Bracketing in Online Shopping – Lesson 2 from 1M Returns
What Happens When Customers Don’t Trust Size Charts? They Bracket Instead.
After analysing 1 million online returns, we’ve identified a clear trend: when shoppers lack confidence in size charts, they resort to bracketing in online shopping. This practice, where customers order multiple sizes of the same item and return those that don’t fit, significantly impacts retailers. It leads to increased return rates, logistical challenges, and added costs. Our data reveals that bracketing rates vary depending on the retailer type and product category, but one thing is certain: better fit guidance leads to fewer returns.
The Prevalence and Drivers of Bracketing in Online Shopping
Bracketing vs Haul Culture: Understanding the Key Differences
Bracketing and haul culture are both driving high return rates, but they stem from different consumer behaviours. Bracketing is primarily about uncertainty – shoppers order multiple sizes of the same item to ensure they get the best fit. Haul culture, on the other hand, is driven by social media trends, where mass purchasing is more about aesthetics and content creation than necessity.
The Rise of Bracketing in E-Commerce
Bracketing is the bigger issue facing retailers, as it’s rooted in a fundamental lack of confidence in online sizing. Unlike haul culture, which is more prevalent among influencers and content-driven shoppers, bracketing affects a much wider audience and spans all demographics. Studies show that:
- 40% of shoppers engage in bracketing due to inconsistent sizing (Statista).
- 69% of Gen Z consumers admit to over-ordering compared to 16% of Baby Boomers (Woke Waves)..
- Return rates for fashion items range between 30-40%, compared to in-store return rates of just 8-10%. The average return rate for online apparel orders overall is 24.4%, further illustrating the return challenges faced by online retailers (Coresight Research, Soocial).
Prime AI sees bracketing rates at multi-brand retailers ranging from 15-40%, while for single-brand retailers, the rate is lower at 3-10%, depending on the product mix. Certain categories, such as dresses and bottoms, are particularly prone to bracketing. Additionally, younger shoppers tend to bracket more frequently than older demographics.
However, the introduction of AI-driven size recommendations makes a significant difference. Customers who use a size finder before purchasing bracket 80% less than those who do not. This highlights the power of accurate fit guidance in reducing unnecessary returns.
The Role of Haul Culture in Return Rates
Haul culture, while not the primary cause of bracketing, contributes to over-purchasing and high return rates. Influencer-driven shopping habits encourage bulk buying, often with the intention of returning a significant portion of the order. Social media trends exacerbate this behaviour by glamorising excess consumption.
A key distinction is that haul shoppers may buy for content but return for convenience, whereas bracketers return due to lack of fit confidence. Both impact retailers, but bracketing remains the larger financial burden due to its sheer scale.
(Woke Waves) highlights how Gen Z’s social shopping habits are shifting, influencing retailers to rethink return policies and fit solutions.
Identifying and Addressing Serial Returners
Many shoppers engaged in haul culture could also be classified as serial returners – customers who habitually order excessive amounts and return most of their purchases. This behaviour significantly erodes retailers’ profit margins, as free returns enable these shoppers to repeatedly engage in costly return cycles without contributing meaningfully to sales.
Prime AI helps online retailers identify, track, and manage serial returners, allowing businesses to take action and regain control over their profitability. For example, at one multi-brand retailer, Prime AI’s analysis over a 24-month period uncovered that just 95 shoppers were responsible for 5.1% of total refunds, while contributing only 0.8% of total sales. In other words, these shoppers were placing large orders, frequently returning items, and rarely keeping anything.
This means online retailers are losing money due to the logistical and operational costs associated with processing their orders and returns. To counteract this, the retailer implemented targeted return fees for these specific high-return customers, effectively reducing unnecessary returns while maintaining fairness for loyal shoppers.
Would you like Prime AI to monitor your refunds and flag serial returners for proactive management? We provide dynamic monitoring and intelligent rule-based decision-making integrated into your refund systems. Prime AI can automatically identify which customers should be charged return fees based on your business rules, helping you protect your profitability while ensuring a fair and sustainable return policy.
Talk to us today to take control of your return management and maximise your margins.
The Financial and Environmental Impact of Bracketing in Online Shopping
The impact of online shopping returns extends far beyond just operational costs. Studies show that return rates for online apparel orders vary significantly by category, with general apparel returns averaging 24.4%, while dresses in the US and Europe see return rates as high as 55%. This is in stark contrast to the 8-10% return rate for in-store purchases (Coresight Research, Soocial). This gap highlights the challenges online retailers face in reducing returns, with sizing issues and bracketing being major contributors.
The implications of bracketing are profound:
- Economic Costs: In the UK, serial returners – those who frequently return online purchases – are responsible for sending back £6.6 billion worth of items annually, accounting for nearly a quarter of the £27 billion forecasted returns (The Guardian).
- Environmental Concerns: The logistics of processing returns contribute to increased carbon emissions and waste, challenging sustainability efforts within the retail industry.
Retailer Losses: Returns cost retailers up to 30% of the item’s production cost, making high return rates a major drain on profits. This includes processing, restocking, and reselling returned items, which can often be more expensive than the initial production cost. (Coresight Research)
The Role of ‘Buy Now, Pay Later’ in Bracketing
Payment options like Buy Now, Pay Later (BNPL) have further fuelled bracketing behaviour. The ability to order multiple sizes without upfront payment reduces the perceived risk for shoppers but increases return rates for retailers. Research by the Bank for International Settlements (BIS) indicates that BNPL users experience higher delinquency rates than traditional credit users, suggesting a potential link between BNPL usage and increased return rates (BIS).
Studies indicate that BNPL users exhibit higher return rates compared to traditional payment methods, as flexible financing lowers the psychological barrier to bulk purchasing. Prime AI’s internal analysis has also shown an upward trend in return rates among BNPL shoppers, reinforcing this industry-wide concern. While offering flexible payment solutions can drive conversions, retailers must carefully assess the trade-off. Monitoring return patterns post-BNPL adoption is crucial to avoid unexpected spikes in returns.
Predictive vs. Statistical Sizing: Why Prime AI Is More Powerful
Most size recommendation tools rely on statistical sizing methods, which use basic historical purchase data and return rates to estimate a customer’s fit. While this approach can reduce some uncertainty, it does not account for granular body shapes, fabric behaviour, or real-time customer preferences.
Predictive AI sizing, on the other hand, goes beyond simple statistics. Prime AI’s technology dynamically learns from millions of data points, including fabric stretch, colour variations, and even regional size discrepancies. This approach ensures that recommendations are far more accurate and personalised, leading to a significant reduction in bracketing behaviour.
To combat the challenges posed by bracketing in online shopping, Prime AI offers an advanced Predictive AI Size Finder technology designed to enhance the online shopping experience and reduce returns:
- Predictive AI-Powered Recommendations: By analysing customer inputs such as height, weight, age, and fit preferences, Prime AI delivers highly accurate size recommendations without requiring photos or manual measurements.
- Granular Sizing & Fit Precision: Unlike standard size charts, Prime AI considers brand-specific sizing variations, fabric elasticity, and colour-specific adjustments. This ensures the most precise recommendations available, as certain dyes and treatments can subtly alter garment fit.
- Advanced Fit Adjustments: Prime AI understands how different fabrics, styles, and cuts affect fit, ensuring ultra-precise recommendations for every product and customer profile.
- User-Friendly Integration: The solution seamlessly integrates with Shopify, Magento, Salesforce and custom e-commerce platforms, ensuring a smooth user experience.
- Operational Benefits: Retailers using Prime AI’s Size Finder see significant reductions in size-related returns – 30% – which improves profit margins and sustainability. This leads to increased customer confidence, as shoppers can buy with certainty, resulting in higher satisfaction and conversion rates. Brands like O’Neill, Playful Promises, and Aristocracy have seen measurable improvements, with reduced returns and enhanced customer trust in their online sizing recommendations.
Bracketing Solution: How Prime AI's Size Finder Eliminates Guesswork
Reducing bracketing requires a combination of technology, consumer education, and better fit guidance. AI-powered tools are already making a measurable impact:
- Predictive AI-Driven Fit Technology Adoption: A recent report by McKinsey found that retailers using AI-powered personalisation, including fit and sizing recommendations, saw a significant improvement in customer satisfaction and a potential reduction in return rates of up to 20% (McKinsey). However, Prime AI’s advanced Predictive AI technology goes even further, achieving an average 28% reduction in return rates across its clients – outperforming market estimates.
- Consumer Preferences for Personalised Experiences: A survey revealed that 80% of consumers are more likely to make a purchase when brands offer personalised experiences, highlighting the importance of tailored shopping journeys in enhancing customer satisfaction and reducing returns (McKinsey).
- Impact on Return Rates: Implementing virtual try-on solutions has been shown to reduce product returns by 25%, demonstrating the potential of AI-powered tools in addressing sizing issues and minimising bracketing behaviour (SEO Sandwitch).
Final Thoughts
Bracketing remains a costly issue for retailers, but solutions are available. AI-driven sizing tools, better return policies, and improved consumer education can significantly reduce return rates while maintaining a positive shopping experience.
Prime AI’s Predictive AI Size Finder is already helping leading retailers slash returns and increase conversions. Want to see how it can work for your store? Request a demo today and start reducing bracketing now!
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