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 "Clothing" as an answer. 25 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.

A survey conducted in 2024 revealed that 72 percent of shoppers in France preferred to return products bought online with parcel shop drop-offs. Home collection was another popular way of returning online purchases. Parcel lockers were preferred by 17 percent of surveyed shoppers.

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.

In 2024, next to an add-to-cart rate of 9.3%, a cart abandonment rate of 72.8%, and a conversion rate of 2.5%, the eCommerce Benchmark KPIs in Japan also consist of an AOV of US$116.7, a discount rate of 10.3%, and a return rate of 7.4%.

In 2024, next to an add-to-cart rate of 11.2%, a cart abandonment rate of 77.5%, and a conversion rate of 2.5%, the eCommerce Benchmark KPIs in the UAE also consist of an AOV of US$120.8, a discount rate of 10.6%, and a return rate of 6.4%.

A survey conducted in 2024 revealed that the preferred method for returning online purchases in Italy was in a parcel shop, with 44 percent of survey respondents reporting as much. Home collection was the second-most popular option, with 43 percent.

In 2024, next to an add-to-cart rate of 11.1%, a cart abandonment rate of 75.8%, and a conversion rate of 2.7%, the global eCommerce Benchmark KPIs also consist of an AOV of US$116.1, a discount rate of 13.2%, and a return rate of 10.1%.

In 2024, next to an add-to-cart rate of 9.4%, a cart abandonment rate of 74.8%, and a conversion rate of 2.4%, the eCommerce Benchmark KPIs in Malaysia also consist of an AOV of US$91.7, a discount rate of 12%, and a return rate of 4.9%.

A 2024 study indicated that return of online purchases on parcel shops was the preferred method for Dutch shoppers. According to the study findings, 13 percent of shoppers in the Netherlands favored home collection as a return method for products bought online.

When asked about "Most returned online purchases by category", most UK respondents pick "Clothing" 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.

In 2024, next to an add-to-cart rate of 9%, a cart abandonment rate of 84.8%, and a conversion rate of 1.4%, the eCommerce Benchmark KPIs in Indonesia also consist of an AOV of US$77.8, a discount rate of 15%, and a return rate of 5.2%.

In 2024, next to an add-to-cart rate of 9.1%, a cart abandonment rate of 82.6%, and a conversion rate of 1.6%, the eCommerce Benchmark KPIs in Singapore also consist of an AOV of US$157.4, a discount rate of 10.5%, and a return rate of 6.7%.

In 2024, next to an add-to-cart rate of 9.4%, a cart abandonment rate of 74.9%, and a conversion rate of 2.4%, the eCommerce Benchmark KPIs in Pakistan also consist of an AOV of US$63.2, a discount rate of 16.1%, and a return rate of 5.1%.

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 🔍📊

In 2024, next to an add-to-cart rate of 10.5%, a cart abandonment rate of 79.3%, and a conversion rate of 2.2%, the eCommerce Benchmark KPIs for Fashion in India also consist of an AOV of US$62.9, a discount rate of 20.5%, and a return rate of 16.9%.

The favorite way for German shoppers to return their online purchases was through parcel shops, with 59 percent of respondents to a survey reporting this in 2025. Parcel locker was the second-most popular, with 26 percent.

In 2024, next to an add-to-cart rate of 9.2%, a cart abandonment rate of 79.4%, and a conversion rate of 1.9%, the eCommerce Benchmark KPIs in Vietnam also consist of an AOV of US$80.2, a discount rate of 14.2%, and a return rate of 4.7%.

The e-commerce personalization platform market is experiencing robust growth, driven by the increasing need for businesses to enhance customer engagement and drive sales conversions in the competitive digital landscape. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This growth is fueled by several key factors. Firstly, the rising adoption of omnichannel strategies necessitates personalized experiences across all touchpoints, from website browsing to email marketing. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enabling more sophisticated personalization techniques, such as predictive analytics and real-time recommendations. Thirdly, the growing preference for personalized shopping experiences among consumers is directly influencing businesses' investment in these platforms. The market segments show strong growth across all sectors, with Apparel & Footwear and Electronics & Jewelry leading the charge due to their high-value product categories and potential for tailored product suggestions. Larger enterprises currently dominate the market share, but the increasing accessibility and affordability of personalization solutions are propelling adoption among SMEs. Geographic distribution reveals North America and Europe as major markets, but Asia-Pacific is showing significant growth potential, fueled by the region's burgeoning e-commerce sector and expanding digital infrastructure. Competition in the e-commerce personalization platform market is intense, with established players like Oracle and SAP alongside agile startups such as SearchSpring and Nosto vying for market share. The success of these vendors depends on their ability to offer innovative features, seamless integrations, robust analytics capabilities, and exceptional customer support. Future growth will be driven by the continued development of AI-powered personalization, the integration of emerging technologies like augmented reality (AR) and virtual reality (VR) into personalization strategies, and the expansion into new markets and verticals. The ability to demonstrate a clear return on investment (ROI) through measurable improvements in conversion rates, customer lifetime value, and average order value will be critical for platform providers to maintain a competitive edge. Furthermore, addressing data privacy concerns and adhering to evolving regulations will be crucial for long-term success.

The return order volume for purchases made online was at **** percent in 2022 in India. This was a decrease as compared to the previous year when the return volume stood at over ** percent. Moreover, the majority of the returns belonged to the fashion segment under apparel and footwear.