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...

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.

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...

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.

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.

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.

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

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.

High Fashion Market Outlook

The global high fashion market size is set to witness a substantial growth, with projections indicating an increase from USD 134 billion in 2023 to approximately USD 275 billion by 2032, reflecting a commendable compound annual growth rate (CAGR) of around 8.2%. This growth is primarily driven by the increasing consumer inclination towards luxury and premium fashion products, a trend that is further amplified by the rising disposable incomes and evolving lifestyle preferences across the globe. The high fashion market is experiencing a dynamic transformation, fueled by technological advancements, increased online presence, and a burgeoning demand for personalized and unique fashion experiences.

A significant growth factor for the high fashion market is the continuous influence of social media and digital platforms, which have revolutionized the way consumers interact with fashion brands. Platforms like Instagram, TikTok, and Pinterest have significantly increased the visibility of high fashion brands, enabling them to reach a global audience instantly. Influencer marketing has become a potent tool for these brands, allowing them to create trends and engage with consumers on a deeper level. This digital shift has not only enhanced brand visibility but also driven sales by providing consumers with immediate access to fashion products and trends. Furthermore, the integration of augmented reality (AR) in online shopping experiences is also contributing to market growth by offering consumers a virtual try-before-you-buy experience.

Another pivotal growth driver is the rising awareness and demand for sustainable and ethical fashion. There is an increasing consumer shift towards brands that are committed to environmental sustainability, ethical labor practices, and the use of eco-friendly materials. This shift is compelling high fashion brands to adopt sustainable practices and materials, which not only helps them to cater to the changing consumer preferences but also gives them a competitive edge in the market. Many high fashion brands are launching eco-friendly collections and are transparent about their production processes, thereby appealing to the environmentally conscious consumers who are willing to pay a premium for sustainable products.

Moreover, the high fashion market is being significantly influenced by globalization and urbanization. The expansion of global retail networks and the proliferation of fashion weeks across the world are making high fashion more accessible to a broader audience. Cities like Paris, Milan, and New York continue to be fashion capitals, setting global trends that are embraced worldwide. Additionally, emerging markets, particularly in Asia Pacific and Latin America, are witnessing a growing appetite for high-end fashion as middle-class populations expand and urbanization accelerates, leading to increased consumer spending capacity.

Fashion Berets have emerged as a noteworthy trend within the high fashion accessory segment, capturing the attention of both designers and consumers alike. These stylish headpieces, often associated with timeless elegance and a touch of European flair, are making a significant comeback. Fashion berets are being reimagined by luxury brands, incorporating modern materials and innovative designs that appeal to contemporary tastes. Their versatility allows them to be paired with a variety of outfits, from casual streetwear to sophisticated evening ensembles, making them a must-have accessory for fashion-forward individuals. As fashion berets continue to gain popularity, they are becoming a symbol of chic sophistication, adding a unique element to the high fashion landscape.

Regionally, the high fashion market is witnessing varied growth patterns. North America and Europe continue to dominate the market due to the presence of prominent fashion houses and a strong consumer base with high purchasing power. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing urbanization and the rise of affluent middle-class consumers in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, fueled by the improving economic conditions and increasing western influence on fashion trends. Each region presents unique opportunities and challenges, contributing to the diverse growth trajectory of the global high fashion market.

Product Type Analysis

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Description

Context Semantic Segmentation is one of major tasks in Computer Vision. It is the pixel-wise classification of an image into object classes. This dataset contains 1000 images and segmentation masks pairs of individual people's clothing. With 59 object classes and a relatively lesser data, the task of modelling is expected to be a challenging one! The data needs no preprocessing, all images are of same size, same format, and ready to model.

Content The dataset contains 1000 images and 1000 corresponding semantic segmentation masks each of size 825 pixels by 550 pixels in PNG format. The segmentation masks belong to 59 classes, the first being the background of individuals, and the rest belong to 58 clothing classes such as shirt, hair, pants, skin, shoes, glasses and so on. A CSV file containing the list of 59 classes is included in the dataset. The dataset contains data in both JPEG formats and PNG formats. However, JPEG is found to be lossy, while PNG is lossless with the essence of Originality.

Introducing a Compact Clothing Dataset: This dataset showcases a collection of fashion items across six distinct categories — T-Shirts, Shirts, Pants, Shorts, Shoes, and Sneakers. With a focus on diversity, it provides a snapshot of styles, colors, and patterns, making it an ideal resource for small-scale fashion analysis, image recognition, and machine learning applications.

Key Features:

Diversity Across Categories:

The dataset encapsulates an array of clothing categories, ensuring representation from various facets of the fashion landscape. From casual wear such as T-Shirts and Shorts to more formal attire like Shirts and Pants, and even encompassing footwear options with Shoes and Sneakers, this collection is designed to be inclusive and versatile. Snapshot of Styles:

With a keen emphasis on diversity, the dataset provides a snapshot of contemporary fashion trends. Users can explore a wide array of styles, from minimalist designs to vibrant patterns, facilitating a nuanced understanding of the evolving preferences in the fashion industry. Ideal for Small-Scale Analysis:

Tailored for small-scale fashion analysis, this dataset is perfect for researchers, developers, and enthusiasts looking to conduct insightful studies on trends, consumer preferences, and stylistic variations within a limited scope. Image Recognition and Machine Learning Applications:

The dataset is well-suited for image recognition and machine learning applications. Researchers and practitioners can leverage this resource to train and test algorithms, develop fashion recommendation systems, and explore the intersection of technology and style. Accessible Resource:

Whether you are a novice in the field of machine learning or an experienced researcher, this dataset serves as a valuable and accessible resource. Its compact size makes it particularly suitable for educational purposes, enabling learners to grasp fundamental concepts in image classification and pattern recognition. Usage Scenarios:

The Compact Clothing Dataset finds utility in a variety of scenarios, including but not limited to:

Developing and testing image classification algorithms. Training machine learning models for fashion recognition. Conducting small-scale fashion trend analyses. Exploring the correlation between styles and color patterns. In summary, this Compact Clothing Dataset emerges as an indispensable tool for those delving into the multifaceted world of fashion analysis and machine learning applications. With its thoughtful curation and focus on diversity, it paves the way for innovative exploration and understanding of the intricate nuances within the realm of clothing and style.

The Intelligent Multi-Layer Clothing Classification Dataset is designed for dynamic thermal comfort evaluation in energy-efficient environments. It provides data on clothing attributes, environmental conditions, and physiological responses to help develop AI-driven models for optimizing thermal adaptation.

This dataset supports clothing classification based on fabric type, thickness, and coverage while integrating IoT sensor data (temperature, humidity, air velocity, radiant heat) and wearable device data (skin temperature, heart rate variability, perspiration rate). Additionally, Clo values from infrared thermal imaging ensure accurate heat insulation estimation.

The dataset is ideal for machine learning models in thermal comfort analysis, smart clothing systems, and personalized climate control applications.

Key Features: Clothing Data: Fabric type, thickness, coverage, and shape.

Environmental Data: Temperature, humidity, air velocity, radiant heat.

Physiological Data: Skin temperature, heart rate variability, perspiration rate.

Infrared Imaging Data: Clo value estimation for insulation analysis.

Target Variable: Clothing classification (Single Layer, Double Layer, Multi-Layer).

Global Clothing and Accessories of Furskin Market Size Value Per Capita by Country, 2023 Discover more data with ReportLinker!

Microplastic pollution is one of the emerging global environmental issues. The fashion industry is a major source of microplastic pollution due to the massive consumption of synthetic fibres. Textile, garment, and footwear manufacturing contribute to the microplastic emission into sewage waters. Cities are major contributors to microplastic emissions and the implementers of microplastic control. It is important to compile the inventories of microplastic emissions of the fashion industry for cities, and fewer studies have investigated the inventories of microplastic emissions for key industries in cities. This study, for the first time, compiles microplastic emission inventories of the fashion industry for Chinese cities. The inventories are constructed using 21 cities and 4 production processes of the fashion industry. The socioeconomic data of the fashion industry in cities such as the output of industrial products and the gross output value of the fashion industry are included in the datasets as well. The dataset can provide transparent, comparable, and verifiable data support for the microplastic pollution prevention and control strategies in the fashion industry, and provide references for the construction of microplastic emission inventories for other key industries.

Raising environmental awareness and product development are two separate and costly investments that many small and medium-sized fashion businesses cannot afford to achieve sustainability. Therefore, there is a need to decide which factors more significantly impact consumer loyalty and purchase intention toward eco-friendly fashion. Thus, the study employs a mixed-methods approach to research how customers' perception about product-service quality and their environmental awareness and pro-environmental behavior impact purchase intention and customer loyalty toward eco-friendly fashion products among Vietnamese Gen Z's. Most interviewees acknowledged that their primary source of knowledge for eco-friendly fashion was social media. The qualitative results further show that the knowledge and attitude toward eco-friendly fashion practices are insufficient to convince young customers to afford eco-friendly fashion products. The quantitative results show that while customer-related factors play a more significant role in stimulating purchase intention, only product-service quality factors impact loyalty. Hence, the study suggests that businesses should prioritize improving service and product quality rather than funding green marketing when targeting the Vietnamese Gen Z. Government should prioritize financial and technological support for fashion firms to develop high-quality eco-friendly fashion to ensure the availability of products in the market.

This report shows one way to manage enterprise value on the example of companies in the apparel industry. The calculations and assumptions were made based on the Balanced Scorecard method and on the basis of data from the financial statements of the analyzed companies, with clasped the values read for the industry.

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.

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

Success.ai’s Retail Data for Retail Professionals in APAC offers a comprehensive and accurate dataset tailored for businesses and organizations aiming to connect with key players in the retail industry across the Asia-Pacific region. Covering roles such as retail managers, merchandisers, supply chain specialists, and executives, this dataset provides verified LinkedIn profiles, work emails, and professional histories.

With access to over 700 million verified global profiles, Success.ai ensures your outreach, marketing, and collaboration strategies are powered by continuously updated, AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to excel in the dynamic and competitive APAC retail market.

Why Choose Success.ai’s Retail Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of retail professionals across APAC.
   • AI-driven validation ensures 99% accuracy, reducing inefficiencies and boosting engagement outcomes.

2. Comprehensive Coverage of APAC’s Retail Sector

   • Includes professionals from key retail hubs such as China, Japan, South Korea, India, Australia, and Southeast Asia.
   • Gain insights into market trends, consumer behavior, and retail innovations unique to the APAC region.

3. Continuously Updated Datasets

   • Real-time updates capture changes in roles, organizations, and industry dynamics.
   • Stay aligned with evolving trends and capitalize on emerging opportunities in the retail sector.

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: Access detailed retail data for professionals and organizations across the APAC region.
• Verified Contact Details: Gain work emails, phone numbers, and LinkedIn profiles for precise targeting.
• Professional Histories: Understand career trajectories, areas of expertise, and contributions to the retail sector.
• Regional Insights: Leverage actionable data on consumer preferences, supply chain challenges, and market trends.

Key Features of the Dataset:

1. Comprehensive Retail Professional Profiles

   • Identify and connect with professionals managing retail operations, merchandising, supply chains, and customer engagement strategies.
   • Target decision-makers involved in e-commerce, brick-and-mortar retail, and omnichannel strategies.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (fashion, electronics, grocery), geographic location, or job function.
   • Tailor campaigns to align with specific business needs, such as technology adoption, marketing strategies, or vendor partnerships.

3. Regional and Industry-specific Insights

   • Leverage data on APAC’s retail trends, consumer purchasing patterns, and logistics challenges.
   • Refine strategies to align with unique market dynamics and customer expectations.

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 Outreach

   • Promote retail technology solutions, marketing tools, or supply chain services to retail professionals in the APAC region.
   • Use verified contact data for multi-channel outreach, including email, phone, and LinkedIn campaigns.

2. Partnership Development and Collaboration

   • Build relationships with retail chains, e-commerce platforms, and logistics providers seeking strategic partnerships.
   • Foster collaborations that enhance customer experiences, expand distribution networks, or improve operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze regional retail trends, consumer behavior, and supply chain innovations to refine product offerings and business strategies.
   • Benchmark against competitors to identify growth opportunities and high-demand solutions.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in the retail industry recruiting for roles in merchandising, operations, and digital transformation.
   • Provide workforce optimization platforms or training solutions tailored to the retail sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality retail data at competitive prices, ensuring strong ROI for your marketing, sales, and business outreach efforts.

2. Seamless Integration

   • Integrate verified retail data into CRM systems, analytics platforms, or marketing tools via APIs or downloadable formats, streamlining workflows and enhancing productivity.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions, refine targeting, and boost conv...

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.