In the UK alone, more than 20% of online non-food purchases are returned, with forecasts suggesting £27 billion worth of returns in 2024.
Returns present not only a logistical challenge, but also an opportunity to gain insight into customers' preferences. In Europe, online returns rarely result from “difficult customers” but rather reflect the discrepancy between shoppers' expectations and their actual experience.
Psychology has a name for this: expectation–confirmation theory (Oliver, 1980). It explains that satisfaction, and by extension, the likelihood of returning an item, depends on whether the experience confirms what the buyer anticipated. If reality underperforms their expectations (“what I thought I’d get for this price, by this date, in this quality/fit”), they experience negative disconfirmation, which often triggers regret and corrective action; usually, a return.
While companies excel at capturing our interest with innovative product presentations, delivery promises and demonstrations, it is ultimately the product itself that matters.
Consider the launch of Meta’s AI Glasses at Meta Connect 2025. The live on-stage demo famously failed, forcing Mark Zuckerberg to confront the situation in real time. While the product may still succeed, the incident illustrates how powerful expectation gaps can be: a single moment where reality falls short of the promise can shake confidence, slow adoption, and create hesitation in the market.
The good news? Expectation design is not only possible, it is one of the most cost-effective ways to protect your margins. By being clear, concrete, and honest about what customers will experience, we can close the expectation gap, reduce regret, and keep more products out of the returns pile.
Research by Dzyabura, El Kihal, Hauser and Ibragimov (2023) found that product images can predict return rates at an item level. When visuals over-gloss colour, texture, or scale, customers feel misled the moment they unbox their purchase.
A practical approach: keep your hero shots for marketing impact, but add a reality anchor: a well-lit, non-glossy image (or a short video) showing true colour and scale, perhaps next to a familiar object. This subtle addition reduces “it looked different online” disappointment before the product even ships.
In the fashion industry, size and fit are the main reasons for returns. Zalando reports a reduction of around 10% in size-related returns wherever its size-advice tools are used. Generic size charts are not enough; customers need SKU-specific guidance.
For electronics and home goods, carrying out a quick compatibility check (e.g. OS version, doorway width) before “Add to Cart” helps eliminate surprises later, without adding friction.
Vague promises such as '3–7 business days' can lead to unrealistic expectations. When timing is uncertain, people discount the future: perceived value drops, post-purchase anxiety rises and support teams are flooded with “Where is my order?” requests.
Instead, show the earliest reliable delivery date and order cut-off time on the product page and in the shopping basket. Follow through with a short post-purchase timeline and proactive updates. This isn't about speed at all costs, it's about credibility and consistency.
Returns often spike when hidden effort appears after delivery – whether that’s unexpected assembly, care requirements, or missing accessories. Expectation–confirmation theory predicts this: discovering 'extra work' late is experienced as a disappointment.
Adding a “Day One Expectations” block on the product page (“15–20 mins assembly; Phillips screwdriver; video guide included”) can prevent regret. Linking a first-use guide right in the order confirmation email keeps the promise alive after purchase.
Expectation design is not just for show – it’s a reliability system. When imagery calibrates reality, fit questions are answered up front, delivery timing is clear and kept, and first-use effort is transparent, customers get what they believed they were buying.
The result? Fewer returns, fewer WISMO queries, higher repeat rates, and improved margins.
Here's a simple test for your own site: could a first-time customer predict the total cost, the arrival date and the effort required for the first use without hesitation? If so, you have aligned expectations with reality and made returns a manageable exception rather than an expensive norm.
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