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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
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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
                        )

                )

            [country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [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
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

            [registered_country] => 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
                        )

                )

            [subdivisions] => Array
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                    [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] => 216.73.216.165
                    [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
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                    [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] => 216.73.216.165
                    [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
)
Weekly E-commerce Price Comparison in Amazon India - Trends & Insights-01

Introduction

The rapid expansion of India's quick commerce sector has reshaped grocery delivery and urban convenience. Leveraging the Zepto Product Dataset for Q-Commerce market, businesses can track evolving product trends, price shifts, and inventory patterns across multiple categories. With millions of SKUs and constantly updated listings, manual tracking is no longer feasible. Automated extraction of Zepto's product data allows enterprises to gain actionable insights for decision-making.

From 2020 to 2025, the Indian quick commerce market has seen a 40% growth in product listings, reflecting increased consumer demand and broader category coverage. Insights derived from Zepto Product Dataset for Q-Commerce market empower retailers, app developers, and analysts to understand real-time trends, optimize pricing, and enhance service offerings.

Real-time analytics not only track product launches but also monitor sales performance, enabling businesses to anticipate demand and strategize efficiently. By integrating Zepto Product Dataset for Q-Commerce market into market intelligence workflows, companies can ensure scalability, accuracy, and responsiveness in India's fast-paced quick commerce landscape.

What Does Zepto Product Dataset Reveal About Q-Commerce Trends?

The Zepto Product Dataset for Q-Commerce market provides businesses with an unparalleled view of product availability, category expansion, and emerging trends across India's fast-moving urban markets. By extracting and analyzing data from Zepto, retailers and app developers can understand the dynamics of product supply, consumer preferences, and market demand. Between 2020 and 2025, the dataset shows a remarkable 40% increase in product listings, signaling rapid growth and diversification of India's quick commerce sector.

The dataset covers multiple categories, including fresh produce, packaged foods, personal care, and household essentials. Detailed analysis of category distribution helps businesses prioritize high-demand areas, plan inventory, and forecast demand. For instance, fresh produce maintains consistent growth, representing a vital segment for daily consumer needs, whereas packaged goods account for the majority of listings in terms of volume.

Year Total Products Listed Fresh Produce % Packaged Goods % Personal Care %
2020 25,000 30% 50% 20%
2021 30,500 32% 48% 20%
2022 36,000 33% 47% 20%
2023 42,000 34% 45% 21%
2024 48,000 35% 44% 21%
2025 52,000 36% 43% 21%

Beyond products, the Zepto Quick Commerce Delivery Datasets reveal operational insights such as delivery speed, fulfillment efficiency, and order reliability. Analysis shows that on-time deliveries improved by 20% between 2020 and 2025, reflecting the platform's investment in logistics.

By leveraging Zepto Product Datasets for Market Research, companies can gain insights into new product trends, seasonal demand spikes, and category-specific growth rates. These datasets help businesses identify gaps in offerings, plan promotional campaigns, and make informed investment decisions. Ultimately, the Zepto product dataset serves as a key tool for understanding India's fast-paced Q-commerce ecosystem, enabling organizations to stay ahead of competitors while optimizing operational strategies.

How Can Real-Time Product Dataset Analysis Improve Decision Making?

Real-time access to Zepto's dataset transforms business decision-making by enabling immediate visibility into product launches, pricing changes, and stock levels. Real-Time Product Dataset Analysis from Zepto allows businesses to track thousands of products simultaneously, identify trends, and adjust strategies proactively rather than reactively.

Automated systems process live feeds of product listings and inventory updates, ensuring that businesses have the most current information to optimize operations. Companies using this dataset can determine which products are gaining popularity, which categories require replenishment, and which items are underperforming.

Year New Product Launches Price Updates Stock Alerts
2020 5,000 1,200 800
2021 6,200 1,500 950
2022 7,400 1,800 1,100
2023 8,500 2,100 1,300
2024 9,700 2,500 1,450
2025 10,500 2,800 1,600

Businesses can combine real-time monitoring with predictive analytics to anticipate demand, manage inventory, and reduce stockouts. Using Scrape Zepto Product Data for Analyzing India's Quick Commerce, companies can compare product performance across multiple stores and regions, enabling efficient allocation of resources and better pricing strategies.

Furthermore, real-time analysis supports pricing intelligence. Scrape Zepto Sales Data for Quick Commerce allows businesses to adjust prices dynamically based on competitor activity, promotional campaigns, and market trends. This ensures competitiveness and maximizes profit margins.

By integrating Zepto Product Dataset for Q-Commerce market into business intelligence workflows, enterprises can make informed, data-driven decisions that directly impact operational efficiency, customer satisfaction, and revenue growth. The speed and accuracy of real-time analytics have become critical differentiators in India's competitive Q-commerce landscape.

Leverage real-time Zepto product dataset analysis to make smarter decisions, optimize pricing, forecast demand, and stay ahead in Q-commerce.
Contact Us Today!

What Insights Can Be Derived About India's Q-Commerce Market?

India's quick commerce market is growing at an unprecedented pace, driven by consumer demand for convenience, rapid delivery, and broad product availability. Through India Q-Commerce Market Insights via Zepto Dataset, businesses can track urban and tier-2 city adoption rates, analyze basket size trends, and monitor category performance.

Year Urban Orders % Tier-2 City Orders % Average Basket Value INR
2020 65% 35% 350
2021 67% 33% 360
2022 70% 30% 370
2023 72% 28% 380
2024 74% 26% 390
2025 76% 24% 400

Insights from Quick Commerce Price Monitoring in India highlight how consumers respond to discounts and promotions, helping businesses tailor offers to maximize sales. Additionally, Zepto Grocery Store Dataset provides a detailed view of store-level product availability and fulfillment performance, enabling companies to benchmark against competitors.

Extract Zepto Supermarket Data allows retailers to track competitor pricing, stock-outs, and promotions in real time. This intelligence supports strategic decisions such as regional inventory allocation, targeted promotions, and expansion into underserved markets.

By analyzing historical and real-time data, businesses can anticipate demand trends, optimize logistics, and enhance customer satisfaction. Insights from the Zepto Product Dataset for Q-Commerce market empower decision-makers with actionable intelligence, ensuring competitiveness and operational efficiency in India's rapidly evolving quick commerce sector.

How Does Scraping Zepto Data Streamline Operations?

Automated web scraping is central to operational efficiency in Q-commerce. Quick Commerce & Grocery Data Scraping Services enable businesses to extract structured data from Zepto, covering products, prices, stock levels, and promotions without manual intervention.

Year Products Monitored Data Refresh Rate (hrs) Accuracy %
2020 20,000 24 85%
2021 25,000 12 87%
2022 30,000 8 89%
2023 36,000 6 91%
2024 44,000 4 93%
2025 52,000 2 95%

By utilizing Web Scraping Services, companies can continuously monitor product listings and pricing changes, ensuring accurate data feeds for operational dashboards and business intelligence tools. Automation reduces human error, ensures timely updates, and enables seamless integration with inventory and planning systems.

For example, real-time extraction of Zepto data allows operations teams to track low-stock products, anticipate replenishment needs, and maintain optimal inventory. Retailers can proactively manage logistics, plan warehouse allocation, and optimize delivery routes based on product demand patterns identified through the dataset.

Furthermore, Zepto Product Dataset for Q-Commerce market supports cross-store benchmarking, allowing companies to evaluate competitor performance, pricing strategies, and product availability. This operational insight strengthens supply chain efficiency, improves responsiveness, and ultimately enhances customer satisfaction by reducing stock-outs and missed opportunities.

How Can Zepto Data Enhance Pricing Strategies?

Pricing is a critical factor in Q-commerce, where consumers are price-sensitive and promotions drive loyalty. Access to Zepto Grocery Data Scraping API allows businesses to track real-time prices, monitor competitor adjustments, and optimize pricing dynamically.

Year Avg. Discount % Price Updates per Day Competitive Price Accuracy %
2020 5% 50 80%
2021 6% 70 82%
2022 7% 90 85%
2023 8% 110 87%
2024 9% 130 90%
2025 10% 150 92%

By integrating Web Scraping API Services, companies can automatically adjust prices in response to competitor movements, demand fluctuations, and seasonal trends. This ensures profitability while maintaining competitiveness and customer satisfaction.

Pricing analysis also supports promotional campaigns. Businesses can identify which categories benefit most from discounts and adjust offers to maximize sales. Insights from Scrape Zepto Product Data for Analyzing India's Quick Commerce help optimize revenue, reduce margin erosion, and ensure strategic allocation of promotions across categories and regions.

Ultimately, leveraging Zepto data for pricing intelligence enables businesses to adopt a proactive approach, responding to market changes faster than competitors and increasing operational efficiency in India's high-growth Q-commerce market.

Use Zepto data to optimize pricing strategies, monitor competitors, implement dynamic discounts, and maximize revenue in India's quick commerce market.
Contact Us Today!

How Does Zepto Dataset Support Market Research?

The Zepto Product Datasets for Market Research provide rich insights for understanding consumer preferences, category growth, and market trends. Businesses can analyze product launches, seasonal variations, and category-specific demand to inform marketing and operational strategies.

Year Research Projects Data Points Collected Insights Delivered
2020 10 50,000 8
2021 14 75,000 12
2022 20 100,000 15
2023 26 125,000 18
2024 32 150,000 21
2025 40 180,000 25

By scraping and analyzing Zepto data, businesses can identify trending products, measure category performance, and anticipate market shifts. Scrape Zepto Sales Data for Quick Commerce supports demand forecasting, competitor benchmarking, and pricing optimization.

In addition, insights from Quick Commerce Price Monitoring in India help identify price-sensitive categories, peak buying times, and regional preferences. This allows businesses to optimize promotions, stock allocation, and expansion strategies.

With structured datasets and actionable analytics, Zepto data enables informed decisions, reduces market risk, and improves operational efficiency. Companies can leverage these insights to maintain competitiveness, enhance customer satisfaction, and drive growth in India's evolving Q-commerce sector.

How Can Real-Time Analytics Drive Competitive Advantage?

Real-Time Product Dataset Analysis from Zepto provides immediate visibility into stock availability, pricing changes, and new product launches. Businesses leveraging this data gain a competitive edge by acting faster than competitors.

Year Real-Time Alerts Sent Data Latency (mins) Accuracy %
2020 100 60 85%
2021 150 50 87%
2022 200 40 89%
2023 250 30 91%
2024 300 20 93%
2025 350 10 95%

By integrating Zepto Quick Commerce Delivery Datasets and automated monitoring, companies can anticipate demand, adjust pricing dynamically, and optimize inventory in real time. Insights derived from Zepto data also guide product launches, marketing campaigns, and regional expansion strategies.

Real-time analytics enables operational agility, ensuring businesses can respond quickly to competitors' pricing and product adjustments. Leveraging Zepto Product Dataset for Q-Commerce market, organizations gain a data-driven advantage, enhancing revenue, customer satisfaction, and market share.

How Actowiz Solutions Can Help?

Actowiz Solutions offers end-to-end Quick Commerce & Grocery Data Scraping Services that empower businesses to extract, analyze, and act on Zepto datasets efficiently. Using the Zepto Product Dataset for Q-Commerce market, Actowiz enables real-time tracking of product listings, prices, and trends to support strategic decision-making.

Our team leverages Web Scraping Services and Web Scraping API Services to automate data extraction and deliver structured datasets for immediate analysis. Retailers, app developers, and analysts can optimize pricing, inventory, and product recommendations while gaining actionable insights into consumer behavior.

By integrating Zepto data into business workflows, Actowiz Solutions ensures accuracy, scalability, and compliance. Companies can monitor competitive activity, anticipate demand changes, and enhance operational efficiency across India's fast-evolving Q-commerce landscape.

Conclusion

The Zepto Product Dataset for Q-Commerce market reveals the dynamic growth of India’s quick commerce sector, with a 40% increase in product listings from 2020 to 2025. Real-time insights from Zepto datasets enable businesses to track trends, monitor pricing, and optimize inventory efficiently.

Actowiz Solutions’ expertise in Scrape Zepto Product Data for Analyzing India’s Quick Commerce empowers companies to leverage these insights for competitive advantage. Automated web scraping pipelines ensure continuous, accurate, and scalable data delivery, supporting strategic decisions and improving customer experience.

By integrating Zepto Quick Commerce Delivery Datasets, price monitoring, and real-time analytics into operational workflows, businesses can optimize product offerings, enhance market responsiveness, and drive growth. Actowiz Solutions transforms raw Zepto data into actionable intelligence, helping brands remain competitive and data-driven in India’s rapidly evolving quick commerce market. Contact us for more details!

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
(
    [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
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                )

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                    [geoname_id] => 6255149
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                            [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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            [country] => Array
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                    [iso_code] => US
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                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [location] => Array
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            [postal] => Array
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            [registered_country] => Array
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [subdivisions] => Array
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                    [0] => Array
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                            [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] => 216.73.216.165
                    [prefix_len] => 22
                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [0] => code
                    [1] => geonameId
                    [2] => names
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        )

    [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
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                    [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] => 216.73.216.165
                    [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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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

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

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Nov 04, 2025

Real-Time Price Scraping to Track Black Friday Deals on Amazon, Walmart & Target

Discover how Real-Time Price Scraping to Track Black Friday Deals on Amazon, Walmart & Target helps shoppers monitor discounts, compare prices, and maximize savings.

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Scraping Zepto Grocery Data for Price Comparison to Power Real-Time Meal Planning Insights

Discover how Scraping Zepto Grocery Data for Price Comparison helped a meal planning app automate real-time pricing insights, optimize budgets, and enhance user experience.

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Adidas Price Discounts Analysis 2025 - Global Black Friday Trends and Consumer Insights from Data Scraping

Explore the Adidas Price Discounts Analysis 2025, uncovering global Black Friday trends, price fluctuations, and consumer insights through advanced data scraping techniques.

Nov 04, 2025

Real-Time Price Scraping to Track Black Friday Deals on Amazon, Walmart & Target

Discover how Real-Time Price Scraping to Track Black Friday Deals on Amazon, Walmart & Target helps shoppers monitor discounts, compare prices, and maximize savings.

Nov 03, 2025

How Zepto Product Dataset for Q-Commerce Market Reveals Trends and 40% Increase in Product Listings Across India?

Discover how Zepto Product Dataset for Q-Commerce Market reveals trends and a 40% increase in product listings, highlighting India’s evolving quick commerce landscape.

Nov 02, 2025

Real-Time Grocery Price Trends - Scrape Black Friday Grocery Deals Data from Blinkit, Zepto & BigBasket – Monitor 2,000+ Deals Instantly

Get real-time insights on Black Friday grocery deals by using Scrape Black Friday Grocery Deals Data from Blinkit, Zepto & BigBasket to monitor 2,000+ deals instantly.

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Scraping Zepto Grocery Data for Price Comparison to Power Real-Time Meal Planning Insights

Discover how Scraping Zepto Grocery Data for Price Comparison helped a meal planning app automate real-time pricing insights, optimize budgets, and enhance user experience.

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How Price Intelligence Dashboard for Grocery Price Tracking Helped Retailers Optimize Pricing Across Blinkit, BigBasket, and Zepto

Discover how the Price Intelligence Dashboard for Grocery Price Tracking helped retailers optimize pricing, track live prices, and boost profitability across Blinkit, BigBasket, and Zepto.

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Scraping Macy’s & Kohl’s for Retail Competitiveness to Benchmark Market Performance and Trends

Explore how Scraping Macy’s & Kohl’s for Retail Competitiveness provides actionable insights to benchmark pricing, promotions, and market trends effectively.

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Adidas Price Discounts Analysis 2025 - Global Black Friday Trends and Consumer Insights from Data Scraping

Explore the Adidas Price Discounts Analysis 2025, uncovering global Black Friday trends, price fluctuations, and consumer insights through advanced data scraping techniques.

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Real-Time API Scraping from Myntra, Ajio & Nykaa to Track Fashion Trends and Pricing

Discover how Real-Time API Scraping from Myntra, Ajio & Nykaa provides actionable insights to track fashion trends, pricing, and market intelligence effectively.

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Real-Time Electronics Price Tracking for Black Friday - Insights from 2025 Sales Trends and Consumer Behavior

Discover Real-Time Electronics Price Tracking for Black Friday 2025, revealing sales trends, discounts, and consumer behavior insights for smarter retail decisions.

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