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Actowiz Metrics Now Live!

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Actowiz Metrics Now Live!

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Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

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Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

Unlock Smarter , Faster Analytics!

Actowiz Metrics Now Live!

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Actowiz Metrics Now Live!

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(
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [zh-CN] => 美国
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            [registered_country] => Array
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                            [ru] => США
                            [zh-CN] => 美国
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                                    [fr] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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    [country:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

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

    [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
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [validAttributes:protected] => Array
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.152
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [1] => autonomousSystemOrganization
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
                        (
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

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

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

    [subdivisions:protected] => Array
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            [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
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                            [0] => en
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                    [validAttributes:protected] => Array
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)

 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
)

Introduction

In India’s fast-paced and diverse food industry, staying ahead of the curve is essential for success. With thousands of eateries ranging from local street food vendors to upscale gourmet cafes, the market is constantly evolving. Businesses relying on outdated or generic insights risk missing key trends and losing potential customers. The most effective way to gain a competitive edge? Scrape Restaurant Data India for accurate, real-time, and location-specific insights. Whether you're looking to Extract Cafe Listings India, analyze regional cuisine preferences, or track competitor performance, India Cafe and Restaurant Data scraping provides a goldmine of information. With category-based segmentation and Restaurant Type Data Scraping, businesses can make smarter, data-driven decisions. Stay ahead in this dynamic space with Real-time Restaurant Data Extraction tailored to your niche, target audience, and geographic preferences. Embrace the power of data and watch your food venture thrive in one of the world’s most vibrant culinary markets.

Why Food Trend Tracking Matters?

In India’s dynamic and highly competitive food ecosystem, tracking food trends has become crucial for stakeholders like restaurants, food delivery platforms, FMCG brands, and market researchers. Staying in sync with evolving customer preferences empowers these players to make smart, strategic choices around product offerings, pricing models, promotions, and expansion strategies. Leveraging Real-time Restaurant Data Extraction and Food Business Data Scraping India allows businesses to react instantly to changing demands and capitalize on new opportunities.

1. Understanding Changing Consumer Tastes

As dietary preferences shift towards health-conscious, plant-based, or regional cuisines, capturing these trends is key.

Cuisine Category	Growth (YoY %)	Popular Regions
Plant-based	22%	Bengaluru, Mumbai
Regional Indian (Bengali, Rajasthani)	17%	Kolkata, Jaipur
Korean & Japanese	28%	Delhi NCR, Pune

Using Scraping Restaurant Categories India, businesses can track which food types are trending, where, and among which demographics.

2. Launching Region-Specific Menus

Customizing offerings by geography boosts customer satisfaction and engagement.

Region	Top Trending Dish	Local Cuisine Penetration (%)
South India	Andhra Spicy Biryani	64%
North India	Chole Bhature	58%
East India	Macher Jhol	61%

With Cafe and Restaurant Insights India, restaurants can align menu offerings with regional demand, reducing waste and increasing sales.

3. Predicting Demand for New Cuisines

Anticipating demand allows for faster adaptation and better inventory planning.

Cuisine Type	Search Spike (%)	Adoption by Restaurants (%)
Korean BBQ	36%	12%
Vegan Desserts	29%	8%
Mediterranean	19%	9%

Food Business Data Scraping India helps spot demand patterns before they become saturated.

4. Analyzing Pricing Dynamics and Discount Trends

Understanding pricing fluctuations and popular discounting strategies is essential for competitiveness.

Segment	Avg. Price Increase (6 Months)	Most Common Discount
Cloud Kitchens	11%	20% off
QSR Chains	7%	Buy 1 Get 1 Free
Cafes	5%	₹100 Off on Orders ₹500+

With Restaurant Market Analysis India and Competitor Analysis for Restaurants India, brands can make pricing strategies more dynamic and location-specific.

Unlock powerful insights with real-time food trend tracking—optimize menus, pricing, and strategy with Scrape Restaurant Data India today!

Contact Us Today!

What Kind of Data Can Be Scraped?

In today's hyper-competitive food service landscape, access to real-time, structured data gives businesses the edge they need. Actowiz Solutions offers comprehensive solutions to Scrape Restaurant Data India, enabling brands, food delivery platforms, market researchers, and FMCG players to extract a wide range of valuable information for decision-making and strategy.

By using advanced Real-time Restaurant Data Extraction tools, businesses can access precise, timely, and location-specific datasets. This structured data includes:

Types of Data Extracted

Data Type	Description	Use Case Example
Menu Items & Pricing	Full list of food and drink offerings with rates	Pricing comparison, menu optimization
Customer Ratings & Reviews	Star ratings, user feedback, sentiment trends	Reputation management, quality control
Restaurant Type Data Scraping	Categorization into QSRs, cafes, fine dining, cloud kitchens	Market segmentation, service modeling
Cuisine & Dietary Preferences	Vegan, gluten-free, regional cuisine tags	Targeted marketing, custom meal planning
Opening Hours & Delivery Info	Operational timing and delivery support	Logistics, delivery zone mapping

This comprehensive India Cafe and Restaurant Data fuels deeper consumer understanding and competitive analysis.

Sample Data Coverage (2020–2025)

Tracking trends over time helps businesses make future-ready strategies.

Year	Avg Menu Items per Outlet	Avg User Rating	Discount Frequency (%)
2020	47	3.8	18%
2021	52	4.0	22%
2022	60	4.1	27%
2023	65	4.3	31%
2024	70	4.4	35%

Top 5 Cuisine Tags (2024)

Cuisine Type	Presence Across Listings (%)
North Indian	78%
Chinese	65%
South Indian	58%
Italian	45%
Fast Food/QSR	88%

Using Extract Cafe Listings India, businesses can tailor menus to prevailing consumer tastes and preferences across locations.

Restaurant Category Distribution by Tier-1 Cities (2024)

City	Cafes (%)	QSRs (%)	Fine Dining (%)
Delhi NCR	32	46	22
Mumbai	35	44	21
Bengaluru	30	48	22
Hyderabad	29	50	21

This detailed Restaurant Type Data Scraping supports strategic placement, expansion, and category-based promotion efforts.

Whether you're a startup or an established food brand, leveraging India Cafe and Restaurant Data with Scrape Restaurant Data India services from Actowiz Solutions ensures smarter decisions backed by real, actionable insights.

Use Cases of Restaurant Data Scraping

The power of Real-time Restaurant Data Extraction goes far beyond just listing eateries. It unlocks deep, actionable insights for a range of stakeholders—from food manufacturers and restaurant chains to analysts and delivery platforms. By choosing to Scrape Restaurant Data India, businesses gain access to accurate, structured, and location-specific information. Here's how different industries can use this data effectively:

1. Food Business Data Scraping India for Product Development

FMCG brands and food manufacturers can monitor trending ingredients, dishes, and dietary preferences through India Cafe and Restaurant Data. By analyzing menus and reviews, they can shape new product lines that resonate with evolving consumer tastes.

Trending Cuisine (2024)	Product Opportunity Example	Consumer Sentiment Score
Korean	Gochujang sauces, instant noodles	4.3/5
Vegan/Plant-based	Meat alternatives, vegan snacks	4.5/5
South Indian	Dosa mixes, chutneys	4.2/5

Food Business Data Scraping India ensures product innovation is based on real-time demand.

2. Extract Cafe Listings India for Market Expansion

Restaurant chains and delivery aggregators use Extract Cafe Listings India to identify areas with high consumer interest, favorable ratings, and untapped opportunities.

City	Avg. Footfall (Daily)	Avg. User Rating	New Outlet Potential
Pune	420	4.3	High
Ahmedabad	390	4.1	Moderate
Chandigarh	360	4.4	High

With Cafe and Restaurant Insights India, companies can confidently plan their next launch.

3. Competitor Analysis for Restaurants India

Track competitor pricing, seasonal promotions, and user reviews through Restaurant Market Analysis India.

Competitor	Avg. Meal Price	Popular Offer	Avg. Rating
Brand A	₹250	20% Off on Combos	4.0
Brand B	₹230	₹100 Off above ₹500	4.2

Competitor Analysis for Restaurants India enables strategic responses and performance benchmarking.

4. Scraping Restaurant Categories India for Market Segmentation

Understand the balance of casual dining, QSRs, and fine dining in different geographies through Restaurant Type Data Scraping.

City	Casual Dining (%)	QSR (%)	Fine Dining (%)
Mumbai	38	42	20
Delhi NCR	35	45	20
Bengaluru	40	40	20

With Scraping Restaurant Categories India, businesses get granular insights into local market composition.

Using tools like Scrape Restaurant Data India, Cafe and Restaurant Insights India, and Restaurant Market Analysis India, brands can stay ahead in the ever-evolving Indian food scene. From product development to expansion planning, data is the secret ingredient to success.

Boost your business with data-driven decisions—leverage restaurant data scraping for expansion, analysis, and competitive edge today!

Contact Us Today!

How Actowiz Solutions Can Help?

At Actowiz Solutions, we specialize in delivering custom scraping solutions that are scalable, secure, and fully compliant with data policies. Our advanced tools enable Real-time Restaurant Data Extraction from leading platforms like Zomato, Swiggy, and Google Maps. We provide rich, structured Cafe and Restaurant Insights India in formats such as CSV, JSON, or via API, ensuring seamless integration with your existing systems. With advanced filtering capabilities based on cuisine type, user ratings, city, and more, our solutions help you access highly segmented and relevant datasets tailored to your needs.

Whether you’re looking to Scrape Restaurant Data India for competitor analysis, expansion planning, or trend tracking, our services are built to support even the most complex scraping requirements. We also offer scheduled reports and real-time data delivery through robust API integrations. Additionally, our support for historical data tracking enables long-term Restaurant Market Analysis India, empowering your business to make intelligent, data-driven decisions with confidence and speed.

Conclusion

India’s food scene is constantly evolving, and businesses need data-driven strategies to stay competitive. By choosing to Scrape Restaurant Data India, you gain access to trends, pricing intelligence, and market insights in real time.

Ready to transform your food business with smart data? Contact Actowiz Solutions today for a customized data scraping solution! You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!

GeoIp2\Model\City Object
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                            [ru] => США
                            [zh-CN] => 美国
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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            [traits] => Array
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                    [prefix_len] => 22
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    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [ja] => 北アメリカ
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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    [country:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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    [locales:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [validAttributes:protected] => Array
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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                            [ru] => США
                            [zh-CN] => 美国
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [2] => isInEuropeanUnion
                    [3] => isoCode
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                    [4] => names
                    [5] => type
                )

        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.152
                    [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
)

Start Your Project

+1 ▼

Select Service Web Scraping Real-Time API Access Data Dashboards Datasets Review & Sentiment Analysis Price Monitoring Custom Solution

Select Industry E-Commerce Quick Commerce Food Delivery Travel & OTA Grocery & Retail Real Estate Others

Target Country Web Scraping Services Mobile App Scraping Web and App Scraping API Datasets Dashboard

A$лвR$C$CHF¥Kčkr€£HK$FtRp₪₹kr¥₩Mex$RMkrNZ$₱złleikrS$฿₺$R Budget Range $500 - $1,000 - Starter Budget (POC/Single Site) $1,000 - $2,500 - Basic Plan (Small Business Needs) $2,500 - $5,000 - Growth Phase (Multi-site/Data Volume Projects) $5,000 - $10,000 - Business-Scale (Automation/API Integrations) $10,000 - $25,000 - Enterprise Projects (Real-Time Systems, Global Data) $25,000 - $50,000 - Enterprise+ (Custom Dashboards, Ongoing Support) $50,000+ - Strategic Partnership (End-to-End Data Ops)

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

🔒 "Your data is secure with us. NDA available."

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Ecommerce

Q-Commerce

Travel & OTA

Food Delivery

Real Estate

Social Media

Trusted by Global Leaders – Secured by International Standards

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★

'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

2 min

★★★★★

“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”

Iulen Ibanez

CEO / Datacy.es

1 min

★★★★★

“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”

Febbin Chacko

-Fin, Small Business Owner

1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Request Demo Access

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

All

Blog

Case Studies

Infographics

Report

Sep 24, 2025

Web Crawling for US Grocery Platforms - Discovering Market Leaders and Key Insights

Explore how web crawling for US grocery platforms reveals market leaders, consumer trends, and key insights shaping the future of online grocery.

Price Comparison Study - How Menu Price Comparison for Swiggy and Zomato Improves Retail Insights

Menu Price Comparison for Swiggy and Zomato: Real-time menu data extraction helps retailers track prices, optimize menus, and gain actionable insights.

Festive Price Surge Tracker: Amazon Fresh vs BigBasket vs JioMart in India

Track how prices of sweets, snacks, and groceries surged across Amazon Fresh, BigBasket, and JioMart during Diwali & Navratri in India with Actowiz festive price insights.

Ride-Hailing Competition in NYC - Uber, Lyft & Yellow Cab Pricing Analysis

Ride-Hailing Price Comparison in NYC - An in-depth analysis of Uber, Lyft, and Yellow Cab fares, highlighting cost trends and competitive insights.

Sep 24, 2025

Web Crawling for US Grocery Platforms - Discovering Market Leaders and Key Insights

Explore how web crawling for US grocery platforms reveals market leaders, consumer trends, and key insights shaping the future of online grocery.

Sep 24, 2025

How Data Scraping for Luxury Retailers Reveals Regional Buying Patterns and Market Insights?

Discover how data scraping for luxury retailers uncovers regional buying patterns, consumer trends, and market insights to drive smarter business decisions.

Sep 24, 2025

How Sephora API for Beauty Market Trends Analysis Helps Brands Forecast Demand with Data and AI Insights?

Discover how Sephora API for beauty market trends analysis, combined with AI insights, helps brands forecast demand and stay ahead of consumer trends.

Read More

Price Comparison Study - How Menu Price Comparison for Swiggy and Zomato Improves Retail Insights

Menu Price Comparison for Swiggy and Zomato: Real-time menu data extraction helps retailers track prices, optimize menus, and gain actionable insights.

Grocery Price Tracking for Blinkit, BigBasket & Zepto - Real-Time Scraping to Optimize Retail Pricing

Grocery Price Tracking for Blinkit, BigBasket & Zepto: Real-time scraping insights to optimize retail pricing, monitor competitors, and boost sales efficiency.

Analyzing Audience Engagement with the MX Player Viewership Dataset - Insights for Content Strategy

Explore audience behavior with the MX Player Viewership Dataset and uncover insights to optimize content strategy and boost viewer engagement effectively.

Read More

Festive Price Surge Tracker: Amazon Fresh vs BigBasket vs JioMart in India

Track how prices of sweets, snacks, and groceries surged across Amazon Fresh, BigBasket, and JioMart during Diwali & Navratri in India with Actowiz festive price insights.

Top 5 Brands Offering Deepest Discounts on Clothes This Navratri

Score big this Navratri 2025! Discover the top 5 brands offering the biggest clothing discounts and grab stylish festive outfits at unbeatable prices.

Top 10 Most Ordered Grocery Items During Navratri 2025

Discover the top 10 most ordered grocery items during Navratri 2025. Explore popular festive essentials for fasting, cooking, and celebrations.

Read More

Ride-Hailing Competition in NYC - Uber, Lyft & Yellow Cab Pricing Analysis

Ride-Hailing Price Comparison in NYC - An in-depth analysis of Uber, Lyft, and Yellow Cab fares, highlighting cost trends and competitive insights.

Unlocking Price Trends – Blinkit vs BigBasket Market Data Analysis 2025 with Comparative Price Intelligence

Discover key insights from Blinkit vs BigBasket Market Data Analysis 2025—unlock price trends and boost growth with comparative price intelligence.

Wine vs. Beer vs. Spirits - Alcohol Consumption Trends in Travel Hubs (NYC, Dubai, London)

Explore Alcohol Consumption Trends in Travel Hubs comparing wine, beer, and spirits in NYC, Dubai, and London with key insights and data analysis.

Read More

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Time Zone Flexibility

Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.

Clear Communication

Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.

Uncompromising Quality

Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations

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