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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
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                    [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
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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
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            [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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            [registered_country] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [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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                )

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

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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    [locales:protected] => Array
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            [0] => en
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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
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

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

    [traits:protected] => GeoIp2\Record\Traits Object
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                (
                    [ip_address] => 216.73.216.150
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
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                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
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                    [19] => staticIpScore
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        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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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            [validAttributes:protected] => Array
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        )

    [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
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                                )

                        )

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

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
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)
 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
)
Case Study Naver Store Seasonal Sales Analysis – Discount Trends During Korean Chuseok Festival-0

Introduction

The Indian festive season is one of the most competitive battlegrounds for e-commerce companies. Both Amazon and Flipkart roll out massive promotional campaigns, price drops, and category-specific offers to capture the attention of millions of online shoppers. Businesses aiming to thrive during this period need advanced Competitive Price Intelligence to identify pricing patterns, discounting tactics, and product performance insights across the two platforms. Actowiz Solutions undertook a detailed case study to Scrape Amazon vs. Flipkart Festive Deal Strategies Data and help its client achieve deeper market clarity. By capturing real-time festive promotions, the client was able to spot gaps, benchmark against competitors, and refine its sales strategy. This project not only delivered actionable insights but also created a scalable system that consistently monitors top categories and accelerates deal detection by 30%, offering unmatched visibility during high-volume festive sales.

The Client

The client is a mid-sized consumer electronics brand preparing for aggressive online sales during India’s peak festive season. Competing directly with established e-commerce leaders, they wanted to track festive deals across multiple categories in real time. Their primary focus was on televisions, smartphones, home appliances, accessories, and wearable devices. The brand was aware that both Amazon and Flipkart were investing heavily in festive promotions and needed a smarter approach to monitor them. They approached Actowiz Solutions with a request to Scrape Amazon vs. Flipkart Festive Deal Strategies Data and decode competitors’ online moves. The client wanted clear visibility into price updates, discount frequency, product positioning, and promotion mechanics. To stay ahead of rivals, they also sought Competitive Benchmarking insights that would enable them to adapt pricing decisions on the go while aligning promotional spends to outperform rivals in high-growth segments.

Key Challenges

Key Challenges-01

The client faced multiple challenges that restricted their ability to act quickly in the marketplace. Firstly, Amazon and Flipkart update deals dynamically, with discounts and flash sales changing by the hour. This meant the client could not rely on manual monitoring, as the delay would result in missed opportunities. Secondly, extracting and structuring festive promotion data across categories like smartphones, appliances, and wearables was complex due to high-frequency updates and variations in listings. The inability to compare price shifts across platforms made it difficult to react in time. Additionally, competitor promotions were often bundled with add-on benefits, exchange offers, or cashback deals, complicating the analysis. Without a robust Price Comparison Software, the client struggled to track subtle variations that could influence purchase decisions. Most importantly, the client lacked a unified dashboard that could consolidate real-time updates into actionable insights for their sales and marketing teams.

Key Solutions

The-Client

Actowiz Solutions developed a customized data intelligence system designed specifically to Scrape Amazon vs. Flipkart Festive Deal Strategies Data in real time. The solution captured hourly updates across five high-priority categories and highlighted price changes, bundled offers, and limited-time discounts. The system also integrated deep insights from a Flipkart Product and Review Dataset, enabling the client to correlate deals with consumer sentiment and identify which promotions were driving higher traction. Alongside this, Actowiz deployed advanced Amazon Data Scraping capabilities to extract competitor product listings, festive promotions, and pricing adjustments within seconds of updates going live. The solution did not stop at raw data extraction—it structured the findings into category-specific dashboards that made Ecommerce Data Scraping insights instantly accessible. To further strengthen decision-making, Actowiz integrated predictive analytics to detect emerging deal patterns. This enabled the client to respond proactively, positioning their own festive offers strategically and maximizing consumer conversions.

Client Testimonial

“Partnering with Actowiz Solutions completely transformed our approach to festive season sales. Their expertise in Scrape Festive Deal Tactics from Amazon and Flipkart gave us real-time visibility into competitors’ promotions and helped us adjust our strategies instantly. The integration of advanced data intelligence and structured insights ensured that we did not just monitor but actively optimized our sales campaigns during the high-demand festive period. Thanks to Actowiz, we could react faster, plan smarter, and gain a competitive edge that translated into measurable revenue growth.”

— Head of E-commerce Strategy, Leading Electronics Brand

Conclusion

This case study demonstrates how Actowiz Solutions leveraged advanced Web Scraping Services to give the client a decisive edge during India’s most competitive online shopping period. By enabling the client to Scrape Amazon vs. Flipkart Festive Deal Strategies Data, Actowiz helped them decode real-time promotions, achieve 30% faster price updates, and analyze five top-performing categories with precision. The solution also supported initiatives like Amazon vs. Flipkart Festive Promotion Data Scraper, Extract Seasonal Sale Strategies from Amazon and Flipkart, and Real-time data scraping for Amazon vs Flipkart discount monitoring—all designed to optimize festive campaign decisions. With the power to Extract competitor pricing strategies during Indian festive sales, the client transformed uncertainty into opportunity, reinforcing Actowiz Solutions’ position as a trusted partner for data-driven growth. As festive e-commerce battles intensify each year, proactive intelligence through Extract E-commerce Festive Pricing Data becomes not just an advantage but a necessity.

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:

Fintech / Digital Payments

Result

Accurate daily voucher &

cashback visibility across platforms

★★★★★

“Actowiz Solutions helped us automate daily voucher and cashback data collection across PhonePe, Paytm, Flipkart, and Hubble. The API-driven delivery significantly improved offer accuracy and operational efficiency.”

Product Manager, Fintech Platform (India)

✓ Daily voucher & cashback tracking via Push & Pull APIs

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
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

Actowiz Insights Hub

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

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Optimizing Customer Loyalty with Grab Rewards Data Scraping - Points, Tiers, and Rewards Analysis

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