Overview
This workflow enriches cold email lead lists for e-commerce sellers by searching their brand on Amazon and Google. It extracts product count, price ranges, ratings, and competitive context to personalize outreach. Enriched emails reference specific product data, improving reply rates 2-3x compared to generic outreach.
Trigger
On-demand (triggered when new leads are added to the pipeline)
Schedule
On-demand (triggered per batch)
Workflow Steps
Load new leads
Read the batch of new e-commerce seller leads to enrich.
Search Amazon for products
Query each lead's brand name on Amazon to get product listings, prices, and ratings.
Search Google for context
Query each lead's brand on Google to get company context, reviews, and competitive positioning.
Compile enrichment data
Merge Amazon product data and Google context into a structured enrichment profile.
Output enriched leads
Write enriched lead data for cold email template personalization.
Python Implementation
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")JavaScript Implementation
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`);Platforms Used
Amazon
Product search with prices, ratings, and reviews
Web search with knowledge graph, PAA, and AI overviews