Maps-Based Competitor Insights for Restaurants & Grocery Stores – Technical Tutorial

Introduction

Understanding competition is critical for:

• Q-commerce brands
• restaurant chains
• coffee outlets
• grocery stores
• FMCG brands
• cloud kitchens
• hyperlocal delivery platforms

Google Maps is one of the most accurate real-time sources for competitor intelligence because it provides:

• store density
• ratings
• reviews
• service areas
• peak hours
• photos
• menu/amenity metadata
• category-based listings

This tutorial shows how Actowiz Solutions builds Competitor Insight Engines for restaurants and grocery stores using:

• Google Maps scraping
• category-based search
• geo-grid coverage
• store attribute extraction
• sentiment clustering
• competitor gap analysis

All examples use Python, Selenium, Requests, TextBlob, and Lat-Lng grid crawling.

How Do You Set Up Your Environment?

Install dependencies:

pip install selenium
pip install undetected-chromedriver
pip install pandas
pip install textblob
pip install requests
pip install lxml
pip install beautifulsoup4

Imports:

import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from time import sleep
import pandas as pd
from textblob import TextBlob
import re

What Competitor Data Can Be Extracted From Google Maps?

This pipeline captures:

• Competitor Name
• Category (Restaurant, Café, Grocery Store, Bakery, etc.)
• Rating (4.2, 3.8, etc.)
• Number of Reviews
• Address & Geolocation
• Popular Times / Peak Hours (if available)
• Dining Options (Dine-in, Delivery, Takeaway)
• Grocery Attributes (Express, Pickup, Fuel attached, In-store shopping)
• Menu photos / store photos (optional)
• Price range
• Review sentiment

Example output:

{
  "name": "Fresh Market",
  "category": "Grocery Store",
  "rating": 4.4,
  "reviews": 815,
  "address": "Brooklyn, NY, USA",
  "lat": 40.6231,
  "lng": -73.9752,
  "services": ["Pickup", "Delivery"],
  "sentiment": 0.63
}

How Do You Search Competitors by Category on Google Maps?

Popular competitor categories:

• Restaurants
  • "pizza near me"
  • "burger near me"
  • "coffee shops Manhattan"
  • "best biryani Bangalore"
• Grocery Stores
  • "grocery store near me"
  • "supermarket London"
  • "organic store Berlin"
  • "24-hour grocery Dubai"

Example:

search = "grocery stores in Manhattan"
url = f"https://www.google.com/maps/search/{search.replace(' ', '+')}"
browser = uc.Chrome()
browser.get(url)
sleep(4)

How Do You Scroll to Load More Competitors?

All results load inside a scrollable container.

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

for _ in range(120):
    browser.execute_script("arguments[0].scrollTop = arguments[0].scrollHeight", panel)
    sleep(1)

How Do You Extract Competitor Listing Cards?

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

How Do You Parse Each Competitor’s Details?

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

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

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

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

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

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

    competitors.append({
        "name": name,
        "rating_raw": rating,
        "reviews_raw": reviews,
        "category": category,
        "address": address,
        "maps_url": url
    })

How Do You Extract Latitude, Longitude, and Place ID?

Google Maps URLs contain coordinate metadata.

def extract_lat_lng(url):
    try:
        lat, lng = re.findall(r"@([-0-9\.]+),([-0-9\.]+)", url)[0]
        return float(lat), float(lng)
    except:
        return None, None

def extract_place_id(url):
    try:
        pid = re.findall(r"!1s([^!]+)!8m", url)[0]
        return pid
    except:
        return None

for comp in competitors:
    comp["lat"], comp["lng"] = extract_lat_lng(comp["maps_url"])
    comp["place_id"] = extract_place_id(comp["maps_url"])

How Do You Clean Rating & Review Counts?

def clean_rating(r):
    nums = re.findall(r"\d+\.?\d*", r)
    return float(nums[0]) if nums else None

def clean_reviews(r):
    nums = re.findall(r"\d+", r.replace(",", ""))
    return int(nums[0]) if nums else None

for comp in competitors:
    comp["rating"] = clean_rating(comp["rating_raw"])
    comp["reviews"] = clean_reviews(comp["reviews_raw"])

How Do You Extract Competitor Reviews for Sentiment?

Open each competitor’s page → click reviews.

1. Visit URL

sentiments = []

for comp in competitors[:25]:
    browser.get(comp["maps_url"])
    sleep(3)

2. Open Reviews Panel

try:
    btn = browser.find_element(By.XPATH, '//button[contains(@aria-label,"reviews")]')
    btn.click()
    sleep(2)
except:
    continue

3. Scroll Reviews

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

for _ in range(30):
    browser.execute_script("arguments[0].scrollTop = arguments[0].scrollHeight", panel)
    sleep(1)

4. Extract Reviews

cards = browser.find_elements(By.XPATH, '//div[@data-review-id]')

for r in cards:
    try: text = r.find_element(By.CLASS_NAME, "wiI7pd").text
    except: text = ""

    try: rating = r.find_element(By.CLASS_NAME, "fzvQZe").get_attribute("aria-label")
    except: rating = ""

    sentiments.append({
        "competitor": comp["name"],
        "text": text,
        "rating": rating
    })

How Do You Run Sentiment Analysis on Competitor Reviews?

df_sent = pd.DataFrame(sentiments)

def polarity(t):
    return TextBlob(t).sentiment.polarity

df_sent["sentiment"] = df_sent["text"].apply(polarity)

Output sample:

How Do You Calculate Competitor Strength Scores?

1. Weighted Sentiment Score

score = df_sent.groupby("competitor")["sentiment"].mean()

2. Rating + Review Count Influence

df_comp = pd.DataFrame(competitors)

df_comp["strength_score"] = (
    df_comp["rating"] * 0.6 +
    (df_comp["reviews"] / df_comp["reviews"].max()) * 0.4
)

How Do You Map Competitors Geographically?

Export coordinates:

df_geo = df_comp[["name", "lat", "lng", "rating", "strength_score"]]
df_geo.to_csv("competitor_locations.csv", index=False)

Load into:

• QGIS
• Mapbox
• Google Data Studio
• PowerBI maps

This generates:

• density heatmaps
• competitor clusters
• white-space opportunity zones

How Do You Build a Competitor Heatmap?

Use latitude/longitude grid to visualize:

• Restaurant saturation
• Grocery chain density
• Competitor clusters by category

Example grid analysis (pseudocode):

df_geo["grid_lat"] = df_geo["lat"].round(2)
df_geo["grid_lng"] = df_geo["lng"].round(2)

heat = df_geo.groupby(["grid_lat","grid_lng"])["name"].count()

How Do You Identify Low-Competition Zones?

heat.reset_index().sort_values("name").head(50)

This shows areas with the least number of competitors → best places to open a new store.

What Are the Limitations of Competitor Scraping?

• Google Maps changes DOM structure often
• Hard rate limits → need proxies
• Review extraction is slow
• Stores may appear multiple times under similar queries
• Large cities require grid-based crawling

Why Use Actowiz Solutions for Competitor Insights?

Use this tutorial if:

• you want small-scale competitor scraping
• city-level insights
• one-time analysis

Use Actowiz Solutions when:

• you want nationwide competitor mapping
• weekly refreshes
• 50K–500K store extractions
• automatic sentiment clustering
• category-level pricing intelligence
• market-entry feasibility scoring
• API or dashboard-based competitor intelligence

Actowiz provides:

• competitor heatmaps
• neighborhood trend dashboards
• store-level performance clustering
• chain-wise benchmark reports
• multi-country competitor coverage

Conclusion

In this guide, you learned how to:

• search competitors by category on Google Maps
• extract store listings
• capture coordinates and Place IDs
• scrape reviews for sentiment
• compute competitor strength scores
• build heatmaps for restaurants & grocery stores
• identify low-competition zones
• export competitor datasets

This is the foundation for hyperlocal expansion planning, market-entry strategy, and store network optimization powered by Actowiz Solutions.

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