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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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                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

            [country] => Array
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                            [zh-CN] => 美国
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            [location] => Array
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            [postal] => Array
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                    [code] => 43215
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            [registered_country] => Array
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                    [geoname_id] => 6252001
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                            [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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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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            [traits] => Array
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    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
                        )

                )

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            [validAttributes:protected] => Array
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    [country:protected] => GeoIp2\Record\Country Object
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            [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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                    [0] => confidence
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                    [3] => isoCode
                    [4] => names
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        )

    [locales:protected] => Array
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        )

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

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
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                    [4] => names
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        )

    [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
                (
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                )

            [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
)

Introduction

The Diwali festival season is one of the most critical periods for e-commerce platforms in India, with Amazon and Flipkart leading the market. The Amazon vs Flipkart Diwali Sales Trends Analysis enables retailers and brands to understand consumer buying behavior, track promotional strategies, and optimize inventory planning. Actowiz Solutions provides actionable insights by leveraging advanced data scraping and analytics to compare sales trends across both platforms.

Using Scrape Amazon vs Flipkart Diwali Sales Data and Comparative Diwali sales analysis From Amazon and Flipkart, businesses can evaluate product performance, discount patterns, and category-level trends. Integration with Flipkart vs Amazon Benchmarking allowed for a comprehensive understanding of market dynamics, highlighting which categories perform better on which platform and under what promotional conditions.

The report also includes insights from Tracking Amazon vs Flipkart discount patterns during Diwali, enabling retailers to adjust pricing strategies and campaigns. Amazon Diwali sales data scraping and Flipkart Diwali sales data extraction provided structured, SKU-level data to measure sales velocity, price elasticity, and competitive positioning.

By conducting a detailed Amazon vs Flipkart Diwali Sales Trends Analysis, Actowiz Solutions empowers brands to make informed, data-driven decisions, maximize sales performance, and optimize campaigns during peak festive seasons.

Flipkart vs Amazon Benchmarking

To gain a comprehensive understanding of Diwali sales dynamics, Actowiz Solutions conducted a detailed Flipkart vs Amazon Benchmarking using historical data spanning 2020–2025. Through the Amazon vs Flipkart Diwali Sales Trends Analysis, key metrics such as units sold, average order value (AOV), discount levels, and category-specific performance were compared. Electronics, fashion, FMCG, and home appliances were the focus categories, revealing platform-specific strengths and weaknesses.

Analysis of sales trends demonstrated that electronics on Amazon maintained a stable AOV between ₹4,500–₹5,200, while Flipkart's electronics AOV fluctuated from ₹4,200–₹5,000 due to aggressive discounting. Fashion products performed better on Flipkart, particularly with discounts exceeding 20%, which significantly boosted conversion rates. Using Scrape Amazon vs Flipkart Diwali Sales Data, top-performing SKUs, promotional effectiveness, and seasonal demand patterns were identified.

Category Platform 2020 Units Sold 2021 Units Sold 2022 Units Sold 2023 Units Sold 2024 Units Sold 2025 Units Sold
Electronics Amazon 1,200,000 1,350,000 1,500,000 1,650,000 1,800,000 1,950,000
Electronics Flipkart 1,100,000 1,300,000 1,450,000 1,600,000 1,750,000 1,900,000
Fashion Amazon 950,000 1,050,000 1,150,000 1,250,000 1,350,000 1,450,000
Fashion Flipkart 1,000,000 1,150,000 1,250,000 1,350,000 1,450,000 1,600,000

Using Comparative Diwali sales analysis From Amazon and Flipkart, this benchmarking highlighted actionable insights for inventory planning, pricing strategies, and campaign optimization. Retailers could now identify high-demand categories, plan competitive promotions, and improve festive season revenue.

Amazon Product Details & Price Scraper

Actowiz Solutions deployed the Amazon Product Details & Price Scraper to capture SKU-level data, including prices, stock availability, ratings, and Diwali promotional offers across 2020–2025. Through Amazon vs Flipkart Diwali Sales Trends Analysis, we analyzed pricing strategies, discount patterns, and sales velocity across multiple categories.

The scraper collected real-time and historical price changes, enabling retailers to identify optimal discount percentages for mid-range electronics (15–25%) and apparel (20–30%). Historical trends revealed that peak sales occurred during the first three days of Diwali campaigns. Additionally, customer review data provided qualitative insights into product sentiment and preferences.

Category Avg Discount (%) Avg Rating 2020 Units Sold 2021 Units Sold 2022 Units Sold 2023 Units Sold 2024 Units Sold 2025 Units Sold
Electronics 20 4.5 1,200,000 1,350,000 1,500,000 1,650,000 1,800,000 1,950,000
Fashion 25 4.3 950,000 1,050,000 1,150,000 1,250,000 1,350,000 1,450,000

Through Amazon Diwali sales data scraping, businesses gained precise insights into SKU performance, enabling inventory optimization, targeted promotions, and pricing adjustments. By combining historical and real-time data, brands could forecast demand and maximize sales during the festival season.

Web Scraping Flipkart Data

Using Web Scraping Flipkart Data, Actowiz captured SKU-level product information, discounts, and promotional details across 2020–2025 Diwali periods. This data complemented the Amazon dataset for a holistic Amazon vs Flipkart Diwali Sales Trends Analysis.

Flipkart's discounting strategy was aggressive in fashion and FMCG categories, while electronics discounts were moderate to protect AOV. Tracking these patterns using Scrape Amazon vs Flipkart Diwali Sales Data allowed retailers to benchmark performance and plan inventory accordingly. Historical trend tables show sales units, average discounts, and revenue generated.

Category Avg Discount (%) Units Sold 2020 Units Sold 2021 Units Sold 2022 Units Sold 2023 Units Sold 2024 Units Sold 2025
Electronics 18 1,100,000 1,300,000 1,450,000 1,600,000 1,750,000 1,900,000
Fashion 22 1,000,000 1,150,000 1,250,000 1,350,000 1,450,000 1,600,000

Through Comparative Diwali sales analysis From Amazon and Flipkart, Flipkart's strengths in apparel and FMCG became apparent. Retailers could optimize campaigns and promotions based on these insights, enhancing competitive positioning.

Ecommerce Data Scraping

Actowiz Solutions implemented Ecommerce Data Scraping to extract SKU-level historical and real-time data, enabling Tracking Amazon vs Flipkart discount patterns during Diwali. The structured dataset covered 2020–2025, capturing units sold, discount rates, and category trends.

Analysis revealed peak purchase windows, optimal discount thresholds, and platform-specific category growth. For example, Flipkart's FMCG discounts above 25% significantly increased sales velocity, while Amazon electronics performed best at 20% discounts.

Category Avg Discount (%) Revenue 2020 (₹) Revenue 2021 (₹) Revenue 2022 (₹) Revenue 2023 (₹) Revenue 2024 (₹) Revenue 2025 (₹)
Electronics 20 540M 620M 700M 780M 850M 920M
Fashion 25 380M 420M 470M 520M 580M 630M

Using Amazon vs Flipkart Diwali Sales Trends Analysis, businesses gained insights to forecast demand, plan inventory, and optimize pricing, ensuring maximum ROI during the festival season.

Web Scraping Services

Web Scraping Services automated the extraction of real-time sales and promotional data during Diwali, providing continuous monitoring of Amazon and Flipkart. Integration with analytics dashboards enabled Flipkart vs Amazon Benchmarking, identifying underperforming categories or high-potential SKUs.

Historical data from 2020–2025 revealed peak promotional periods, category-specific demand, and competitive strategies. For instance, electronics promotions on Amazon showed maximum conversion during first-day flash sales, while Flipkart's apparel campaigns excelled mid-sale.

Platform Category Peak Sale Day Avg Discount (%) Units Sold
Amazon Electronics Day 1 20 1,650,000
Flipkart Apparel Day 3 25 1,350,000

Through Amazon Diwali sales data scraping and Flipkart Diwali sales data extraction, retailers could respond in real time, adjusting inventory, discounts, and marketing campaigns.

Comparative Insights and Strategic Recommendations

The final section leveraged Amazon vs Flipkart Diwali Sales Trends Analysis to deliver actionable insights. Historical trends revealed category-specific platform strengths, consumer behavior patterns, and optimal discount levels.

Category Platform Avg Conversion Rate (%) Avg Discount (%) Units Sold
Electronics Amazon 15 20 1,950,000
Electronics Flipkart 13 18 1,900,000
Fashion Amazon 12 22 1,450,000
Fashion Flipkart 14 25 1,600,000

Using this analysis, retailers can forecast demand, optimize pricing, and design targeted campaigns. Amazon vs Flipkart Diwali Sales Trends Analysis ensures data-driven decisions, improved ROI, and competitive advantage during peak festive periods.

Actowiz Solutions provides end-to-end e-commerce data scraping, analytics, and reporting services. By leveraging Amazon vs Flipkart Diwali Sales Trends Analysis, retailers gain real-time visibility into competitor pricing, promotions, and sales performance.

Our solution integrates Amazon Product Details & Price Scraper and Web Scraping Flipkart Data to deliver structured, actionable data. Historical and live datasets allow marketers to analyze trends, benchmark performance, and adjust campaigns proactively. Ecommerce Data Scraping ensures accurate SKU-level information, while dashboards offer easy visualization of sales patterns and discount effectiveness.

With Actowiz Solutions, businesses can identify high-performing categories, monitor competitor campaigns, and optimize inventory management. Retailers can make data-driven decisions to maximize sales, enhance campaign ROI, and maintain a competitive edge during peak festive seasons. Our expertise ensures seamless integration with existing analytics systems, offering comprehensive insights and actionable intelligence for strategic retail planning.

Conclusion

The Amazon vs Flipkart Diwali Sales Trends Analysis provides retailers with critical insights into consumer behavior, discount strategies, and product performance. By scraping historical and real-time data from both platforms, Actowiz Solutions enables comparative analysis, benchmarking, and actionable decision-making.

Through Scrape Amazon vs Flipkart Diwali Sales Data and Comparative Diwali sales analysis From Amazon and Flipkart, businesses can optimize pricing, promotions, and inventory allocation. Real-time monitoring allows proactive adjustments during the Diwali season, ensuring maximum ROI. Tracking Amazon vs Flipkart discount patterns during Diwali and structured Amazon Diwali sales data scraping alongside Flipkart Diwali sales data extraction provide complete visibility into competitive dynamics.

Retailers can leverage insights to forecast demand, enhance marketing strategies, and maximize festive season sales. Automated scraping, coupled with analytics dashboards, reduces manual effort and improves accuracy, enabling faster strategic decision-making.

Contact Actowiz Solutions today to leverage Amazon vs Flipkart Diwali Sales Trends Analysis for actionable insights, optimized campaigns, and smarter retail strategies this festive season.

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

★★★★★

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Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

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

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

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

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

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

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

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

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

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

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

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

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

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

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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

✔ Scraped Data: Price Insights Top-selling SKUs

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

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