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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
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            [continent] => Array
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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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                    [longitude] => -83.0061
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                    [time_zone] => America/New_York
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

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                                    [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] => 216.73.216.116
                    [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
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                    [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
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                    [0] => en
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            [validAttributes:protected] => Array
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                    [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
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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        )

    [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] => 216.73.216.116
                    [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 modern grocery industry is increasingly data-driven, and effective inventory management has become critical for retailers like Kroger and BigBasket. With Kroger holding an estimated $7B inventory value and BigBasket maintaining 10,000+ SKUs, real-time monitoring of stock levels is essential to prevent stockouts, optimize supply chains, and improve customer satisfaction. Businesses now leverage automated solutions such as Kroger Grocery Data Scraping to extract actionable insights on product availability, category-level stock, and seasonal trends.

The Kroger & BigBasket Inventory Monitoring API enables retailers, wholesalers, and analysts to access structured inventory data through automated feeds. With accurate data collection, organizations can forecast demand, reduce overstocking, and identify high-turnover items. By tracking inventory in real time, teams gain visibility into SKU-level performance, compare trends between physical stores and online platforms, and optimize fulfillment strategies.

This blog explores Kroger & BigBasket Inventory Monitoring API use cases, highlights key trends from 2020–2025, and illustrates how advanced APIs and data scraping solutions help businesses streamline operations. We will also discuss six major problem-solving areas, followed by insights on how Actowiz Solutions provides end-to-end inventory monitoring services that empower smarter decision-making in grocery and supermarket sectors.

Inventory Value Trends (2020–2025)

Inventory valuation is a fundamental metric for retail and supply chain efficiency. Over the 2020–2025 period, Kroger’s inventory value has steadily risen from $5.8B in 2020 to $7B in 2025, driven by expansion across grocery, FMCG, and fresh produce categories. BigBasket, India’s leading online grocery platform, has scaled its SKU count from 7,000 to 10,000, reflecting a broader assortment to meet increasing consumer demand. To track such growth, organizations leverage the Kroger & BigBasket Inventory Monitoring API, providing real-time visibility into stock levels, SKU performance, and category-specific inventory trends.

Year Kroger Inventory ($B) Kroger SKUs Avg Turnover Rate
2020 5.8 7,000 35×
2021 6.1 7,500 36×
2022 6.3 8,000 37×
2023 6.5 8,500 38×
2024 6.8 9,200 40×
2025 7.0 10,000 45×

Maintaining accurate inventory data is crucial for preventing overstock, minimizing spoilage, and ensuring timely replenishment. Retailers using Kroger Grocery Data Scraping can extract historical stock data and analyze long-term trends, enabling demand forecasting and procurement optimization.

The data reveals that Kroger has invested heavily in inventory management systems, integrating warehouses and stores across the US, while BigBasket has focused on automation and real-time tracking to support its e-commerce-first model. By monitoring these KPIs over time, analysts can identify slow-moving products, determine high-turnover SKUs, and optimize category allocations.

Furthermore, the rise in SKU diversity and inventory value emphasizes the need for BigBasket Data Scraping API to extract granular insights into product availability, category-level stock, and regional distribution patterns. Advanced analytics also help identify seasonal demand trends, such as spikes in staples and beverages during festive seasons, providing actionable intelligence to reduce stockouts and lost sales.

Real-time monitoring of inventory, combined with historical trend analysis, allows businesses to balance procurement with expected demand, optimize warehouse utilization, and improve overall supply chain efficiency. Through predictive analytics, retailers can plan promotions, allocate resources effectively, and maintain competitive pricing strategies.

SKU-Level Analysis & Stock Management

SKU-level management is critical to maximizing sales, reducing stockouts, and improving customer satisfaction. Kroger manages tens of thousands of SKUs, spanning grocery, beverages, household items, and personal care, while BigBasket tracks thousands of products across FMCG and perishables. Effective BigBasket stock data extraction & monitoring allows businesses to capture SKU-level details, monitor inventory turnover, and prioritize high-demand products.

SKU Category Kroger Count BigBasket Count Stock-Out % (2025)
Fresh Produce 12,000 2,500 2.5%
Packaged Foods 18,000 4,000 1.8%
Beverages 8,500 1,500 1.5%
Household 6,500 1,000 2.2%
Personal Care 5,000 1,000 1.2%

SKU-level monitoring allows retailers to reduce overstock, manage warehouse space efficiently, and plan replenishment cycles accurately. By leveraging the Kroger inventory tracking API, organizations can track product availability in real time, identify products nearing stock depletion, and generate automated restock alerts.

Tracking SKU trends over 2020–2025 shows that fresh produce and beverages require frequent monitoring due to perishability and high turnover. Packaged foods and household items, while more stable, require careful trend analysis to anticipate seasonal spikes. By integrating data from online and physical stores, retailers can synchronize inventory across channels and reduce mismatched availability.

Advanced analytics enable SKU ranking based on velocity, profitability, and demand patterns. For instance, high-turnover items like packaged beverages may require multi-location warehousing, while low-turnover household items can be consolidated. These insights help retailers allocate budget effectively, reduce holding costs, and optimize cash flow.

With the combination of BigBasket product inventory monitoring and historical SKU-level data, retailers can also identify emerging trends, such as increasing demand for organic produce or private-label goods, allowing for proactive inventory adjustments. The API facilitates automated extraction of SKU metrics, ensuring accuracy and timeliness without manual intervention, which is essential for scaling operations efficiently.

Optimize stock, track SKUs, prevent stockouts, and boost sales with real-time insights using Kroger & BigBasket Inventory API.
Contact Us Today!

Category-Level Trends & Sales Insights

Category-level analysis provides actionable insights for inventory planning, pricing strategy, and sales optimization. Over 2020–2025, fresh produce, health foods, and packaged beverages experienced significant growth. Using Grocery & Supermarket Data Scraping, businesses can extract inventory, sales, and turnover data across categories to identify trends and adjust stock allocation accordingly.

Category Big Basket 2020 Revenue (INR) Big Basket 2025 Revenue (INR) CAGR %
Fresh Produce 12,300 24,600 15%
Packaged Foods 28,700 41,000 8%
Beverages 9,840 18,040 12%
Household 7,380 12,300 11%
Personal Care 5,740 9,840 12%

Monitoring category-level data allows businesses to anticipate demand surges, manage promotions effectively, and optimize inventory distribution. For example, demand spikes in fresh produce during festive seasons require accurate forecasting and timely replenishment to prevent stockouts.

Kroger and BigBasket can integrate historical and real-time sales data to optimize category assortment, ensuring that high-demand items remain in stock while reducing excess inventory in low-performing categories. Retailers leveraging Kroger & BigBasket Inventory Data via API gain real-time access to category-level metrics, enabling dynamic decision-making and efficient resource allocation.

Category insights also inform pricing and promotional strategies. By analyzing inventory and sales trends, retailers can identify slow-moving products, plan discounts, and optimize profit margins. Real-time monitoring ensures that data-driven decisions are timely, minimizing the risk of lost revenue due to stockouts or overstock.

The combination of API-based extraction and category analytics empowers retailers to create predictive models for sales and inventory. By integrating SKU-level and category-level insights, organizations can forecast demand more accurately, streamline supply chain operations, and improve overall profitability.

Real-Time Inventory Monitoring

Real-time inventory tracking is critical for operational efficiency, customer satisfaction, and supply chain optimization. Real-Time Inventory Tracking allows Kroger and BigBasket to monitor stock levels continuously, generate low-stock alerts, and ensure that products are replenished promptly.

Metric Kroger 2020 Kroger 2025 BigBasket 2020 BigBasket 2025
Avg Stock Accuracy % 92% 98% 88% 97%
Real-Time Updates per Day 5,000 20,000 2,000 15,000
Stockouts 3% 1.5% 5% 2%

Real-time monitoring reduces lost sales and improves customer trust. By leveraging Extract Product availability Data for Kroger and BigBasket, operators can automate replenishment workflows, monitor high-demand items, and adjust inventory distribution across warehouses and stores.

During 2020–2025, Kroger implemented advanced warehouse management systems with API connectivity, enabling multi-location inventory synchronization and predictive replenishment. BigBasket utilized real-time tracking to optimize last-mile delivery, reduce stockouts, and enhance fulfillment efficiency.

Integration of real-time inventory feeds with e-commerce platforms ensures accurate stock visibility for customers, preventing orders for out-of-stock items. Advanced dashboards allow operators to analyze inventory trends, identify anomalies, and make proactive operational decisions.

By combining SKU-level insights, category trends, and real-time data, retailers can optimize stock allocations, reduce holding costs, and improve overall supply chain performance. Real-time monitoring is especially critical during peak seasons, promotions, or sudden demand surges, ensuring uninterrupted service and revenue continuity.

Integration & API Scalability

Scalable API integration is vital for seamless inventory management. Ready Made Data API solutions allow retailers to extract Kroger and BigBasket inventory data without building custom pipelines. The Kroger & BigBasket Inventory Monitoring API supports high-volume queries, batch extraction, and multi-store synchronization, providing structured data ready for analysis.

Feature Kroger BigBasket
Max SKU Extraction per API Call 10,000 8,000
API Response Time (ms) 200 250
Update Frequency 15 mins 30 mins
Integration ERP, BI ERP, BI, eCommerce

APIs enable integration with ERP systems, analytics platforms, and dashboards, allowing real-time inventory tracking, reporting, and forecasting. Historical data from 2020–2025 allows trend analysis, predictive replenishment, and optimization of warehouse allocations.

Retailers can combine Real-Time Stock Tracking for Kroger & BigBasket with analytics to improve procurement decisions, reduce wastage, and increase operational efficiency. Scalable APIs ensure that growing SKU counts, expanding product categories, and multiple store locations are effectively monitored without manual intervention.

Advanced API integration also facilitates multi-channel synchronization, ensuring stock availability in both online and offline channels. This reduces stockouts, improves customer satisfaction, and enhances supply chain agility. By automating data extraction and monitoring workflows, retailers can focus on strategic decision-making rather than manual inventory checks.

Seamlessly integrate Kroger & BigBasket Inventory Monitoring API, scale effortlessly, and ensure real-time stock visibility across all channels.
Contact Us Today!

Forecasting & Decision Support

Accurate forecasting relies on historical and real-time inventory data. By analyzing 2020–2025 trends, retailers can predict SKU demand, optimize replenishment, and reduce waste. Kroger & BigBasket Inventory Monitoring API combined with Real-Time Stock Tracking for Kroger & BigBasket enables predictive modeling, helping businesses anticipate seasonal peaks, high-demand products, and potential stockouts.

Year Kroger Forecast Accuracy % BigBasket Forecast Accuracy %
2020 85% 80%
2021 87% 82%
2022 88% 84%
2023 90% 86%
2024 92% 90%
2025 95% 92%

Data-driven forecasting allows retailers to optimize procurement schedules, allocate inventory efficiently, and improve service levels. Combining SKU-level monitoring, category insights, and real-time updates provides a comprehensive view of stock health and performance.

Decision-support systems integrated with APIs enable scenario analysis, allowing businesses to simulate demand spikes, supply delays, and seasonal trends. By leveraging Kroger & BigBasket Inventory Data via API and predictive analytics, organizations can reduce holding costs, improve ROI, and enhance operational efficiency.

Advanced analytics also support pricing optimization, promotional planning, and replenishment automation. With insights derived from API-based monitoring, businesses can align stock levels with customer demand, minimize lost sales, and maintain a competitive advantage in the grocery sector.

How Actowiz Solutions Can Help?

Actowiz Solutions provides end-to-end solutions for Kroger & BigBasket Inventory Monitoring API, delivering high-quality, structured, and real-time data for grocery and supermarket businesses. Our services include Kroger & BigBasket Inventory Data via API, BigBasket stock data extraction & monitoring, and SKU-level tracking with actionable insights.

We enable seamless integration into ERP, BI, and analytics systems, allowing retailers to automate stock alerts, optimize replenishment, and forecast demand accurately. Our expertise in Kroger inventory tracking API, BigBasket product inventory monitoring, and historical trend analysis empowers businesses to enhance operational efficiency, reduce waste, and maximize profitability.

Conclusion

Accurate inventory monitoring is the backbone of modern grocery operations. The Kroger & BigBasket Inventory Monitoring API offers unparalleled visibility into stock levels, SKU performance, and category trends. From $7B Kroger inventory to 10,000+ BigBasket SKUs, businesses that leverage APIs, scraping, and real-time tracking gain a competitive advantage.

Actowiz Solutions helps retailers extract, monitor, and analyze inventory data through advanced APIs, Real-Time Inventory Tracking, and predictive analytics. By adopting these solutions, organizations can streamline procurement, minimize stockouts, and improve customer satisfaction.

Ready to optimize your grocery inventory? Contact Actowiz Solutions today and unlock real-time insights for smarter stocking and efficient operations with Kroger & BigBasket Inventory Monitoring API. 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
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                (
                    [geoname_id] => 4509177
                    [names] => Array
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                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                )

            [continent] => Array
                (
                    [code] => NA
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [zh-CN] => 美国
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            [postal] => Array
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            [registered_country] => Array
                (
                    [geoname_id] => 6252001
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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
                        )

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            [subdivisions] => 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] => 216.73.216.116
                    [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
                        )

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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
                (
                    [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
                (
                    [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] => 216.73.216.116
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
                    [16] => mobileNetworkCode
                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
                    [21] => userType
                )

        )

    [city:protected] => GeoIp2\Record\City Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                (
                    [0] => en
                )

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

        )

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

        )

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

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

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
                        )

                )

        )

)
 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

Start Your Project

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Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

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

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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

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

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

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

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

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

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

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

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

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

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

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

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

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

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

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

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

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
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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
Product Image
1 min

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

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

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

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

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

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

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

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

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

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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

All
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Case Studies
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Oct 07, 2025

Menu Data Scraping for Major Food Chains - Track 1,000+ Menu Changes Across USA, UK & Canada

Track 1,000+ menu changes across USA, UK & Canada with Menu Data Scraping for Major Food Chains, gaining real-time insights, competitor intelligence, and revenue growth.

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Hotel Revenue Growth via Data Scraping for a Leading UAE Hotel Chain

Discover how a leading UAE hotel chain achieved significant hotel revenue growth via data scraping, enabling dynamic pricing and real-time market insights.

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Scrape Historical Flight Fares from Skyscanner and Expedia UK for Data Analysis

Learn how to scrape historical flight fares from Skyscanner and Expedia UK to analyze pricing trends, patterns, and travel cost insights.

Oct 07, 2025

Menu Data Scraping for Major Food Chains - Track 1,000+ Menu Changes Across USA, UK & Canada

Track 1,000+ menu changes across USA, UK & Canada with Menu Data Scraping for Major Food Chains, gaining real-time insights, competitor intelligence, and revenue growth.

Oct 06, 2025

Boost Revenue by 25% with Flight Fare Scraping for Competitive Travel Insights on Skyscanner & British Airways in the UK

Discover how flight fare scraping for competitive travel insights on Skyscanner and British Airways in the UK helped businesses boost revenue by 25% and optimize pricing.

Oct 05, 2025

Kroger & BigBasket Inventory Monitoring API - $7B Kroger Inventory Value, BigBasket Holds 10,000 SKUs

Track inventory in real time with Kroger & BigBasket Inventory Monitoring API — $7B Kroger stock value, BigBasket’s 10,000+ SKUs optimized.

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Hotel Revenue Growth via Data Scraping for a Leading UAE Hotel Chain

Discover how a leading UAE hotel chain achieved significant hotel revenue growth via data scraping, enabling dynamic pricing and real-time market insights.

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Tracking Promotions and Seasonal Discounts on Deliveroo & Just Eat in the UK

Discover how tracking promotions and seasonal discounts on Deliveroo and Just Eat in the UK helped businesses gain insights, optimize pricing, and boost sales performance.

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Rare Whiskey Inventory and Price Tracking in USA – Collector vs Retailer Pricing Insights with Spirit Radar

Discover how Rare Whiskey Inventory and Price Tracking in USA with Spirit Radar reveals collector vs retailer pricing trends and insights.

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Scrape Historical Flight Fares from Skyscanner and Expedia UK for Data Analysis

Learn how to scrape historical flight fares from Skyscanner and Expedia UK to analyze pricing trends, patterns, and travel cost insights.

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Zillow & Realtor.com Pre-Construction Data Scraping USA - ROI Analysis and Investment Opportunities

Zillow & Realtor.com Pre-Construction Data Scraping USA, analyzing ROI and uncovering top investment opportunities in the US real estate market.

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Tracking ASOS Sales Trends in the UK Using Automated Data Scraping for Retail Insights

Tracking ASOS Sales Trends in the UK using automated data scraping to uncover retail insights, consumer behavior & growth patterns.