Los primeros tres segundos de un vídeo de TikTok determinan si los espectadores siguen viéndolo o se alejan. Este tutorial utiliza la API de Scavio TikTok para extraer datos de video para contenido de alto rendimiento en su nicho, luego analiza qué patrones de gancho se correlacionan con las tasas de participación más altas. Al comparar las descripciones de los videos, las proporciones de visualización y me gusta y los recuentos de contenido compartido entre diferentes estilos de gancho, puede identificar qué patrones de apertura funcionan mejor. Cada llamada API cuesta $0,005.
Requisitos previos
- Python 3.9+ instalado
- solicita biblioteca instalada
- Una clave API de Scavio de scavio.dev
- Un nicho o tema para analizar los ganchos
Guia paso a paso
Paso 1: Recopile videos de alto rendimiento en su nicho
Busque videos en su nicho y obtenga los de mejor desempeño. Las descripciones de sus vídeos suelen reflejar el estilo de gancho utilizado en los primeros segundos.
Python
import os, requests, time, re
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 search_niche_videos(niche: str, count: int = 30) -> list:
resp = requests.post(f'{TT_URL}/search/videos', headers=TT_H,
json={'keyword': niche, 'count': count, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
results = []
for v in videos:
stats = v.get('stats', {})
views = stats.get('playCount', 0)
likes = stats.get('diggCount', 0)
results.append({
'desc': v.get('desc', ''),
'views': views,
'likes': likes,
'comments': stats.get('commentCount', 0),
'shares': stats.get('shareCount', 0),
'like_rate': likes / views if views > 0 else 0,
'share_rate': stats.get('shareCount', 0) / views if views > 0 else 0,
})
results.sort(key=lambda x: x['views'], reverse=True)
return results
videos = search_niche_videos('python programming tutorial')
print(f'Collected {len(videos)} videos')Paso 2: Clasifica patrones de gancho a partir de descripciones de videos
Clasifique los vídeos por su estilo de gancho según el texto de descripción. Los tipos de ganchos comunes incluyen ganchos de preguntas, ganchos de reclamos, ganchos de instrucciones y ganchos de controversia.
Python
HOOK_PATTERNS = {
'question': [r'^(did you|have you|do you|why|what if|how)', r'\?'],
'claim': [r'^(this|the|I found|I discovered|most people)', r'(secret|trick|hack|mistake)'],
'how_to': [r'^(how to|step|tutorial|learn|guide)', r'(in \d+ (?:seconds|minutes|steps))'],
'controversy': [r'(stop|dont|never|wrong|bad|overrated)', r'(unpopular|hot take|controversial)'],
'listicle': [r'^(\d+ |top \d+|best \d+)', r'(things|tips|tools|ways|reasons)'],
}
def classify_hook(desc: str) -> str:
desc_lower = desc.lower()
scores = {}
for hook_type, patterns in HOOK_PATTERNS.items():
score = sum(1 for p in patterns if re.search(p, desc_lower))
scores[hook_type] = score
best = max(scores, key=scores.get)
return best if scores[best] > 0 else 'other'
for v in videos[:5]:
hook = classify_hook(v['desc'])
print(f' [{hook:12s}] {v["like_rate"]:.1%} like rate | {v["desc"][:50]}')Paso 3: Analizar la participación por tipo de gancho
Agrupe vídeos por tipo de gancho y compare las tasas de participación promedio. Esto revela qué estilos de gancho generan el mejor rendimiento en su nicho.
Python
def analyze_hooks(videos: list) -> dict:
by_hook = {}
for v in videos:
hook = classify_hook(v['desc'])
if hook not in by_hook:
by_hook[hook] = []
by_hook[hook].append(v)
report = {}
for hook_type, vids in by_hook.items():
avg_like_rate = sum(v['like_rate'] for v in vids) / len(vids)
avg_share_rate = sum(v['share_rate'] for v in vids) / len(vids)
avg_views = sum(v['views'] for v in vids) / len(vids)
report[hook_type] = {
'count': len(vids),
'avg_like_rate': avg_like_rate,
'avg_share_rate': avg_share_rate,
'avg_views': avg_views,
'best_video': max(vids, key=lambda x: x['views'])['desc'][:50],
}
print('Hook Analysis Report')
print('=' * 65)
for hook, stats in sorted(report.items(), key=lambda x: x[1]['avg_like_rate'], reverse=True):
print(f' {hook:12s} | {stats["count"]:3d} videos | {stats["avg_like_rate"]:5.1%} like rate | {stats["avg_views"]:>10,.0f} avg views')
print(f' | Best: {stats["best_video"]}')
return report
report = analyze_hooks(videos)Ejemplo en Python
Python
import os, requests, re
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
TT_H = {'Authorization': f'Bearer {SCAVIO_KEY}', 'Content-Type': 'application/json'}
def analyze_hooks(niche):
resp = requests.post('https://api.scavio.dev/api/v1/tiktok/search/videos', headers=TT_H,
json={'keyword': niche, 'count': 20, 'cursor': 0})
videos = resp.json().get('data', {}).get('videos', [])
hooks = {'question': [], 'how_to': [], 'claim': [], 'other': []}
for v in videos:
desc = v.get('desc', '').lower()
stats = v.get('stats', {})
views = stats.get('playCount', 1)
like_rate = stats.get('diggCount', 0) / views
if '?' in desc or desc.startswith(('did', 'why', 'what')): cat = 'question'
elif desc.startswith(('how to', 'tutorial', 'learn')): cat = 'how_to'
elif any(w in desc for w in ['secret', 'hack', 'trick']): cat = 'claim'
else: cat = 'other'
hooks[cat].append(like_rate)
for cat, rates in hooks.items():
avg = sum(rates) / len(rates) if rates else 0
print(f'{cat:12s}: {len(rates):3d} videos, {avg:.1%} avg like rate')
analyze_hooks('python programming')Ejemplo en JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
const TT_H = { Authorization: `Bearer ${SCAVIO_KEY}`, 'Content-Type': 'application/json' };
async function analyzeHooks(niche) {
const resp = await fetch('https://api.scavio.dev/api/v1/tiktok/search/videos', {
method: 'POST', headers: TT_H, body: JSON.stringify({ keyword: niche, count: 20, cursor: 0 })
}).then(r => r.json());
const hooks = { question: [], how_to: [], claim: [], other: [] };
for (const v of (resp.data?.videos || [])) {
const desc = (v.desc || '').toLowerCase();
const views = v.stats?.playCount || 1;
const likeRate = (v.stats?.diggCount || 0) / views;
let cat = 'other';
if (desc.includes('?') || desc.startsWith('did') || desc.startsWith('why')) cat = 'question';
else if (desc.startsWith('how to') || desc.startsWith('tutorial')) cat = 'how_to';
else if (['secret', 'hack', 'trick'].some(w => desc.includes(w))) cat = 'claim';
hooks[cat].push(likeRate);
}
for (const [cat, rates] of Object.entries(hooks)) {
const avg = rates.length ? rates.reduce((a, b) => a + b, 0) / rates.length : 0;
console.log(`${cat}: ${rates.length} videos, ${(avg * 100).toFixed(1)}% avg like rate`);
}
}
analyzeHooks('python programming');Salida esperada
JSON
Collected 30 videos
Hook Analysis Report
=================================================================
question | 8 videos | 8.2% like rate | 890,000 avg views
| Best: Did you know Python can do this in one lin
claim | 6 videos | 7.5% like rate | 1,200,000 avg views
| Best: This Python trick will save you hours of c
how_to | 10 videos | 5.8% like rate | 650,000 avg views
| Best: How to build an API in 60 seconds with Fas
controversy | 3 videos | 9.1% like rate | 780,000 avg views
| Best: Stop using print for debugging in Python
other | 3 videos | 4.2% like rate | 320,000 avg views