Sélectionner les bons créateurs TikTok pour des partenariats de marque nécessite plus que le nombre d'abonnés. Le taux d'engagement, la cohérence du contenu, l'adéquation du public et la trajectoire de croissance comptent tous. Ce tutoriel construit un pipeline de notation utilisant l'API Scavio TikTok qui évalue les créateurs sur plusieurs dimensions et produit un score de partenariat composite. Chaque évaluation de créateur coûte 2-3 crédits (0,010-0,015 $).
Prérequis
- Python 3.9+ installé
- bibliothèque requests installée
- Une clé API Scavio provenant de scavio.dev
- Une liste de noms d'utilisateur de créateurs TikTok à évaluer
Parcours
Étape 1: Récupérer les données de profil du créateur
Obtenir les informations de profil pour chaque créateur, y compris le nombre d'abonnés, le nombre d'abonnements, le total de likes et la bio. Cela donne des indicateurs de base.
Python
import requests, os
API_KEY = os.environ['SCAVIO_API_KEY']
TT_URL = 'https://api.scavio.dev/api/v1/tiktok'
def get_creator_profile(username: str) -> dict:
resp = requests.post(f'{TT_URL}/user/info',
headers={'Authorization': f'Bearer {API_KEY}',
'Content-Type': 'application/json'},
json={'username': username})
resp.raise_for_status()
user = resp.json().get('data', {}).get('user', {})
stats = resp.json().get('data', {}).get('stats', {})
return {
'username': username,
'nickname': user.get('nickname', ''),
'bio': user.get('signature', ''),
'verified': user.get('verified', False),
'followers': stats.get('followerCount', 0),
'following': stats.get('followingCount', 0),
'likes': stats.get('heartCount', 0),
'videos': stats.get('videoCount', 0),
}
# Test with a creator
profile = get_creator_profile('charlidamelio')
for key, val in profile.items():
print(f' {key}: {val}')Étape 2: Analyser l'engagement du contenu récent
Rechercher les vidéos récentes du créateur et calculer les mesures d'engagement. Un engagement élevé par rapport aux abonnés signale un public actif et engagé.
Python
def analyze_engagement(username: str, video_count: int = 10) -> dict:
resp = requests.post(f'{TT_URL}/search/videos',
headers={'Authorization': f'Bearer {API_KEY}',
'Content-Type': 'application/json'},
json={'keyword': f'@{username}', 'count': video_count, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
if not videos:
return {'engagement_rate': 0, 'avg_views': 0, 'videos_analyzed': 0}
total_engagement = 0
total_views = 0
for v in videos:
stats = v.get('stats', {})
views = stats.get('playCount', 0)
likes = stats.get('diggCount', 0)
comments = stats.get('commentCount', 0)
shares = stats.get('shareCount', 0)
total_views += views
total_engagement += likes + comments + shares
avg_views = total_views / len(videos)
engagement_rate = (total_engagement / total_views * 100) if total_views > 0 else 0
return {
'engagement_rate': round(engagement_rate, 2),
'avg_views': int(avg_views),
'total_engagement': total_engagement,
'videos_analyzed': len(videos)
}
engagement = analyze_engagement('charlidamelio')
for key, val in engagement.items():
print(f' {key}: {val}')Étape 3: Calculer le score composite du créateur
Combiner les mesures de profil et les données d'engagement en un seul score de partenariat de 0 à 100. Pondérer le taux d'engagement le plus lourdement car il prédit les performances de campagne.
Python
def score_creator(username: str) -> dict:
profile = get_creator_profile(username)
engagement = analyze_engagement(username)
# Engagement rate score (0-40 points) - most important
er = engagement['engagement_rate']
if er >= 5:
er_score = 40
elif er >= 3:
er_score = 30
elif er >= 1:
er_score = 20
else:
er_score = er * 20
# Follower tier score (0-25 points)
followers = profile['followers']
if followers >= 1_000_000:
follower_score = 25 # mega
elif followers >= 100_000:
follower_score = 22 # macro
elif followers >= 10_000:
follower_score = 20 # micro (often best ROI)
elif followers >= 1_000:
follower_score = 15 # nano
else:
follower_score = 5
# Content volume score (0-20 points)
videos = profile['videos']
volume_score = min(videos / 50 * 20, 20)
# Verified bonus (0-15 points)
verified_score = 15 if profile['verified'] else 0
total = round(er_score + follower_score + volume_score + verified_score, 1)
return {
'username': username,
'score': total,
'tier': 'mega' if followers >= 1_000_000 else 'macro' if followers >= 100_000 else 'micro' if followers >= 10_000 else 'nano',
'engagement_rate': er,
'followers': followers,
'avg_views': engagement['avg_views'],
'credits_used': 2
}Étape 4: Noter et classer un lot de créateurs
Évaluer plusieurs créateurs et les classer par score. Exporter les résultats pour votre équipe de partenariats de marque.
Python
import time
def evaluate_creators(usernames: list) -> list:
scored = []
for username in usernames:
try:
result = score_creator(username)
scored.append(result)
print(f' [{result["score"]:5.1f}] @{username} ({result["tier"]}) '
f'ER:{result["engagement_rate"]}% '
f'Followers:{result["followers"]:,}')
except Exception as e:
print(f' [ERROR] @{username}: {e}')
time.sleep(0.5)
scored.sort(key=lambda x: -x['score'])
total_credits = sum(c['credits_used'] for c in scored)
print(f'\nEvaluated {len(scored)} creators')
print(f'Credits used: {total_credits} (${total_credits * 0.005:.3f})')
return scored
creators = ['charlidamelio', 'khaby.lame', 'addisonre']
results = evaluate_creators(creators)Exemple Python
Python
import requests, os, time
API_KEY = os.environ['SCAVIO_API_KEY']
TT = 'https://api.scavio.dev/api/v1/tiktok'
def score_creator(username):
profile = requests.post(f'{TT}/user/info',
headers={'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'},
json={'username': username}).json().get('data', {})
stats = profile.get('stats', {})
followers = stats.get('followerCount', 0)
videos_resp = requests.post(f'{TT}/search/videos',
headers={'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json'},
json={'keyword': f'@{username}', 'count': 10, 'cursor': 0})
videos = videos_resp.json().get('data', {}).get('videos', [])
total_eng = sum(v.get('stats', {}).get('diggCount', 0) + v.get('stats', {}).get('commentCount', 0)
for v in videos)
total_views = sum(v.get('stats', {}).get('playCount', 0) for v in videos)
er = (total_eng / total_views * 100) if total_views > 0 else 0
return {'username': username, 'followers': followers, 'engagement_rate': round(er, 2)}
for u in ['charlidamelio', 'khaby.lame']:
r = score_creator(u)
print(f'@{r["username"]}: {r["followers"]:,} followers, {r["engagement_rate"]}% ER')
time.sleep(0.5)Exemple JavaScript
JavaScript
const API_KEY = process.env.SCAVIO_API_KEY;
const TT = 'https://api.scavio.dev/api/v1/tiktok';
async function scoreCreator(username) {
const profile = await fetch(`${TT}/user/info`, {
method: 'POST',
headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ username })
}).then(r => r.json());
const stats = profile.data?.stats || {};
const videos = await fetch(`${TT}/search/videos`, {
method: 'POST',
headers: { 'Authorization': `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
body: JSON.stringify({ keyword: `@${username}`, count: 10, cursor: 0 })
}).then(r => r.json());
const vids = videos.data?.videos || [];
const views = vids.reduce((s, v) => s + (v.stats?.playCount || 0), 0);
const eng = vids.reduce((s, v) => s + (v.stats?.diggCount || 0) + (v.stats?.commentCount || 0), 0);
return { username, followers: stats.followerCount || 0, er: views ? (eng/views*100).toFixed(2) : 0 };
}
scoreCreator('charlidamelio').then(r => console.log(`@${r.username}: ${r.followers} followers, ${r.er}% ER`));Sortie attendue
JSON
username: charlidamelio
followers: 155000000
engagement_rate: 3.45
avg_views: 12500000
videos_analyzed: 10
[ 85.0] @charlidamelio (mega) ER:3.45% Followers:155,000,000
[ 78.2] @khaby.lame (mega) ER:2.80% Followers:162,000,000
[ 72.5] @addisonre (mega) ER:2.10% Followers:88,000,000
Evaluated 3 creators
Credits used: 6 ($0.030)