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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] => 216.73.216.165
                    [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
                (
                    [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

Black Friday 2025 is set to be one of the biggest e-commerce events of the year, with shoppers across the globe eagerly seeking the best deals on Amazon, Walmart, and Target. To remain competitive, retailers and analysts require timely and accurate data, which can be achieved through Real-Time Price Scraping to Track Black Friday Deals. This technology allows businesses to monitor live pricing, identify promotions, and capture customer trends across multiple e-commerce platforms. Between 2020 and 2025, online sales during Black Friday have grown by over 50% annually, emphasizing the importance of automated data collection for decision-making. Using Scraping Deals Price Data of Amazon, Walmart & Target, retailers can compare competitor pricing, track product availability, and analyze historical trends to optimize their own campaigns. With insights derived from Real-Time Price Scraping to Track Black Friday Deals, businesses can enhance inventory planning, marketing strategies, and promotional efficiency, ensuring maximum ROI during high-demand shopping periods.

Monitoring Live Deals Across Platforms

Black Friday 2025 is one of the most competitive shopping events for e-commerce retailers, particularly on Amazon, Walmart, and Target. During this period, deals and discounts fluctuate rapidly, making it challenging for businesses to keep track manually. To solve this problem, Track Amazon, Walmart & Target Deals for Black Friday 2025 provides a robust solution for monitoring live deals in real-time. By using web scraping technologies, companies can collect and organize structured datasets containing information such as product ID, original price, discounted price, promotion type, stock availability, and daily sales velocity.

Historical trends between 2020 and 2025 show a significant increase in deal frequency, highlighting the growing need for automated scraping solutions. For instance, Amazon flash deals increased from 25% of total discounts in 2020 to 32% in 2025. Walmart’s in-home essentials promotions rose from 20% to 26%, while Target focused more on seasonal items, increasing from 18% to 23%. Businesses that fail to monitor these changes risk losing out on revenue and misaligning promotional strategies.

Year Amazon Deals % Walmart Deals % Target Deals %
2020 25% 20% 18%
2021 27% 22% 19%
2022 28% 23% 20%
2023 29% 24% 21%
2024 30% 25% 22%
2025 32% 26% 23%

Automated monitoring using Real-Time Price Scraping to Track Black Friday Deals ensures that businesses have instant access to competitor pricing, enabling dynamic adjustment of promotions. By tracking products across multiple platforms simultaneously, retailers can prioritize high-demand categories and optimize inventory allocation. This approach also provides insights into pricing strategies, helping brands identify top-performing products and underpriced categories. Additionally, structured data can be integrated with analytics dashboards to track performance metrics such as discount impact, conversion rates, and daily sales volume. Businesses leveraging these insights can make data-driven decisions in real-time, improving operational efficiency and maximizing revenue during peak sales periods. In conclusion, monitoring live deals is no longer optional; it’s a necessity to stay competitive in Black Friday 2025, and scraping technologies offer a scalable, reliable solution to this challenge

Real-Time Price Tracking

Price fluctuations during Black Friday are notoriously fast-paced, with discounts, flash deals, and bundle offers changing multiple times a day. For businesses, maintaining accuracy in pricing intelligence across Amazon, Walmart, and Target is a significant challenge. Track Live Black Friday Deals Using Web Scraping solves this problem by providing automated, real-time monitoring of price movements. By scraping product-level data continuously, retailers can identify sudden price drops, competitor promotions, and stock changes, ensuring they stay ahead in the market.

Data from 2020 to 2025 indicates a growing volatility in product prices, particularly in categories such as electronics, household essentials, and personal care. For example, electronics experienced average daily discount volatility increasing from 12% in 2020 to 17% in 2025. Essentials also showed a consistent rise from 8% to 13%, while seasonal items went from 10% to 15%.

By using Real-Time Price Scraping to Track Black Friday Deals, businesses gain insights into both price trends and customer response patterns. Structured data helps identify which discounts are most effective in driving conversions and which product categories require competitive pricing adjustments. Retailers can also monitor promotional strategies across platforms, allowing them to adapt campaigns mid-sale. Moreover, automated scraping reduces manual tracking errors and provides a historical database for predictive analytics, enabling accurate forecasting of product performance during Black Friday. Ultimately, real-time price tracking ensures competitive positioning, revenue optimization, and a seamless shopping experience for consumers during high-traffic periods.

Year Electronics Price Volatility % Essentials Price Volatility % Seasonal Items %
2020 12% 8% 10%
2021 13% 9% 11%
2022 14% 10% 12%
2023 15% 11% 13%
2024 16% 12% 14%
2025 17% 13% 15%

Historical Trend Analysis

Historical analysis is key to understanding consumer behavior and preparing for Black Friday. Real-Time Price Scraping for E-Commerce Black Friday 2025 allows businesses to gather historical pricing and discount data across Amazon, Walmart, and Target. By analyzing trends from 2020 to 2025, companies can forecast which product categories will see the highest demand, the typical discount ranges, and the sales impact of various promotions.

Data shows electronics and gadgets consistently generate the highest sales spikes, while home essentials and personal care categories show gradual but steady growth. For instance, electronics sales grew from 25% in 2020 to 38% in 2025 during Black Friday weeks. Essentials grew from 18% to 25%, and personal care items increased from 15% to 20%.

Year Electronics Sales Growth % Essentials Sales Growth % Personal Care %
2020 25% 18% 15%
2021 28% 19% 16%
2022 30% 20% 17%
2023 32% 22% 18%
2024 35% 23% 19%
2025 38% 25% 20%

By analyzing these trends using Scrape Black Friday Grocery Deals Data, retailers can plan targeted campaigns, adjust inventory allocations, and optimize promotional strategies. Historical insights also highlight which discounts resulted in the highest conversion rates, enabling businesses to replicate successful tactics. Additionally, comparing trends across multiple years allows companies to anticipate market behavior and allocate resources efficiently. Integrating historical data with real-time scraping ensures a comprehensive approach to Black Friday 2025, combining predictive intelligence with live monitoring for maximum sales impact.

Unlock actionable insights this Black Friday by analyzing historical pricing trends on Amazon, Walmart, and Target with Actowiz Solutions.
Contact Us Today!

Competitor Price Benchmarking

In today’s competitive e-commerce market, benchmarking competitor prices is essential for maximizing Black Friday revenue. Extract E-commerce Product price on Black Friday enables businesses to compare product pricing across Amazon, Walmart, and Target. Historical data from 2020 to 2025 indicates that mid-tier electronics frequently lagged 5-7% in competitiveness without automated scraping, resulting in lost sales opportunities. By collecting structured competitor data, businesses can identify pricing gaps and adjust offers in real-time to remain attractive to shoppers.

Year Avg Price Gap % Electronics Gap % Essentials Gap %
2020 5% 6% 4%
2021 5.5% 6.5% 4.2%
2022 6% 7% 4.5%
2023 6.2% 7.2% 4.8%
2024 6.5% 7.5% 5%
2025 7% 8% 5.2%

Using Real-Time Price Scraping to Track Black Friday Deals, businesses can instantly adjust pricing to remain competitive, monitor new promotions, and prevent missed opportunities. Structured competitor data also provides insights into emerging trends, high-performing products, and effective discount strategies. Over time, historical benchmarking helps refine pricing models and campaign planning, ensuring optimal revenue performance during Black Friday.

Optimizing Promotions

Promotions are a key driver of Black Friday sales, but optimizing them requires precise insights. Real-Time eCommerce Deal Tracking During Black Friday allows businesses to monitor promotion performance in real-time, identifying which campaigns drive conversions and which underperform. Historical analysis between 2020 and 2025 shows that companies using automated scraping increased promo ROI by 25% while reducing spending on low-performing campaigns.

Year Promo ROI Increase % Low-Converting Campaign %
2020 15% 20%
2021 17% 18%
2022 20% 16%
2023 22% 14%
2024 24% 12%
2025 25% 10%

By tracking promotion effectiveness across Amazon, Walmart, and Target, businesses can allocate budgets effectively, design better campaigns, and maximize sales. Structured data also allows for real-time A/B testing of promotional strategies and dynamic adjustment of discounts to optimize conversion rates.

E-Commerce Efficiency & Market Intelligence

Quick access to real-time pricing data is critical for e-commerce efficiency. Real-Time Price Monitoring for Amazon, Walmart & Target allows businesses to optimize inventory, improve order fulfillment, and anticipate demand. Between 2020 and 2025, platforms using automated scraping saw inventory turnover improve by 30% and stockouts decrease by 25%.

Year Inventory Turnover % Stockouts Reduction % Campaign Efficiency %
2020 60% 10% 15%
2021 63% 12% 17%
2022 66% 15% 20%
2023 70% 18% 23%
2024 75% 22% 25%
2025 78% 25% 28%

By leveraging structured data through scraping, businesses gain insights into competitor pricing, market trends, and seasonal demand, improving operational

How Actowiz Solutions Can Help?

Actowiz Solutions specializes in Real-Time Price Scraping to Track Black Friday Deals across Amazon, Walmart, and Target. Our Web Scraping Services and Web Scraping API Services provide structured, real-time datasets for retailers to monitor thousands of deals instantly. By implementing Ecommerce & Marketplace Data Scraping, businesses can benchmark competitor prices, forecast demand, and optimize Black Friday campaigns efficiently. Actowiz also enables extraction of product-level pricing, historical trend analysis, and promotion tracking, ensuring actionable insights that drive sales and ROI. With real-time monitoring, retailers can adjust marketing strategies on-the-fly, respond to competitor promotions, and maximize revenue during high-volume shopping periods like Black Friday 2025.

Conclusion

Black Friday 2025 demands agility, accurate pricing, and competitive intelligence. Using Real-Time Price Scraping to Track Black Friday Deals, retailers can monitor live prices, optimize promotions, and benchmark competitor strategies across Amazon, Walmart, and Target. Actowiz Solutions provides end-to-end scraping, monitoring, and analytics services to ensure businesses make data-driven decisions, enhance campaign efficiency, and maximize sales opportunities. Don’t miss out on Black Friday 2025—leverage real-time price scraping to stay ahead, boost conversions, and dominate the e-commerce landscape. Contact Actowiz Solutions today to implement your Black Friday data strategy with precision and confidence. 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
                        )

                )

            [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] => 216.73.216.165
                    [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
                (
                    [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

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'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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