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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
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                            [en] => Columbus
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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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            [continent] => Array
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                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

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                    [iso_code] => US
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                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [location] => Array
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                    [iso_code] => US
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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                    [ip_address] => 216.73.216.213
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    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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            [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
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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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
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                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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                    [2] => isInEuropeanUnion
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                    [4] => names
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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] => 216.73.216.213
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
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                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
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                    [10] => isp
                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

        )

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

        )

    [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
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            [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
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                            [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
)
Navratri Mega Sale Price Tracking

Introduction

With cocktail culture rapidly evolving in India’s metropolitan cities, understanding consumer preferences through digital platforms has become essential for beverage companies, restaurant consultants, and bar owners. Actowiz Solutions was approached by a client who wanted to Scrape Cocktail Trends From Zomato in Mumbai & Bangalore to monitor seasonal shifts, new recipe launches, and trending flavors. By combining advanced data extraction with Liquor Data Scraping Services, we enabled them to extract cocktail trend data from Zomato in India with precision and efficiency. The project not only focused on current market dynamics but also forecasted changes in consumer demand. Using scalable crawlers and automated processes, the client gained access to accurate datasets covering menus, pricing, ingredients, and variations across thousands of listings. This case study highlights how Actowiz Solutions transformed Zomato data into actionable insights that shaped strategy and decision-making for the client’s operations.

The Client

The client, a beverage market research company, was focused on helping liquor brands, hospitality groups, and mixologists align their offerings with customer demand. Their primary objective was to extract Zomato cocktail trends in Mumbai and Bangalore to benchmark popularity across regions and measure how urban customers responded to innovative drink menus. They wanted deeper insights into flavor trends, seasonal offerings, and price positioning to guide their advisory services. However, they faced limitations with manual monitoring and fragmented reports, which left gaps in competitive benchmarking. By leveraging Customer Ratings & Reviews Analytics, the client aimed to add context to raw menu data by understanding how consumers rated and reviewed cocktails. Partnering with Actowiz Solutions allowed them to access a structured, real-time pipeline of cocktail trend intelligence that could be delivered to stakeholders with accuracy and consistency.

Key Challenges

Key Challenges-01

The client’s major challenge was scale. Zomato hosts thousands of listings across Mumbai and Bangalore, with menus changing frequently. Tracking cocktails, identifying new launches, and correlating this information with consumer sentiment was impossible through manual methods. Additionally, the client wanted Zomato data extraction for cocktail trends 2025 to prepare a forward-looking analysis of upcoming consumer behaviors. Another difficulty was the complexity of structured versus unstructured data. While menu items provided clarity, understanding seasonality and availability required correlating multiple datasets. For instance, they needed to know if a cocktail was listed only for summer or available year-round, which made product availability tracking a vital aspect of the project. Moreover, competitors in the space were already adopting automated tools, meaning any delay in deploying web crawlers could put the client at a disadvantage. The challenge was to design a solution that ensured accurate, scalable, and timely delivery of cocktail trend insights.

Key Solutions

The-Client

Actowiz Solutions designed a comprehensive solution to Scrape Cocktail Trends From Zomato in Mumbai & Bangalore by deploying automated crawlers built for flexibility and scale. Our framework was developed to scrape Zomato menus to analyze cocktail trends in Mumbai while also being capable of adapting to different restaurant categories and pricing models. We extended this capability to extract Zomato menus for cocktail trends in Bangalore, ensuring a balanced regional comparison between the two cities. Special modules were built to extract Zomato cocktail menu data for seasonal trend analysis, allowing the client to detect shifts in summer cocktails, winter specials, and festive offerings. With Web Scraping Zomato, our team ensured data normalization, cleansing, and integration into dashboards for trend mapping. We also incorporated a Zomato Scraping API, enabling real-time access to structured datasets without delays. To support historical analysis, the client was provided with a Zomato Web Scraping Dataset, offering detailed archives of past cocktail menu shifts. These measures created a scalable ecosystem for continuous insights and competitive benchmarking.

Client Testimonial

“Actowiz Solutions has completely transformed how we analyze cocktail trends across metro cities. Their ability to Scrape Cocktail Trends From Zomato in Mumbai & Bangalore gave us unprecedented access to menu insights, seasonal patterns, and consumer preferences. The integration of Zomato data extraction for cocktail trends 2025 and seasonal trend analysis has been invaluable in helping our clients prepare for upcoming market shifts. The expertise, responsiveness, and accuracy of Actowiz’s solutions have positioned us as leaders in cocktail trend research.”

— Head of Insights, Beverage Market Research Company

Conclusion

This project demonstrates how data-driven strategies can reshape beverage and hospitality intelligence. By helping the client Scrape Cocktail Trends From Zomato in Mumbai & Bangalore, Actowiz Solutions delivered a framework that automated trend tracking, captured seasonal changes, and benchmarked consumer preferences across two of India’s most vibrant cities. From extracting Zomato cocktail trends in Mumbai and Bangalore to building a scalable Zomato Web Scraping Dataset, our solution created a foundation for both real-time monitoring and long-term forecasting. By integrating tools like Zomato Scraping API and leveraging contextual insights such as customer ratings & reviews analytics and product availability, the client gained a 360-degree view of the cocktail landscape. Actowiz Solutions continues to empower businesses with advanced data intelligence, enabling them to anticipate demand, refine offerings, and maintain a strong competitive edge in a rapidly evolving market.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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.

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 Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
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.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
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!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
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

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
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Amazon India vs Flipkart vs Snapdeal Product Data Mapping – Comparing Prices, Seller Networks, and SKU Match Rates

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How Web Scraping Grab Taxi Data Helps Brands Decode Real-Time Ride Prices, Routes & Demand Trends?

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Extracting GrabTaxi Fare & Availability Data to Improve Ride-Hailing Price Transparency

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How We Helped a Hospitality Brand Track 700+ Properties by Scraping Booking.com Hotel Prices in France

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Driving Smarter Marketplace Decisions with Seller Competition & Pricing Intelligence on Amazon India and Snapdeal

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Scraping Top-Selling GrabMart Products - Top Categories & SKUs Across Singapore, Malaysia & Thailand

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City-Wise Demand & Delivery Time Analysis for NIC Ice Cream - Solving Last-Mile Challenges in Quick Commerce

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