The Problem
Cold email campaigns targeting e-commerce sellers lack enrichment data. You know someone sells on Amazon, but not their product categories, price range, review volume, or competitive position. Generic outreach converts poorly. Enriched outreach that references specific product data converts 2-3x better.
The Scavio Solution
Enrich e-commerce seller lead lists by searching their brand or product names on Amazon and Google via Scavio. Extract product count, price ranges, review ratings, and competitive positioning. Use this data to personalize cold email templates with specific, relevant details about the prospect's products.
Before
Before enrichment, cold emails were generic: 'I see you sell on Amazon.' Open rate: 18%. Reply rate: 2%. The prospect received 20 similar emails daily.
After
After enrichment, emails reference specific data: 'Your top product has 847 reviews at 4.3 stars, but competitor X has 2,100 reviews at 4.6. We help close that gap.' Open rate: 32%. Reply rate: 8%. Cost per enriched lead: $0.01 (2 queries at $0.005).
Who It Is For
SDRs and sales teams doing cold outreach to e-commerce sellers. Agencies selling Amazon optimization services. SaaS companies targeting Amazon/Walmart sellers.
Key Benefits
- Personalize cold emails with real product data from Amazon and Google
- Enrichment cost: $0.01/lead (2 API queries)
- 2-3x improvement in reply rates with data-specific outreach
- Competitive context (rival products, review gaps) adds urgency
- Batch enrichment of 500 leads costs $5.00
Python Example
import requests
API_KEY = "your_scavio_api_key"
def enrich_ecom_lead(brand: str) -> dict:
# Search Amazon for their products
amazon_res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "amazon", "query": brand},
timeout=15,
)
amazon_data = amazon_res.json() if amazon_res.ok else {}
products = amazon_data.get("organic", [])[:5]
# Search Google for brand context
google_res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": f"{brand} reviews"},
timeout=15,
)
google_data = google_res.json() if google_res.ok else {}
return {
"brand": brand,
"product_count": len(products),
"top_product": products[0].get("title", "") if products else "",
"price_range": [p.get("price") for p in products if p.get("price")],
"avg_rating": round(sum(p.get("rating", 0) for p in products if p.get("rating")) / max(len([p for p in products if p.get("rating")]), 1), 1),
"google_snippets": [r.get("snippet", "") for r in google_data.get("organic", [])[:3]],
}
lead = enrich_ecom_lead("Anker")
print(f"{lead['brand']}: {lead['product_count']} products, avg rating {lead['avg_rating']}")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function enrichEcomLead(brand) {
const [amazonRes, googleRes] = await Promise.all([
fetch("https://api.scavio.dev/api/v1/search", { method: "POST", headers: { "x-api-key": API_KEY, "content-type": "application/json" }, body: JSON.stringify({ platform: "amazon", query: brand }) }),
fetch("https://api.scavio.dev/api/v1/search", { method: "POST", headers: { "x-api-key": API_KEY, "content-type": "application/json" }, body: JSON.stringify({ platform: "google", query: `${brand} reviews` }) }),
]);
const products = ((await amazonRes.json()).organic ?? []).slice(0, 5);
return { brand, productCount: products.length, topProduct: products[0]?.title ?? "" };
}
const lead = await enrichEcomLead("Anker");
console.log(`${lead.brand}: ${lead.productCount} products`);Platforms Used
Amazon
Product search with prices, ratings, and reviews
Web search with knowledge graph, PAA, and AI overviews