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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => 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
                        )

                )

            [continent] => 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
                        )

                )

            [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
                        )

                )

            [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] => 216.73.216.128
                    [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] => 216.73.216.128
                    [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

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Milk Basket Datasets – Scrape Milk Basket product availability data

Scrape Milk Basket product availability data for comprehensive Milk Basket datasets. Monitor Milk Basket data to strategize assortment, especially for multi-pincode assortment with Milk Basket. Utilize Milk Basket Quick Commerce data scraping for assortment analytics and optimization. Collect Milk Basket Q-Commerce data to analyze product availability trends and make informed decisions. Gain insights into Milk Basket assortment strategies and enhance Milk Basket product availability with meticulous data monitoring.

Improved Milk Basket Datasets

After the initial scraping process, Milk Basket datasets are enriched with additional data points, providing deeper insights into consumer behavior and market dynamics.

Enhanced Milk Basket datasets offer deeper insights into consumer behavior and market dynamics, driving informed decisions and optimizing strategies for sustainable growth.

Frame.svg
IdProduct_NameBrandCategoryPriceUnit_SizeAvailabilityRatingReviews_CountDiscountDelivery_FeeStock_QuantitySKUDescriptionOrganicExpiry_DateProduct_URL
1Full Cream MilkAmulDairy₹ 55.001 literIn Stock4.5220None₹ 0.00500AMUL-MILK-001Fresh Amul full cream milk, rich and creamy.No15-07-2025https://milkbasket.com/product/amul-full-cream-milk-1-liter-001
2Cow GheeGovardhanDairy₹ 480.00500 mlIn Stock4.71505% off₹ 0.00150GOV-GHEE-002Pure cow ghee, made traditionally.No01-01-2026https://milkbasket.com/product/govardhan-cow-ghee-500ml-002
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Optimizing Assortment Strategies

Leverage Milk Basket Datasets to analyze consumer behavior, monitor product availability, and refine assortment strategies for enhanced customer satisfaction and market competitiveness.

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Optimizing-Assortment-Strategies-01

Pincode-Specific Assortment

Scrape Milk Basket Quick Commerce data to monitor product availability across various pincode regions, enabling tailored assortment strategies to meet diverse customer demands effectively.

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Pincode-Specific-Assortment-01

Assortment Analytics

Extract valuable insights from Milk Basket Datasets to identify consumer preferences, trends, and demand patterns, facilitating data-driven decision-making and optimized inventory management strategies.

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Assortment-Analytics-01

Multi-Pincode Assortment Planning

Use Milk Basket Datasets to plan and optimize product assortment across multiple pincode regions, ensuring efficient distribution and maximizing customer convenience and satisfaction.

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Features of Datasets

Get structured and easy-to-use datasets for each use case.

Structure Maintenance

We maintain data sets depending on structural changes in the website.

Customized Output Fields

You can define customized Output Fields depending on your business needs.

24/7 Assistance

We offer all-time support to our clients whenever they ask us.

Subscription

Depending on predefined scheduled subscription to data feeds of updated records.

Multiple Options for Delivery

Google Cloud PubSun, Microsoft Azure, Amazon S3, and SFTP.

Multiple Output File Formats

Get datasets in various formats like Excel, CSV, ndJSON, or JSON.

Dedicated Account Manager

We assign a dedicated account manager to manage your data scraping.

Data Scaling

Assign servers to manage thousands of data requests.

Data with Quality Assurance

Don't worry about the data quality; focus on better decision-making with accurate and reliable data.

You focus on significant business activities; we will handle your data needs.

Hassle-free Discovery

Click all the data points appearing on the websites that you find challenging to uncover if you try to collect them yourself.

Complete and Correct

Get precise, complete, and fresh datasets covering millions of web pages with billions of data fields.

Enriched Data

Boost the usability and value of your data sets by adding several data resources to generate a valuable and accurate dataset.

Leaders in Compliance

Actowiz Solutions is fully committed to complying with data protection rules and regulations, including CCPA and GDPR.

Prefer to scrape the data yourself?

Try our Web Scraper IDE, built on our patented proxy and unblocking technology.

Different Formats

We will deliver datasets in formats like CSV, Excel, ndJSON, or JSON to your preferred storage system.

We use Google Drives, Cloud Storage, AWS, SFTP, and other platforms to deliver the data.

Flexible Pricing

Pricing will depend on your requirements for data quantity and difficulty level. So share your requirements with our team, and we will quote after analyzing your requirements.

  • Free samples for already available datasets
  • Price begins from 0.001 USD per record
  • Dedicated account manager and support system
Monitor-SKUs,-sales-volume,

15,000+
Customers
Worldwide

Monitor-SKUs,-sales-volume,

Category Leader
#1 in Web Data
Monitor-SKUs,-sales-volume,

Analyze-entire-product

Used by: Business Leaders, Decision Makers, Analysts, Academics, Software Engineers, IT Professionals

Operational-dashboard

Fortune 500 Companies, Academic Institutions, Small-Medium Businesses, NGOs

FAQ
Amazon Dataset consists of various data fields per your business needs, like Product name, seller ID, reviews, ratings, description, brands, prices, category, discount offers, ASIN, etc.
Yes, we will provide updates to your datasets on customized frequency.

Yes, you can procure an Amazon subset with limited data points and less cost.

If you are not planning to procure an Amazon dataset, consider scraping Amazon data using our scraper.

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 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

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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

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Blog
Case Studies
Infographics
Report
Nov 29, 2025

Travel Fare Scraping Insights – Comparing Expedia, MakeMyTrip, and Booking.com for Accurate Real-Time Travel Prices

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Extract McDonalds USA Store Locations Data at Scale - Mapping 10,000+ McDonald’s Stores with Automated Scraping Technology

Discover how automated scraping was used to extract McDonalds USA store locations data at scale, mapping 10,000+ outlets with precise geospatial and operational insights.

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Enhancing Deep Learning Models With Image Scraping

Enhance deep learning performance with large-scale image scraping. Build diverse, high-quality training datasets to improve AI accuracy, object detection, and model generalization.

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Top 10 Real-World Use Cases of Web Scraping in 2025 for Smarter Business Decisions

Explore the top 10 real-world use cases of web scraping in 2025, enabling smarter business decisions, market insights, competitor analysis, and data-driven growth.
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Travel Fare Scraping Insights – Comparing Expedia, MakeMyTrip, and Booking.com for Accurate Real-Time Travel Prices

Travel Fare Scraping Insights – Compare real-time prices across Expedia, MakeMyTrip, and Booking.com to uncover trends, save costs, and make smarter travel decisions.

Nov 28, 2025

How Real-Time Grocery Price Comparison via Scraping Helps Track Price Differences on Blinkit, Zepto & BigBasket?

Real-Time Grocery Price Comparison via Scraping helps analyze product costs across Blinkit, Zepto and BigBasket, enabling smarter shopping decisions.

Nov 27, 2025

How to Scrape News Articles With Python and AI Using a Powerful News Scraper?

Learn how to scrape news articles efficiently with Python and AI using a powerful News Scraper, automating content collection for analysis, insights, and research.

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Extract McDonalds USA Store Locations Data at Scale - Mapping 10,000+ McDonald’s Stores with Automated Scraping Technology

Discover how automated scraping was used to extract McDonalds USA store locations data at scale, mapping 10,000+ outlets with precise geospatial and operational insights.

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How Nike.com Price Scraping With Python Helped Monitor Flash Sales and Dynamic Pricing Trends

Discover how Nike.com price scraping with Python enabled real-time monitoring of flash sales and dynamic pricing, helping brands track discounts and trends efficiently.

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Scaling Fashion Product Data Extraction from H&M, Zara, SHEIN & Myntra to Power Real-Time Trend and Pricing Analytics

Scalable fashion product data extraction from H&M, Zara, SHEIN & Myntra, enabling real-time trend insights, pricing analytics, and competitive benchmarking.

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Enhancing Deep Learning Models With Image Scraping

Enhance deep learning performance with large-scale image scraping. Build diverse, high-quality training datasets to improve AI accuracy, object detection, and model generalization.

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Dark Store Location Optimization - Data-Driven Strategy Explained

Uncover how data-driven strategies optimize dark store locations, boosting quick commerce efficiency, reducing costs, and improving delivery speed.

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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.

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Top 10 Real-World Use Cases of Web Scraping in 2025 for Smarter Business Decisions

Explore the top 10 real-world use cases of web scraping in 2025, enabling smarter business decisions, market insights, competitor analysis, and data-driven growth.
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Data Scraping Trends for United States - Insights and Forecasts for 2025–2030

Explore the latest Data Scraping Trends for United States from 2025–2030, uncovering emerging technologies, market opportunities, and business insights.
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USA Adidas Store Insights 2025 – Analyzing Retail Footprint Using Adidas Stores Location Dataset

Explore the USA Adidas retail footprint in 2025 with our Research Report using the Adidas Stores Location Dataset to analyze store locations and trends.
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