Lancer une campagne de hashtag TikTok sans suivi, c'est voler à l'aveugle. Vous devez savoir : combien de vidéos ont utilisé votre hashtag, le nombre total de vues générées, quels créateurs ont suscité le plus d'engagement, et comment la campagne évolue dans le temps. Ce tutoriel construit un outil de suivi de campagne hashtag en utilisant les endpoints de l'API Scavio TikTok. Suivez les performances de la campagne, identifiez les contenus les plus performants et générez des rapports à 0,005 $ par appel API.
Prérequis
- Python 3.9+ installé
- Bibliothèque requests installée
- Une clé API Scavio depuis scavio.dev
- Une campagne de hashtag TikTok à suivre
Parcours
Étape 1: Récupérer les données d'aperçu du hashtag
Utilisez l'endpoint hashtag pour obtenir le nombre total de vidéos et les statistiques de vues pour votre hashtag de campagne.
Python
import os, requests, time, json
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'}
def get_hashtag_overview(hashtag: str) -> dict:
"""Get hashtag stats overview."""
resp = requests.post(f'{TT_URL}/hashtag', headers=TT_H,
json={'hashtag': hashtag})
data = resp.json().get('data', {})
challenge = data.get('challengeInfo', data)
stats = challenge.get('stats', data.get('stats', {}))
return {
'hashtag': hashtag,
'video_count': stats.get('videoCount', 0),
'view_count': stats.get('viewCount', 0),
'timestamp': datetime.now().isoformat(),
}
overview = get_hashtag_overview('tiktokmademebuyit')
print(f"Hashtag: #{overview['hashtag']}")
print(f"Videos: {overview['video_count']:,}")
print(f"Views: {overview['view_count']:,}")Étape 2: Récupérer et analyser les vidéos de la campagne
Récupérez les vidéos sous votre hashtag de campagne. Analysez les taux d'engagement, identifiez les meilleurs créateurs et suivez les thèmes de contenu.
Python
def get_campaign_videos(hashtag: str, count: int = 20) -> list:
"""Get videos from a hashtag campaign."""
resp = requests.post(f'{TT_URL}/hashtag/videos', headers=TT_H,
json={'hashtag': hashtag, 'count': count, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
results = []
for v in videos:
stats = v.get('stats', {})
author = v.get('author', {})
views = stats.get('playCount', 0)
likes = stats.get('diggCount', 0)
comments = stats.get('commentCount', 0)
shares = stats.get('shareCount', 0)
eng_rate = (likes + comments + shares) / views if views > 0 else 0
results.append({
'video_id': v.get('id', ''),
'description': v.get('desc', '')[:100],
'author': author.get('uniqueId', ''),
'author_followers': author.get('stats', {}).get('followerCount', 0) if isinstance(author.get('stats'), dict) else 0,
'views': views,
'likes': likes,
'comments': comments,
'shares': shares,
'engagement_rate': eng_rate,
})
results.sort(key=lambda x: x['views'], reverse=True)
return results
videos = get_campaign_videos('tiktokmademebuyit')
print(f'Campaign videos: {len(videos)}')
print('\nTop performers:')
for v in videos[:5]:
print(f' {v["views"]:>12,} views | {v["engagement_rate"]:5.1%} eng | @{v["author"]:15s} | {v["description"][:30]}')Étape 3: Identifier les meilleurs créateurs et les schémas de contenu
Découvrez quels créateurs génèrent le plus d'engagement pour la campagne et quels schémas de contenu fonctionnent le mieux.
Python
def analyze_campaign_creators(videos: list) -> dict:
"""Identify top creators and content patterns."""
creator_stats = {}
for v in videos:
author = v['author']
if author not in creator_stats:
creator_stats[author] = {'videos': 0, 'total_views': 0,
'total_likes': 0, 'total_engagement': 0}
cs = creator_stats[author]
cs['videos'] += 1
cs['total_views'] += v['views']
cs['total_likes'] += v['likes']
cs['total_engagement'] += v['engagement_rate']
# Calculate averages and rank
ranked = []
for author, stats in creator_stats.items():
stats['author'] = author
stats['avg_engagement'] = stats['total_engagement'] / stats['videos'] if stats['videos'] else 0
stats['avg_views'] = stats['total_views'] / stats['videos'] if stats['videos'] else 0
ranked.append(stats)
ranked.sort(key=lambda x: x['total_views'], reverse=True)
# Engagement analysis
all_eng = [v['engagement_rate'] for v in videos]
avg_eng = sum(all_eng) / len(all_eng) if all_eng else 0
all_views = [v['views'] for v in videos]
return {
'unique_creators': len(creator_stats),
'top_creators': ranked[:10],
'avg_engagement': avg_eng,
'total_views': sum(all_views),
'avg_views': sum(all_views) / len(all_views) if all_views else 0,
}
analysis = analyze_campaign_creators(videos)
print(f"Unique creators: {analysis['unique_creators']}")
print(f"Total views: {analysis['total_views']:,}")
print(f"Avg engagement: {analysis['avg_engagement']:.1%}")
print(f"\nTop creators:")
for c in analysis['top_creators'][:5]:
print(f" @{c['author']:15s} | {c['videos']} videos | {c['total_views']:>10,} views | {c['avg_engagement']:.1%} eng")Étape 4: Construire le rapport de suivi de campagne
Générez un rapport complet de campagne. Suivez le hashtag dans le temps en exécutant ce processus périodiquement et en comparant les instantanés.
Python
TRACKING_FILE = 'campaign_tracker.json'
def load_tracking_history() -> list:
if os.path.exists(TRACKING_FILE):
with open(TRACKING_FILE) as f:
return json.load(f)
return []
def save_tracking_snapshot(snapshot: dict):
history = load_tracking_history()
history.append(snapshot)
with open(TRACKING_FILE, 'w') as f:
json.dump(history, f, indent=2)
def campaign_report(hashtag: str) -> dict:
"""Generate a full campaign tracking report."""
overview = get_hashtag_overview(hashtag)
time.sleep(0.3)
videos = get_campaign_videos(hashtag, count=20)
analysis = analyze_campaign_creators(videos)
snapshot = {
'timestamp': datetime.now().isoformat(),
'hashtag': hashtag,
'video_count': overview['video_count'],
'total_views': analysis['total_views'],
'unique_creators': analysis['unique_creators'],
'avg_engagement': analysis['avg_engagement'],
'top_creator': analysis['top_creators'][0]['author'] if analysis['top_creators'] else '',
'credits_used': 2,
'cost': 0.010,
}
save_tracking_snapshot(snapshot)
# Check growth vs previous snapshot
history = load_tracking_history()
if len(history) >= 2:
prev = history[-2]
view_growth = snapshot['total_views'] - prev.get('total_views', 0)
creator_growth = snapshot['unique_creators'] - prev.get('unique_creators', 0)
print(f'Growth since last check:')
print(f' Views: +{view_growth:,}')
print(f' Creators: +{creator_growth}')
print(f'\nCampaign Report: #{hashtag}')
print(f' Total videos: {overview["video_count"]:,}')
print(f' Sample views: {analysis["total_views"]:,} (from {len(videos)} videos)')
print(f' Unique creators: {analysis["unique_creators"]}')
print(f' Avg engagement: {analysis["avg_engagement"]:.1%}')
print(f' Top creator: @{snapshot["top_creator"]}')
print(f' Tracking cost: ${snapshot["cost"]}')
print(f' History: {len(history)} snapshots')
return snapshot
campaign_report('tiktokmademebuyit')Exemple Python
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
TT_H = {'Authorization': f'Bearer {SCAVIO_KEY}', 'Content-Type': 'application/json'}
def track_hashtag(hashtag):
# Hashtag overview
resp = requests.post('https://api.scavio.dev/api/v1/tiktok/hashtag', headers=TT_H,
json={'hashtag': hashtag})
stats = resp.json().get('data', {}).get('stats', {})
time.sleep(0.3)
# Recent videos
resp2 = requests.post('https://api.scavio.dev/api/v1/tiktok/hashtag/videos', headers=TT_H,
json={'hashtag': hashtag, 'count': 10, 'cursor': 0})
videos = resp2.json().get('data', {}).get('videos', [])
total_plays = sum(v.get('stats', {}).get('playCount', 0) for v in videos)
creators = len(set(v.get('author', {}).get('uniqueId', '') for v in videos))
print(f'#{hashtag}: {stats.get("videoCount", 0):,} total videos')
print(f' Recent sample: {total_plays:,} plays from {creators} creators')
track_hashtag('tiktokmademebuyit')Exemple JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
const TT_H = { Authorization: `Bearer ${SCAVIO_KEY}`, 'Content-Type': 'application/json' };
async function trackHashtag(hashtag) {
const overview = await fetch('https://api.scavio.dev/api/v1/tiktok/hashtag', {
method: 'POST', headers: TT_H, body: JSON.stringify({ hashtag })
}).then(r => r.json());
const stats = overview.data?.stats || {};
const videos = await fetch('https://api.scavio.dev/api/v1/tiktok/hashtag/videos', {
method: 'POST', headers: TT_H, body: JSON.stringify({ hashtag, count: 10, cursor: 0 })
}).then(r => r.json());
const vids = videos.data?.videos || [];
const plays = vids.reduce((s, v) => s + (v.stats?.playCount || 0), 0);
const creators = new Set(vids.map(v => v.author?.uniqueId)).size;
console.log(`#${hashtag}: ${(stats.videoCount || 0).toLocaleString()} total videos`);
console.log(` Recent: ${plays.toLocaleString()} plays from ${creators} creators`);
}
trackHashtag('tiktokmademebuyit');Sortie attendue
JSON
Hashtag: #tiktokmademebuyit
Videos: 45,200,000
Views: 892,000,000,000
Campaign videos: 20
Top performers:
8,234,567 views | 7.2% eng | @shopqueen | This product is everything I
5,891,234 views | 5.8% eng | @dealsunder50 | Amazon finds you NEED from T
Unique creators: 18
Total views: 28,450,000
Avg engagement: 5.4%
Campaign Report: #tiktokmademebuyit
Total videos: 45,200,000
Sample views: 28,450,000 (from 20 videos)
Unique creators: 18
Avg engagement: 5.4%
Top creator: @shopqueen
Tracking cost: $0.010
History: 1 snapshots