Resumen
Este flujo de trabajo enriquece cold correo electronico lead listas for e-commerce sellers by busqueda their marca on Amazon y Google. It extrae producto conteo, price ranges, ratings, y competitive context to personalize outreach. Enriquecido correos electronicos reference especifico producto datos, improving reply rates 2-3x comparado to generic outreach.
Desencadenador
On-demand (activado cuando nuevo leads son added to el pipeline)
Programación
On-demand (activado per batch)
Pasos del flujo de trabajo
Cargar nuevo leads
Leer el batch of nuevo e-commerce seller leads to enriquecer.
Search Amazon for productos
Consulta cada lead's nombre de marca on Amazon to obtener producto listings, prices, y ratings.
Search Google for context
Consulta cada lead's marca on Google to obtener company context, resenas, y competitive positioning.
Compile enriquecimiento datos
Fusionar Amazon producto datos y Google context en un structured enriquecimiento perfil.
Salida enriquecido leads
Escribir enriquecido lead datos for cold correo electronico plantilla personalization.
Implementacion en Python
import requests
import json
API_KEY = "your_scavio_api_key"
def enrich_lead(brand: str) -> dict:
# Amazon products
amazon_res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "amazon", "query": brand},
timeout=15,
)
products = amazon_res.json().get("organic", [])[:5] if amazon_res.ok else []
# Google context
google_res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": f"{brand} reviews"},
timeout=15,
)
google_snippets = [r.get("snippet", "") for r in google_res.json().get("organic", [])[:3]] if google_res.ok else []
prices = [p.get("price") for p in products if p.get("price")]
ratings = [p.get("rating") for p in products if p.get("rating")]
return {
"brand": brand,
"product_count": len(products),
"price_range": f"${min(prices):.2f}-${max(prices):.2f}" if prices else "N/A",
"avg_rating": round(sum(ratings) / len(ratings), 1) if ratings else None,
"top_product": products[0].get("title", "") if products else "",
"google_context": google_snippets,
}
leads = ["Anker", "TOZO", "JBL"]
for brand in leads:
data = enrich_lead(brand)
print(f"{data['brand']}: {data['product_count']} products, {data['price_range']}, avg {data['avg_rating']} stars")Implementacion en JavaScript
const API_KEY = "your_scavio_api_key";
async function enrichLead(brand) {
const [amazonRes, googleRes] = await Promise.all([
fetch("https://api.scavio.dev/api/v1/search", { method: "POST", headers: { "x-api-key": API_KEY, "content-type": "application/json" }, body: JSON.stringify({ platform: "amazon", query: brand }) }),
fetch("https://api.scavio.dev/api/v1/search", { method: "POST", headers: { "x-api-key": API_KEY, "content-type": "application/json" }, body: JSON.stringify({ platform: "google", query: `${brand} reviews` }) }),
]);
const products = ((await amazonRes.json()).organic ?? []).slice(0, 5);
return { brand, productCount: products.length, topProduct: products[0]?.title ?? "" };
}
const data = await enrichLead("Anker");
console.log(`${data.brand}: ${data.productCount} products`);Plataformas utilizadas
Amazon
Búsqueda de productos con precios, calificaciones y reseñas
Búsqueda web con grafo de conocimiento, PAA y resúmenes de IA