Google Maps Structured vs Raw Scraping

Google Maps scraping returns incomplete data because Maps only displays the most relevant businesses for a given viewport, not all of them. Structured search APIs that query Google's local pack return the same business data as typed JSON without proxy management, CAPTCHA solving, or selector maintenance.

Why Maps Scraping Breaks

Google Maps dynamically loads results based on zoom level, scroll position, and user context. A scraper that captures results at one zoom level misses businesses that only appear at different zoom levels. The viewport problem means a scraper targeting "dentists in Chicago" might capture 40 results while the actual dataset contains 2,000+. Pagination in Maps is scroll-based, not page-based, which makes reliable complete extraction nearly impossible without significant infrastructure.

On top of data completeness issues, Maps aggressively detects automation. Playwright sessions get interrupted by CAPTCHA challenges after 20-30 requests. Residential proxies help but add $15-50/month in infrastructure cost, and Google periodically invalidates proxy pools.

The Local Pack Alternative

When you search "dentists in Chicago" on Google, the local pack shows a ranked list of businesses with names, addresses, ratings, review counts, phone numbers, and categories. This is the same data people try to scrape from Maps, but it is available as structured JSON through SERP APIs.

import os, requests

API_KEY = os.environ["SCAVIO_API_KEY"]

def get_local_businesses(query, location="Chicago, IL"):
    res = requests.post("https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": API_KEY, "Content-Type": "application/json"},
        json={"query": query, "country_code": "us", "location": location})
    data = res.json()
    local = data.get("local_results", [])
    for biz in local:
        print(f"{biz.get('title')} | {biz.get('rating')} stars | "
              f"{biz.get('reviews')} reviews | {biz.get('address')}")
    return local

businesses = get_local_businesses("dentists")
print(f"Found {len(businesses)} local results")

Coverage Comparison

A single SERP query returns the local pack (typically 3-10 results for the top positions), plus organic results that often include directory listings (Yelp, Healthgrades, Angi) containing additional businesses. To expand coverage, vary queries: "dentists in Chicago Loop", "dentists in Lincoln Park", "pediatric dentist Chicago", "emergency dental Chicago". Ten queries at $0.005 each = $0.05 covers more ground than a Maps scraper running for an hour.

DataForSEO offers a dedicated Google Maps endpoint at $0.002/query in live mode ($0.0006 in queue) that returns up to 400 businesses per query. For pure Maps data at high volume, DataForSEO is cheaper per-result. Scavio's advantage is combining local pack data with organic, PAA, and other SERP features in one call, which matters when you want both business listings and context about what Google surfaces for that query.

When Scraping Still Wins

SERP APIs return what Google shows searchers, which is curated and ranked. If you need every single business in a category regardless of Google's relevance algorithm, you need either the Google Places API (official, $17/1k requests) or a dedicated scraping tool like Apify's Google Maps Scraper ($49/mo for 40k results). The Google Places API is the most reliable option for exhaustive coverage, though it costs more per result.

For lead generation workflows where you need the top 10-20 businesses per neighborhood, SERP-based local pack data is sufficient and dramatically simpler to maintain. For market research requiring complete business census data, invest in the Places API or accept the maintenance cost of scraping.

Building a Multi-Source Lead Pipeline

import os, requests

API_KEY = os.environ["SCAVIO_API_KEY"]
H = {"x-api-key": API_KEY, "Content-Type": "application/json"}

neighborhoods = ["Loop", "Lincoln Park", "Wicker Park", "Lakeview", "Hyde Park"]
categories = ["dentist", "chiropractor", "physical therapy"]

all_leads = []
seen_names = set()

for cat in categories:
    for hood in neighborhoods:
        query = f"{cat} {hood} Chicago"
        res = requests.post("https://api.scavio.dev/api/v1/search",
            headers=H, json={"query": query, "country_code": "us"})
        data = res.json()
        for biz in data.get("local_results", []):
            name = biz.get("title", "")
            if name not in seen_names:
                seen_names.add(name)
                all_leads.append({
                    "name": name,
                    "category": cat,
                    "neighborhood": hood,
                    "rating": biz.get("rating"),
                    "reviews": biz.get("reviews"),
                    "address": biz.get("address"),
                    "phone": biz.get("phone"),
                })

print(f"Unique leads: {len(all_leads)}")
print(f"API cost: {len(neighborhoods) * len(categories) * 0.005:.2f}")

This pattern scales to any metro area. Fifteen queries across 5 neighborhoods and 3 categories costs $0.075 and typically yields 50-100 unique businesses with ratings, review counts, and contact information.