Google Maps Scraping: Reviews, Business Data & Lead Generation

For B2B sales teams and local SEO agencies, Google Maps is the world’s largest database of leads. Whether you need to extract business emails, phone numbers, or analyze customer sentiment from reviews, the data is there—but getting it out is a nightmare.

In this guide, we compare the three main ways to scrape Google Maps in 2026: The Open Source Way (Free but unstable), The Subscription Way (Apify/Outscraper), and The API Way (SearchCans).

Why Scraping Google Maps is Harder Than Search

Unlike standard Google Search results (which are static HTML), Google Maps is a heavy, dynamic web application.

Infinite Scroll

You can’t just curl a URL. You need to simulate scrolling to load more reviews.

Complex DOM

Google uses obfuscated class names that change frequently, breaking DIY scrapers instantly.

Data Volume

A single popular restaurant might have 5,000 reviews. Extracting them all requires thousands of interactions.

Method 1: The Open Source / DIY Route

If you browse GitHub, you will find tools like google-maps-reviews-scraper. These typically use Selenium or Playwright to control a Chrome browser.

The Workflow:

1. Launch a headless browser.
2. Navigate to the business URL.
3. Click “More reviews”.
4. Scroll down… wait… scroll down… wait.

The Problem:

While free, this method is slow and fragile. If Google detects your automated driver (which is easy in 2026), you get CAPTCHA-blocked immediately. Furthermore, managing the infinite scroll logic for thousands of items is computationally expensive.

For more on why DIY approaches fail, see our article on bypassing 429 errors.

Method 2: The Subscription SaaS (Apify / Outscraper)

Platforms like Apify and Outscraper offer specialized “Google Maps Scrapers” as a service.

Apify

Offers a powerful “Google Maps Reviews Scraper” actor. It extracts reviews, rating, text, and owner responses.

Cost

You usually pay a monthly subscription (e.g., $49/mo) for “Compute Units.” Large scrapes can drain these units quickly.

Outscraper

Positions itself as a “Pay as you go” service but focuses on exporting to CSV/Excel for non-coders.

The Verdict:

These tools are great for non-technical users who want a CSV file, but for developers building an automated pipeline, the “per-result” pricing or monthly subscription can be overkill.

Method 3: The Developer’s API (SearchCans)

If you are a developer building a Lead Gen tool or a Review Monitor, you want raw JSON data via an API, without managing browser infrastructure or paying huge monthly fees.

SearchCans provides a dedicated Google Maps endpoint that handles the scrolling and parsing for you.

Comparison: Cost & Flexibility

Feature	Apify	SearchCans
Pricing Model	Monthly Sub (Compute Units)	Pay-As-You-Go ($0.56/1k requests)
Output	JSON/CSV	JSON
Speed	Queue-based (can be slow)	Real-time API
Setup	Account + Actor config	1 API Request

For a complete price breakdown, check out our 2026 pricing comparison.

Code Example: Extracting Reviews with Python

Instead of writing 200 lines of Selenium code, you can get reviews with one request:

import requests

API_KEY = "YOUR_SEARCHCANS_KEY"

def get_reviews(query):
    url = "https://www.searchcans.com/api/search"
    params = {
        "s": query,          # e.g., "Starbucks New York reviews"
        "t": "maps",         # Use the Maps engine
        "d": 20,             # Number of results
        "p": 1
    }
    headers = {
        "Authorization": f"Bearer {API_KEY}",
        "Content-Type": "application/json"
    }
    
    response = requests.post(url, json=params, headers=headers)
    return response.json()

# Result: Structured JSON with reviewer name, rating, text, and date.
# Similar to SerpApi's structure but at a fraction of the cost

Use Cases for Maps Data

1. Lead Generation

Extract business contact information for B2B outreach:

def extract_business_info(location, category):
    query = f"{category} in {location}"
    data = get_reviews(query)
    
    businesses = []
    for item in data.get('data', []):
        businesses.append({
            'name': item.get('title'),
            'address': item.get('address'),
            'phone': item.get('phone'),
            'rating': item.get('rating'),
            'website': item.get('website')
        })
    
    return businesses

# Extract all restaurants in Manhattan
restaurants = extract_business_info("Manhattan NY", "restaurants")

2. Sentiment Analysis

Monitor customer sentiment across multiple locations:

def analyze_reviews(business_name):
    reviews = get_reviews(f"{business_name} reviews")
    
    positive = 0
    negative = 0
    
    for review in reviews.get('data', []):
        rating = review.get('rating', 0)
        if rating >= 4:
            positive += 1
        elif rating <= 2:
            negative += 1
    
    return {
        'positive': positive,
        'negative': negative,
        'sentiment': 'positive' if positive > negative else 'negative'
    }

3. Competitive Intelligence

Track how your business compares to competitors:

def compare_competitors(your_business, competitors):
    all_businesses = [your_business] + competitors
    comparison = {}
    
    for business in all_businesses:
        data = get_reviews(business)
        comparison[business] = {
            'avg_rating': data.get('avg_rating'),
            'total_reviews': data.get('total_reviews'),
            'recent_reviews': len([r for r in data.get('data', []) if r.get('is_recent')])
        }
    
    return comparison

Integration with CRM Systems

Automatically enrich your CRM with Maps data:

def enrich_crm_leads(company_names):
    enriched_data = []
    
    for company in company_names:
        maps_data = get_reviews(company)
        
        enriched_data.append({
            'company': company,
            'phone': maps_data.get('phone'),
            'website': maps_data.get('website'),
            'rating': maps_data.get('avg_rating'),
            'address': maps_data.get('address')
        })
    
    return enriched_data

Best Practices

1. Rate Limiting: Even with unlimited concurrency, add small delays between requests to be respectful
2. Data Validation: Always validate extracted phone numbers and emails
3. Duplicate Detection: Maps data can have duplicate listings
4. Geo-targeting: Use specific location parameters for better results

For more on handling high-volume scraping, see our guide on scaling AI agents with unlimited concurrency.

Conclusion

For one-off exports, use a CSV tool. But for building scalable applications that need to monitor reviews or find leads continuously, SearchCans offers the most cost-effective and developer-friendly API in 2026.

Start mining local leads today. Check out our complete documentation or explore more web scraping tutorials.

👉 Get your API Key at SearchCans.com