Poor Fit Leads to Lost Customers – Lesson 3: Stop Return-Driven Churn
Analysing 1 million online returns revealed a critical insight: poor fit doesn’t just cause returns – it drives customers away for good. Studies indicate that negative return experiences significantly impact customer loyalty, with many consumers switching brands due to such issues. Research shows that 55% of consumers would stop buying from a company after several bad experiences, and 84% won’t repurchase after a poor returns experience (PwC).
Customers who encounter repeated sizing problems are far more likely to switch to competitors who provide better fit accuracy and clearer sizing guidance. Once trust in a retailer’s sizing is broken, regaining it is nearly impossible.
As the third lesson in our return analysis series, we uncover why sizing issues lead to lost customers, share real customer frustrations, and provide actionable strategies to reduce churn through better size recommendations.
The Hidden Cost of Poor Fit: Customer Churn
Most retailers focus on return rates, but what’s even more concerning is the customer loss that stems from repeated fit failures. Prime AI’s analysis of over 1 million returns found that around 70% of online returns are due to incorrect sizing. This aligns with industry research, such as a McKinsey study, which also reports high sizing-related return rates (McKinsey).. However, the real issue isn’t just the return itself—it’s the loss of trust.
When a shopper encounters multiple sizing issues, their frustration grows. 50% of consumers would switch brands after one bad experience, which applies broadly to e-commerce and fashion retail, increasing to 80% after multiple poor experiences (Harvard Business Review). A fashion retailer may be losing customers permanently without even realising it—poor fit leads to lost customers if not addressed..
When a shopper encounters multiple sizing issues, their frustration grows. 80% of consumers say they have switched brands due to poor customer experience (Qualtrics). And incorrect fit is a major pain point. A fashion retailer may be losing customers permanently without even realising it.
Real Customer Complaints About Sizing Issues
Customer reviews often highlight frustration with sizing inconsistencies, leading to brand abandonment. Many retailers have also faced backlash due to changes in their return policies, further frustrating customers and leading to churn. Here are a few real examples:
- “When 60% of your reviews mention sizing inconsistencies, it’s clear that customers are frustrated.” (LinkedIn)
- “66% of consumers are likely to abandon a brand after a poor customer service experience – including sizing issues.” (Marketing Charts)
- “Vanity sizing is eroding customer trust – shoppers don’t know what to expect from brand to brand.” (Wikipedia)
- “PrettyLittleThing has faced backlash after reducing its return window from 28 days to 14 days, leading to customer dissatisfaction and online complaints.” (BBC)
- “ASOS plans to implement a £3.95 fee for customers returning goods, following a wider industry shift towards charging for returns.” (The Guardian)
- “Brands under the Boohoo Group, including Debenhams and Warehouse, have introduced a £1.99 return fee for ‘Unlimited’ delivery subscribers, causing customer dissatisfaction.” (The Sun)
- “ASOS is expected to announce significant changes to its return policy, following a trend among retailers to introduce return fees.” (The Wall Street Journal)
These frustrations aren’t isolated incidents, they reflect a widespread issue that drives customers away. Addressing sizing inconsistencies is crucial to maintaining customer trust and loyalty.
These aren’t just one-off complaints. They highlight the frustration many online shoppers feel when sizing is unreliable. And frustrated customers don’t just return products, they leave brands entirely.
Why Brands Must Learn From Return Data
Many retailers treat returns as an inevitable part of e-commerce. However, return data holds crucial insights that can prevent future losses. Tracking sizing-related returns allows brands to identify patterns and adjust product sizing information accordingly (Semrush).
A recent study found that retailers who actively use return data to improve sizing saw a 20% decrease in returns over a year. This not only reduces operational costs but also boosts customer satisfaction (HubSpot).
Key Strategies to Improve Sizing Accuracy
- Implement Predicitive AI Size Finders
- Size recommendation technology, like Prime AI’s predictive Clothing Size & Fit Finder, helps shoppers select the right fit without guesswork.
- Industry Example: Amazon’s AI Fit Tool
Amazon has introduced an AI-powered Virtual Fit Tool, distinct from traditional size recommendation tools, as it uses large language models (LLMs) to analyse customer reviews, returns data, and size charts. This enables more precise fit recommendations by identifying sizing inconsistencies and common return reasons. to tackle return rates caused by incorrect sizing. The tool analyses customer purchase history and body data to recommend the best-fitting items, aiming to reduce return rates and boost customer satisfaction. This move demonstrates how AI-driven fit recommendations are becoming a key strategy in reducing returns. (Vogue Business)- Retailers implementing AI-driven sizing solutions have seen significant reductions in return rates, with some reports indicating decreases of as much as 50%, depending on the technology and implementation (Emerald), demonstrating the power of AI in reducing returns and increasing customer confidence (Mirrorsize).
- Store Customer Preferences
- Retailers that allow customers to save fit preferences and past sizing history see higher repeat purchase rates.
- ASOS’s ‘Fit Assistant’ helps customers select the correct size by analysing past purchases and similar shopper data, aiming to improve fit accuracy and reduce return rates (ASOS).
- Offer Personalised Size Recommendations
- AI-driven solutions analyse body measurements, brand-specific sizing charts, and customer reviews to provide a tailored fit suggestion.
- Retailers that offer AI-powered size recommendations can significantly reduce return rates by improving fit accuracy and customer confidence. AI-driven tools help brands refine size charts, analyse return patterns, and provide better recommendations, leading to measurable improvements in customer satisfaction and fewer returns (Vogue Business).
The Financial Impact of Fit-Related Returns
Retailers often underestimate the cost of returns beyond logistics. Returns cost retailers up to 30% of a product’s value, factoring in shipping, restocking, and customer service expenses, making them a significant financial burden (Vogue Business). Poor customer retention practices lead to higher acquisition costs and lost lifetime value (LTV). Research shows:
- Acquiring new customers costs 5x more than retaining existing ones (Semrush).
- Customers with seamless sizing experiences are much more likely to become repeat buyers (HubSpot).
- Handling costs for returns can significantly impact profit margins (Exploding Topics).
Financial Breakdown: For every pound spent on acquiring a new customer, only a fraction is spent on retaining existing ones. This disparity highlights the economic benefits of focusing on customer retention through improved sizing accuracy (Semrush).
By reducing fit-related returns, brands not only cut operational costs but also retain more loyal customers. Digital fitting technologies can reduce fit-related return costs by up to 80%, demonstrating their effectiveness in lowering operational expenses and improving customer satisfaction (Emerald). Additionally, 75% of shoppers are less likely to buy again from a retailer that charges return fees, with research showing that 88% of U.S. consumers have stopped shopping with a retailer after the introduction of a paid returns policy (Chain Store Age).
Conclusion: Reducing Returns Means Retaining Customers
Fit-related returns do more than eat into profit margins – poor fit leads to lost customers – they damage brand loyalty. Customers frustrated by poor fit are likely to churn, and once lost, they rarely return.
Retailers must take proactive steps because poor fit leads to lost customers. Improving sizing accuracy using size finder technology, customer preference tracking, and AI-driven size recommendations. By doing so, they can reduce returns, improve customer satisfaction, and ultimately increase retention rates.
Your Next Step: Implement a predeicitve AI size recommendation tool and start leveraging return data to enhance fit accuracy. Customers will reward you with repeat purchases, and fewer returns.
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