How Data Reduces Returns in Online Shopping
Online Shopping
Jul 31, 2025
AI and data-driven tools are transforming online shopping by reducing returns and enhancing fit accuracy, addressing a critical issue in the fashion industry.
Online shopping faces a massive returns problem, especially in fashion. In 2023, 24.4% of online apparel purchases were returned, costing U.S. retailers $25.1 billion in processing fees and creating environmental challenges. The main reasons? Poor fit, product misrepresentation, and uncertainty. Over 70% of returns stem from sizing issues, while "bracketing" (ordering multiple sizes to return the rest) has become common.
But there's a solution. AI and data-driven tools are helping reduce returns, improve accuracy, and boost customer confidence. Virtual try-ons, personalized recommendations, and machine learning predict better fits, cutting return rates by up to 64%. Retailers like Amazon, Zalando, and H&M are already seeing higher conversions and fewer returns by using these technologies.
Key takeaways:
Returns cost billions annually and harm the environment.
AI-powered virtual try-ons and size recommendations address fit issues.
Retailers using data-driven tools report reduced returns and higher sales.
The future of online shopping lies in using data to tackle uncertainty, helping both businesses and shoppers save time, money, and resources.
Boost Sales and Reduce Returns with AI for fashion retail | Silicon & Silk EP17
The Problem: High Return Rates in Online Fashion
Recent data paints a troubling picture for online fashion retailers: more than half of consumers who bought clothing online in the past 90 days have made at least one return. Returning items has become standard practice.
The numbers are staggering. Online fashion return rates often surpass 30%, with some reports showing customers sending back up to 40% of their purchases. In 2022 alone, 41% of online shoppers returned items due to sizing issues. These rates far exceed those of traditional brick-and-mortar stores, emphasizing the unique hurdles faced by online retailers. But why are so many items being returned, and what are the financial and environmental consequences of this trend?
Why Customers Return Online Purchases
The top reasons for returns? Poor fit (39%) and products not meeting visual expectations (28%). Without the ability to touch fabrics, see true colors, or try items on, customers are left guessing - and often guessing wrong.
This uncertainty has fueled a behavior known as "bracketing", where shoppers order multiple sizes or styles, fully intending to return what doesn’t work. Younger consumers, in particular, are driving this trend. Among mobile clothing shoppers, 50.9% are aged 18 to 24, 28.7% fall between 25 and 34, and only 4.5% are aged 45 to 54. These digitally savvy, younger shoppers are more comfortable with the buy-and-return cycle that has become synonymous with online shopping.
The Cost of Returns for Businesses and the Environment
The impact of returns extends far beyond customer habits - it’s a financial and ecological burden. Returns can eat up 10–20% of a retailer’s total revenue, while processing costs may account for up to 30% of the original sale price. This can slash gross margins by as much as 20%.
The financial toll is immense. In 2024, over $890 billion in retail merchandise was returned in the U.S. alone. Globally, e-commerce returns exceeded $800 billion annually in 2023, with fashion leading the pack.
The environmental cost is equally alarming. E-commerce returns contribute up to 24 million metric tons of CO₂ emissions each year. Clothing returns in the U.S. alone generate emissions equivalent to those of 3 million cars. In 2022, more than 9.5 billion pounds of returned products ended up in landfills, and discarded clothing has turned 741 acres of Chile’s Atacama Desert into a dumping ground.
Return logistics only add to the problem. Online shopping produces 4.8 times more packaging waste than in-store purchases. Additionally, returns can increase the emissions from the original delivery by as much as 30%. Processing a return often takes three times longer than delivering a new item, further straining supply chains.
"Returns are an environmental issue and are now also becoming an issue for the business model of many of these brands." - ActiveSustainability.com
In total, returns account for 25% of all emissions in e-commerce, compared to just 7% for physical stores. This stark difference highlights the hidden environmental price of online shopping.
"These products use precious resources which are becoming scarce and we are throwing them away unnecessarily." - Sarah Needham, Centre for Sustainable Fashion at University of the Arts London
To tackle these challenges, companies need smarter, data-driven solutions to improve sizing accuracy and reduce unnecessary returns.
How Data Changes Online Shopping Experiences
Tackling the issue of high return rates, data-driven personalization is reshaping how we shop online. Instead of treating all customers the same, retailers are turning to artificial intelligence to analyze massive amounts of customer data. The result? Shopping experiences tailored to individual preferences, leading to better fit accuracy and happier customers.
Modern AI systems dig into browsing habits, purchase histories, and demographics to build detailed shopper profiles and understand body types. This data helps predict the right fit, addressing one of the biggest challenges in online shopping - sizing issues. The impact is clear: AI personalization can boost revenue by 10–30% and increase conversion rates by 35%. Plus, 91% of shoppers are more likely to buy from brands that offer relevant recommendations and deals. This personalized approach not only enhances the shopping journey but also reduces returns by providing more accurate fit suggestions, as we’ll explore further.
Using Customer Data for Better Fit and Style Recommendations
Top retailers are proving that personalized recommendations drive results. Zalando, for instance, uses AI-powered suggestions to improve click-through rates by 40%, while ASOS’s "Complete the Look" emails have increased repeat purchases by 24%.
H&M’s mobile app takes it a step further by creating a custom storefront for each user based on their browsing history and style preferences. It’s not just about showing more options - it’s about showing the right options.
And customers are starting to expect this level of personalization. 71% of shoppers want companies to deliver tailored interactions, while 76% report frustration when those experiences are missing. Even more compelling, customers are 10 times more likely to become a brand's top-tier buyers when personalized experiences resonate strongly.
The benefits for retailers are just as striking. Personalization can increase revenue by 5–15%, improve marketing ROI by up to 50%, and raise conversion rates by 10–15%. It also leads to a 20% boost in customer satisfaction, proving that tailoring the experience pays off.
How Machine Learning Predicts Fit
Machine learning takes personalization to the next level by predicting fit and identifying potential return risks in real time. These algorithms analyze patterns in customer data to forecast which items are likely to be returned, enabling retailers to act before issues arise.
For example, ThredUp uses AI and machine learning to manage returns by studying customer preferences, item details, purchase habits, and seasonal trends. This allows them to predict not just what customers will buy, but what they’re likely to keep.
Amazon has introduced AI-driven "fit wardrobes" that consider individual body shapes to recommend sizes with higher accuracy, reducing returns in the fashion category. By understanding how different brands fit various body types, Amazon can suggest sizes that work better for each shopper.
Meanwhile, Measmerize’s Size Advisor combines machine learning, SKU-level data, and real-time customer inputs to deliver highly accurate size recommendations. These AI-driven tools can slash return rates by 25–40%, a significant improvement compared to the average return rate of 56% .
Machine learning also enables proactive strategies. For instance, it flags at-risk purchases and triggers follow-up actions like targeted emails or simplified exchange processes to address potential issues before they escalate.
"I would say preventing returns up front is probably the easiest place to deploy AI, and where we're seeing merchants use it the most." – Kristen Kelly, VP of Product at Loop Returns
This shift from reacting to returns to preventing them altogether is transforming how retailers operate. By leveraging AI, forward-thinking brands are not just solving problems - they’re staying ahead of them.
AI-Powered Virtual Try-On: A Solution to Reduce Returns
Shopping for clothes online can be tricky - shoppers often worry about fit and style without the chance to try items on. While tools like personalized recommendations and machine learning have made strides in improving this experience, AI-powered virtual try-on technology takes it to the next level. By offering a realistic preview of how clothing fits and moves, this technology directly tackles the uncertainty of online shopping.
What Is AI-Powered Virtual Try-On?
AI-powered virtual try-on uses augmented reality and artificial intelligence to provide a real-time, interactive preview of how clothing looks on a person. Unlike static product photos or basic size charts, this tech lets shoppers see themselves wearing the items virtually, creating a highly engaging experience.
Here’s how it works: Computer vision analyzes the user's body shape, size, and posture, while AI algorithms - trained on extensive datasets - map clothing onto the user's image. These algorithms account for human dimensions, fabric properties, and even how garments stretch and move. By incorporating 3D body scanning and advanced computer graphics, the system creates a personalized 3D avatar for each user. The result? A lifelike representation that goes beyond simply overlaying images, delivering an accurate sense of fit and movement.
One standout example is BetterMirror, which uses this advanced approach to provide shoppers with realistic previews of how clothes fit and move on their bodies. The platform analyzes individual data, such as purchase history, preferences, and browsing habits, to offer tailored recommendations.
"Immersive, accurate, and personal. This is the next wave of virtual try-ons."
– Alexandr Gergardt, Head of the ML department at Onix
How Virtual Try-Ons Reduce Returns
AI-powered virtual try-ons don’t just enhance the shopping experience - they also significantly reduce product returns. For industries where fit and sizing are critical, virtual try-ons have been shown to cut return rates by as much as 64%. By allowing customers to see how an item will look on their unique body type, they make more confident, informed purchase decisions. This has also led to a 200% boost in customer engagement.
Consider Avon, which implemented a virtual try-on feature and saw a 320% jump in conversions and a 33% increase in average order value. Similarly, AI-generated heat maps for garment comfort have demonstrated a fit prediction accuracy of 94.1%. In the footwear industry, Nike’s AR-powered virtual try-on lets users scan their feet for precise sizing recommendations, solving one of the biggest challenges in online shoe shopping.
Beyond individual purchases, brands in fashion and cosmetics have reported impressive results. Conversion rates have risen by up to 40%, while return rates have dropped nearly 20%. Sephora’s Virtual Artist app, which uses facial recognition to recommend makeup shades, recorded over 200 million shades tried on and 8.5 million visits within two years.
BetterMirror builds on these advancements, offering a virtual try-on experience that not only boosts customer confidence but also reduces the likelihood of returns.
"AR, coupled with AI, makes for a highly personalized shopping experience... We often see an increase in e-commerce basket size, add-on purchases, and conversion rates with AR/AI. Further, we also see a significant reduction in product returns that can save millions in cost savings."
– Kevin Nicholas, CMO of Growth Marketing
Benefits of Data-Driven Solutions for Retailers and Shoppers
Data-driven virtual try-on solutions are changing the game for online shopping. By combining AI's predictive capabilities with personalized experiences, these tools offer clear advantages for both retailers and customers. They not only improve customer confidence but also drive better business outcomes for retailers.
Better Customer Satisfaction and Trust
Virtual try-ons take the uncertainty out of online shopping. Instead of guessing, customers can make more informed decisions, which builds trust. For instance, over 51% of shoppers are less likely to return items when they use virtual try-ons, and 90% are willing to spend more when they have a positive shopping experience. Personalization plays a big role here - 81% of shoppers say a good experience makes them more likely to shop again.
How Retailers Improve Performance Metrics
Retailers see impressive results, too. Augmented reality (AR) and AI-driven tools can increase conversion rates by 40% and boost the average order value by 20%. On top of that, these technologies help reduce return rates. Some brands report a 25% drop in returns, while one global apparel company achieved an 80% reduction thanks to an AI-powered size recommendation tool.
Customer engagement also gets a significant lift. Retailers using virtual try-ons have seen up to a 200% increase in engagement. This translates to longer browsing times, more product views, and ultimately, higher sales.
Take BetterMirror as an example. By offering an AI-powered virtual try-on solution, they’ve managed to reduce returns while boosting conversions. Their platform addresses customer hesitation and creates a personalized shopping experience that benefits both shoppers and retailers.
According to Think With Google, customers are 40% more likely to spend beyond their original budget when the shopping experience feels personalized. Virtual try-on technology allows retailers to deliver tailored experiences to thousands of shoppers at once. This creates a win-win scenario: happier customers and a more profitable, efficient retail ecosystem.
Conclusion: The Future of Data-Driven Online Shopping
The future of online shopping is being shaped by data and AI, with the potential to boost revenues by 10–15% and redefine personalization - something 73% of business leaders are already anticipating.
The numbers back this up. The AI-enabled e-commerce market is expected to hit $16.8 billion by 2030, growing at an annual rate of 15.7%. Technologies like virtual try-ons are making a real difference, with return rates dropping by up to 25%, and specialty apparel brands reporting 15–40% increases in conversion rates after adopting these solutions.
Take BetterMirror, for example. This platform is tackling one of online shopping’s biggest pain points: uncertainty. By using AI-powered virtual try-on tools, it allows customers to see how clothes will fit and move on their bodies, giving them realistic previews. This not only builds confidence but also significantly cuts down on returns.
Personalization has become a non-negotiable for today’s consumers. Shoppers now expect tailored experiences, and businesses that fail to deliver risk falling behind. Christina Inge from Harvard's Professional & Executive Development highlights the importance of leveraging AI:
"There is a saying going around now - and it is very true - that your job will not be taken by AI. It will be taken by a person who knows how to use AI. So, it is very important for marketers to know how to use AI."
Emerging technologies like natural language processing and computer vision are poised to take retail analysis even further. Data-driven companies are already proving their advantage, being 23 times more likely to acquire new customers and typically 20 times more profitable than their competitors. For retailers, this isn’t just a trend - it’s a clear directive: embrace AI and data-driven tools to stay relevant and meet evolving customer demands.
The shift is already happening. Right now, 9 out of 10 businesses are actively investing in AI, and it’s predicted to save over $11 billion in annual costs this year alone. As virtual try-on tools and other AI-driven solutions grow more advanced, we’ll likely see even lower return rates and higher customer satisfaction across the e-commerce world. The message is clear: the future of online shopping belongs to those who harness the power of data and AI.
FAQs
How do AI-powered virtual try-ons help reduce return rates in online shopping?
AI-powered virtual try-ons leverage cutting-edge technology to provide lifelike, tailored previews of how clothes will fit and move on your body. By mimicking the fit and appearance of garments, these tools allow shoppers to make decisions with greater confidence.
This approach tackles one of the biggest challenges in online shopping: uncertainty about size and fit. When customers can visualize how an item will look and feel, they’re less likely to face disappointment. This not only reduces the number of returns but also makes for a smoother, more satisfying shopping experience.
How do high return rates in e-commerce affect the environment, and how can data-driven tools help reduce them?
High return rates in online shopping come with a heavy environmental cost. Each year, they contribute millions of metric tons of CO2 emissions. These emissions stem from extra shipping, the waste generated from packaging, and the unfortunate disposal of many returned items that can't be resold.
One way to address this problem is through AI-powered virtual try-ons. These tools can improve fit accuracy and boost customer confidence before they make a purchase. By cutting down the chances of returns, these solutions don't just enhance the shopping experience - they also help reduce the environmental impact of e-commerce.
How does machine learning help improve fit accuracy and reduce returns in online shopping?
Machine learning takes the guesswork out of finding the right fit by analyzing a combination of customer body measurements, previous purchases, and product details. With this data-driven system, shoppers get personalized size recommendations, making it easier to choose items that fit well.
This approach not only helps customers feel more confident in their choices but also reduces the likelihood of ordering the wrong size. For retailers, this means happier customers, fewer returns, and a smoother shopping experience for everyone involved.