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Weekly E-commerce Price Comparison in Amazon India - Trends & Insights-01

Introduction

Weekly E-commerce Price Comparison in Amazon India - Trends & Insights-01

Google Maps has become the world’s biggest directory for everything — from restaurants and salons to hospitals, gyms, real-estate properties, and service providers. Today, companies across all industries rely on Google Places data extraction to understand businesses, locations, customer ratings, market presence, and local competition.

Whether you’re in marketing, sales, logistics, analytics, delivery, travel, or mapping — Google Places data is gold.

In this long-form blog, we’ll explore:

  • What Google Places data includes
  • Why extracting 5,000+ listings is so valuable
  • Business use cases
  • How large-scale extraction works
  • What sample datasets look like
  • SEO benefits
  • Why Actowiz Solutions is the best choice for accurate, scalable extraction

Let’s dive in.

Google Places is the backbone of local search. Every time a user searches “restaurants near me”, “gyms in Dubai”, “salons in New York”, or “hospitals in Bangalore”, Google fetches the information from the Places API and Maps database.

This includes:

  • Business names
  • Addresses
  • Ratings
  • Reviews
  • Categories
  • Photos
  • Menus
  • Location coordinates
  • Working hours
  • Phone numbers
  • Popular times
  • Amenities

Extracting this data helps businesses get real-time market intelligence.

Actowiz Solutions enables companies to extract 5,000 – 100,000+ Google Places entries with 99% accuracy.

Why Google Places Data Extraction Matters

Companies need large-scale Maps data to grow in 2025. Here's why:

1. Local SEO & Competitor Benchmarking

Businesses compare their listing with nearby competitors:

  • Who ranks higher?
  • Who has better ratings?
  • Which categories perform best?
  • What keywords appear in top reviews?
  • What amenities attract more customers?

This helps brands improve local visibility.

2. Sales Prospecting & Lead Generation

Google Places allows businesses to find:

  • Restaurants
  • Cafes
  • Clinics
  • Retail shops
  • Beauty salons
  • Automotive centers
  • Educational institutes
  • Hotels
  • Real estate listings

For B2B companies, this becomes a powerful prospecting tool.

Example: A POS (Point of Sale) company extracts 5,000+ restaurants to sell payment devices.

3. Market Research & Location Intelligence

Companies track:

  • Density of stores in an area
  • Popular business categories
  • Consumer sentiment
  • Review keywords
  • Operational timings
  • Growth hotspots

This helps in expansion planning and competitor mapping.

4. Delivery & Logistics Planning

Food-tech, e-commerce, and grocery delivery platforms use Maps data to:

  • Plot serviceable zones
  • Identify new delivery-friendly regions
  • Verify store coordinates
  • Monitor working hours
  • Map customer demand
5. Real Estate & Retail Growth Analysis

Developers extract Google Places data to study:

  • Neighborhood quality
  • Schools nearby
  • Restaurants & grocery options
  • Medical facilities
  • Entertainment & lifestyle infrastructure

This supports investment decisions.

6. App & Website Directories

Travel, lifestyle, and local search apps use Google Places data to populate:

  • Business lists
  • Ratings
  • Photos
  • Maps & location pins
  • Category pages

What Data Can Actowiz Solutions Extract from Google Places?

Actowiz Solutions can extract complete business data, even from 5,000+ maps entries.

Here's everything included:

Business Information
  • Business Name
  • Category
  • Subcategory
  • Address
  • City
  • Zip Code
  • Country
Contact & Location Data
  • Phone Number
  • Website
  • Latitude
  • Longitude
  • Google Plus Code
Ratings & Reviews
  • Total Reviews
  • Average Rating
  • Individual Review Text
  • Reviewer Name (if available)
  • Timestamp (date posted)
  • Review Sentiment
  • Rating Breakdown (1-5 stars)
Operational Information
  • Opening & Closing Hours
  • Open/Closed Status
  • Holiday Hours
  • Peak Visit Times
Amenities
  • Parking
  • Wheelchair Access
  • WiFi Availability
  • Delivery / Takeaway
  • Outdoor Seating
Media & Visual Data
  • Photos
  • Image URLs
  • Photo Timestamps
  • Menu Photos (for restaurants)
Advanced Data
  • Popular Times Graph
  • Permanently Closed Flag
  • Temporarily Closed Flag
  • Price Level ($, $$, $$$)
  • Menu (if listed on Maps)
  • Food & service attributes
  • Inventory tags (for stores)

Sample Google Places Dataset (Example)

Below is a sample dataset based on a restaurant search query:

Sample Dataset: Restaurants in New York City
Business Name Rating Reviews Address Phone Category Website
Joe's Pizza 4.5 8,200 Greenwich Village, NY (212) 555-7654 Pizza joespizza.com
Amma's Kitchen 4.3 2,900 Midtown, NY (212) 555-2389 Indian ammaskitchen.com
Shake Shack 4.2 15,400 Madison Square Park (212) 555-3311 Fast Food shakeshack.com
The Blue Dog 4.6 1,200 Times Square (212) 555-9876 Café thebluedog.com
Phở Saigon 4.4 740 Lower Manhattan (212) 555-6721 Vietnamese phosaigon.com
Sample Dataset: Gyms in Dubai
Business Name Rating Location Phone Status Price Level
Fitness First 4.1 JLT 800-1234 Open $$$
GymNation 4.6 Al Quoz 04-555-1212 Open $$
UFC Gym 4.4 Business Bay 04-555-9898 Open $$$
Max Gym 4.0 Bur Dubai 04-777-1213 Open $
Warehouse Gym 4.5 DIFC 04-444-9873 Open $$$

This is the exact format Actowiz delivers to clients — clean, structured, ready for dashboards or CRMs.

How Actowiz Solutions Extracts 5,000+ Google Places Entries (Process)

Collecting such large datasets requires advanced infrastructure.

Step 1: Define Keywords

Example:

  • “restaurants in Dubai”
  • “grocery stores in Mumbai”
  • “gyms in New York”
  • “car repair shops in London”
Step 2: Collect All Location-Based Results

Every city, district, or targeted area is mapped.

Step 3: Scrape All Relevant Business Profiles

Our crawlers fetch:

  • Main listing
  • Metadata
  • Reviews
  • Coordinates
Step 4: Data Cleaning

We remove:

  • Duplicates
  • Incomplete entries
  • Spam reviews
Step 5: Format the Dataset

CSV / Excel / JSON / API output — client's choice.

Step 6: Deliver Results

Fast turnaround even for 5,000+ entries.

Why Accuracy Matters So Much

When you extract 5,000+ listings, even a small error rate becomes huge.

Actowiz Solutions ensures:

  • 99% accuracy
  • Verified location coordinates
  • Correct category classification
  • Proper address formatting
  • Clean phone numbers
  • Eliminated duplicates
  • No missing fields

Accuracy + scale = competitive advantage.

Top Industries Using Google Places Data Extraction

Google Places data is now used across:

1. Restaurants & Food-Tech
  • Delivery app optimization
  • Menu mapping
  • Area demand analysis
2. Marketing Agencies
  • Competitor benchmarking
  • Local SEO strategy
  • Lead generation
3. Retail & FMCG
  • Store density mapping
  • Product distribution planning
4. Real Estate
  • Neighborhood analysis
  • Lifestyle mapping
5. Logistics
  • Service area planning
  • Delivery route optimization
6. Hospitality & Travel
  • Hotel, restaurants, attractions list building
7. SaaS Platforms
  • Business directories
  • Location-based apps

Google Places Data for SEO & Growth

Local search is exploding. 90% of customers choose a business based on map listings.

Your clients need to know:

  • Who ranks on top
  • Who gets the best reviews
  • Which categories dominate
  • What keywords customers mention
  • Why some listings outperform others

With extracted review text, you can analyze:

  • Sentiment
  • Common issues
  • Popular menu items
  • Pricing feedback

This drives better business decisions.

Why Actowiz Solutions Is the Best for Google Places Extraction

Actowiz Solutions specializes in location-based intelligence.

What Makes Us Unique
  • Handle 5,000 – 100,000+ listings
  • Fast turnaround
  • Country-level coverage
  • Supports USA, UK, UAE, India, Europe, Asia
  • Custom filters for categories
  • Clean, structured data
  • Bulk review extraction
  • API integration
  • Real-time scraping available
  • Highly accurate coordinate mapping
  • Affordable pricing

Types of Google Places Projects We Handle

Here are real use cases we regularly manage:

1. “Restaurants in 100 U.S. Cities”

5,000+ listings, 25 categories, 50,000 reviews.

2. “All Medical Clinics Across UAE”

Clinic name, specialties, insurance partners, reviews.

3. “Gyms & Fitness Centers in India”

Membership price, facilities, ratings.

4. “Tourist Attractions Across Europe”

Photos, review trends, distances.

5. “Real Estate Neighborhood Mapping”

Schools, grocery stores, hospitals, parks nearby.

Sample Output Formats

Actowiz Solutions delivers data in:

  • Excel
  • CSV
  • JSON
  • SQL
  • API feed
  • Dashboard-ready format

Final Thoughts — Google Places Data Is the Future of Local Intelligence

Google Places is the single most powerful source of local business intelligence. Extracting this data helps companies:

  • Understand markets
  • Identify competitors
  • Find new customers
  • Analyze reviews
  • Improve SEO
  • Build directories
  • Plan expansion
  • Enhance delivery services
  • Make better strategic decisions

Actowiz Solutions helps businesses extract high-accuracy, clean, ready-to-use data for any location or business category — even 5,000+ entries at once.

If you're building a location-based product, analyzing markets, or improving local search presence, Google Places data extraction is the key.

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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US
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                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
                (
                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
                (
                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                )

            [traits] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => geonameId
                    [2] => names
                )

        )

    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [locales:protected] => Array
        (
            [0] => en
        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.153
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
                )

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [validAttributes:protected] => Array
                (
                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
                )

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

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