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

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                            [fr] => Amérique du Nord
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [longitude] => -83.0061
                    [metro_code] => 535
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            [postal] => Array
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                    [code] => 43215
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                    [iso_code] => US
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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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                                    [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
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                    [names] => 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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                            [ru] => США
                            [zh-CN] => 美国
                        )

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            [validAttributes:protected] => Array
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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                )

            [validAttributes:protected] => Array
                (
                    [0] => queriesRemaining
                )

        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

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            [validAttributes:protected] => Array
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.213
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
                (
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                    [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
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                    [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
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                )

            [validAttributes:protected] => Array
                (
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                    [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
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                                    [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
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                            [3] => names
                        )

                )

        )

)

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

Start Your Project

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  • Track Black Friday Discount for Zara, Nike & SHEIN

Optimizing Retail Promotions - Track Black Friday Discount for Zara, Nike & SHEIN in the U.S. Market

Explore how to Track Black Friday Discount for Zara, Nike & SHEIN in the U.S., optimizing retail promotions and pricing strategies effectively.

Download Case Studies

GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
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                            [en] => Columbus
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                            [fr] => Columbus
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                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                            [ru] => США
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                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

                        )

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            [traits] => Array
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                )

        )

    [continent:protected] => GeoIp2\Record\Continent Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
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                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
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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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            [validAttributes:protected] => Array
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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
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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        )

    [locales:protected] => Array
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    [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
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                    [geoname_id] => 6252001
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                            [fr] => États Unis
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                            [ru] => США
                            [zh-CN] => 美国
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        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [3] => isoCode
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
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                (
                    [ip_address] => 216.73.216.213
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                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [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
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            [validAttributes:protected] => Array
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        )

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

            [validAttributes:protected] => Array
                (
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                    [1] => accuracyRadius
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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
                                )

                        )

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

                    [validAttributes:protected] => Array
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                            [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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Navratri Mega Sale Price Tracking

Introduction

The Black Friday season is a critical period for online retail, with significant sales and promotional activity across major brands. Accurate tracking of discounts enables businesses to optimize pricing, promotions, and inventory strategies. Actowiz Solutions demonstrated the potential of Track Black Friday discount for Zara, Nike & SHEIN by leveraging Ecommerce Data Scraping to collect real-time pricing and promotional data across multiple U.S. online stores. This approach allowed for monitoring discount trends, evaluating competitor strategies, and identifying the most attractive offers. By automating data collection, businesses can gain timely insights, streamline decision-making, and enhance revenue during high-demand sales events.

About the Client

This case study focuses on a demonstration project targeting U.S. e-commerce stores of leading retail brands: Zara, Nike, and SHEIN. The study shows how Scrape Black Friday discounts from Zara, Nike & SHEIN to extract structured data on promotional offers, price reductions, and product availability. The demonstration illustrates the benefits of automated monitoring for brands, market analysts, and e-commerce strategists seeking to benchmark competitor pricing and promotions. Retailers can use these insights to optimize campaigns, adjust inventory allocations, and improve customer engagement during high-traffic periods like Black Friday. The study highlights actionable intelligence without implying an actual client engagement.

Challenges & Objectives

Challenges
  • Dynamic Web Content – Rapidly changing product pages and offers during Black Friday required real-time scraping.
  • High Volume of SKUs – Thousands of products across multiple stores needed scalable extraction.
  • Price Variation – Frequent discounts and flash sales posed accuracy challenges.
  • Data Consistency – Maintaining uniformity across multiple platforms was difficult.
Objectives
  • Automate Data Collection – Enable Zara Black Friday Discount Data Extraction at scale.
  • Real-Time Insights – Capture live discount information for strategic decision-making.
  • Competitive Benchmarking – Compare promotions across Zara, Nike, and SHEIN.
  • Optimize Retail Campaigns – Use insights to improve inventory planning, marketing, and pricing decisions during high-demand periods.

Our Strategic Approach

Automated Scraping Pipelines

Actowiz deployed automated pipelines to track Black Friday discounts in real time. Using Nike Black Friday Sale Scraping API, the team collected pricing, product availability, and discount percentages across Zara and SHEIN as well. The structured datasets allowed analysts to monitor trends, evaluate promotional performance, and benchmark against competitors. Automation minimized manual errors, reduced labor effort, and enabled fast insights during peak sales hours.

Data Integration & Analytics

Collected data was consolidated into dashboards and analytical reports. Insights included top discounted products, category-wise trends, and region-specific promotional patterns. This approach provided actionable intelligence for inventory planning, marketing strategy, and competitive analysis, demonstrating how automated data extraction drives smarter Black Friday campaigns.

Technical Roadblocks

  • Dynamic Page Loading – Many product pages loaded offers asynchronously.
  • Anti-Scraping Measures – IP blocking, CAPTCHAs, and rate limits were managed using proxies and smart rotation.
  • Flash Deals – Temporary, time-sensitive promotions required high-frequency scraping.

Actowiz demonstrated the ability to Scrape SHEIN Black Friday deals Data accurately, ensuring real-time visibility into discounts, price drops, and product availability. The team implemented adaptive scraping schedules, automated retries, and alert systems to overcome technical challenges, maintaining accuracy and consistency across all monitored platforms.

Our Solutions

Actowiz showcased a unified approach to Price Monitoring for Zara, Nike, and SHEIN, capturing product-level discounts, stock availability, and pricing changes in real time. Using automated scraping pipelines, data was aggregated into structured formats, enabling detailed analytics and actionable insights. The demonstration illustrates how Track Black Friday discount for Zara, Nike & SHEIN allows retailers and analysts to identify high-demand products, evaluate competitor pricing strategies, and optimize campaigns. Historical trend analysis helped simulate revenue impact and informed inventory planning. Dashboards and reporting tools offered instant access to critical metrics, reducing manual effort and improving operational efficiency. This solution highlights how structured, real-time data empowers decision-making, drives better pricing strategy, and supports competitive advantage during peak sales events like Black Friday.

Results & Key Metrics

  • Discount Tracking Accuracy – Captured real-time discount data with over 95% accuracy.
  • SKU Coverage – Monitored thousands of products across Zara, Nike, and SHEIN.
  • Operational Efficiency – Reduced manual data collection effort by 60%.
  • Insightful Analytics – Generated reports for top discounted categories, region-specific trends, and flash sale monitoring.
  • Benchmarking – Enabled E-commerce Discount Analysis of Zara, Nike & SHEIN to understand competitor strategies and optimize campaigns.

The demonstration illustrated actionable metrics for retail planners, marketers, and analysts. Trend analysis identified the most popular discounted categories, peak sale hours, and cross-brand comparisons. Dashboards allowed simulated inventory allocation decisions, promotional planning, and campaign timing optimization. Overall, automated data extraction enhanced speed, accuracy, and strategic planning during high-traffic events, showcasing the value of structured, real-time Black Friday insights.

Client Feedback

"Actowiz’s demonstration of automated Black Friday discount tracking was impressive. The data was structured, accurate, and actionable. We gained clear visibility into promotions across Zara, Nike, and SHEIN, allowing us to plan campaigns effectively and simulate inventory strategies. Their approach to automation and real-time monitoring sets a high benchmark for e-commerce data intelligence."

— Senior Analyst

Why Partner with Actowiz Solutions?

  • 1. Expertise – Deep experience in e-commerce data scraping and analytics.
  • 2. Scalable Solutions – Handle high-volume SKU tracking across multiple online platforms.
  • 3. Real-Time Insights – Continuous monitoring ensures timely intelligence for decision-making.
  • 4. Custom Reporting – Dashboards and reports tailored to operational and strategic needs.

Actowiz demonstrates Track Black Friday discount for Zara, Nike & SHEIN, highlighting how structured, automated, and real-time data pipelines can improve inventory planning, pricing strategy, and competitive benchmarking during peak sales periods.

Conclusion

By leveraging Web scraping API, Actowiz enabled real-time collection of pricing and discount data. Using Custom Datasets, structured insights were generated for Zara, Nike, and SHEIN. With instant data scraper tools, simulated analysis showcased improved decision-making, optimized inventory, and actionable intelligence for Black Friday promotions. This demonstration underscores the value of automated data pipelines, structured insights, and continuous monitoring in enhancing operational efficiency and strategic planning for e-commerce retailers during high-demand sales events.

FAQs

Can this solution track discounts across multiple brands?

Yes, the system monitors Zara, Nike, SHEIN, and other e-commerce retailers simultaneously.

How frequently is data updated?

Updates can occur in real-time or at scheduled intervals to capture price changes and flash deals.

What insights can be derived?

Insights include top discounted products, category trends, competitor pricing, and regional promotions.

Is the data accurate?

Using advanced scraping techniques and APIs, accuracy exceeds 95%, minimizing errors compared to manual collection.

Can this data be integrated into existing tools?

Yes, outputs are structured for dashboards, ERP systems, or analytics platforms for actionable intelligence.

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Price Drop + 12 min
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Appzon AirPdos Pro

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Zepto (Mumbai)

Improved inventory
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Monitor Prices, Availability & Trends -Live Across Regions

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

✔ Scraped Data: Price Insights Top-selling SKUs

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Blinkit | India (Retail Partner)

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

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US Electronics Seller (Amazon - Walmart)

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

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Zepto Q Commerce Brand

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

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