Problem-First Thinking is the Key To Virtual Try-On Success

This is an excerpt from our white paper AI and AR Virtual Try-On: Reducing Returns Through a Strategic Approach to Implementation.

Without a specific, measurable business problem as the starting point, AR and AI Virtual Try-On becomes an expensive experiment that's difficult to justify continuing or expanding. However, in a highly competitive environment like global retail, the pressure to succeed and gain an edge over your rivals can throw logic out of the window.

This is often driven by a common scenario. A senior executive discovers that a competitor is using a new innovation. Fuelled by the fear of falling behind, both in reality and in the perception of the consumer, the knee-jerk reaction is to immediately get the technology team working on an implementation. After all, the board has been pushing for innovation, while vendors are selling hard. But with little thought put into what practical problem it can solve, the likelihood is that the project is judged on technical success rather than business impact.

The sophistication trap

Before companies know it, they’ve fallen into ‘the sophistication trap’. The technology itself is successfully deployed and genuinely impressive to use, which makes it easy to mistake technical achievement for business value. And this creates serious issues.

Because technology choices have been made before understanding which customer behaviours actually need changing, ROI becomes impossible to measure because success metrics were never clearly defined. Engineering teams end up building impressive technology that customers don't need or use.

This wastes considerable time, effort and investment – not to mention the impact it can have on a team’s morale. Keen to save face and justify their decision, senior executives point to engagement metrics rather than business outcomes. So, it’s easy to see that once set in motion, this can be a difficult spiral to break out of. However, seeing the real-world business impact of taking a problem-first approach to AI and AR Virtual Try-On can certainly help, like in the following example.

A first-hand example of the problem-first approach

In the Home and Garden category, furniture has the greatest customer return rate. At 22.7% it’s the third highest across all subcategories surveyed by Rocket Returns in 2025, behind only shoes (31.4%) and women's fashion (27.8%).

When one of the UK’s largest homeware retailers was investigating this costly problem for itself, the team found returns disproportionately driven by sofas and other large items. The stated reason: these products were too big to fit through doors and up staircases.

This single issue was having a knock-on negative impact across the business. Each return was costing the company in terms of logistics, because unsuitable items needed to be collected. The return became a lost sale. Restocking’s workload was increased. Valuable customer service time was taken up handling the customer query.

Drilling down into the problem, we quantified the costs, were able to calculate the return rate, cost per return and total annual cost of the issue – and subsequently, the savings to the business if the problem could be solved.

Working with the retailer, we came up with the hypothesis that if customers could virtually verify the fit items before purchase, they'd choose more wisely or decide not to purchase at all rather than buying and returning. The key point here being that the ROI case was built before choosing the technology to solve the problem and implementing it. If AR, for example, could reduce the return rate even by half, it would generate a specific amount in annual value. The question wasn't: “How can we use AR?”, but rather: "What would actually prevent these specific returns?"

Adding another dimension

Harnessing the physical space scanning capabilities that AR can deliver, we developed a virtual tool that could be operated by customers via a new feature in the retailer’s mobile app, which was able to measure door dimensions. With the user interface focused on the fit verification question rather than general browsing, it was quick and easy to use. Success metrics were also established, tied directly to return rates, not engagement nor impressions. This provided clarity for assessing ROI related to the specific problem the tool was designed to solve.

The AR Virtual Try-On tool was a proof of concept that delivered a clear projection for a reduction in returns such that a clear ROI was expected, which justified the continued investment in technology and its expansion across the business.

Starting with a measurable business problem, then using the right technology to solve it enabled the project to be tightly focused on both impact and value. Building the ROI case in advance ensured any investment was more than covered by the costs saved through the implementation.

The real lesson here? That AR and AI are not valuable as technologies alone.

Only as part of a solution that solves a key business challenge.

Read our white paper AI and AR Virtual Try-On: Reducing Returns Through a Strategic Approach to Implementation to find out how to develop a strategic framework to ensure AI and AR Virtual Try-On is applied appropriately from a problem-first perspective to solve measurable business challenges.