---name: finding-tiktok-shop-trending-productsdescription: >Triggers when the user asks to: find trending TikTok Shop products, discover what products arelicense: Apache-2.0metadata: author: apidojoversion: 1.0apify-actor: apidojo/tiktok-scraper---

Finding TikTok Shop Trending Products

Identifies products gaining momentum on TikTok Shop by analyzing creator promotion density and engagement. A product promoted by many creators simultaneously is a strong viral commerce signal.

Prerequisites

• APIFY_TOKEN environment variable set
• Optional: Apify MCP server installed

Inputs

Parameter	Type	Required	Default	Notes
startUrls	array	Optional	[]	TikTok URLs — user profiles, hashtags, music pages, search, locations
keywords	array	Optional	[]	Search keywords/terms to find posts
sortType	string	Optional	RELEVANCE	Sort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED
location	string	Optional	—	ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB)
maxItems	number	Optional	Unlimited	Maximum posts to return across the run
includeSearchKeywords	boolean	Optional	false	Add the matched search keyword field to each post
customMapFunction	string	Optional	—	JavaScript function to transform each output object

Workflow

Progress:
- [ ] Step 1: Search TikTok Shop hashtags for category
- [ ] Step 2: Extract products from promotional posts
- [ ] Step 3: Count creator promotion density per product
- [ ] Step 4: Score product trend momentum
- [ ] Step 5: Deliver trending product list

Step 1: Search Posts

Recommended — run_actor.js (handles waiting, output, and file saving automatically):

# Quick answer (prints table to chat)
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}'

# Save as CSV
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.csv --format csv

# Save as JSON
node scripts/run_actor.js \
  --actor "apidojo~tiktok-scraper" \
  --input '{"param": "value"}' \
  --output YYYY-MM-DD_results.json --format json

APIFY_TOKEN must be set in environment or .env file.

If Apify MCP is available:

Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
  "keywords": ["#tiktokshop[category]", "#[category]deals", "#tiktokmademebuyit"],
  "maxItems": 300
}

REST API fallback:

curl -X POST \
  "https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"keywords": ["#tiktokmademebuyit", "#skincaretiktokshop"], "maxItems": 300}'

Step 2: Score Products

Group posts by product name from captions. For each product:

promotion_density = count(unique creators promoting product)
total_reach = sum(playCount for all promoting posts)
engagement_rate = sum(diggCount) / total_reach * 100

trend_score = (promotion_density / 5, max 1) * 0.35
            + (total_reach / 1000000, max 1) * 0.35
            + (engagement_rate > 3 ? 1 : engagement_rate/3) * 0.30

Trend window: 70%+ promotions in last 7 days = PEAKING; spread evenly = SUSTAINED; mostly > 7 days = FADING

Step 3: Edge Cases

• Product name extraction unreliable: Extract most specific noun phrase from caption alongside shop link
• Same product different variants: Group by product name similarity; sum reach across variants
• Saturated trend (> 50 creators): Window may be closing; flag as LATE_STAGE
• Counterfeit signals: Price far below market (e.g. < $5 for normally $30+ item) → flag

Output Format

# TikTok Shop Trending Products: [CATEGORY]
Posts analyzed: [N] | Unique products: [N] | Date: [DATE]

## Trending Now (Peak Momentum)
| Product | Creators | Total Views | Avg Eng Rate | Stage | Score |
|---------|---------|------------|-------------|-------|-------|

## Sustained Performers
| Product | Creators | Total Views | Stage |
|---------|---------|-----------|-------|

## Saturation Alert
[Products with > 30 creators — difficult to enter profitably]

Troubleshooting

Few products identified: Check bio links of top promoting creators for product page. Trend already peaked: Run every 3-4 days to catch trends early. Suspicious pricing: Flag and verify before sourcing.