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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 ) ) [continent] => Array ( [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 ) ) [country] => 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 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => 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 ) ) [subdivisions] => Array ( [0] => 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 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.150 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [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 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [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 [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [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 ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.150 [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 ( [0] => en ) [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 )
Discover how our Myntra dataset helped a retailer analyze fashion products, forecast trends accurately, and optimize inventory for better sales.
Note: You’ll receive it via email shortly after submitting the form.
In the highly competitive Indian fashion e-commerce market, staying ahead of trends and optimizing pricing is crucial for profitability. Actowiz Solutions provided a comprehensive Myntra dataset to a leading fashion retailer, enabling them to analyze thousands of SKUs, track trending fashion products, and optimize their pricing strategies.
The dataset included structured information on product listings, prices, discounts, reviews, and seasonal promotions, offering actionable insights for inventory planning, trend forecasting, and dynamic pricing. By leveraging this Myntra dataset, the retailer gained visibility into consumer preferences, emerging fashion trends, and competitor pricing, allowing them to make data-driven decisions. Integration with analytics platforms enabled SKU-level analysis, promotional impact assessment, and timely pricing adjustments, reducing manual research time and improving operational efficiency.
Furthermore, the data supported End of Reason Sale price analysis and identified high-demand SKUs in real time. Retailers could adjust pricing and stock based on real-time insights, optimizing margins, reducing waste, and ensuring that popular fashion products were always available. The combination of historical data (2020–2025) and live updates empowered the client to forecast demand accurately, maximize seasonal campaigns, and maintain a competitive edge in the fast-moving fashion landscape.
The client is a mid-sized Indian fashion retailer operating both online and offline channels, catering primarily to urban millennials and young professionals. Their product range includes clothing, footwear, accessories, and seasonal fashion items, with a focus on trendy, fast-fashion products.
Facing frequent shifts in consumer preferences and high competition from platforms like Myntra, Flipkart, and Amazon Fashion, the retailer needed a data-driven solution to maintain profitability. Actowiz Solutions provided a Dynamic pricing model using Myntra fashion dataset, enabling automated pricing decisions based on competitor prices, historical sales, and market trends.
With structured, real-time insights, the retailer could optimize stock levels, anticipate high-demand items, and forecast trends for upcoming seasons. By combining SKU-level pricing, promotion data, and historical analytics, the client improved campaign planning and inventory management. The solution allowed them to track fashion products across multiple categories, identify popular items early, and dynamically adjust prices to respond to competitor strategies and market demand.
Additionally, using the Dynamic pricing model using Myntra fashion dataset, the retailer improved overall sales efficiency, reduced stockouts, and minimized excess inventory, leading to higher profitability and customer satisfaction. This end-to-end data-driven approach provided actionable insights for merchandising teams, enabling smarter decisions and proactive market response.
Using the Myntra Fashion product dataset, Actowiz Solutions implemented a dynamic pricing model that considered competitor prices, historical sales, and ongoing promotions. The model allowed real-time price adjustments for thousands of SKUs across clothing, footwear, and accessories, ensuring competitiveness while maximizing margins.
Integrated dashboards provided insights into category performance, price gaps, and margin impact, empowering decision-makers to act quickly. SKU-level insights allowed the client to prioritize high-demand products, anticipate promotional impact, and optimize pricing during peak seasons. Automated alerts were configured for sudden price drops or competitor campaigns, allowing the client to react instantly.
This Dynamic pricing model reduced human errors, minimized revenue loss due to underpricing, and enabled higher profitability for fast-moving fashion products. By continuously learning from historical data, the model also forecasted demand for emerging trends, allowing proactive inventory management.
Actowiz leveraged historical data from the Myntra Fashion product dataset to analyze the effectiveness of End of Reason Sale campaigns. This analysis tracked SKU-level sales performance, discount elasticity, and stock turnover, providing actionable insights for future promotions.
The retailer was able to segment high-performing fashion products, forecast demand for specific SKUs, and design dynamic discount strategies that optimized revenue while maintaining healthy margins. Insights from past campaigns revealed patterns in consumer purchasing behavior, such as preferred discount percentages and popular categories during sale periods.
By integrating End of Reason Sale price analysis with real-time SKU-level monitoring, the client could anticipate demand spikes, avoid stockouts, and ensure that trending fashion products were available throughout the sale, improving overall customer satisfaction and profitability.
Actowiz Solutions delivered a Myntra India Product Listings Dataset capturing prices, discounts, reviews, stock status, and promotional information for thousands of fashion SKUs. The solution enabled SKU-level tracking, dynamic pricing, and trend forecasting.
Historical and real-time data integration allowed the client to anticipate market shifts, optimize pricing during End of Reason Sale campaigns, and adjust inventory according to demand. The structured datasets could seamlessly integrate with ERP and analytics dashboards, providing actionable insights for merchandising, pricing, and campaign planning. Automated scraping pipelines ensured continuous updates, maintaining dataset reliability and accuracy, while enabling proactive decision-making for fast-changing fashion products.
Additionally, the solution supported SKU-level price tracking, allowing the retailer to monitor product performance, detect market opportunities, and strategically plan future campaigns for maximum revenue impact.
Additional metrics highlighted improved customer satisfaction, faster response to competitor campaigns, and better alignment of marketing strategies with consumer preferences.
“Actowiz Solutions transformed our approach to fashion trend forecasting and pricing. The Myntra dataset provided real-time insights, enabling us to optimize pricing, reduce stock issues, and boost profitability. Their support and technology integration were seamless.”
— Head of Merchandising, Indian Fashion Retailer
By leveraging the Myntra dataset, combined with Web scraping API, Custom Datasets, and instant data scraper technology, the retailer optimized pricing, forecasted fashion trends more accurately, and improved margins. SKU-level monitoring and End of Reason Sale price analysis enabled smarter inventory and promotional planning, providing a competitive edge in the Indian fashion market.
Ready to enhance trend forecasting and optimize fashion product pricing? Contact Actowiz Solutions today to harness the power of Myntra datasets!
It includes prices, discounts, promotions, stock status, reviews, and product details for thousands of fashion SKUs.
Yes, SKU-level and category-level tracking is supported for precise monitoring.
Real-time updates ensure pricing, stock, and promotion data are always current.
Historical and real-time data enable accurate predictions of emerging fashion trends and seasonal demand.
Absolutely. The datasets are structured for easy integration with dashboards, ERPs, and pricing systems.
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
Coffee / Beverage / D2C
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
Real Estate
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×
Organic Grocery / FMCG
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
Quick Commerce
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
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
Beverage / D2C
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
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
Real results from real businesses using Actowiz Solutions
In Stock₹524
Price Drop + 12 minin 6 hrs across Lel.6
Price Drop −12 thr
Improved inventoryvisibility & planning
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✔ Scraped Data: Price Insights Top-selling SKUs
"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"
✔ Scraped Data, SKU availability, delivery time
With hourly price monitoring, we aligned promotions with competitors, drove 17%
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