A comprehensive dataset providing insights into the fashion industry, including market size, employment statistics, trends, and analysis on the apparel sector for 2025.

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in Asia provides a robust dataset tailored for businesses seeking to connect with key players in Asia’s thriving fashion and luxury goods industries. Covering roles such as brand managers, designers, retail executives, and supply chain leaders, this dataset includes verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles and 130 million profiles focused on Asia, Success.ai ensures your outreach, marketing, and business development strategies are supported by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution positions you to succeed in Asia’s competitive and ever-growing fashion markets.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries across Asia.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency.

2. Comprehensive Coverage of Asian Fashion Professionals

   • Includes profiles from major fashion hubs such as China, India, Japan, South Korea, and Southeast Asia.
   • Gain insights into regional consumer trends, emerging fashion markets, and luxury goods opportunities.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and product launches.
   • Stay aligned with evolving industry trends and capitalize on new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with professionals across the global fashion and apparel industries, with a focus on Asia.
• 130M+ Profiles in Asia: Gain detailed insights into professionals shaping the region’s fashion and luxury goods markets.
• Verified Contact Details: Access work emails, phone numbers, and business locations for precise targeting.
• Leadership Insights: Engage with designers, brand managers, and retail leaders driving Asia’s fashion trends.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with decision-makers in apparel design, luxury goods branding, retail operations, and supply chain management.
   • Target individuals leading innovation in sustainable fashion, fast fashion, and digital transformation.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (luxury goods, ready-to-wear, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs, such as emerging e-commerce platforms or regional fashion preferences.

3. Industry and Regional Insights

   • Leverage data on consumer behaviors, market growth, and regional trends in Asia’s fashion and luxury goods sectors.
   • Refine marketing strategies, product development, and partnership outreach based on actionable insights.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Expansion

   • Design targeted campaigns to promote apparel, luxury goods, or retail solutions to fashion professionals in Asia.
   • Leverage multi-channel outreach, including email, phone, and social media, to maximize engagement.

2. Product Development and Consumer Insights

   • Utilize data on regional trends and consumer preferences to guide product development and marketing strategies.
   • Collaborate with brand managers and designers to tailor collections or launch new offerings aligned with market demands.

3. Partnership Development and Retail Collaboration

   • Build relationships with retail chains, luxury brands, and supply chain leaders seeking strategic alliances.
   • Foster partnerships that expand distribution channels, enhance brand visibility, or improve operational efficiencies.

4. Market Research and Competitive Analysis

   • Analyze trends in Asia’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the fast-paced fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workfl...

Second-Hand Fashion Dataset

Update Sep. 19th, 2024

• Some problematic and duplicate images have been removed from version 2.- All "gold dataset" data from station1 and station3 has been moved to a single test100 folder.- JSON errors have been fixed - all JSON files should be parsed correctly now.

The new dataset has 31,638 items (+ about 100 items in test100 folder) instead of the 31,997 items in version 2.

Overview

The dataset originates from projects focused on the sorting of used clothes within a sorting facility. The primary objective is to classify each garment into one of several categories to determine its ultimate destination: reuse, reuse outside Sweden (export), recycling, repair, remake, or thermal waste.

The dataset has 31,638 clothing items, a massive update from the 3,000 items in version 1. The dataset collection started under the Vinnova funded project "AI for resource-efficient circular fashion" in Spring, 2022 and involves collaboration among three institutions: RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB. The dataset has received further support through the EU project, CISUTAC (cisutac.eu).

Project page

• Webpage: second-hand-fashion- Contact: farrukh.nauman@ri.se

Dataset Details

• The dataset contains 31,638 clothing items, each with a unique item ID in a datetime format. The items are divided into three stations: station1, station2, and station3. The station1 and station2 folders contain images and annotations from Wargön Innovation AB, while the station3 folder contains data from Myrorna AB. Each clothing item has three images and a JSON file containing annotations.

• Three images are provided for each clothing item: 1. Front view. 2. Back view. 3. Brand label close-up. About 4000-5000 brand images are missing because of privacy concerns: people's hands, faces, etc. Some clothing items did not have a brand label to begin with.

• Image resolutions are primarily in two sizes: 1280x720 and 1920x1080. The background of the images is a table that used a measuring tape prior to January 2023, but later images have a square grid pattern with each square measuring 10x10 cm.

• Each JSON file contains a list of annotations, some of which require nuanced interpretation (see labels.py for the options): - usage: Arguably the most critical label, usage indicates the garment's intended pathway. Options include 'Reuse,' 'Repair,' 'Remake,' 'Recycle,' 'Export' (reuse outside Sweden), and 'Energy recovery' (thermal waste). About 99% of the garments fall into the 'Reuse,' 'Export,' or 'Recycle' categories. - trend: This field refers to the general style of the garment, not a time-dependent trend as in some other datasets (e.g., Visuelle 2.0). It might be more accurately labeled as 'style.' - material: Material annotations are mostly based on the readings from a Near Infrared (NIR) scanner and in some cases from the garment's brand label. - Damage-related attributes include: - condition (1-5 scale, 5 being the best) - pilling (1-5 scale, 5 meaning no pilling) - stains, holes, smell (each with options 'None,' 'Minor,' 'Major'). Note: 'holes' and 'smell' were introduced after November 17th, 2022, and stains previously only had 'Yes'/'No' options. For station1 and station2, we introduced additional damage location labels to assist in damage detection:

    "damageimage": "back",
    "damageloc": "bottom left",
    "damage": "stain ",
    "damage2image": "front",
    "damage2loc": "None",
    "damage2": "",
    "damage3image": "back",
    "damage3loc": "bottom right",
    "damage3": "stain"
  
  Taken from `labels_2024_04_05_08_47_35.json` file. Additionally, we annotated a few hundred images with bounding box annotations that we aim to release at a later date.  - `comments`: The comments field is mostly empty, but sometimes contains important information about the garment, such as a detailed text description of the damage. 
  

• Whenever possible, ISO standards have been followed to define these attributes on a 1-5 scale (e.g., pilling).

• Gold dataset: 100 garments were annotated multiple times by different annotators for annotator agreement comparisons. These 100 garments are placed inside a separate folder test100.

• The data has been annotated by a group of expert second-hand sorters at Wargön Innovation AB and Myrorna AB.

• Some attributes, such as price, should be considered with caution. Many distinct pricing models exist in the second-hand industry: - Price by weight - Price by brand and demand (similar to first-hand fashion) - Generic pricing at a fixed value (e.g., 1 Euro or 10 SEK) Wargön Innovation AB does not set the prices in practice and their prices are suggestive only (station1 and station2). Myrorna AB (station3), in contrast, does resale and sets the prices.

Comments

• We received feedback on our version 1 that some images were too blurry or had poor lighting. The image quality has slightly improved, but largely remains similar to release 1. - Some users did not prefer a tar.gz format that we uploaded in version 1 of the dataset. We have now switched to .zip for convenience.- Extra care was taken not to leak personal information. This is why you will not see any entries for annotator attribute in the JSON files in station1/sep2023 since people used their real names. Since then, we used internally assigned IDs. - Many brand images contained people's hands, faces, or other personal information. We have removed about 4000-5000 brand images for privacy reasons. - Please inform us immediately if you find any personal information revelations in the dataset: - Farrukh Nauman (RISE AB): farrukh.nauman@ri.se, - Susanne Eriksson (Wargön Innovation AB): susanne.eriksson@wargoninnovation.se, - Gabriella Engstrom (Wargön Innovation AB): gabriella.engstrom@wargoninnovation.se.

We went through 100k images four times to ensure no personal information is leaked, but we are human and can make mistakes.

Partners

The data collection for this dataset has been carried out in collaboration with the following partners:

1. RISE Research Institutes of Sweden AB: RISE is a leading research institute dedicated to advancing innovation and sustainability across various sectors, including fashion and textiles.

2. Wargön Innovation AB: Wargön Innovation is an expert in sustainable and circular fashion solutions, contributing valuable insights and expertise to the dataset creation.

3. Myrorna AB: Myrorna is Sweden's oldest chain of stores for collecting clothes and furnishings that can be reused.

License

CC-BY 4.0. Please refer to the LICENSE file for more details.

Acknowledgments

This dataset was made possible through the collaborative efforts of RISE Research Institutes of Sweden AB, Wargön Innovation AB, and Myrorna AB, with funding from Vinnova and support from the EU project CISUTAC. We extend our gratitude to all the expert second-hand sorters and annotators who contributed their expertise to this project.

ZARA UK Fashion Dataset offers an extensive collection of fashion product data from ZARA's UK online store, providing a detailed overview of available items. This dataset is valuable for analyzing the European fashion retail market, particularly in the UK, and includes fields such as product titles, URLs, SKUs, MPNs, brands, prices, currency, images, breadcrumbs, country, availability, unique IDs, and timestamps for when the data was scraped.

Key Features:

• Product Details: Includes title, URL, SKU (Stock Keeping Unit), MPN (Manufacturer Part Number), and brand for each product, helping to uniquely identify and differentiate items.
• Pricing Information: Features the price of each product along with the currency used (GBP) to understand the pricing strategies of ZARA in the UK market.
• Visual Data: High-quality images of each product, essential for visual merchandising analysis and online consumer behavior studies.
• Categorical Information: Breadcrumbs data provide context on the product's placement within ZARA's website structure, helping to analyze navigation and product hierarchy.
• Geographical Focus: Specific to the UK market, making it relevant for studies on British fashion retail and consumer trends.
• Availability Status: Includes real-time availability data, which is crucial for understanding stock levels, popular products, and restocking practices.
• Unique Identifiers: Each product is tagged with a uniq_id, ensuring data integrity and making it easier to track and analyze over time.
• Data Collection Timestamp: The scraped_at field records the exact time and date when the data was collected, aiding in time-based analysis of inventory and pricing.

Potential Use Cases:

• Market Research: Analyze UK and European fashion trends, consumer preferences, and competitive positioning within the fast fashion sector.
• E-commerce Analysis: Study ZARA's product placement, pricing, and availability to optimize online retail strategies.
• Stock Management: Use SKU and availability data to predict inventory needs and enhance supply chain efficiency.
• Brand Analysis: Examine the impact of brand identity on consumer choices and product performance in the UK market.
• Academic Research: Ideal for research projects focused on fashion retail, marketing strategies, and consumer behavior in Europe.

Data Sources:

The data is meticulously collected from ZARA's official UK website and other reliable retail databases, reflecting the latest product offerings and market dynamics specific to the UK and European fashion markets.

• ZARA US Retail Products Dataset: Explore over 10,000 product records from ZARA's USA online store, including titles, prices, images, and availability.
  
  
• Fashion Products Dataset from GAP.com: Access detailed product information from GAP's online store, featuring over 4,500 fashion items with attributes like price, brand, color, reviews, and images.
  
  
• Myntra Fashion Products Dataset: A comprehensive dataset from Myntra.com, offering over 12,000 fashion products with detailed attributes for in-depth analysis.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows six condensed maps of the distribution of plants producing the following: leather footwear, womens and childrens factory made clothing, synthetic textiles and silks, mens factory made clothing, cotton textiles, and rubber products. All data for these maps is for 1954 with the exception of the rubber products map which is for 1955. Each map is accompanied by a bar graph and pie chart. The bar graphs show the value of production by major categories of products. The pie charts show the percentage distribution of persons employed in each manufacturing industry by province.

In this study, 403 Chinese consumers generalizable to the broader population were surveyed on their motivations to shop for fashion apparel in both high street and e-commerce environments. Statistical analysis was undertaken through multiple T-Tests and MANOVA with the assistance of SPSS and G*Power.

To increase the profits of international brands, this paper presents the motivations of Chinese consumers to engage in fashion retail, building upon established theory in hedonic and utilitarian motivations. With China set to capture over 24% of the $212 billion fashion market, international brands need to understand the unique motivations of Chinese consumers in order to capitalise on the market. However, the motivations of Chinese people to engage in fashion retail are as yet undefined, limiting the ability for international fashion retailers to operate with prosperity in the Chinese market.

Success.ai’s Fashion & Apparel Data for Apparel, Fashion & Luxury Goods Professionals in North America offers a comprehensive dataset designed to help businesses connect with decision-makers and key professionals in the dynamic fashion and apparel industry. Covering roles such as designers, brand managers, retail executives, and supply chain leaders, this dataset provides verified contact details, professional insights, and actionable business data.

With access to over 700 million verified global profiles, including 130 million in North America, Success.ai ensures your marketing, outreach, and business development strategies are powered by accurate, continuously updated, and AI-validated information. Backed by our Best Price Guarantee, this solution is indispensable for thriving in North America’s competitive fashion market.

Why Choose Success.ai’s Fashion & Apparel Data?

1. Verified Contact Data for Targeted Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of professionals in apparel, fashion, and luxury goods industries.
   • AI-driven validation ensures 99% accuracy, improving communication efficiency and minimizing data gaps.

2. Comprehensive Coverage of North American Fashion Professionals

   • Includes profiles of professionals from major fashion hubs such as New York, Los Angeles, Chicago, and Miami.
   • Gain insights into regional trends, consumer preferences, and market opportunities in the fashion and luxury goods sectors.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, brand expansions, and market dynamics.
   • Stay aligned with evolving industry trends and seize new opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with apparel, fashion, and luxury goods professionals worldwide, with a focus on North America.
• 130M+ Profiles in North America: Gain verified contact details and actionable insights into the region’s top professionals.
• Contact Details: Access work emails, phone numbers, and business addresses for precision targeting.
• Leadership Insights: Connect with C-suite executives, brand managers, and designers driving innovation in the fashion industry.

Key Features of the Dataset:

1. Professional Profiles in Fashion and Apparel

   • Identify and connect with professionals responsible for product design, brand management, retail operations, and luxury goods marketing.
   • Target individuals leading creative initiatives, logistics, and digital transformation in the fashion industry.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (apparel, luxury goods, footwear), geographic location, or job function.
   • Tailor campaigns to align with specific market needs such as sustainable fashion, e-commerce, or retail expansion.

3. Regional Trends and Industry Insights

   • Leverage data on emerging fashion trends, consumer demands, and market growth in North America.
   • Refine marketing and product development strategies to align with audience expectations and market opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Brand Outreach

   • Design targeted campaigns to promote apparel, luxury goods, or supply chain solutions to professionals in the fashion industry.
   • Use verified contact data for multi-channel outreach, including email, phone, and social media.

2. Product Development and Innovation

   • Utilize insights into consumer preferences and fashion trends to guide product design and marketing strategies.
   • Collaborate with designers and brand managers to refine collections or launch new products.

3. Partnership Development and Collaboration

   • Build relationships with apparel brands, luxury retailers, and fashion designers exploring strategic alliances.
   • Foster partnerships that expand market reach, enhance brand visibility, or improve supply chain efficiency.

4. Market Research and Competitive Analysis

   • Analyze trends in North America’s fashion industry to refine business strategies, identify market gaps, and anticipate consumer demands.
   • Benchmark against competitors to stay ahead in the rapidly evolving fashion landscape.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality fashion and apparel data at competitive prices, ensuring strong ROI for your marketing, sales, and product development efforts.

2. Seamless Integration

   • Integrate verified fashion data into CRM systems, analytics platforms, or marketing tools via APIs or dow...

Sustainable Fashion Market Size, Trends and Insights By Product Type (Apparel, Footwear, Accessories, Jewellery, Bags, Others), By Fabric Type (Recycled Fabrics, Organic Fabrics, Regenerated Fabrics, Natural Fibers, Alternate Fibers, Others), By Distribution Channel (Online, Offline, Brand Outlets, Independent Boutiques, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033.

Reports Description

Global Sustainable Fashion Market was valued at USD 8.1 Billion in 2024 and is expected to reach USD 33.1 Billion by 2033, at a CAGR of 22.9% during the forecast period 2024 – 2033.

The eco-friendly and socially conscious fashion industry known as the sustainable fashion business, is expected to grow rapidly. Sustainable fashion refers to a movement and process of fostering change in fashion products and the fashion system towards greater ecological integrity and social justice. It encompasses the entire lifecycle of clothing, from design and production to distribution, use, and disposal.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=51954

China Garment & Apparel: Industrial Sales Value: Delivery Value for Export: Year to Date data was reported at 375,787.380 RMB mn in Dec 2018. This records an increase from the previous number of 348,432.210 RMB mn for Nov 2018. China Garment & Apparel: Industrial Sales Value: Delivery Value for Export: Year to Date data is updated monthly, averaging 235,719.050 RMB mn from Jan 2012 (Median) to Dec 2018, with 84 observations. The data reached an all-time high of 516,773.050 RMB mn in Dec 2014 and a record low of 52,955.220 RMB mn in Feb 2018. China Garment & Apparel: Industrial Sales Value: Delivery Value for Export: Year to Date data remains active status in CEIC and is reported by China Textile Industry Association. The data is categorized under China Premium Database’s Textile Sector – Table CN.RSC: Textile Industry: Garment and Apparel.

This dataset is about books. It has 1 row and is filtered where the book is Social innovation for business success : shared value in the apparel industry. It features 7 columns including author, publication date, language, and book publisher.

With the upgrading of consumption concepts, the fashion industry has huge market potential. According to statistics from authoritative organizations, the global market value of the fashion industry has exceeded US$3 trillion. At the same time, AI technology is also developing continuously, but the technology still faces many challenges in the process of integrating with the fashion industry. In order to promote the combination of AI technology and clothing fashion, Alibaba's "Image Harmony" team teamed up with the Department of Textiles and Clothing of the Hong Kong Polytechnic University to launch the industry's first large-scale high-quality fashion data set that meets both clothing professionalism and machine learning requirements, focusing on machines. There are two basic issues in cognitive fashion: clothing key point positioning and clothing attribute label identification. The clothing attribute label recognition data set was generated under this background. Clothing attribute tags are an important foundation for the clothing knowledge system, which is huge and complex. We have professionally organized and abstracted the clothing attributes, and built a label knowledge system that is consistent with the cognitive process, structured and meets the requirements of machine learning. The clothing attribute tag recognition technology born from this can be widely used in clothing image retrieval, tag navigation, clothing matching and other application scenarios. The image data is collected from Alibaba e-commerce data. This research topic focuses on local attribute identification of clothing products. All clearly identifiable attribute labels in the picture require prediction. Considering the complexity of clothing knowledge, this data set only retains the product image data of a single subject (single model or single piece tile), so that researchers can focus on solving the challenges in the attribute labeling task.

Forecast: Leather Apparel Market Size Value in Austria 2022 - 2026 Discover more data with ReportLinker!

The North American textile fabrics industry, valued at approximately $99.82 billion in 2025, is projected to experience steady growth with a Compound Annual Growth Rate (CAGR) of 3.85% from 2025 to 2033. This growth is fueled by several key drivers. Increased demand for apparel and home textiles from a growing and increasingly affluent population is a significant factor. Furthermore, advancements in textile technology, particularly in sustainable and functional fabrics, are driving innovation and expanding application areas. The rise of e-commerce and fast fashion continues to influence market dynamics, although concerns surrounding sustainability and ethical sourcing are creating countervailing pressures. Strong performance in segments like clothing and household applications, coupled with the popularity of cotton and synthetic materials, contribute significantly to the overall market size. However, factors like fluctuating raw material prices and global economic uncertainty pose potential restraints. Competitive pressures from international manufacturers also necessitate continuous innovation and efficiency improvements within the industry. The leading players in this market, encompassing both large multinational corporations and smaller specialized firms, are strategically adapting their product lines and manufacturing processes to meet evolving consumer preferences and environmental concerns. The regional breakdown shows North America as a dominant market within the textile fabrics landscape. Considering the provided data and industry knowledge, we can reasonably estimate that the North American market encompasses a substantial portion – approximately 70% – of the overall market size, with the remaining share distributed across other regions globally. The United States, as the largest economy in North America, drives a significant portion of the regional demand, followed by Canada and Mexico. The growth trajectory within North America is expected to mirror the overall global trend, with consistent year-on-year increases driven by the factors outlined above. The segmentation across applications (clothing, industrial, household) and material types (cotton, jute, silk, synthetics, wool) will continue to influence market dynamics, with potential shifts driven by consumer preferences and technological advancements in material science and manufacturing processes. This report provides a detailed analysis of the North American textile fabrics industry, covering the period from 2019 to 2033. It leverages a robust data set, including historical data (2019-2024), an estimated year (2025), and a comprehensive forecast (2025-2033) to provide valuable insights for industry stakeholders. The report delves into market segmentation by application (clothing, industrial/technical applications, household applications), material type (cotton, jute, silk, synthetics, wool), and process (woven, non-woven), offering a granular understanding of market dynamics. Key search terms include: North America textile market, textile fabric industry trends, woven fabrics market, non-woven fabrics market, cotton textile market, synthetic textile market, textile industry growth, North American textile manufacturers. Recent developments include: February 2023: Huntsman Corporation (NYSE: HUN) announced that it has completed the sale of its Textile Effects division to Archroma, a portfolio company of SK Capital Partners. The agreed purchase price was USD 593 million in cash plus assumed pension liabilities. Huntsman expects the net after-tax cash proceeds to be approximately USD 540 million before customary post-closing adjustments., December 2022: India and Canada are negotiating a free trade agreement (FTA) to boost bilateral trade. The Indian textile industry has suggested various rules related to the agreement. The conditions of the proposed FTA will determine if it will help get more export orders. Currently, India’s apparel exports to Canada are negligible., August 2022: Huntsman Corporation, an American multinational manufacturer and marketer of chemical products, has announced that Archroma, a global leader in sustainable specialty chemicals and solutions and a portfolio company of the US-based private investment firm SK Capital Partners, has entered into a definitive agreement to acquire the Textile Effects business.. Key drivers for this market are: Increasing demand for clothing and accessories, Availability of raw materials at low prices for textile manufacturers. Potential restraints include: Increasing demand for clothing and accessories, Availability of raw materials at low prices for textile manufacturers. Notable trends are: Increasing demand for North America's apparels driving the market.

Artificial Intelligence in Fashion Design Market Outlook

According to our latest research, the global Artificial Intelligence in Fashion Design market size reached USD 1.98 billion in 2024 and is projected to grow at a robust CAGR of 36.2% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a valuation of USD 28.2 billion. This remarkable growth is primarily driven by increasing digital transformation across the fashion industry, the need for enhanced personalization, and growing investments in AI-powered design tools and solutions. The adoption of AI technologies is fundamentally reshaping how fashion products are designed, manufactured, and marketed, offering significant opportunities for innovation and operational efficiency.

One of the key growth factors fueling the Artificial Intelligence in Fashion Design market is the rising demand for hyper-personalized consumer experiences. Today's fashion consumers expect brands to understand their unique preferences, style, and fit. AI algorithms enable brands to analyze vast datasets from social media, purchase histories, and customer feedback to predict trends and recommend products tailored to individual tastes. This level of personalization not only enhances customer satisfaction but also drives higher conversion rates and brand loyalty. Moreover, AI-powered virtual fitting rooms and style assistants are reducing product returns and improving the overall shopping experience, which is particularly significant for online retailers and e-commerce platforms.

Another major driver is the increasing pressure on fashion brands to accelerate their design cycles and respond quickly to changing market trends. Artificial Intelligence enables rapid prototyping, automated pattern generation, and real-time trend forecasting, allowing designers to bring new collections to market faster than ever before. AI-driven tools can scan global fashion trends, analyze consumer sentiment, and generate design suggestions, significantly reducing the time and cost associated with traditional design processes. This agility is crucial in a highly competitive industry where speed-to-market can determine a brand's success or failure.

Sustainability concerns are also propelling the adoption of AI in fashion design. The industry faces mounting scrutiny over its environmental impact, from resource-intensive production processes to excess inventory and waste. AI solutions are helping brands optimize supply chains, predict demand more accurately, and minimize overproduction. By leveraging AI for material selection, waste reduction, and sustainable design practices, fashion companies are not only meeting regulatory requirements but also aligning with consumer expectations for ethical and eco-friendly products. This shift towards sustainable fashion, powered by AI, is expected to be a significant growth catalyst over the next decade.

Regionally, North America and Europe are leading the adoption of AI in fashion design, driven by a strong presence of established fashion houses, technology startups, and significant R&D investments. The Asia Pacific region, however, is emerging as the fastest-growing market, fueled by a burgeoning middle class, rapid digitalization, and the rise of local fashion brands. Latin America and the Middle East & Africa are also witnessing increasing adoption, albeit at a slower pace, as brands in these regions begin to embrace AI-driven solutions to enhance competitiveness and cater to evolving consumer preferences.

Component Analysis

The Artificial Intelligence in Fashion Design market by component is segmented into Software, Hardware, and Services. The software segment dominates the market, accounting for the largest revenue share in 2024, as fashion brands increasingly invest in AI-powered design platforms, trend forecasting tools, and virtual fitting solutions. These software solutions are critical for automating repetitive design tasks, analyzing consumer data, and generating actionable insights that inform both creat

image made with Dall-e and used Chatgpt to enhance clarity on data description.

Description:

This dataset encompasses responses from 403 Chinese consumers, representative of the broader population. The survey focused on understanding the motivations behind fashion apparel shopping in both brick-and-mortar and e-commerce environments. It aims to provide insights into the unique motivations of Chinese consumers in the fashion retail sector, which is critical for international brands targeting the rapidly growing Chinese fashion market.

The Measurable AI Temu & Fast Fashion E-Receipt Dataset is a leading source of email receipts and transaction data, offering data collected directly from users via Proprietary Consumer Apps, with millions of opt-in users.

We source our email receipt consumer data panel via two consumer apps which garner the express consent of our end-users (GDPR compliant). We then aggregate and anonymize all the transactional data to produce raw and aggregate datasets for our clients.

Use Cases Our clients leverage our datasets to produce actionable consumer insights such as: - Market share analysis - User behavioral traits (e.g. retention rates) - Average order values - Promotional strategies used by the key players. Several of our clients also use our datasets for forecasting and understanding industry trends better.

Coverage - Asia (Japan, Thailand, Malaysia, Vietnam, Indonesia, Singapore, Hong Kong, Phillippines) - EMEA (Spain, United Arab Emirates, Saudi, Qatar) - Latin America (Brazil, Mexico, Columbia, Argentina)

Granular Data Itemized, high-definition data per transaction level with metrics such as - Order value - Items ordered - No. of orders per user - Delivery fee - Service fee - Promotions used - Geolocation data and more - Email ID (can work out user overlap with peers and loyalty)

Aggregate Data - Weekly/ monthly order volume - Revenue delivered in aggregate form, with historical data dating back to 2018.

Most of our clients are fast-growing Tech Companies, Financial Institutions, Buyside Firms, Market Research Agencies, Consultancies and Academia.

Our dataset is GDPR compliant, contains no PII information and is aggregated & anonymized with user consent. Contact business@measurable.ai for a data dictionary and to find out our volume in each country.

Forecast: Leather-Based or Leather Apparel Market Size Value in Poland 2023 - 2027 Discover more data with ReportLinker!

In 2023, the market size of the Indian textile industry was worth *** billion U.S. dollars, a decrease from previous year. The market size is likely to increase to *** billion dollars by 2030.

Articles of Apparel and Clothing Accessories of Furskin Market Size Value Per Capita in Mexico, 2023 Discover more data with ReportLinker!

This data reports on the volume, value, unit value, and import market share of certain textile and apparel imports from China.