Google Maps Business Listings Scraping Tutorial

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Introduction

Google Maps is the largest publicly accessible business directory in the world. It powers everything from:

lead generation
competitor benchmarking
location intelligence
retail expansion planning
hyperlocal data modeling
brand presence tracking
aggregator feeds
store verification
logistics planning

Extracting 1,000,000+ Google Maps business listings can support:

FMCG distribution networks
retail store expansion
QSR & restaurant benchmarking
service area optimization
franchise mapping
local SEO audits
GIS & route optimization

But scraping Google Maps at scale is extremely challenging because:

scrolling loads dynamic elements
reviews load in nested containers
place details load asynchronously
listings don’t have static URLs
Google aggressively blocks bots
inconsistent layouts across regions
pagination is infinite-scroll-based

This technical guide shows how Actowiz Solutions builds large-scale Google Maps scraping pipelines using:

Selenium (undetected mode)
Requests + reverse-engineered calls
Proxy rotation
Modular ETL pipelines
Caching logic
CSV + database storage
Slow scroll simulation
API-like Place Details extraction

Let’s build a workflow capable of scraping 1 million business listings.

Step 1: Install Required Libraries

pip install selenium
pip install undetected-chromedriver
pip install requests
pip install beautifulsoup4
pip install pandas
pip install fake-useragent
pip install lxml

We’ll use:

Selenium → handle scrolling + JS content
Requests → hit API-like endpoints after extracting Place IDs
Pandas → dataset export

Step 2: Identify Target Search Queries

Examples:

“restaurants in New York”
“salons in Dubai”
“grocery stores in London”
“pharmacies in Mumbai”
“coffee shops in Paris”

Google Maps search URL:

https://www.google.com/maps/search/

Step 3: Launch Undetected Chrome (To Avoid Blocking)

import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from time import sleep

browser = uc.Chrome()
browser.get("https://www.google.com/maps/search/restaurants+in+new+york")
sleep(5)

Step 4: Scroll to Load Thousands of Listings

Google Maps loads 20–30 listings at a time inside the left-side results pane.

Detect scroll pane:

scrollable = browser.find_element(By.CLASS_NAME, "m6QErb")

Scroll multiple times:

for _ in range(300):  # adjust for millions
    browser.execute_script("arguments[0].scrollTop = arguments[0].scrollHeight", scrollable)
    sleep(1.5)

For 1 million listings, you’ll need:

1,000+ scroll cycles
Parallel execution across multiple servers

Step 5: Extract Listing Containers

Each listing is wrapped inside a containing a business card.

cards = browser.find_elements(By.XPATH, '//div[contains(@class, "Nv2PK")]')

Step 6: Extract Business Details (Name, Rating, Category, Location)

records = []

for card in cards:
    try:
        name = card.find_element(By.CLASS_NAME, "qBF1Pd").text
    except:
        name = ""

    try:
        rating = card.find_element(By.CLASS_NAME, "MW4etd").text
    except:
        rating = ""

    try:
        reviews = card.find_element(By.CLASS_NAME, "UY7F9").text
    except:
        reviews = ""

    try:
        category = card.find_element(By.CLASS_NAME, "W4Efsd").text
    except:
        category = ""

    try:
        address = card.find_element(By.CLASS_NAME, "rllt__details").text
    except:
        address = ""

    try:
        url = card.find_element(By.TAG_NAME, "a").get_attribute("href")
    except:
        url = ""

    records.append({
        "name": name,
        "rating": rating,
        "review_count": reviews,
        "category": category,
        "address": address,
        "maps_url": url
    })

Step 7: Extract Place ID (Needed for API-like Calls)

Google Maps URLs contain:

.../data=!3m1!4b1!4m5!3m4!1s!...

Extract Place ID via regex.

import re

def extract_place_id(url):
    match = re.findall(r"1s(.*)!2m", url)
    return match[0] if match else None

for r in records:
    r["place_id"] = extract_place_id(r["maps_url"])

Step 8: Reverse-Engineer Google’s NearbySearch / Details API Call

Google often serves structured JSON via:

https://www.google.com/maps/preview/place/details?authuser=0&hl=en&gl=us&pb=!1m2!1s!...

We can fetch details using Requests:

import requests

def fetch_place_details(place_id):
    try:
        url = f"https://www.google.com/maps/preview/place/details?authuser=0&hl=en&gl=us&pb=!1m2!1s{place_id}"
        r = requests.get(url, headers={"User-Agent": "Mozilla/5.0"})
        return r.text
    except:
        return "{}"

Parse JSON-like structure from Google’s "pb" encoded data:

This requires custom parsers (Actowiz uses internal decoders), but simple fields like phone, hours, plus code, and website can be parsed using regex.

def extract_field(raw, key):
    try:
        idx = raw.index(key)
        snippet = raw[idx:idx+200]
        value = re.findall(r'"([^"]+)"', snippet)[1]
        return value
    except:
        return ""

Attach:

for r in records:
    raw = fetch_place_details(r["place_id"])
    r["phone"] = extract_field(raw, "phone")
    r["website"] = extract_field(raw, "website")
    r["plus_code"] = extract_field(raw, "plus_code")

Step 9: Extract Coordinates (Latitude & Longitude)

Coordinates appear inside listing URLs:

def extract_lat_long(url):
    try:
        match = re.findall(r"@([\d\.\-]+),([\d\.\-]+)", url)[0]
        return float(match[0]), float(match[1])
    except:
        return None, None

for r in records:
    r["latitude"], r["longitude"] = extract_lat_long(r["maps_url"])

Step 10: Handle Scaling (Extracting 1 Million+ Records)

To scrape 1M records:

Strategy 1 — Keyword Expansion

Generate 500+ queries per country:

“restaurants in <city>”
“gyms in <city>”
“pharmacies in <city>”
“electronics store in <city>”

Strategy 2 — Geo-grid Tiling

Cut the map into grids:

latitude increments: 0.05
longitude increments: 0.05

Search each grid:

https://www.google.com/maps/search/restaurants/@,,14z

Strategy 3 — Proxy Rotation

Use:

residential proxies
mobile proxies
IP rotation every 5 requests

Strategy 4 — Parallelization

Run scraper on:

50+ Docker containers
multiple regions
auto-restart on failure

Strategy 5 — Caching Place IDs

Avoid duplicate businesses across queries.

Step 11: De-duplicate Records

Use Place ID as unique key:

import pandas as pd

df = pd.DataFrame(records)
df = df.drop_duplicates(subset=["place_id"])

Step 12: Export Final Dataset

df.to_csv("google_maps_business_listings.csv", index=False)

Step 13: Build a Lead Gen or Store Locator Dataset

Field	Example
name	Starbucks
rating	4.3
reviews	230
category	Coffee shop
address	Manhattan, NY
phone	+1 212 555 1234
website	starbucks.com
latitude	40.7128
longitude	-74.0060
place_id	ChIJ________
maps_url	https://maps.google.com/...

Step 14: Build Analytics From Dataset

Top business categories
Density maps
Heatmaps for store clusters
Competitor overlap analysis
Rating distribution
Review sentiment
Area-based revenue proxies

Technical Challenges in Google Maps Scraping

Challenge	Explanation
Strong anti-bot protection	Causes blocks after 20–50 scrolls
Dynamic containers	review and detail blocks load asynchronously
infinite scroll	pagination is not linear
inconsistent HTML	varies by region
throttling	rapid requests trigger captcha

Actowiz Solutions solves this using:

browser fingerprinting
human-like scroll patterns
rotating proxies
load balancing across 30–50 nodes
place ID caching
retry-on-failure engines

When Should You Use Actowiz Solutions?

Choose Actowiz if you need:

1M–50M Google Maps listings
Hourly or daily updates
Multi-country intelligence (USA, UAE, India, Europe)
Review sentiment analysis
Category-level heatmaps
Hyperlocal competitor insights
API access to Google Maps intelligence
Fully enriched business listings

We support extraction across:

Google Maps
Bing Maps
Apple Maps
Yelp
Zomato
Swiggy
TripAdvisor

Conclusion

In this tutorial, you learned how to:

scrape Google Maps business listings
scroll dynamically for thousands of entries
extract place IDs
fetch details via API-like preview URLs
extract phone, website, hours
fetch coordinates
scale scraping to 1 million+ records
remove duplicates
export datasets
build large-scale business intelligence

This becomes the foundation of a full-scale location intelligence pipeline.

You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

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Business Listings Extraction from Google Maps (1 Million Records) – Full Technical Tutorial

Dec 14, 2025

Introduction

Step 1: Install Required Libraries

Step 2: Identify Target Search Queries

Step 3: Launch Undetected Chrome (To Avoid Blocking)

Step 4: Scroll to Load Thousands of Listings

Step 5: Extract Listing Containers

Step 6: Extract Business Details (Name, Rating, Category, Location)

Step 7: Extract Place ID (Needed for API-like Calls)

Step 8: Reverse-Engineer Google’s NearbySearch / Details API Call

Step 9: Extract Coordinates (Latitude & Longitude)

Step 10: Handle Scaling (Extracting 1 Million+ Records)

Strategy 1 — Keyword Expansion

Strategy 2 — Geo-grid Tiling

Strategy 3 — Proxy Rotation

Strategy 4 — Parallelization

Strategy 5 — Caching Place IDs

Step 11: De-duplicate Records

Step 12: Export Final Dataset

Step 13: Build a Lead Gen or Store Locator Dataset

Step 14: Build Analytics From Dataset

Technical Challenges in Google Maps Scraping

When Should You Use Actowiz Solutions?

Conclusion

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