In 2021, European online shoppers aged 18 to 24 returned the highest proportion of items purchased on the web. With an online return rate of over ** percent, young adults located in Switzerland were the most prolific returners out of the *** countries analyzed.

When asked about "Most returned online purchases by category", most U.S. respondents pick ********** as an answer. ** percent did so in our online survey in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

As of February 2025, the average cost of returning an online purchase to leading retailers in the United Kingdom (UK) was ***** British pounds. The median fee was much lower, at around**** British pounds.

When asked about "Most returned online purchases by category", most UK respondents pick ********** as an answer. ** percent did so in our online survey in 2025. Looking to gain valuable insights about customers of online shops across the globe? Check out our reports about consumers of online shops worldwide. These reports offer the readers a comprehensive overview of customers of eCommerce brands: who they are; what they like; what they think; and how to reach them.

Comprehensive dataset of ecommerce return rates across product categories, including industry averages, seasonal variations, and demographic breakdowns. Features current 2024 data and 2025 projections.

This statistic shows the results of a survey on the annual average rate of product returns in the online retail sector in Germany in 2013. During the survey period it was found that ** percent of responding online retailers stated to have a return rate of under **** percent.

China Banks' Wealth Management Product Average Rate of Return (BWMPR) data was reported at 4.380 % in Nov 2018. This records a decrease from the previous number of 4.470 % for Oct 2018. China Banks' Wealth Management Product Average Rate of Return (BWMPR) data is updated monthly, averaging 4.580 % from Jul 2014 (Median) to Nov 2018, with 53 observations. The data reached an all-time high of 5.170 % in Apr 2015 and a record low of 3.730 % in Nov 2016. China Banks' Wealth Management Product Average Rate of Return (BWMPR) data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Banks' Wealth Management Product: Payment. Since Jun 2016 (inclusive), the scope of average yield is closed-end product, previous is all products. 从2016年6月(含) 以来，平均收益率为封闭式产品口径，之前为全部产品口径。

Graph and download economic data for Average Price: Cola, Non-Diet, Return Bottles, 6 or 8 Pack (Cost per 16 Ounces/473.2 Milliliters) in U.S. City Average (APU0000717111) from Jan 1980 to Jul 1986 about other food items, retail, price, and USA.

Graph and download economic data for Average Price: Cola, Non-Diet, Return Bottles, 6 or 8 Pack (Cost per 16 Ounces/473.2 Milliliters) in the Midwest Census Region - Urban (APU0200717111) from Jan 1980 to Aug 1986 about other food items, retail, price, and USA.

Snapshot img

India Online Fashion Retail Market Size 2025-2029

The India online fashion retail market size is forecast to increase by USD 36.01 billion, at a CAGR of 22.2% between 2024 and 2029.

Major Market Trends & Insights

By Product - Apparel segment was valued at USD 8.26 billion in 2022
By Gender - Women segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 314.31 billion
Market Future Opportunities: USD 36.01 billion 
CAGR : 22.2%

Market Summary

The market has witnessed significant growth, fueled by the increasing adoption of digital technologies and the rise in internet and smartphone penetration. According to recent reports, India's online fashion market is projected to reach USD 35 billion by 2025, growing at a steady pace. This expansion is driven by the convenience and accessibility offered by e-commerce platforms, which allow consumers to shop from the comfort of their homes. Moreover, the presence of various payment options, including credit/debit cards, digital wallets, and cash on delivery, has further boosted the market's growth. In contrast, traditional brick-and-mortar stores face challenges such as high rental costs and limited product offerings, making e-commerce an attractive alternative.
The fashion industry's online segment includes various categories, such as apparel, footwear, and accessories, with apparel being the largest and fastest-growing segment. As the market continues to evolve, we can expect to see increased competition, innovative marketing strategies, and personalized shopping experiences.

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

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market exhibits a significant presence in the country's burgeoning e-commerce sector. According to recent estimates, the market currently accounts for over 3% of India's total retail sector, with a growth rate of approximately 25% year-on-year. Looking forward, this figure is projected to reach nearly 5% by 2025. A comparison of key performance indicators reveals that online fashion retailers in India have achieved impressive customer engagement. For instance, the average customer lifetime value stands at INR 25,000, while the conversion rate for mobile commerce reaches 35%. Furthermore, the market's growth is driven by factors such as increasing internet penetration, improving digital infrastructure, and the rising popularity of social commerce.
In terms of competition, players in the online fashion retail space continue to invest in various strategies to differentiate themselves. These include website traffic analysis, customer segmentation models, and personalization algorithms, among others. Despite challenges such as payment processing fees, e-commerce logistics, and returns and exchanges, the market's potential for growth remains strong. In conclusion, the market presents a compelling opportunity for businesses looking to expand their reach and capitalize on the country's growing digital economy. With a projected growth rate of 25% year-on-year and a customer lifetime value of INR 25,000, the market's potential for revenue generation is significant.
Furthermore, the increasing popularity of mobile commerce and social commerce trends underscores the importance of a robust digital presence for fashion retailers.

How is this India Online Fashion Retail Market segmented?

The online fashion retail in India industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Apparel
  Footwear
  Bags and accessories


Gender

  Women
  Men
  Children


Price Range

  Economy
  Mid-Range
  Premium


Platform

  Mobile Apps
  Web Portals


Geography

  APAC

    India

By Product Insights

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

In the dynamic and evolving online fashion retail landscape in India, the apparel segment experiences consistent growth. Consumers prioritize fashionable and comfortable clothing, driving retailers to cater to diverse consumer segments. The market encompasses a wide range of clothing categories for men, women, children, and infants. Top wear apparel, including tops, blouses, dresses, casual shirts, formal shirts, T-shirts, sweaters, sweatshirts, tank tops, and vests, currently accounts for a significant market share. Meanwhile, bottom wear, consisting of trousers, jeans, jeggings, pants, shorts, and skirts, also experiences steady demand. Intimates and sleepwear, such as pajamas, bathrobes, shapewear, slips, socks, underwear, and briefs, are essential categories that cater to consumers' daily needs.

Children's and inf

Retail Sales in the United States increased 3.90 percent in July of 2025 over the same month in the previous year. This dataset provides - United States Retail Sales YoY - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Focus group discussions with women in Manchester and Edinburgh on female consumer perceptions about online shopping for fashion, and product visualisation technology on shop websites. Participants discussed how they currently shop online and in shops and how they prefer to visualise items on shop websites. After showcasing novel visualisation technologies allowing shoppers to interact with models on the shop website and scrunching fabrics, participants discussed their views and preferences for such innovations allowing more visual and tactile interactions in online shopping. This study aims to address the challenge of the lack of tactile input which currently characterises online fashion shopping, by developing existing applied research to make it more applicable to the UK fashion retail sector. The inability to touch products during the purchase decision-making process is one of the key challenges for fashion retailers. Product returns rates for fashion currently average 25 per cent and the abandoned shopping basket rate for online retail in the UK currently stands at around 65 per cent. The research objectives are: (1)to assess the application of a novel form of image interactivity technology in the fashion retail context. Specifically the research seeks to address the perceptual gap between digital and physical product evaluation both online and in the physical store environment via the use of novel touchscreen technology, developed by Heriot-Watt University; (2)to assess the potential of image interactivity technology in fulfilling consumers' utilitarian and hedonic online shopping motives; (3) to identify the barriers and facilitators of adoption by fashion retailers, with a focus on SMEs. The methodology will include the collection and analysis of quantitative data from retailer websites (Google analytics), supplemented by qualitative data gathered from interviews with retailers and focus groups with consumers.

China WMCP: Duration: Avg Annualized Rate of Return: Close-end Fixed Income Product: Up to 1 Year data was reported at 3.340 % pa in Mar 2025. This records a decrease from the previous number of 3.560 % pa for Feb 2025. China WMCP: Duration: Avg Annualized Rate of Return: Close-end Fixed Income Product: Up to 1 Year data is updated monthly, averaging 3.235 % pa from Aug 2022 (Median) to Mar 2025, with 32 observations. The data reached an all-time high of 4.160 % pa in Aug 2022 and a record low of 1.880 % pa in Dec 2022. China WMCP: Duration: Avg Annualized Rate of Return: Close-end Fixed Income Product: Up to 1 Year data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Puyi Standard: Average Annualized Rate of Return: Duration: Wealth Management Product.

Abstract

Microenterprise sectors are a dominant feature in urban areas of low- and middle-income countries. As much as a third of the labor force in these economies is self-employed. Those involved in retail trade—street vendors and owners of small shops and restaurants—are a plurality of small scale enterprises. These vendors earn their living using their own labor and small amounts of capital. They generally lack access to loans from formal financial institutions, relying on their own savings and perhaps informal loans from family members or friends. Surveys indicate that the lack of access to finance is one of their most often mentioned complaints.

This study uses data from the Mexican National Survey of Microenterprises (ENAMIN) to estimate returns to capital. A randomized experiment was designed to generate data which allow a consistent measure of returns to capital in microenterprises. Data was collected from a panel of microenterprises in the city of Leon, in Mexico over a period of five quarters. The baseline survey was carried out in November 2005. After the first through fourth rounds of the survey, treatments were administered in the form of either cash or equipment to randomly selected enterprises in the sample. The treatments generate shocks to capital stock which are random, uncorrelated with either the ability of the enterprise owner or the prospects for the business.

An unbiased estimate of returns to capital has important policy implications in several areas. First, the returns from investment determine the interest rates which borrowers are willing to pay to microlending organizations. Higher returns imply a higher likelihood of developing financially sustainable microlenders. Second, if returns are low below some investment threshold, then these low returns may act as an entry barrier, preventing high ability entrepreneurs without access to capital from entering. If, on the other hand, returns to capital are high at very low levels of investment, then capital-constrained entrepreneurs should be able to enter and grow to a desired size by reinvesting profits earned in the enterprise. In that case, capital constraints will have short term costs, but fewer long term effects on outcomes. High returns at low very low capital stock levels suggest that credit constraints will not lead to poverty traps.

Geographic coverage

Leon, Mexico. Leon is the fifth largest city in Mexico, with a metropolitan area population of approximately 1.4 million. The city is the center of Mexico's shoe and leather industries, and is also home to an active microenterprise sector.

Analysis unit

• Microenterprise

Universe

The research team set out to select a sample of enterprises with less than 100,000 pesos (approximately US$1000) in capital stock, excluding land and buildings. The sample was limited to enterprises engaged in retail trade and owned by males aged 22-55. In order to cover only full-time work, the owners were required to be working 35 hours or more a week in the baseline period.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame was based on the 10% public use sample of the 2000 population census for the city of Leon. Data was examined at the level of the smallest geographical unit available in the public sample, the UPM (unidad primaria de muestreo). For each UPM, the research team calculated for males 22-55 years of age the average education level and the percentage self-employed in the retail sector. They also calculated the percentage of households in the UPM with a male household head present. Using these data, 20 UPMS were selected with high rates of retail self employment and modest average levels of education.

The screening survey identified enterprises owned by males 22-55 years of age in the retail sector, operating without paid employees. Enterprises with paid employees are very likely to exceed our upper limit of 100,000 pesos of capital stock, so the lack of paid employees was used as an initial screen for capital stock. Where the screening survey was administered to the owners, we also asked for the value of the capital stock excluding land and buildings, measured at replacement cost.

The sample is limited to males aged 22-55 operating in the retail sector. The average enterprise has been operating for just over five years. Only 20 percent of the enterprises were started within a year of the baseline survey. Almost 20 percent are at least ten years old. Sales average 5,700 pesos per month, and profits 3,486 pesos per month. The median levels of sales and profits are similar, 5,000 and 3,000 pesos per month, respectively. We asked owners for profits before accounting for any compensation for their own time, so the profit levels should be viewed as including the opportunity cost of the time spent in the enterprise by the owner. As a result of this, profits are never reported as being negative.

Mode of data collection

Face-to-face [f2f]

Research instrument

The study employed several questionnaires that are explained below. - Survey screen: the screening questionnaire used to determine eligibility for the study - Survey baseline: the baseline survey of enterprises - Household Survey round 1: the baseline survey of households attached to the enterprise - Round 2, Round 3, Round 4, Round 5 surveys: follow-up surveys of enterprises - Round 5 household survey: follow-up survey of the household - Digit span recall showcard: showcard of digits used for digitspan recall test

The survey instrument was modeled after the Mexican National Survey of Microenterprises (ENAMIN) survey. In the first round, detailed information was gathered on the capital invested in the enterprise, separated into tools, machinery and equipment, vehicles, real estate and buildings, and inventories and finished and unfinished goods. Operational data was also gathered on the firm--revenues, expenses and profits-for the preceding month, and personal information about the owner. In each subsequent survey, firms were asked about changes in capital stock, either purchase of new assets or sales of existing assets, and operational data for another month of the survey.

In Europe, clothing items had the highest fashion return rates in 2022, a study revealed. About ** percent of dress purchases got returned, while skirts followed with roughly ** percent. Being a popular category among online shoppers, shoewear reached significant online return rates, too. Over ** percent of backless slippers orders were sent back in the considered year.

China WMCP: Duration: Avg Annualized Rate of Return: Open-end Fixed Income Product: Up to 1 Month data was reported at 1.790 % pa in Mar 2025. This records a decrease from the previous number of 2.270 % pa for Feb 2025. China WMCP: Duration: Avg Annualized Rate of Return: Open-end Fixed Income Product: Up to 1 Month data is updated monthly, averaging 2.985 % pa from Aug 2022 (Median) to Mar 2025, with 32 observations. The data reached an all-time high of 4.430 % pa in Apr 2023 and a record low of -4.530 % pa in Dec 2022. China WMCP: Duration: Avg Annualized Rate of Return: Open-end Fixed Income Product: Up to 1 Month data remains active status in CEIC and is reported by Puyi Standard. The data is categorized under China Premium Database’s Financial Market – Table CN.ZAM: Puyi Standard: Average Annualized Rate of Return: Duration: Wealth Management Product.

In 2022, the rate of fashion item-related returns was almost ** percent in Germany. This was higher compared to rest of Europe where the rate was around ** percent. Furthermore, the fashion category had by far the highest rate of returns compared with other categories both in Germany and Europe.

Customer360Insights

The Customer360Insights dataset is a synthetic collection meticulously designed to mirror the multifaceted nature of customer interactions within an e-commerce platform. It encompasses a wide array of variables, each serving as a pillar to support various analytical explorations. Here’s a breakdown of the dataset and the potential analyses it enables:

Dataset Description

• Customer Demographics: Includes FullName, Gender, Age, CreditScore, and MonthlyIncome. These variables provide a demographic snapshot of the customer base, allowing for segmentation and targeted marketing analysis.
• Geographical Data: Comprising Country, State, and City, this section facilitates location-based analytics, market penetration studies, and regional sales performance.
• Product Information: Details like Category, Product, Cost, and Price enable product trend analysis, profitability assessment, and inventory optimization.
• Transactional Data: Captures the customer journey through SessionStart, CartAdditionTime, OrderConfirmation, OrderConfirmationTime, PaymentMethod, and SessionEnd. This rich temporal data can be used for funnel analysis, conversion rate optimization, and customer behavior modeling.
• Post-Purchase Details: With OrderReturn and ReturnReason, analysts can delve into return rate calculations, post-purchase satisfaction, and quality control.

Types of Analysis

• Descriptive Analytics: Understand basic metrics like average monthly income, most common product categories, and typical credit scores.
• Predictive Analytics: Use machine learning to predict credit risk or the likelihood of a purchase based on demographics and session activity.
• Customer Segmentation: Group customers by demographics or purchasing behavior to tailor marketing strategies.
• Geospatial Analysis: Examine sales distribution across different regions and optimize logistics. Time Series Analysis: Study the seasonality of purchases and session activities over time.
• Funnel Analysis: Evaluate the customer journey from session start to order confirmation and identify drop-off points.
• Cohort Analysis: Track customer cohorts over time to understand retention and repeat purchase patterns.
• Market Basket Analysis: Discover product affinities and develop cross-selling strategies.

This dataset is a playground for data enthusiasts to practice cleaning, transforming, visualizing, and modeling data. Whether you’re conducting A/B testing for marketing campaigns, forecasting sales, or building customer profiles, Customer360Insights offers a rich, realistic dataset for honing your data science skills.

Curious about how I created the data? Feel free to click here and take a peek! 😉

📊🔍 Good Luck and Happy Analysing 🔍📊

Graph and download economic data for Average Price: Cola, Non-Diet, Return Bottles, 6 or 8 Pack (Cost per 16 Ounces/473.2 Milliliters) in the West Census Region - Urban (APU0400717111) from Jan 1980 to Nov 1985 about other food items, retail, price, and USA.

Instant Retail E-Commerce Platform Market Outlook

The global Instant Retail E-Commerce Platform market size is projected to grow from USD 45.3 billion in 2023 to USD 112.7 billion by 2032, reflecting a robust CAGR of 10.7% over the forecast period. The significant growth in this market is primarily driven by the increasing adoption of digital shopping experiences, the rapid advancement in technology, and the growing demand for personalized customer experiences.

The growth of the Instant Retail E-Commerce Platform market is fueled by several critical factors. Firstly, the shift in consumer behavior towards online shopping has been accelerated by the COVID-19 pandemic, which necessitated the need for contactless shopping experiences. Consumers are increasingly demanding faster, more convenient shopping solutions, which has led to the adoption of instant retail e-commerce platforms. These platforms offer immediate purchase and delivery services, meeting the high expectations for speed and convenience that modern consumers have.

Secondly, advancements in technology, including the proliferation of mobile devices and high-speed internet, are pivotal growth drivers. The increasing penetration of smartphones and improved internet connectivity have made it easier for consumers to shop online anytime and anywhere. Moreover, the integration of artificial intelligence and machine learning in e-commerce platforms has enhanced customer experiences by providing personalized recommendations, predictive analytics, and more efficient inventory management, further encouraging the adoption of these platforms.

The E-Commerce Profit Model is a critical aspect for businesses operating in the digital retail space. This model focuses on generating revenue through various streams such as product sales, subscription services, and advertising. By leveraging data analytics, companies can optimize pricing strategies, enhance customer retention, and increase average order values. The ability to offer personalized promotions and loyalty programs further contributes to profitability. As the e-commerce landscape becomes more competitive, businesses must continuously innovate their profit models to maintain a sustainable edge. Understanding customer behavior and preferences is key to developing effective profit strategies that align with market demands.

Additionally, the growing emphasis on customer satisfaction and engagement is pushing retailers to adopt instant retail e-commerce solutions. These platforms enable businesses to offer a seamless shopping experience, from browsing to checkout, and ensure quick delivery times. The ability to provide a superior customer experience through features such as real-time tracking, multiple payment options, and hassle-free returns is crucial in gaining a competitive edge in the crowded e-commerce marketplace.

From a regional perspective, the market's growth is unevenly distributed across different geographies. North America is expected to dominate the market due to the high adoption rate of advanced technologies and the presence of major e-commerce players. However, the Asia Pacific region is projected to exhibit the highest growth rate owing to the increasing internet penetration, rising middle-class population, and growing disposable incomes. Countries like China and India are at the forefront of this growth, driven by their massive consumer base and government initiatives promoting digital commerce.

Platform Type Analysis

The Instant Retail E-Commerce Platform market can be segmented by platform type into web-based and mobile-based platforms. Web-based platforms are traditional e-commerce websites accessible via desktop and laptop browsers. They provide a comprehensive shopping experience with detailed product listings, customer reviews, and various payment options. Although web-based platforms remain popular, their growth rate is relatively slower compared to mobile-based platforms due to the increasing preference for mobile shopping.

Mobile-based platforms, on the other hand, are designed specifically for smartphones and tablets. They offer a more streamlined and user-friendly interface tailored to the smaller screens of mobile devices. The rise in mobile commerce, or m-commerce, is a significant trend driving this segment's growth. Consumers favor mobile-based platforms for their convenience and ease of use, allowing them to shop on-the-go. The integration of mobile payment soluti