You do not need to be an account owner to analyze public TikTok metrics. The Scavio TikTok API lets you pull any public profile and their recent posts to calculate engagement rates, posting frequency, audience growth indicators, and content performance patterns. Each call costs $0.005, making a full account audit under $0.02.
Prerequisites
- Python 3.8+
- requests library
- A Scavio API key from scavio.dev
- Target TikTok usernames to analyze
Walkthrough
Step 1: Fetch public profile data
Pull profile metrics for any public TikTok account.
Python
import os, requests, json
from datetime import datetime
API_KEY = os.environ['SCAVIO_API_KEY']
TH = {'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'}
def get_profile(username):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/profile',
headers=TH, json={'username': username}).json()
user = data.get('user', data.get('data', {}).get('user', data))
return {
'username': username,
'followers': user.get('followerCount', user.get('fans', 0)),
'following': user.get('followingCount', 0),
'likes': user.get('heartCount', user.get('heart', 0)),
'videos': user.get('videoCount', 0),
'verified': user.get('verified', False),
'bio': user.get('signature', '')[:100],
'sec_uid': user.get('secUid', '')
}
profile = get_profile('charlidamelio')
for k, v in profile.items():
if k != 'sec_uid':
val = f'{v:,}' if isinstance(v, int) else v
print(f' {k}: {val}')Step 2: Analyze recent post performance
Fetch recent posts and calculate engagement metrics.
Python
def get_posts(sec_uid):
data = requests.post('https://api.scavio.dev/api/v1/tiktok/user/posts',
headers=TH, json={'sec_user_id': sec_uid}).json()
posts = data.get('videos', data.get('data', {}).get('videos', []))
return [{'desc': p.get('desc', '')[:60],
'plays': p.get('stats', {}).get('playCount', 0),
'likes': p.get('stats', {}).get('diggCount', 0),
'comments': p.get('stats', {}).get('commentCount', 0),
'shares': p.get('stats', {}).get('shareCount', 0),
'created': p.get('createTime', 0)} for p in posts]
def analyze_engagement(posts, followers):
if not posts or not followers:
return {}
total_eng = sum(p['likes'] + p['comments'] + p['shares'] for p in posts)
total_plays = sum(p['plays'] for p in posts)
avg_plays = total_plays / len(posts)
avg_likes = sum(p['likes'] for p in posts) / len(posts)
er = total_eng / total_plays * 100 if total_plays else 0
follower_er = total_eng / (followers * len(posts)) * 100
return {
'avg_plays': int(avg_plays), 'avg_likes': int(avg_likes),
'engagement_rate': round(er, 2), 'follower_er': round(follower_er, 2),
'total_posts_analyzed': len(posts)
}
posts = get_posts(profile['sec_uid'])
metrics = analyze_engagement(posts, profile['followers'])
for k, v in metrics.items():
print(f' {k}: {v:,}' if isinstance(v, int) else f' {k}: {v}')Step 3: Calculate posting frequency and patterns
Determine how often the account posts and identify timing patterns.
Python
from datetime import datetime
def posting_patterns(posts):
if len(posts) < 2:
return {}
timestamps = sorted([p['created'] for p in posts if p['created']])
if not timestamps:
return {}
# Calculate posting frequency
time_span_days = (timestamps[-1] - timestamps[0]) / 86400 if len(timestamps) > 1 else 1
freq = len(posts) / max(time_span_days, 1)
# Day of week distribution
days = [datetime.fromtimestamp(t).strftime('%A') for t in timestamps if t]
day_counts = {}
for d in days:
day_counts[d] = day_counts.get(d, 0) + 1
best_day = max(day_counts, key=day_counts.get) if day_counts else 'Unknown'
return {
'posts_per_day': round(freq, 2),
'posts_per_week': round(freq * 7, 1),
'most_active_day': best_day,
'time_span_days': round(time_span_days, 0)
}
patterns = posting_patterns(posts)
print('Posting patterns:')
for k, v in patterns.items():
print(f' {k}: {v}')Step 4: Generate full account audit report
Combine all analytics into a comprehensive account report.
Python
def audit_account(username):
print(f'\n=== TikTok Account Audit: @{username} ===')
profile = get_profile(username)
cost = 0.005
print(f'\nProfile:')
print(f' Followers: {profile["followers"]:,} | Following: {profile["following"]:,}')
print(f' Total likes: {profile["likes"]:,} | Videos: {profile["videos"]}')
print(f' Verified: {profile["verified"]} | Bio: {profile["bio"][:50]}')
posts = get_posts(profile['sec_uid'])
cost += 0.005
metrics = analyze_engagement(posts, profile['followers'])
print(f'\nEngagement ({len(posts)} recent posts):')
print(f' Avg plays: {metrics.get("avg_plays", 0):,}')
print(f' Avg likes: {metrics.get("avg_likes", 0):,}')
print(f' Play-based ER: {metrics.get("engagement_rate", 0)}%')
print(f' Follower-based ER: {metrics.get("follower_er", 0)}%')
patterns = posting_patterns(posts)
print(f'\nPosting:')
print(f' Frequency: {patterns.get("posts_per_week", 0)} posts/week')
print(f' Most active: {patterns.get("most_active_day", "Unknown")}')
# Top performing post
if posts:
top = max(posts, key=lambda p: p['plays'])
print(f'\nTop post: {top["desc"][:50]}... ({top["plays"]:,} plays)')
print(f'\nAudit cost: ${cost:.3f}')
audit_account('charlidamelio')Python Example
Python
import os, requests
TH = {'Authorization': f'Bearer {os.environ["SCAVIO_API_KEY"]}', 'Content-Type': 'application/json'}
def audit(username):
p = requests.post('https://api.scavio.dev/api/v1/tiktok/profile',
headers=TH, json={'username': username}).json()
user = p.get('user', p.get('data', {}).get('user', p))
followers = user.get('followerCount', 0)
posts_data = requests.post('https://api.scavio.dev/api/v1/tiktok/user/posts',
headers=TH, json={'sec_user_id': user.get('secUid', '')}).json()
vids = posts_data.get('videos', posts_data.get('data', {}).get('videos', []))[:10]
total_likes = sum(v.get('stats', {}).get('diggCount', 0) for v in vids)
total_plays = sum(v.get('stats', {}).get('playCount', 0) for v in vids)
er = total_likes / total_plays * 100 if total_plays else 0
print(f'@{username}: {followers:,} followers, {er:.2f}% ER, {len(vids)} posts. Cost: $0.010')
audit('charlidamelio')JavaScript Example
JavaScript
const TH = { 'Authorization': `Bearer ${process.env.SCAVIO_API_KEY}`, 'Content-Type': 'application/json' };
async function audit(username) {
const p = await fetch('https://api.scavio.dev/api/v1/tiktok/profile', {
method: 'POST', headers: TH, body: JSON.stringify({ username })
}).then(r => r.json());
const user = p.user || p.data?.user || p;
const posts = await fetch('https://api.scavio.dev/api/v1/tiktok/user/posts', {
method: 'POST', headers: TH, body: JSON.stringify({ sec_user_id: user.secUid })
}).then(r => r.json());
const vids = (posts.videos || posts.data?.videos || []).slice(0, 10);
const plays = vids.reduce((s, v) => s + (v.stats?.playCount || 0), 0);
const likes = vids.reduce((s, v) => s + (v.stats?.diggCount || 0), 0);
console.log(`@${username}: ${(user.followerCount||0).toLocaleString()} followers, ${(likes/plays*100).toFixed(2)}% ER`);
}
audit('charlidamelio').catch(console.error);Expected Output
JSON
=== TikTok Account Audit: @charlidamelio ===
Profile:
Followers: 155,200,000 | Following: 1,234
Total likes: 11,800,000,000 | Videos: 2,456
Verified: True | Bio: dance + vibes
Engagement (10 recent posts):
Avg plays: 8,900,000
Avg likes: 1,200,000
Play-based ER: 14.52%
Follower-based ER: 0.85%
Posting:
Frequency: 4.2 posts/week
Most active: Tuesday
Top post: New dance challenge with... (23,400,000 plays)
Audit cost: $0.010