Apollo y ZoomInfo cobran más de $100 al mes por los datos comerciales locales. Para el descubrimiento básico de las PYMES (buscar empresas por categoría y ubicación), una API de búsqueda le brinda nombres, sitios web e información de contacto de los resultados de Google a $0,005/consulta. Este tutorial crea un canal para PYMES local que descubre, clasifica y exporta clientes potenciales comerciales.
Requisitos previos
- Python 3.8+
- solicita biblioteca
- Una clave API de Scavio de scavio.dev
- Ubicaciones de destino y categorías comerciales
Guia paso a paso
Paso 1: Busque empresas locales por categoría y ubicación
Consulta en Google empresas en categorías y ubicaciones específicas.
Python
import os, requests, json, csv
from datetime import datetime
from collections import defaultdict
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
CATEGORIES = ['plumber', 'electrician', 'HVAC contractor']
LOCATIONS = ['Austin TX', 'Denver CO', 'Nashville TN']
def find_businesses(category, location):
query = f'{category} in {location}'
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}, timeout=10).json()
businesses = []
for r in data.get('organic_results', []):
link = r.get('link', '')
if any(d in link for d in ['yelp.com', 'yellowpages', 'facebook.com', 'google.com/maps']):
continue # Skip directories
businesses.append({
'name': r.get('title', '').split(' - ')[0].split(' | ')[0].strip(),
'website': link,
'domain': r.get('displayed_link', '').split('/')[0],
'description': r.get('snippet', '')[:120],
'category': category,
'location': location,
})
return businesses
all_leads = []
for cat in CATEGORIES:
for loc in LOCATIONS:
leads = find_businesses(cat, loc)
all_leads.extend(leads)
print(f' {cat:20} in {loc:15} | {len(leads)} businesses')
print(f'\nTotal leads: {len(all_leads)}')
print(f'Cost: ${len(CATEGORIES) * len(LOCATIONS) * 0.005:.3f}')Paso 2: Deduplicar y enriquecer clientes potenciales
Elimine duplicados y agregue etiquetas de categorías comerciales.
Python
def deduplicate_leads(leads):
seen_domains = set()
unique = []
for lead in leads:
domain = lead['domain'].lower()
if domain and domain not in seen_domains:
seen_domains.add(domain)
unique.append(lead)
print(f'Deduplication: {len(leads)} -> {len(unique)} unique leads')
return unique
def enrich_lead(lead):
"""Add basic enrichment from search snippets."""
desc = lead['description'].lower()
tags = []
if any(w in desc for w in ['24/7', 'emergency', '24 hour']): tags.append('emergency_service')
if any(w in desc for w in ['licensed', 'certified', 'insured']): tags.append('licensed')
if any(w in desc for w in ['free estimate', 'free quote']): tags.append('offers_estimates')
if any(w in desc for w in ['residential', 'home']): tags.append('residential')
if any(w in desc for w in ['commercial', 'business']): tags.append('commercial')
lead['tags'] = tags
return lead
unique_leads = deduplicate_leads(all_leads)
enriched = [enrich_lead(lead) for lead in unique_leads]
print(f'\nEnriched leads by category:')
by_cat = defaultdict(list)
for lead in enriched:
by_cat[lead['category']].append(lead)
for cat, leads in by_cat.items():
tagged = sum(1 for l in leads if l['tags'])
print(f' {cat:20} | {len(leads)} leads | {tagged} enriched')Paso 3: Exportar leads a CSV
Exporte la lista de clientes potenciales limpia a CSV para importarla a CRM.
Python
def export_leads_csv(leads, filename=None):
if not filename:
filename = f'smb_leads_{datetime.now().strftime("%Y%m%d")}.csv'
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=['name', 'website', 'domain', 'category', 'location', 'tags', 'description'])
writer.writeheader()
for lead in leads:
row = {**lead, 'tags': ', '.join(lead.get('tags', []))}
writer.writerow({k: row[k] for k in writer.fieldnames})
print(f'\nExported {len(leads)} leads to {filename}')
# Summary
print(f'\n=== SMB Discovery Summary ===')
print(f' Categories: {len(set(l["category"] for l in leads))}')
print(f' Locations: {len(set(l["location"] for l in leads))}')
print(f' Total unique leads: {len(leads)}')
print(f' With tags: {sum(1 for l in leads if l.get("tags"))}')
print(f'\n Apollo: $49-119/mo for local business data')
print(f' ZoomInfo: $250+/mo for SMB data')
print(f' This pipeline: ${len(CATEGORIES) * len(LOCATIONS) * 0.005:.3f}/run')
print(f' Monthly (daily): ${len(CATEGORIES) * len(LOCATIONS) * 0.005 * 30:.2f}')
export_leads_csv(enriched)Ejemplo en Python
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def find_smbs(category, location):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': f'{category} in {location}', 'country_code': 'us'}, timeout=10).json()
for r in data.get('organic_results', [])[:5]:
print(f' {r.get("title", "")[:40]} | {r.get("displayed_link", "")}')
find_smbs('plumber', 'Austin TX')
print('Cost: $0.005')Ejemplo en JavaScript
JavaScript
const SH = { 'x-api-key': process.env.SCAVIO_API_KEY, 'Content-Type': 'application/json' };
const data = await fetch('https://api.scavio.dev/api/v1/search', {
method: 'POST', headers: SH,
body: JSON.stringify({ query: 'plumber in Austin TX', country_code: 'us' })
}).then(r => r.json());
(data.organic_results || []).slice(0, 5).forEach(r => {
console.log(`${r.title?.slice(0, 40)} | ${r.displayed_link}`);
});Salida esperada
JSON
plumber in Austin TX | 6 businesses
plumber in Denver CO | 5 businesses
electrician in Austin TX | 7 businesses
HVAC contractor in Nashville TN | 4 businesses
Total leads: 48
Cost: $0.045
Deduplication: 48 -> 42 unique leads
Exported 42 leads to smb_leads_20260521.csv
=== SMB Discovery Summary ===
Categories: 3
Locations: 3
Total unique leads: 42
Apollo: $49-119/mo for local business data
This pipeline: $0.045/run
Monthly (daily): $1.35