TikTok drives massive e-commerce sales. Products that go viral on TikTok sell out on Amazon within days. Detecting these trends early gives you a sourcing advantage. This tutorial builds a TikTok trend detection pipeline using the Scavio TikTok API endpoints. Search for product-related videos, track engagement velocity (views per hour), and cross-reference with Amazon search data to validate commercial viability. TikTok API calls use the Bearer token auth pattern.
Prerequisites
- Python 3.9+ installed
- requests library installed
- A Scavio API key from scavio.dev
- Basic understanding of e-commerce product sourcing
Walkthrough
Step 1: Search TikTok for product-related videos
Use the TikTok search videos endpoint to find product-related content. High view counts and recent upload dates indicate trending products.
Python
import os, requests, time
from datetime import datetime
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
TT_URL = 'https://api.scavio.dev/api/v1/tiktok'
TT_H = {'Authorization': f'Bearer {SCAVIO_KEY}', 'Content-Type': 'application/json'}
SEARCH_URL = 'https://api.scavio.dev/api/v1/search'
SEARCH_H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def search_tiktok_products(query: str, count: int = 20) -> list:
"""Search TikTok for product-related videos."""
resp = requests.post(f'{TT_URL}/search/videos', headers=TT_H,
json={'keyword': query, 'count': count, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
results = []
for v in videos:
stats = v.get('stats', {})
author = v.get('author', {})
results.append({
'description': v.get('desc', '')[:100],
'author': author.get('uniqueId', ''),
'plays': stats.get('playCount', 0),
'likes': stats.get('diggCount', 0),
'comments': stats.get('commentCount', 0),
'shares': stats.get('shareCount', 0),
'created': v.get('createTime', 0),
'video_id': v.get('id', ''),
})
# Sort by plays descending
results.sort(key=lambda x: x['plays'], reverse=True)
return results
videos = search_tiktok_products('TikTok made me buy it')
print(f'Found {len(videos)} product videos')
for v in videos[:5]:
print(f' {v["plays"]:>12,} plays | @{v["author"]:15s} | {v["description"][:40]}')Step 2: Detect trending product categories
Search multiple product-related hashtags and keywords to identify which categories are trending. Compare engagement rates across categories.
Python
def detect_trending_categories(categories: list) -> list:
"""Search TikTok for each product category and rank by engagement."""
results = []
for category in categories:
videos = search_tiktok_products(category, count=10)
if not videos:
continue
total_plays = sum(v['plays'] for v in videos)
total_likes = sum(v['likes'] for v in videos)
avg_engagement = total_likes / total_plays if total_plays else 0
results.append({
'category': category,
'videos': len(videos),
'total_plays': total_plays,
'total_likes': total_likes,
'avg_engagement': avg_engagement,
'top_video': videos[0] if videos else None,
})
time.sleep(0.3)
results.sort(key=lambda x: x['total_plays'], reverse=True)
return results
categories = [
'cleaning gadget review',
'kitchen organization hack',
'skincare routine product',
'desk setup accessory',
'pet gadget review',
]
trending = detect_trending_categories(categories)
print('TikTok Product Trend Detection')
print('=' * 60)
for t in trending:
print(f" {t['category']:30s} | {t['total_plays']:>12,} plays | {t['avg_engagement']:.1%} eng")
if t['top_video']:
print(f" Top: {t['top_video']['description'][:50]}")Step 3: Track hashtag velocity for trend timing
Use the TikTok hashtag endpoint to check how fast a product hashtag is growing. High view counts on the hashtag indicate peak or pre-peak timing.
Python
def check_hashtag_velocity(hashtag: str) -> dict:
"""Check a hashtag's total views and video volume."""
# Get hashtag info
resp = requests.post(f'{TT_URL}/hashtag', headers=TT_H,
json={'hashtag': hashtag})
hashtag_data = resp.json().get('data', {})
time.sleep(0.3)
# Get recent videos under this hashtag
resp = requests.post(f'{TT_URL}/hashtag/videos', headers=TT_H,
json={'hashtag': hashtag, 'count': 20, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
recent_plays = sum(v.get('stats', {}).get('playCount', 0) for v in videos)
return {
'hashtag': hashtag,
'total_views': hashtag_data.get('stats', {}).get('videoCount', 0),
'recent_videos': len(videos),
'recent_plays': recent_plays,
'avg_plays_per_video': recent_plays // len(videos) if videos else 0,
}
hashtags = ['cleaninghack', 'kitchengadget', 'desksetup', 'skincareproduct']
print('Hashtag Velocity Check')
print('-' * 60)
for tag in hashtags:
data = check_hashtag_velocity(tag)
print(f' #{data["hashtag"]:20s} | {data["recent_plays"]:>10,} recent plays | '
f'{data["avg_plays_per_video"]:>8,} avg/video')
time.sleep(0.3)Step 4: Cross-validate with Amazon search data
A product trending on TikTok only matters for e-commerce if people are actually buying it. Cross-reference TikTok trends with Amazon search to validate commercial viability.
Python
def validate_trend_commercially(product: str) -> dict:
"""Cross-validate a TikTok trend with Amazon data."""
# TikTok data
tt_videos = search_tiktok_products(product, count=10)
tt_plays = sum(v['plays'] for v in tt_videos)
time.sleep(0.3)
# Amazon data
resp = requests.post(SEARCH_URL, headers=SEARCH_H,
json={'query': f'site:amazon.com {product}',
'country_code': 'us', 'num_results': 5})
amazon_results = resp.json().get('organic_results', [])
# Scoring
tiktok_score = min(tt_plays / 100000, 10) # Normalize to 0-10
amazon_score = len(amazon_results) * 2 # 0-10
combined = (tiktok_score + amazon_score) / 2
return {
'product': product,
'tiktok_videos': len(tt_videos),
'tiktok_plays': tt_plays,
'amazon_listings': len(amazon_results),
'tiktok_score': round(tiktok_score, 1),
'amazon_score': round(amazon_score, 1),
'combined_score': round(combined, 1),
'verdict': 'HOT' if combined > 6 else 'WARM' if combined > 3 else 'COLD',
'cost': 0.010, # 1 TikTok + 1 Amazon search
}
products = ['LED sunset lamp', 'portable blender', 'cloud slides shoes']
print('\nTrend Validation Report')
print('=' * 65)
for p in products:
result = validate_trend_commercially(p)
print(f"[{result['verdict']:4s}] {result['product']:25s} | "
f"TT: {result['tiktok_score']}/10 AMZ: {result['amazon_score']}/10 | "
f"Combined: {result['combined_score']}/10")
time.sleep(0.5)
total_cost = len(products) * 0.010
print(f'\nTotal cost: ${total_cost:.3f}')Python Example
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
TT_H = {'Authorization': f'Bearer {SCAVIO_KEY}', 'Content-Type': 'application/json'}
S_H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def detect_trend(product):
# TikTok search
resp = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos', headers=TT_H,
json={'keyword': product, 'count': 10, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
plays = sum(v.get('stats', {}).get('playCount', 0) for v in videos)
time.sleep(0.3)
# Amazon cross-check
resp2 = requests.post('https://api.scavio.dev/api/v1/search', headers=S_H,
json={'query': f'site:amazon.com {product}', 'country_code': 'us', 'num_results': 5})
amazon = len(resp2.json().get('organic_results', []))
verdict = 'HOT' if plays > 500000 and amazon >= 3 else 'WARM' if plays > 100000 else 'COLD'
print(f'[{verdict}] {product}: {plays:,} TT plays, {amazon} AMZ listings')
for p in ['LED sunset lamp', 'portable blender', 'cloud slides']:
detect_trend(p)
time.sleep(0.3)JavaScript Example
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function detectTrend(product) {
const tt = await fetch('https://api.scavio.dev/api/v1/tiktok/search/videos', {
method: 'POST',
headers: { Authorization: `Bearer ${SCAVIO_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ keyword: product, count: 10, cursor: 0 })
}).then(r => r.json());
const plays = (tt.data?.videos || []).reduce((s, v) => s + (v.stats?.playCount || 0), 0);
const amz = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST',
headers: { 'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json' },
body: JSON.stringify({ query: `site:amazon.com ${product}`, country_code: 'us', num_results: 5 })
}).then(r => r.json());
const listings = (amz.organic_results || []).length;
const verdict = plays > 500000 && listings >= 3 ? 'HOT' : plays > 100000 ? 'WARM' : 'COLD';
console.log(`[${verdict}] ${product}: ${plays.toLocaleString()} TT plays, ${listings} AMZ listings`);
}
(async () => { for (const p of ['LED sunset lamp', 'portable blender']) await detectTrend(p); })();Expected Output
JSON
Found 20 product videos
1,234,567 plays | @cleanqueen | This cleaning gadget changed my life
892,345 plays | @organizewithme | Kitchen organization haul from Amazon
TikTok Product Trend Detection
============================================================
skincare routine product | 4,523,000 plays | 8.2% eng
cleaning gadget review | 3,891,000 plays | 6.5% eng
kitchen organization hack | 2,156,000 plays | 7.1% eng
Trend Validation Report
=================================================================
[HOT ] LED sunset lamp | TT: 7.2/10 AMZ: 8.0/10 | Combined: 7.6/10
[WARM] portable blender | TT: 4.5/10 AMZ: 6.0/10 | Combined: 5.3/10
[HOT ] cloud slides shoes | TT: 8.1/10 AMZ: 10.0/10 | Combined: 9.1/10
Total cost: $0.030