The Problem
Franchise development teams, suppliers, and consultants need data about franchise operators across hundreds of locations. Commercial franchise databases cost $1,000-5,000/month. Manual Google research for 200 locations takes 3-5 days. The data is stale by the time the report is complete.
The Scavio Solution
Query Scavio Google for '[franchise brand] [zip code]' across all target locations. Extract local pack data: ratings, review counts, addresses, and competitive context. Build a franchise operator intelligence database at $0.005/query. Monthly refreshes keep data current.
Before
Before search API enrichment, the franchise consultant purchased a $2,000/month database for operator data. The database updated quarterly, meaning data was often 1-3 months stale. Coverage for smaller franchise brands was incomplete.
After
After switching to Scavio, 500 locations are enriched monthly for $2.50. Data is always current (queried live). Coverage includes any franchise visible in Google search, not just brands in the commercial database. Annual savings: $23,970.
Who It Is For
Franchise development teams evaluating operator performance. Suppliers targeting specific franchise locations. Franchise consultants building territory analysis reports.
Key Benefits
- 500 franchise locations enriched for $2.50/month
- Live data vs quarterly database updates
- Coverage of any franchise visible in Google search
- Ratings and review data enable operator performance benchmarking
- Competitive density analysis by zip code included
Python Example
import requests
import json
from pathlib import Path
API_KEY = "your_scavio_api_key"
def enrich_franchise(brand: str, zip_codes: list[str]) -> list[dict]:
results = []
for zip_code in zip_codes:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": f"{brand} {zip_code}"},
timeout=15,
)
if not res.ok:
continue
for r in res.json().get("organic", [])[:5]:
results.append({
"brand": brand,
"zip": zip_code,
"name": r.get("title", ""),
"snippet": r.get("snippet", ""),
"link": r.get("link", ""),
})
return results
locations = enrich_franchise("Chick-fil-A", ["30301", "30302", "30303", "30304", "30305"])
print(f"Enriched {len(locations)} locations")
for loc in locations[:5]:
print(f" {loc['zip']}: {loc['name'][:60]}")JavaScript Example
const API_KEY = "your_scavio_api_key";
async function enrichFranchise(brand, zipCodes) {
const results = [];
for (const zip of zipCodes) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "google", query: `${brand} ${zip}` }),
});
if (!res.ok) continue;
for (const r of ((await res.json()).organic ?? []).slice(0, 5)) {
results.push({ brand, zip, name: r.title ?? "", link: r.link ?? "" });
}
}
return results;
}
const locs = await enrichFranchise("Chick-fil-A", ["30301", "30302"]);
console.log(`Enriched ${locs.length} locations`);Platforms Used
Web search with knowledge graph, PAA, and AI overviews