Los operadores de restaurantes de servicio rápido (QSR) son objetivos B2B de alto valor para proveedores de servicios de alimentos, empresas de puntos de venta y plataformas de entrega. Este tutorial crea un canal que descubre operadores de QSR por marca y ubicación, identifica operadores de unidades múltiples y exporta datos de contacto. Cada búsqueda de ubicación cuesta $0,005.
Requisitos previos
- Python 3.8+
- solicita biblioteca
- Una clave API de Scavio de scavio.dev
- Dirigirse a marcas y mercados de QSR
Guia paso a paso
Paso 1: Búsqueda de ubicaciones y operadores de franquicias QSR
Encuentre ubicaciones de franquicias y extraiga información del operador de los resultados de búsqueda.
Python
import os, requests, json, csv
from datetime import datetime
from collections import defaultdict, Counter
API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}
BRANDS = ['Subway', 'Chick-fil-A', 'Wingstop']
MARKETS = ['Dallas TX', 'Atlanta GA', 'Phoenix AZ']
def find_operators(brand, market):
queries = [
f'{brand} franchise owner {market}',
f'{brand} operator {market} locations',
]
operators = []
for query in queries:
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': query, 'country_code': 'us'}, timeout=10).json()
for r in data.get('organic_results', []):
operators.append({
'brand': brand,
'market': market,
'title': r.get('title', ''),
'link': r.get('link', ''),
'snippet': r.get('snippet', '')[:150],
'source': r.get('displayed_link', ''),
})
return operators
all_operators = []
for brand in BRANDS:
for market in MARKETS:
ops = find_operators(brand, market)
all_operators.extend(ops)
print(f' {brand:15} in {market:12} | {len(ops)} results')
total_queries = len(BRANDS) * len(MARKETS) * 2
print(f'\nTotal results: {len(all_operators)}')
print(f'Cost: ${total_queries * 0.005:.3f}')Paso 2: Extraer y categorizar datos del operador
Analice nombres de operadores, identifique operadores de unidades múltiples y clasifique por fuente.
Python
def categorize_results(operators):
categories = defaultdict(list)
for op in operators:
link = op['link'].lower()
if 'linkedin' in link:
categories['linkedin'].append(op)
elif 'businessjournals' in link or 'qsrmagazine' in link or 'nrn.com' in link:
categories['trade_press'].append(op)
elif 'franchisedisclosure' in link or 'franchise' in link:
categories['franchise_docs'].append(op)
else:
categories['other'].append(op)
print(f'\n=== Results by Source ===')
for cat, items in categories.items():
print(f' {cat:20} | {len(items)} results')
return categories
def find_multi_unit(operators):
"""Identify mentions of multi-unit operators (most valuable targets)."""
multi_unit = []
keywords = ['multi-unit', 'multi unit', 'franchise group', 'operates', 'locations',
'franchisee', 'largest operator']
for op in operators:
text = f'{op["title"]} {op["snippet"]}'.lower()
if any(kw in text for kw in keywords):
multi_unit.append(op)
print(f'\n=== Multi-Unit Operator Mentions ===')
print(f' Found: {len(multi_unit)} mentions')
for op in multi_unit[:5]:
print(f' [{op["brand"]}] {op["title"][:50]}')
print(f' {op["snippet"][:80]}')
return multi_unit
categories = categorize_results(all_operators)
multi_unit = find_multi_unit(all_operators)Paso 3: Lista de operadores de exportación para divulgación
Limpiar y exportar la lista de operadores para uso del equipo de ventas.
Python
def export_operator_list(operators, multi_unit):
# Deduplicate by link
seen = set()
unique = []
for op in operators:
if op['link'] not in seen:
seen.add(op['link'])
unique.append(op)
filename = f'qsr_operators_{datetime.now().strftime("%Y%m%d")}.csv'
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=['brand', 'market', 'title', 'link', 'source', 'snippet'])
writer.writeheader()
for op in unique:
writer.writerow({k: op[k] for k in writer.fieldnames})
print(f'\n=== QSR Operator Pipeline Summary ===')
print(f' Brands searched: {len(BRANDS)}')
print(f' Markets searched: {len(MARKETS)}')
print(f' Total results: {len(operators)}')
print(f' Unique results: {len(unique)}')
print(f' Multi-unit mentions: {len(multi_unit)}')
print(f' Exported to: {filename}')
# Brand breakdown
print(f'\n By Brand:')
brand_counts = Counter(op['brand'] for op in unique)
for brand, count in brand_counts.most_common():
print(f' {brand:15} | {count} results')
print(f'\n Cost: ${len(BRANDS) * len(MARKETS) * 2 * 0.005:.3f}')
print(f' vs. Data brokers: $500+ for QSR operator lists')
export_operator_list(all_operators, multi_unit)Ejemplo en Python
Python
import os, requests
SH = {'x-api-key': os.environ['SCAVIO_API_KEY'], 'Content-Type': 'application/json'}
def find_qsr_ops(brand, market):
data = requests.post('https://api.scavio.dev/api/v1/search',
headers=SH, json={'query': f'{brand} franchise owner {market}', 'country_code': 'us'}, timeout=10).json()
for r in data.get('organic_results', [])[:3]:
print(f' {r.get("title", "")[:50]}')
find_qsr_ops('Subway', 'Dallas 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: 'Subway franchise owner Dallas TX', country_code: 'us' })
}).then(r => r.json());
(data.organic_results || []).slice(0, 3).forEach(r => console.log(r.title));Salida esperada
JSON
Subway in Dallas TX | 14 results
Subway in Atlanta GA | 12 results
Chick-fil-A in Dallas TX | 10 results
Wingstop in Phoenix AZ | 11 results
Total results: 108
Cost: $0.090
=== Results by Source ===
linkedin | 18 results
trade_press | 25 results
franchise_docs | 15 results
=== Multi-Unit Operator Mentions ===
Found: 12 mentions
[Subway] Dallas Franchise Group Operates 45 Subway Loc
=== QSR Operator Pipeline Summary ===
Total results: 108
Unique results: 82
Multi-unit mentions: 12
Cost: $0.090