This statistic shows the functional apparel market value worldwide from 2017 until 2025. In 2017, the functional clothing market was valued at approximately ** billion U.S. dollars and was forecast to reach a value of ***** billion U.S. dollars by 2025.

📨 Direct Mail Marketing Response Dataset

This dataset focuses on predicting which customers are most likely to respond to a direct mail marketing promotion.
It is based on real data from a clothing store chain in New England.

• Rows: 28,799 customers
• Features: 51 fields (customer demographics, purchase history, and marketing interactions)
• Target: RESP (whether a customer responded to a promotion)

📊 Dataset Details

Each row corresponds to a unique customer, with information about spending behavior, product preferences, and marketing exposure.

Key Fields

• HHKEY → Unique encrypted customer ID
• ZIP_CODE → Customer’s ZIP code
• FRE → Number of purchase visits
• MON → Total net sales
• AVRG → Average spend per visit
• AMSPEND, PSSPEND, CSSPEND, AXSPEND → Spend across four different franchise brands
• OMONSPEND, TMONSPEND, SMOSPEND → Spend over past 1, 3, and 6 months
• PREVPD → Spend in the same period last year
• GMP → Gross margin percentage
• PROMOS → Number of marketing promotions on file
• DAYS → Number of days customer has been on file
• FREDAYS, LTFREDAY → Time between purchases (recent & lifetime average)
• CLASSES → Number of different product classes purchased
• STYLES → Number of individual items purchased
• STORES → Number of stores shopped at
• MARKDOWN, COUPONS, MAILED, RESPONDE, RESPONSERATE → Promotion and discount engagement
• HI → Product uniformity (lower = more diverse purchases)
• CLUSTYPE → Lifestyle cluster type (encrypted)
• PERCRET → Percent of returns
• CC_CARD, VALPHON, WEB → Flags for credit card, valid phone number, and web shopper status

🛍 Clothing Category Spend Variables

Variables: PSWEATERS, PKNIT_TOPS, PKNIT_DRES, PBLOUSES,PJACKETS, PCAR_PNTS, PCAS_PNTS, PSHIRTS, PDRESSES, PSUITS, POUTERWEAR, PJEWELRY, PFASHION, PLEGWEAR, PCOLLSPND; AC_CALC20

Percentages of spend across 15 clothing/product categories:

sweaters, knit tops, knit dresses, blouses, jackets, career pants, casual pants, shirts, dresses, suits, outerwear, jewelry, fashion, legwear, collectibles

🎯 Target Variable

• RESP → Binary variable indicating whether the customer responded to a mailed promotion.

Credits: https://iscap.pt/~aazevedo/variaveis.html

The most important thing that apparel shoppers in the United States look for when purchasing clothing and footwear is the price of the product, according to a 2023 survey. This was followed by ratings and reviews and then, with a considerably lower share of responses, images provided by people who had previously purchased the product.

It was estimated in 2017, that the apparel market grew by approximately **** percent compared to the previous year. Further growth was forecast for the following years, with a peak growth rate of around *** percent expected in 2020. The United States and China have the largest apparel markets in the world in terms of revenue. Apparel & footwear market H&M is one of the largest and most recognizable apparel brands worldwide. In 2017, H&M held the third largest market share within the global apparel and footwear market, with a share of *** percent. The apparel and footwear market is quite a fragmented market due to its highly competitive and saturated nature, meaning that no one company holds a large portion of the whole market. The global apparel and footwear market had retail sales of around *** trillion U.S. dollars in 2017. Leading apparel companies Adidas was the third most valuable apparel brand in the world, valued at approximately ***billion U.S. dollars in 2019. Nike, ZARA, and H&M are some of the other most valuable apparel brands worldwide. When it comes to retail sales, the picture is slightly different as many of the biggest apparel brands design and manufacture clothing as opposed to focusing on the retail side of the industry. TJX Companies, Inditex, and H&M were the leading apparel retailers in the world in 2017. Inditex, whose brands include ZARA and Bershka, had sales of around **** billion U.S. dollars that year.

Abstract Purpose: The general objective of this paper is to evaluate the determinant attributes of the perception of clothing quality by the users of a social network and to verify if there are any differences of evaluation of these determinants between genders. Design/methodology/approach: To achieve the objective, a survey was conducted with a sample of 295 consumers. All participants, regardless of gender, were asked to access the SurveyMonkey site link and to answer the questions regarding the quality of clothing for both men and women. Data analysis was performed using descriptive statistics and variance analysis (ANOVA). Findings: The main results show that: 1. The consumers of garments regard as highly important to take into consideration quality attributes when deciding to buy clothes, especially for women in relation to menswear; 2. Women has a higher perception than men as for the evaluation of the quality attributes of both women’s wear and menswear; and, 3. Clothing consumers, in particular consumers of women’s products, only consider to purchase such products if they have, in particular, style, fabric quality and fair price. Originality/value: This research filled in some theoretical and methodological gaps with regard to giving emphasis to gender differences in clothing quality assessment. This is in line with the conclusions of quality research conducted long ago, such as Olson & Jacoby’s (1972), which findings are specific to the type of product and/or consumer investigated. Therefore, generalizations beyond the product or the consumers examined are of dubious validity.

Title: Clothing Fit Dataset

Description: The Clothing Fit Dataset is derived from ModCloth and RentTheRunway, containing measurements related to clothing fit. This dataset provides an opportunity to develop and evaluate models targeting product size recommendations, understanding user preferences, and analyzing customer feedback regarding the fit of clothing items.

Basic Statistics: - ModCloth: - Users: 47,958 - Items: 1,378 - Transactions: 82,790 - RentTheRunway: - Users: 105,508 - Items: 5,850 - Transactions: 192,544

Metadata: - Ratings and Reviews: Customer ratings and textual reviews for the clothing items. - Fit Feedback: Feedback regarding the fit of the clothing items (e.g., small, fit, large, etc.). - User/Item Measurements: Physical measurements of users and items. - Category Information: Information regarding the category of clothing items.

Example (RentTheRunway): Each entry in the dataset contains detailed information about a user's experience with a clothing item, including the fit, user and item measurements, rating, review text, and category. json { "fit": "fit", "user_id": "420272", "bust size": "34d", "item_id": "2260466", "weight": "137lbs", "rating": "10", "rented for": "vacation", "review_text": "An adorable romper! Belt and zipper were a little hard to navigate in a full day of wear/bathroom use, but that's to be expected. Wish it had pockets, but other than that-- absolutely perfect! I got a million compliments.", "body type": "hourglass", "review_summary": "So many compliments!", "category": "romper", "height": "5' 8\"", "size": 14, "age": "28", "review_date": "April 20, 2016" }

Download Links: - Modcloth: Download Link (8.5mb) - Renttherunway: Download Link (31mb)

Citation: If you utilize this dataset, please cite the following paper: Title: Decomposing fit semantics for product size recommendation in metric spaces Authors: Rishabh Misra, Mengting Wan, Julian McAuley Published in: RecSys, 2018 Link to paper

This dataset is valuable for researchers and practitioners in the domain of e-commerce, fashion retail, and recommender systems.

Graph and download economic data for Producer Price Index by Industry: Clothing and Clothing Accessories Retailers (PCU448448) from Dec 2003 to Sep 2025 about apparel, PPI, industry, inflation, price index, indexes, price, and USA.

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US Online Fashion Retail Market Size 2025-2029

The us online fashion retail market size is forecast to increase by USD 303.9 billion at a CAGR of 15.6% between 2024 and 2029.

The Online Fashion Retail Market in the US is experiencing significant growth, driven by the rising trend of online shopping and the increasing popularity of sports apparel and footwear. Consumers are increasingly turning to the convenience and accessibility of online platforms to meet their fashion needs. The sports apparel and footwear industry's growth is further fueling market expansion, as consumers seek out the latest trends and styles in athletic wear. However, this market is not without challenges. Security and privacy concerns related to consumer data have emerged as a significant obstacle. With the increasing amount of personal information being shared online, retailers must prioritize data protection and privacy to maintain consumer trust. Failure to do so could result in reputational damage and lost sales. Retailers must invest in robust cybersecurity measures and transparent data handling practices to mitigate these risks and capitalize on the market's potential.

What will be the size of the US Online Fashion Retail Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic US online fashion retail market, high-end fashion brands are embracing e-commerce optimization, leveraging data analytics tools to personalize shopping experiences and boost sales. Blockchain technology is revolutionizing the industry by ensuring supply chain transparency and ethical production, resonating with consumers' growing demand for sustainable sourcing. Resale platforms and second-hand clothing are gaining traction, as unisex fashion and body positivity continue to influence purchasing decisions. Performance marketing, affiliate marketing, and social commerce are key strategies driving growth, with fashion photography and fashion blogging shaping consumer trends. Brands prioritize customer data privacy while implementing customer loyalty programs and subscription services. Trend analysis, size inclusivity, and fashion forecasting are essential components of successful digital marketing automation. Luxury goods and vintage fashion are thriving, with mobile wallet integration streamlining transactions. Omnichannel retail, fashion journalism, fashion styling, and live streaming are shaping the future of the industry.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userWomenMenKidsProductApparelFootwearsBags and accessoriesTypeMass marketPremiumLuxuryPlatformMobile AppsWeb PortalsPrice RangeEconomyMid-RangePremiumGeographyNorth AmericaUS

By End-user Insights

The women segment is estimated to witness significant growth during the forecast period.

The online fashion retail market in the US is experiencing significant growth, driven by several key trends and factors. Product reviews and customer satisfaction play a crucial role in influencing purchasing decisions, with consumers relying on authentic feedback to make informed choices. Influencer marketing is also a major force, as fashion influencers and celebrities shape trends and promote products through social media channels. Virtual reality and augmented reality technologies are transforming the shopping experience, allowing customers to virtually try on clothes and visualize how they would look. Big data and recommendation algorithms are being leveraged to personalize shopping experiences, while trend forecasting ensures that retailers stay ahead of the curve. Content marketing, machine learning, and data analytics are essential tools for fashion brands, helping them to understand customer preferences and tailor their offerings accordingly. Textile suppliers and apparel manufacturers are integrating sustainable practices to cater to the growing demand for eco-friendly fashion. Mobile commerce and mobile app development are critical for reaching customers on the go, with mobile responsiveness and user interface design key considerations. Conversion rate optimization, data security, and payment gateways are essential for ensuring a seamless shopping experience. Customer service, inventory management, order fulfillment, and shipping logistics are all crucial components of a successful online fashion retail business. Social media marketing, email marketing, and fashion designers collaborations are effective strategies for reaching and engaging customers. Size and fit, fast fashion, and formal wear are popular categories, with quality control and brand loyalty key differentiators.

Clothing Pattern Classification Dataset (MD-Fashion-2)

Introduction

The "Clothing Pattern Classification Dataset" is specifically designed to address the needs of the fashion industry, focusing on the classification of various clothing patterns. This dataset gathers internet-collected images that showcase clothing from different scenarios such as e-commerce platforms, fashion shows, social media, and offline user-generated content. It aims to facilitate the development of AI models that can accurately recognize and classify over 30 common clothing patterns, enhancing online shopping experiences and supporting trend analysis.

If you has interested in the full version of the datasets, featuring 220k annotated images, please visit our website maadaa.ai and leave a request.

Specification

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22149246%2Fc1b327fce8ef2e1f0c9fb525c94f8b4d%2Fclothing-patten.jpg?generation=1724925767341372&alt=media" alt="">

About maadaa.ai

Since 2015, maadaa.ai has been dedicated to delivering specialized AI data services. Our key offerings include:

• Data Collection: Comprehensive data gathering tailored to your needs.

• Data Annotation: High-quality annotation services for precise data labeling.

• Off-the-Shelf Datasets: Ready-to-use datasets to accelerate your projects.

• Annotation Platform: Maid-X is our data annotation platform built for efficient data annotation.

We cater to various sectors, including automotive, healthcare, retail, and more, ensuring our clients receive the best data solutions for their AI initiatives.

This statistic depicts apparel market size projections from 2012 to 2025, by region. The United States' apparel market is expected to grow from *** billion U.S. dollars in 2015 to *** billion U.S. dollars in 2025; a CAGR of two percent.Apparel IndustryDespite the current global economic downturn, the global apparel industry continues to grow at a healthy rate and this, coupled with the absence of switching costs for consumers and great product differentiation, means that rivalry within the industry is no more than moderate. The apparel industry is of great importance to the economy in terms of trade, employment, investment and revenue all over the world. This particular industry has short product life cycles, vast product differentiation and is characterized by great pace of demand change coupled with rather long and inflexible supply processes.Even well-established brands have to work hard to maintain their share of the market. Consumers are demanding more versatile wear with wider functionality, which means retailers continue producing new styles of apparel for men and women.Apparel remains largely a discretionary purchase compared to other consumer goods, making it more prone to economic shocks. The global apparel market has been shaped by three contrasting regional movements - robust growth in emerging markets, fragile recovery in the United States, and a sharp slowdown in Western Europe. During 2015, retail sales at clothing and accessories stores in the United States totaled approximately *** billion U.S. dollars; up from ****** billion U.S. dollars the previous year. Apparel retailing has always been a tough, highly competitive business, and many chains rise dramatically and then fail as price pressure from major discounters like Wal-Mart, Target and Kohl's keep profit margins thin at stores that sell moderately priced apparel.The global apparel market is always changing, attempting to adapt to customer trends and new technology that will allow the consumers shopping experience to be more enjoyable and ergonomic.

Sustainable Fashion Q&A Dataset

This dataset (sustainable_fashion.csv) contains a collection of synthetically generated Question-Answer (Q&A) pairs on sustainable fashion and style, with an emphasis on timeless wardrobe pieces, sustainable choices, and capsule wardrobe principles. The data was created using a large language model with advanced reasoning, prompted with various grounded contexts and real-world examples. It can be used to train or evaluate models that specialize in sustainable fashion advice, styling recommendations, or instruction-following tasks.

Examples:

• What makes a neutral color palette so timeless?

• Which casual shirts stand the test of time?

• How can I dress elegantly while pregnant through a hot summer?

• How do I mix classic and athletic styles in a sustainable way?

• I’m seeking advice for building a seasonless blazer collection. Where do I start?

• I’d like to wear jackets more often. Any tips on pairing different jacket types with basics for a classic vibe?

Core Criteria

1. Conciseness & Directness

   • Offers clear, actionable fashion tips without unnecessary complexity.

2. Personalization

   • Tailors advice to individual budgets, lifestyles, and style preferences.

3. Integration of Concepts

   • Connects sustainability principles, budget constraints, and style guidelines into a unified approach.

4. Tone & Accessibility

   • Maintains a friendly, approachable voice—ideal for newcomers and seasoned eco-conscious dressers alike.

5. Strategic Focus

   • Emphasizes long-term wardrobe value, cost-benefit analyses, and ecological impact in every recommendation.

6. Practical Reality

   • Balances high-quality investments with realistic budgeting, mixing accessible pieces with sustainable choices.

Overview

• Context: The data focuses on classic, long-lasting wardrobe recommendations. Topics include choosing neutral color palettes, selecting high-quality fabrics (like wool), finding universally flattering silhouettes, and embracing sustainability in fashion choices...

• Structure: Each entry is formatted, containing two primary fields:

  • instruction – The user’s question or prompt
  • response – The corresponding answer or advice

• Example Entry (Truncated for Clarity): csv instruction,response "What makes a neutral color palette so timeless?", "Neutral tones like black, navy, beige, and gray offer unmatched versatility..."

Data Generation

• Synthetic Creation:
  This dataset is synthetic—the questions and answers were generated by a large language model. The prompts used in creation were seeded with diverse real-world fashion contexts and examples to ensure groundedness and practical relevance.

• Advanced Reasoning:
  The large language model was employed to simulate more detailed and nuanced fashion advice, making each Q&A pair comprehensive yet concise. Despite the synthetic nature, the reasoning incorporates established fashion principles and best practices.

Dataset Contents

Potential Use Cases

1. Sustainable Fashion Chatbot/Assistant:

   • Train a model to provide on-demand styling advice or recommendations for various occasions.

2. Instruction-Following/QA Models:

   • Ideal for fine-tuning large language models (LLMs) so they can handle fashion-specific questions accurately.

3. Content Generation:

   • Generate blog articles, social media content, or editorial pieces on sustainable and timeless fashion, using the Q&A patterns as seed material.

4. Sustainable Fashion Product Descriptions:

   • Leverage the dataset to help a model create consistent, on-brand descriptions for apparel and accessories.

Getting Started

1. Download the Dataset

   • The data is provided as a csv file where each line is a single record with the keys instruction and response.

2. Data Preprocessing

   • Many Q&A or instruction-based fine-tuning frameworks allow direct ingestion of CSV files.
   • Alternatively, convert the data into your preferred format ( Pandas DataFrame, etc.) for custom processing.

3. Sample Use
   ```python import csv

Load the data

data = [] with open('sustainable_fashion.csv', 'r', encoding='utf-8') as f: reader = csv....

📜 Dataset Overview

This dataset contains 3,400 records of fashion retail sales, capturing various details about customer purchases, including item details, purchase amounts, ratings, and payment methods. It is useful for analyzing customer buying behavior, product popularity, and payment preferences.

📂 Dataset Details

🔍 Key Insights

• The dataset contains 3,400 transactions.
• Missing values are present in:
  • Purchase Amount (USD): 650 missing values
  • Review Rating: 324 missing values
• Payment Method includes multiple categories, allowing analysis of payment trends.
• Date Purchase is in DD-MM-YYYY format, which can be useful for time-series analysis.
• The dataset can help analyze sales trends, customer preferences, and payment behaviors in the fashion retail industry.

📊 Potential Use Cases

• Sales Analysis: Understanding which fashion items are selling the most.
• Customer Insights: Analyzing purchase behaviors and spending patterns.
• Trend Forecasting: Identifying seasonal trends in fashion retail.
• Payment Method Preferences: Understanding how customers prefer to pay.

Graph and download economic data for All Employees, Apparel Manufacturing (CES3231500001) from Jan 1990 to Sep 2025 about apparel, nondurable goods, establishment survey, goods, employment, and USA.

Apparel Market size was valued at around USD 1.34 trillion in 2023 & is projected to reach around USD 1.78 trillion by 2030, at 4.3% CAGR (2025-30).

China Exports of other made textile articles, sets, worn clothing was US$34.45 Billion during 2024, according to the United Nations COMTRADE database on international trade. China Exports of other made textile articles, sets, worn clothing - data, historical chart and statistics - was last updated on November of 2025.

Germany Exports: Volume: Clothing data was reported at 81,707.300 Tonne in Feb 2025. This records a decrease from the previous number of 84,717.500 Tonne for Jan 2025. Germany Exports: Volume: Clothing data is updated monthly, averaging 80,658.950 Tonne from Jan 2008 (Median) to Feb 2025, with 206 observations. The data reached an all-time high of 108,053.100 Tonne in Oct 2019 and a record low of 50,616.600 Tonne in Apr 2020. Germany Exports: Volume: Clothing data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.JA003: Trade Statistics: Volume.

China Number of Employee: Garment & Apparel data was reported at 1,969.000 Person th in Oct 2025. This records a decrease from the previous number of 1,986.000 Person th for Sep 2025. China Number of Employee: Garment & Apparel data is updated monthly, averaging 2,559.000 Person th from Jan 2012 (Median) to Oct 2025, with 131 observations. The data reached an all-time high of 4,621.900 Person th in Dec 2014 and a record low of 1,969.000 Person th in Oct 2025. China Number of Employee: Garment & Apparel data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIF: Garment and Apparel.

Context:

This is a visual image dataset, a comprehensive collection of visual data designed to enhance fashion recommendation systems. This dataset leverages advanced computer vision and machine learning techniques to analyze the visual aspects of women's fashion items, such as color, texture, and style, enabling the development of personalized style suggestions. This dataset is ideal for researchers, data scientists, and developers looking to create innovative fashion recommendation systems and gain deeper insights into the visual aspects of women's fashion. Dive into the world of fashion data and unlock new possibilities with the dataset.

The Global Fast Fashion Market Size Was Worth USD 148.40 Billion in 2024 and Is Expected To Reach USD 179.50 Billion by 2034, CAGR of 14.56%.

This dataset offers a unique insight into the rising trend of sustainable fashion across the globe 🌍. With the world becoming increasingly conscious of environmental impact, this dataset highlights key areas such as materials, production processes, and popular brands promoting eco-friendly fashion. Whether you’re researching trends, consumer behavior, or industry shifts, this data provides valuable context for understanding the impact of sustainability on fashion choices in 2024 👗🌱.