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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [geoname_id] => 4509177
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                        (
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                            [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
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                    [longitude] => -83.0061
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            [postal] => Array
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            [registered_country] => Array
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                    [iso_code] => US
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                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
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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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                    [ip_address] => 216.73.216.110
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    [continent:protected] => GeoIp2\Record\Continent Object
        (
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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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                )

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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
                            [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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            [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
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [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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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [validAttributes:protected] => Array
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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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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.110
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [6] => isAnonymousProxy
                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
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                    [11] => isPublicProxy
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                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

        )

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

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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
                (
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                    [1] => geonameId
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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
                    [2] => latitude
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                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

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

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

        )

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

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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
)
Real-Time Regional Insights with Customizable E-commerce Dashboards

Introduction

Korean consumers often consult Naver blogs for product reviews and Coupang for purchasing—making these two platforms vital for FMCG brands operating in the region. The challenge? Aligning consumer sentiment with price strategy in real time.

Actowiz Solutions partnered with a fast-moving consumer goods (FMCG) company to scrape and analyze blog content from Naver and track daily pricing data from Coupang. This correlation helped the brand time its discounts based on online sentiment, resulting in better click-throughs, conversion uplift, and a measurable increase in ROI.

The Challenge

The-Client
1. Disconnected Sentiment and Sales

Product reviews on Naver often didn’t align with pricing activity. Positive buzz failed to convert when no discounts were active.

2. Blind Spot During Price Wars

Coupang’s rapid price fluctuations meant competitors would frequently run unannounced sales—leading to lost market share.

3. Limited Visibility into Organic Triggers

The brand didn’t know which keywords, blog posts, or user-generated tags drove traffic spikes or conversion drops.

Actowiz Solutions’ Approach

Phase 1: Naver Blog Scraping & Sentiment Mapping

We extracted content from top Naver blogs, scraping over 10,000+ posts per month across:

  • Branded keywords (e.g., “맛있는 [Product Name] 리뷰”)
  • Category keywords (e.g., “간식 추천”, “아침 식사 대용”)
  • Hashtags (e.g., #건강한간식, #FMCG트렌드)

We then used NLP (Natural Language Processing) to classify posts into:

  • Positive
  • Neutral
  • Negative
Sample Naver Blog Sentiment Data:
Date Product Blog Title Sentiment Blog Tags
2025-05-22 Protein Snack Bar “가성비 최고! 매일 먹고 있어요” Positive #헬스간식, #다이어트
2025-05-23 Vegan Chips “좀 싱거워요. 맛은 괜찮은데 가격이 문제” Neutral #건강식품, #비건
2025-05-24 Fruit Juice Pack “아이들 간식으로 최고예요” Positive #어린이간식, #착즙주스
2025-05-25 Oatmeal Cookies “너무 달아요. 성분이 걱정돼요.” Negative #단거주의, #비추천간식
Phase 2: Daily Coupang Price Scraping

We scraped real-time Coupang product data including:

  • Daily price
  • % Discount
  • Star Ratings
  • Number of Reviews
  • Coupon availability
Sample Coupang Price Data:
Date Product Price (KRW) Discount Rating Reviews Coupon
2025-05-22 Protein Snack Bar ₩12,900 10% OFF 4.6 3,218 ₩1,000
2025-05-23 Vegan Chips ₩9,900 - 4.1 1,524 None
2025-05-24 Fruit Juice Pack ₩11,500 15% OFF 4.8 4,002 ₩1,500
2025-05-25 Oatmeal Cookies ₩8,700 5% OFF 3.9 978 None
Phase 3: Sentiment-Price Correlation Engine

Our AI model correlated:

Insight Example:

On May 22, “Protein Snack Bar” saw a 300% increase in positive Naver posts. However, the brand missed running a coupon—leading to stagnant conversions.

The following week, the brand timed a 10% discount with another sentiment spike and saw a 22% rise in conversions.

Sample Analysis Output

Graph: Sentiment Spike vs Discount Timing
The-Client
Visual: Dashboard Snapshot
Metric Value
Top Influencer Blog naver.com/blog/snackqueen1
Avg Sentiment (May 2025) +0.78
Days with Price Mismatch 9

Results & Impact

Metric Before Actowiz After Actowiz
Price-Sentiment Alignment Accuracy 21% 87%
Coupon Conversion Rate 6.4% 12.3%
Time-to-React on Competitor Deals 4 days 12 hours
Blog Engagement-Linked Sales Days 3/month 12/month
Blog-to-Coupang Referral Sales Low +33% uplift

Brand Testimonial

“Before Actowiz, we were operating in the dark. Now, our marketing and pricing teams speak the same language—data. It’s been a total game-changer for Korea.”

— Digital Strategy Lead, Global FMCG Brand

Conclusion

This case study demonstrates that timing is everything. Positive sentiment without pricing action means missed revenue. With Actowiz Solutions, this FMCG brand now reacts faster, promotes smarter, and earns better.

By bridging the consumer voice on Naver with real-time pricing on Coupang, we delivered full-funnel intelligence that helped the client win more Korean shoppers—daily.

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

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