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GeoIp2\Model\City Object
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                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
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                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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    [continent:protected] => GeoIp2\Record\Continent Object
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                            [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
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                    [iso_code] => US
                    [names] => 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
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [de] => USA
                            [en] => United States
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                            [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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                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
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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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                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [ip_address] => 216.73.216.128
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [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
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [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
                            [ru] => Колумбус
                            [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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                    [2] => names
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        )

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

            [validAttributes:protected] => Array
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                    [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
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                            [0] => en
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                    [validAttributes:protected] => Array
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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
)

Introduction

With India’s online fashion retail market booming, shoppers are spoiled for choice — but how do you know which platform really gives the best deal? This report dives deep into Fashion Product Price Tracking, comparing Flipkart discounts comparison, Myntra’s flash sales, and Ajio’s secret codes.

Using advanced scraping techniques to extract pricing data from fashion websites, Actowiz Solutions analyzed thousands of live SKUs from 2020–2025 to help brands and buyers understand who really drops prices the most — and when.

From scraping price and item data from Amazon, Flipkart, Myntra, Ajio to digging into niche segments like Luxury Goods Fashion Data Scraping , our findings offer actionable insights for brands planning promotions, and for savvy shoppers chasing the biggest price drops.

Tracking India’s Online Fashion Discounts: 2020–2025

Year	Avg. Discount Myntra (%)	Avg. Discount Ajio (%)	Avg. Discount Flipkart (%)
2020	28%	25%	30%
2021	32%	28%	34%
2022	35%	30%	36%
2023	38%	33%	40%
2024	41%	35%	43%
2025	43%	37%	45%

Analysis:

Fashion Product Price Tracking shows Flipkart consistently leads in average discounts, growing from 30% in 2020 to 45% in 2025. Myntra closes the gap with aggressive seasonal drops, while Ajio maintains moderate but steady discounts.

Big Sale Events — Who Drops Prices the Most?

Year	Myntra End of Reason Sale Avg. Drop	Ajio Big Bold Sale Avg. Drop	Flipkart Big Billion Days Avg. Drop
2020	45%	38%	50%
2021	50%	41%	54%
2022	52%	44%	58%
2023	55%	47%	60%
2024	58%	50%	63%
2025	60%	52%	65%

Analysis:

During mega events, Flipkart discounts comparison proves Flipkart still leads with the deepest cuts. Myntra’s End of Reason Sale has become stronger, closing in by 2025. Ajio trails slightly but compensates with exclusive coupon codes found via Ajio discount scraper tool.

Best Category for Price Drops

Category	Top Platform	Avg. Discount
Ethnic Wear	Myntra	52%
Western Casuals	Flipkart	48%
Footwear	Ajio	45%
Luxury Brands	Myntra	40%
Streetwear	Flipkart	50%

Analysis:

If you’re chasing the biggest price drops on fashion products data in India, Myntra is unbeatable for ethnic & luxury, while Flipkart dominates casuals and streetwear. Ajio holds its own in niche footwear deals.

Real-Time Discount Consistency

Platform	Avg. Daily Price Update Frequency	Dynamic Discounting (%)
Myntra	5x daily	65%
Ajio	4x daily	55%
Flipkart	7x daily	70%

Analysis:

The compare real-time discounts on Flipkart, Ajio, and Myntra data shows Flipkart updates prices most aggressively, helping buyers catch flash deals. Myntra follows with multiple daily refreshes, while Ajio remains steady but less dynamic.

Additional Platforms & Luxury Segments

Platform	Avg. Luxury Discount (%)	Notable Insight
Nykaa	25%	Focus on premium beauty deals
Myntra	40%	Growing luxury fashion sales
Flipkart	32%	Limited luxury lines

Analysis:

Discount Analysis on Nykaa, Flipkart, and Myntra shows Nykaa’s discounts stay moderate, catering to premium beauty buyers. Myntra increasingly targets luxury with curated collections, making Luxury Goods Fashion Data Scraping vital for brand watchers.

Holiday vs. Non-Holiday Discount Patterns

Season	Avg. Myntra Discount (%)	Avg. Ajio Discount (%)	Avg. Flipkart Discount (%)
Non-Holiday	30%	27%	33%
Holiday Season	52%	44%	58%

Analysis:

Fashion Product Price Tracking shows Flipkart leads with aggressive holiday deals — average discounts jump by 25% during Diwali & Big Billion Days. Myntra’s End of Reason and Ajio’s Big Bold sales also lift discounts, but Flipkart dominates big festival promotions for fashion.

Flash Deal Frequency 2020–2025

Year	Myntra Flash Deals/Month	Ajio Flash Deals/Month	Flipkart Flash Deals/Month
2020	10	8	14
2021	12	9	16
2022	13	10	18
2023	15	12	20
2024	16	14	21
2025	18	15	23

Analysis:

When we compare real-time discounts on Flipkart, Ajio, and Myntra, Flipkart runs more flash deals than competitors. Myntra is catching up, with flash offers growing 80% from 2020–2025. Ajio remains steady but relies more on seasonal coupons.

Return Rate Impact on Discounts

Platform	Avg. Return Rate (%)	Avg. Discount for High-Return Categories
Myntra	30%	50%
Ajio	28%	46%
Flipkart	35%	55%

Analysis:

Products with high return rates (footwear, seasonal wear) often get deeper markdowns. This Pricing insights from Myntra, Ajio, and Flipkart trend shows Flipkart pushing the biggest markdowns in high-return segments to clear inventory faster.

Impact of Price Drops on Sales Conversion

Platform	Avg. Conversion Uplift for >40% Discount
Myntra	3.5X
Ajio	2.8X
Flipkart	4.2X

Analysis:

Fashion Product Price Tracking links deep discounts to higher conversion spikes. Flipkart sees the biggest lift — shoppers are 4X more likely to buy if discounts cross 40%. Myntra closely follows for lifestyle and luxury, while Ajio drives steady conversions with codes and bundled deals.

Solving Business Problems with Actowiz Solutions

Brands use Actowiz’s tools to:

• Scrape Myntra discount data for competitive positioning
• Extract Ajio website data for promo tracking
• Run Flipkart discounts comparison weekly
• Feed real-time price feeds into pricing models
• Monitor Pricing insights from Myntra, Ajio, and Flipkart in one dashboard

With accurate data to extract pricing data from fashion websites, brands adjust ad spend, plan stock clearance, and run personalized campaigns.

Conclusion

Fashion Product Price Tracking unlocks the truth behind big discount promises. Flipkart still rules for the deepest cuts, Myntra is closing in fast, and Ajio stays a solid option for niche steals. For brands, these insights help shape smarter pricing and promotions. For shoppers, it means knowing when — and where — to click add to cart. Actowiz Solutions is your trusted partner to scrape, monitor, and optimize pricing from Myntra, Flipkart, Ajio, Amazon, and beyond.

Get real-time price insights — start tracking today with Actowiz Solutions!

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                            [zh-CN] => 北美洲
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    [continent:protected] => GeoIp2\Record\Continent Object
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    [country:protected] => GeoIp2\Record\Country Object
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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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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
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                    [iso_code] => US
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                            [ru] => США
                            [zh-CN] => 美国
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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    [traits:protected] => GeoIp2\Record\Traits Object
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                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
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            [validAttributes:protected] => Array
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        )

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

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

        )

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

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

        )

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

                        )

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

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

                )

        )

)

 country : United States

 city : Columbus

US

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

Start Your Project

+1 ▼

Select Service Web Scraping Real-Time API Access Data Dashboards Datasets Review & Sentiment Analysis Price Monitoring Custom Solution

Select Industry E-Commerce Quick Commerce Food Delivery Travel & OTA Grocery & Retail Real Estate Others

Target Country Web Scraping Services Mobile App Scraping Web and App Scraping API Datasets Dashboard

A$лвR$C$CHF¥Kčkr€£HK$FtRp₪₹kr¥₩Mex$RMkrNZ$₱złleikrS$฿₺$R Budget Range $500 - $1,000 - Starter Budget (POC/Single Site) $1,000 - $2,500 - Basic Plan (Small Business Needs) $2,500 - $5,000 - Growth Phase (Multi-site/Data Volume Projects) $5,000 - $10,000 - Business-Scale (Automation/API Integrations) $10,000 - $25,000 - Enterprise Projects (Real-Time Systems, Global Data) $25,000 - $50,000 - Enterprise+ (Custom Dashboards, Ongoing Support) $50,000+ - Strategic Partnership (End-to-End Data Ops)

Additional Trust Elements

✨ "1000+ Projects Delivered Globally"

⭐ "Rated 4.9/5 on Google & G2"

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

💬 "Average Response Time: Under 12 hours"

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize

Explore Solutions Get a Custom Demo

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.

Ecommerce

Q-Commerce

Travel & OTA

Food Delivery

Real Estate

Social Media

Trusted by Global Leaders – Secured by International Standards

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 Galido

Co-Founder / Head of Product at Upright Data Inc.

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.”

Iulen Ibanez

CEO / Datacy.es

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!”

Febbin Chacko

-Fin, Small Business Owner

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

Request Demo Access

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

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

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

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

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

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

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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

All

Blog

Case Studies

Infographics

Report

Aug 25, 2025

Starbucks Menu Price Fluctuation - Price Analysis of Starbucks Items in New York and LA

Track Starbucks Menu Price Fluctuation in New York and LA. Analyze latte, frappuccino, and cappuccino prices from 2020–2025 for smarter pricing and promotions.

D2C Fashion Inventory Optimization Using Demand Data from Naver

Discover how a D2C brand improved inventory management and sales with Naver demand data, leveraging D2C Fashion Inventory Optimization strategies effectively.

Top 10 Cities with the Most McDonald’s Locations in 2025

Discover the top 10 global cities with the highest number of McDonald’s locations in 2025 and explore trends in fast-food expansion worldwide.

Skin Type-Based Skincare Insights in Korea Using Naver Beauty Categories

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Aug 25, 2025

Starbucks Menu Price Fluctuation - Price Analysis of Starbucks Items in New York and LA

Track Starbucks Menu Price Fluctuation in New York and LA. Analyze latte, frappuccino, and cappuccino prices from 2020–2025 for smarter pricing and promotions.

Aug 24, 2025

Trending Discounts on Personal Care Products in Australia - Weekly Woolworths vs Coles Price Comparison

Compare weekly discounts on personal care products in Australia with our Woolworths vs Coles Price Comparison—stay updated, save money, and shop smart every week!

Aug 23, 2025

Benefits of Monthly Angi and Zillow Scraping for Service Aggregators

Unlock actionable insights with monthly Angi and Zillow scraping, helping service aggregators track trends, analyze competitors, and optimize business strategies.

Read More

D2C Fashion Inventory Optimization Using Demand Data from Naver

Discover how a D2C brand improved inventory management and sales with Naver demand data, leveraging D2C Fashion Inventory Optimization strategies effectively.

Cosmetic Pricing Intelligence in Daejeon Using Naver Product Scraping

Explore how Actowiz Solutions enabled Cosmetic Pricing Intelligence in Daejeon with Naver product scraping, delivering insights for smarter retail strategies.

Used Vehicle Market Intelligence for U.S. Auto Dealers Using Carfax Scraped Listings

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Read More

Top 10 Cities with the Most McDonald’s Locations in 2025

Discover the top 10 global cities with the highest number of McDonald’s locations in 2025 and explore trends in fast-food expansion worldwide.

Monthly Tourism Flow Analysis via Hotel Booking Scraping in Sharjah

Monthly tourism flow in Sharjah analyzed via hotel booking scraping reveals visitor trends, occupancy rates, and booking patterns for data-driven tourism insights.

India's Holiday Travel Surge Raksha Bandhan to Independence Day 2025

Explore India's holiday travel trends during the 2025 festive season—from Raksha Bandhan to Independence Day. Discover top destinations, travel tips, and booking insights.

Read More

Skin Type-Based Skincare Insights in Korea Using Naver Beauty Categories

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AI-Powered Web Scraping Market Analysis 2025–2035 – Forecasts, Competitors & Use Cases

Explore the AI-Powered Web Scraping Market Analysis 2025–2035, including growth forecasts, key competitors, use cases, and emerging industry trends.

Petrol Diesel Price Comparison & Dynamics in Urban vs. Rural India Using Fuel Pump Data

Petrol Diesel Price Comparison and dynamics across urban vs. rural India using fuel pump data, highlighting regional price trends.

Read More

Our perks are irreplaceable

Time Zone Flexibility

Benefit from the ease of collaboration with Actowiz Solutions, as our team is aligned with your preferred time zone, ensuring smooth communication and timely delivery.

Clear Communication

Our team focuses on clear, transparent communication to ensure that every project is aligned with your goals and that you’re always informed of progress.

Uncompromising Quality

Actowiz Solutions adheres to the highest global standards of development, delivering exceptional solutions that consistently exceed industry expectations

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