La génération de leads basée sur le volume remplit votre CRM de contacts qui n'achètent pas. La génération de leads basée sur l'intention trouve des personnes qui recherchent activement des solutions comme la vôtre. En surveillant les résultats de recherche pour des requêtes d'intention d'achat, vous pouvez identifier les entreprises qui publient des articles de comparaison, demandent des recommandations sur Reddit ou recherchent des concurrents. Ce tutoriel construit un pipeline de signaux d'intention en utilisant l'API Scavio à $0.005 par recherche sur Google et Reddit.
Prérequis
- Python 3.9+ installé
- bibliothèque requests installée
- Une clé API Scavio depuis scavio.dev
- Un ICP (profil client idéal) clair pour le ciblage
Parcours
Étape 1: Définir les requêtes de signaux d'intention
Créez des requêtes de recherche qui révèlent l'intention d'achat dans votre marché. Ce sont les requêtes que vos clients idéaux tapent lorsqu'ils évaluent activement des solutions.
Python
import os, requests, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
URL = 'https://api.scavio.dev/api/v1/search'
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def build_intent_queries(product_category: str, competitors: list) -> list:
"""Generate buying-intent search queries."""
queries = []
# Direct comparison queries
for comp in competitors:
queries.append({'query': f'{comp} alternative 2026', 'intent': 'switching', 'signal': 'high'})
queries.append({'query': f'{comp} vs', 'intent': 'comparing', 'signal': 'high'})
queries.append({'query': f'{comp} pricing too expensive', 'intent': 'price_sensitive', 'signal': 'high'})
# Category queries
queries.append({'query': f'best {product_category} 2026', 'intent': 'researching', 'signal': 'medium'})
queries.append({'query': f'{product_category} for enterprise', 'intent': 'enterprise', 'signal': 'medium'})
# Reddit intent queries
queries.append({'query': f'site:reddit.com recommend {product_category}', 'intent': 'asking', 'signal': 'high'})
queries.append({'query': f'site:reddit.com {product_category} frustrating', 'intent': 'pain_point', 'signal': 'high'})
return queries
queries = build_intent_queries('search API', ['SerpAPI', 'Tavily'])
for q in queries:
print(f'[{q["signal"]:6s}] {q["intent"]:15s} | {q["query"]}')Étape 2: Rechercher et extraire les signaux d'intention
Exécutez les requêtes d'intention et extrayez les prospects des résultats. Les entreprises qui rédigent des articles de comparaison, publient sur Reddit ou publient des avis montrent une intention d'achat active.
Python
import re
def extract_leads_from_results(results: list, query_info: dict) -> list:
"""Extract potential leads from search results."""
leads = []
for r in results:
title = r.get('title', '')
snippet = r.get('snippet', '')
url = r.get('link', '')
# Extract domain as potential lead
domain_match = re.search(r'https?://(?:www\.)?([\w.-]+)', url)
domain = domain_match.group(1) if domain_match else ''
# Skip aggregator/directory sites
skip_domains = ['reddit.com', 'quora.com', 'wikipedia.org', 'youtube.com',
'g2.com', 'capterra.com', 'medium.com']
if any(d in domain for d in skip_domains):
# Reddit posts are leads for community outreach, not company leads
if 'reddit.com' in domain:
leads.append({
'type': 'community',
'platform': 'reddit',
'title': title,
'url': url,
'intent': query_info['intent'],
'signal': query_info['signal'],
})
continue
leads.append({
'type': 'company',
'domain': domain,
'title': title,
'snippet': snippet[:200],
'url': url,
'intent': query_info['intent'],
'signal': query_info['signal'],
})
return leads
def scan_intent_signals(queries: list) -> list:
all_leads = []
for q in queries:
resp = requests.post(URL, headers=H,
json={'query': q['query'], 'country_code': 'us', 'num_results': 5})
results = resp.json().get('organic_results', [])
leads = extract_leads_from_results(results, q)
all_leads.extend(leads)
time.sleep(0.3)
return all_leads
leads = scan_intent_signals(queries[:5]) # Test with first 5 queries
print(f'Found {len(leads)} intent signals')
for l in leads[:5]:
print(f' [{l["signal"]}] {l["type"]}: {l.get("domain", l.get("platform", ""))} - {l["intent"]}')Étape 3: Noter et classer les prospects par force d'intention
Attribuez des scores basés sur le type d'intention, la force du signal et la récence. Les prospects à forte intention qui comparent activement des solutions sont classés en premier.
Python
def score_leads(leads: list) -> list:
"""Score leads by intent strength."""
intent_scores = {
'switching': 10, 'price_sensitive': 9, 'pain_point': 8,
'comparing': 7, 'asking': 6, 'researching': 4, 'enterprise': 5,
}
signal_multiplier = {'high': 1.5, 'medium': 1.0, 'low': 0.5}
for lead in leads:
base = intent_scores.get(lead['intent'], 3)
mult = signal_multiplier.get(lead['signal'], 1.0)
lead['score'] = base * mult
# Sort by score descending
leads.sort(key=lambda x: x['score'], reverse=True)
return leads
def deduplicate_leads(leads: list) -> list:
"""Deduplicate by domain, keeping highest-scored entry."""
seen = {}
for lead in leads:
key = lead.get('domain', lead.get('url', ''))
if key not in seen or lead['score'] > seen[key]['score']:
seen[key] = lead
return sorted(seen.values(), key=lambda x: x['score'], reverse=True)
scored = score_leads(leads)
unique = deduplicate_leads(scored)
print(f'Unique leads: {len(unique)}')
print(f'\nTop intent signals:')
for l in unique[:10]:
domain = l.get('domain', l.get('platform', ''))
print(f' Score: {l["score"]:5.1f} | {l["intent"]:15s} | {domain}')
print(f' {l.get("title", "")[:60]}')Étape 4: Exporter le résultat du pipeline de leads
Exportez les prospects notés vers un CSV pour import dans le CRM. Incluez le type d'intention, la force du signal et l'URL source pour le contexte commercial.
Python
import csv
def export_leads(leads: list, filename: str = 'intent_leads.csv'):
if not leads:
print('No leads to export')
return
fieldnames = ['score', 'type', 'domain', 'intent', 'signal', 'title', 'url']
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction='ignore')
writer.writeheader()
writer.writerows(leads)
high = sum(1 for l in leads if l['score'] >= 10)
medium = sum(1 for l in leads if 5 <= l['score'] < 10)
low = sum(1 for l in leads if l['score'] < 5)
print(f'Exported {len(leads)} leads to {filename}')
print(f' High intent (10+): {high}')
print(f' Medium intent (5-9): {medium}')
print(f' Low intent (<5): {low}')
print(f' API cost: {len(queries)} searches x $0.005 = ${len(queries) * 0.005:.3f}')
export_leads(unique)
print(f'\nVolume lead gen: scrapes 1000 emails, 2% conversion = 20 leads')
print(f'Intent pipeline: finds {len([l for l in unique if l["score"] >= 7])} high-intent signals')Exemple Python
Python
import os, requests, csv, time
SCAVIO_KEY = os.environ['SCAVIO_API_KEY']
H = {'x-api-key': SCAVIO_KEY, 'Content-Type': 'application/json'}
def find_intent_leads(product, competitors):
leads = []
queries = [f'{c} alternative 2026' for c in competitors] + [f'best {product} 2026']
for q in queries:
resp = requests.post('https://api.scavio.dev/api/v1/search', headers=H,
json={'query': q, 'country_code': 'us', 'num_results': 5})
for r in resp.json().get('organic_results', []):
leads.append({'query': q, 'title': r['title'], 'url': r['link']})
time.sleep(0.3)
print(f'Found {len(leads)} intent signals from {len(queries)} queries')
print(f'Cost: ${len(queries) * 0.005:.3f}')
return leads
find_intent_leads('search API', ['SerpAPI', 'Tavily'])Exemple JavaScript
JavaScript
const SCAVIO_KEY = process.env.SCAVIO_API_KEY;
async function findIntentLeads(product, competitors) {
const queries = [...competitors.map(c => `${c} alternative 2026`), `best ${product} 2026`];
const leads = [];
for (const q of queries) {
const resp = 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: q, country_code: 'us', num_results: 5 })
});
for (const r of ((await resp.json()).organic_results || [])) {
leads.push({ query: q, title: r.title, url: r.link });
}
}
console.log(`Found ${leads.length} intent signals from ${queries.length} queries`);
return leads;
}
findIntentLeads('search API', ['SerpAPI', 'Tavily']);Sortie attendue
JSON
Found 18 intent signals
Unique leads: 12
Top intent signals:
Score: 15.0 | switching | blog.example.com
Why We Switched From SerpAPI to a Cheaper Alternati
Score: 13.5 | price_sensitive | startup.io
SerpAPI Pricing: Is It Worth $25/month in 2026?
Score: 12.0 | pain_point | reddit
r/webdev - search API pricing is getting ridiculous
Exported 12 leads to intent_leads.csv
High intent (10+): 4
Medium intent (5-9): 5
Low intent (<5): 3
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