today's rank within the category (1 = best) | | `momentum_status` | `Breakout` / `Rising` / `Stable` / `Declining` / `Fading` / `New` — see below | | `rank_7d_delta` | Rank change over 7 days. Negative = climbed. `-30` = moved up 30 spots | | `price` / `price_str` | Current price | | `price_7d_delta` | Price change over 7 days (USD). Positive = price increase | | `price_vs_top100_avg` | This product's price vs category average. `85.9` = 14% cheaper than average | | `appearances_7d` | Days seen in top 100 over the last 7 collection days (max 7). Below 4 = unstable | | `review_count_7d_delta` | New reviews added in the past 7 days | | `goods_url` | Clean product page URL | **Key-Value Store: `REPORT_SUMMARY`** — category-level summary for the day: | Field | Description | |-------|-------------| | `summary.market_signal` | `Hot` (≥75% Breakout+Rising) / `Warm` (≥50%) / `Cooling` (>40% Declining+Fading) / `Neutral` | | `summary.new_entries_today` | Products appearing in top 100 for the first time today | | `summary.momentum_distribution` | Count per momentum status | | `data_reliability.signal_confidence` | `High` / `Medium` / `Low` — how much to trust momentum signals today | | `data_reliability.note` | Plain-language interpretation of today's signal quality | | `price_trend_7d` | 7-day history of category avg price, discount rate, and momentum distribution | | `hot_picks` | Up to 50 highest-momentum products (all Breakout + top Rising by rank delta) | #### Momentum Status | Value | Meaning | |-------|---------| | `Breakout` | Rank climbed >5 spots AND sales up in 7 days — strongest signal | | `Rising` | Rank improving or sales growing steadily | | `New` | First seen in this category's top 100 today | | `Stable` | No significant movement | | `Declining` | Rank dropping or price rising | | `Fading` | Seen on 3 or fewer of the last 7 days — trend ending | Backtested over 6 days across 18 categories: **68.7% of Breakout products** were still in the top 100 six days later. *** ### How to Read the Data #### Start with `REPORT_SUMMARY` ``` market_signal = "Hot" → Strong growth (≥75% Breakout+Rising) — act now market_signal = "Warm" → Moderate growth (≥50% Breakout+Rising) market_signal = "Cooling" → Category losing momentum (>40% Declining+Fading) market_signal = "Neutral" → Mixed signals, check individual products ``` Check `data_reliability.signal_confidence` before acting. Each run computes this from that day's actual data: ```json "data_reliability": { "signal_confidence": "High", "churn_level": "Low", "tracking_coverage": 0.93, "stable_product_ratio": 0.76, "note": "Momentum signals are reliable. Breakout and Rising products can be acted on directly." } ``` #### Find what's moving Filter `hot_picks` or the Dataset: ``` momentum_status = "Breakout" → Strongest signal. Rank climbing + sales up. momentum_status = "Rising" + rank_7d_delta < -20 → Rising fast + appearances_7d >= 5 → Consistent presence, not a one-day spike momentum_status = "Fading" or "Declining" → Losing ground. Exiting the competitive set. ``` #### Track pricing pressure `price_trend_7d` in `REPORT_SUMMARY` shows 7 days of category average pricing. A falling `avg_price` alongside rising `Breakout` counts is the earliest signal of margin compression in your category. *** ### API Integration Example #### Scheduled daily run ```python from apify_client import ApifyClient client = ApifyClient('YOUR_API_TOKEN') run = client.actor('YOUR_ACTOR_ID').call(run_input={ 'category_id': '36', 'region': 'US', }) ## Category-level signal record = client.key_value_store(run['defaultKeyValueStoreId']).get_record('REPORT_SUMMARY') summary = record['value'] print(f"Market signal: {summary['summary']['market_signal']}") print(f"Signal confidence: {summary['data_reliability']['signal_confidence']}") ## Hot picks — highest-momentum products today for p in summary['hot_picks']: print(f" [{p['rank']}] {p['title']} — {p['price_str']} (rank moved {p['rank_7d_delta']})") ``` #### Filter and store product rows ```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: '36', region: 'US' }); const { items } = await client.dataset(run.defaultDatasetId).listItems(); // Breakout products — flag for competitive review const breakouts = items.filter(p => p.momentum_status === 'Breakout'); // Price movers — products cheaper than category average and climbing const pricePressure = items.filter(p => p.price_vs_top100_avg < 80 && p.rank_7d_delta < -10 ); ``` *** ### Data Notes - US market only - Top 100 ranked by **sales volume** at time of daily collection - `sales_7d_delta` is often `0` due to Temu's bucketed display (`"100K+"`). Use `rank_7d_delta` as the primary trend signal - `disappeared_today` counts cumulative exits over the 7-day window — can exceed 100 - `appearances_7d` and `days_in_category` are capped at 7. During the first 6 days of tracking a new category, `Fading` counts appear inflated — this normalizes automatically after day 7 *** ### Issue History #### 2026-06-05 — Category 871 (Health & Household) runtime failure fix **Symptom:** Running the actor with `category_id: 871` failed at `Actor.push_data()` with `ApifyApiError: Schema validation failed`. All other categories worked normally. **Root cause:** The dataset schema defined `rating` as `"type": "number"` (null not allowed). Category 871 has a high proportion of new products, and Temu does not provide a rating for products with no reviews yet — these arrive as `null`. The schema violation caused Apify's dataset push to reject the entire batch. **Fix:** Changed `rating` type in `.actor/dataset_schema.json` from `"number"` to `["number", "null"]`, consistent with how `original_price` already handles optional numeric fields. # Actor input Schema ## `category_id` (type: `string`): Select the Temu category to analyze. When using the API, pass the numeric ID as a string: 990 = Appliances 1493 = Arts, Crafts & Sewing 580 = Automotive 1167 = Baby & Maternity 7166 = Beachwear 25 = Beauty & Health 7085 = Books & Media 259 = Business, Industry & Science 2640 = Cell Phones & Accessories 248 = Electronics & Accessories 7084 = Food & Grocery 871 = Health & Household 36 = Home & Kitchen 202 = Home Gadgets & Office 352 = Jewelry Accessories 218 = Kids Fashion 1553 = Kids Shoes 731 = Men's Bags & Wallets 1232 = Men's Big & Tall 67 = Men's Clothing 1536 = Men's Shoes 114 = Men's Underwear & Sleepwear 628 = Musical Instruments 885 = Patio, Lawn & Garden 320 = Pet Supplies 1422 = Smart Home 178 = Sports & Outdoors 893 = Tools & Home Improvement 204 = Toys & Games 28 = Women's Clothing 589 = Women's Curve Clothing 1107 = Women's Lingerie & Lounge 95 = Women's Shoes ## `region` (type: `string`): Market region. Currently US only. ## Actor input object example ```json { "category_id": "36", "region": "US" } ``` # Actor output Schema ## `dataset_api` (type: `string`): REST API endpoint to download the 100 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": "36", "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": "36", "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": "36", "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 for brands, retailers, and agencies:\ntop 100 products by sales volume, 7-day momentum tracking, price trend history, and a curated Hot Picks list — covering 30 categories including Fashion, Beauty, and Home.", "version": "0.0", "x-build-id": "H9TYEBfbBsnw2hn3d" }, "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": [ "990", "1493", "580", "1167", "7166", "25", "7085", "259", "2640", "248", "7084", "871", "36", "202", "352", "218", "1553", "731", "1232", "67", "1536", "114", "628", "885", "320", "1422", "178", "893", "204", "28", "589", "1107", "95" ], "type": "string", "description": "Select the Temu category to analyze. When using the API, pass the numeric ID as a string:\n990 = Appliances\n1493 = Arts, Crafts & Sewing\n580 = Automotive\n1167 = Baby & Maternity\n7166 = Beachwear\n25 = Beauty & Health\n7085 = Books & Media\n259 = Business, Industry & Science\n2640 = Cell Phones & Accessories\n248 = Electronics & Accessories\n7084 = Food & Grocery\n871 = Health & Household\n36 = Home & Kitchen\n202 = Home Gadgets & Office\n352 = Jewelry Accessories\n218 = Kids Fashion\n1553 = Kids Shoes\n731 = Men's Bags & Wallets\n1232 = Men's Big & Tall\n67 = Men's Clothing\n1536 = Men's Shoes\n114 = Men's Underwear & Sleepwear\n628 = Musical Instruments\n885 = Patio, Lawn & Garden\n320 = Pet Supplies\n1422 = Smart Home\n178 = Sports & Outdoors\n893 = Tools & Home Improvement\n204 = Toys & Games\n28 = Women's Clothing\n589 = Women's Curve Clothing\n1107 = Women's Lingerie & Lounge\n95 = Women's 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 } } } } } } } } } } ```