It’s too big and it doesn’t fit. A dress? A T-shirt? A pair of shoes? Or how about your returns percentage each year?

Rocketing returns rates are symptomatic of the shopping experience shift in recent years, as the changing room has moved from store to bedroom. They now represent a persistently painful sore for apparel retailers who, while acutely aware of the issue, have yet to truly tackle the deeper systemic challenges that lie beneath the headline figures.

The rise of e-commerce has given customers the freedom to order from home in multiple sizes, colours, and fits to find the right look, leaving retailers to foot the bill for servicing the return and managing the resale of unwanted or damaged items that customers don’t keep.

And what a bill that is. Figures from GlobalData estimate that £6bn worth of goods were returned in the UK alone in 2022, with returns rates regularly settling in the 40-50% range in the fast fashion category. Party and occasion wear can expect to hit 60% or more.

Simply accepting and annually forecasting for this phenomenon as “the cost of doing business” is a limp admission of defeat. At the other end of the spectrum, punishing all customers via blunt instruments such as blanket charges for returns, or instigating policy changes, such as drastically reduced returns windows, is largely unproven and still doesn’t get to the heart of the matter. Indeed, it could have unwanted side-effects: AlixPartners’ recent Returns Study revealed that 64% of respondents wouldn’t shop with a retailer if they had to pay for returns.

Driving returns rates down is a complex, multi-dimensional challenge. Firstly, there are the many reasons for returns. Some, of course, are legitimate cases of flawed goods, while fit, style, and quality all feature regularly as reasons for rejection. However, the reward for successfully addressing the issue is huge: our analysis shows that for a £1bn e-commerce retailer with an average 45% returns rate, a 5% returns rate improvement could translate into an increase of c.£14m in EBITDA.

The reasons why customers return are manifold. “Bracketing” – buying multiple styles and sizes – is a smart move by customers to avoid the issues above, especially if they won’t be inconvenienced by an additional charge for this practice. “Wardrobing” – wearing and returning – or “Staging” – purchasing to showcase on social media and then returning – are two other trends contributing to sky-high returns rates, never mind the multi-million-pound criminal enterprises leveraging fraud in returns for illegal gains.

There are tactical point solutions, such as virtual mirrors and fit apps that measure and interpret your body shape based on algorithms trained on significant data sets. These should be commended for their efforts in guiding customers to the right product at the point of purchase, thereby reducing the chance of a return. Returns portals also provide some insights via reason codes, but unfortunately suffer from limited options and customers simply clicking the first available option to move through the process faster, thus polluting data.

However, the most sophisticated response lies much deeper in the data. The good news is that you probably already have the raw ingredients. So dive in, and see which of these questions you can answer: 

  • What are the item level drivers of returns – size, quality, or something else?
  • Who are the serial returners in your customer base?
  • Are certain products more prone to particular channels of return (e.g., store vs. online)?
  • Are specific suppliers presenting issues and does that vary by category or location?

You won’t be surprised to find yourself searching in disparate digital locations for the answers, or indeed speaking to multiple departments for further information.

Now for a tougher challenge. How about these questions?

  • When should I push a return to store (and what are the economics for different channels)?
  • Should I be consolidating returns or finding alternatives (e.g. local jobbers)?
  • Should I charge for returns (e.g. by order, or waive for ‘premier’ customers) and what might the impact be?
  • What thresholds should be set for free returns?
  • How often do I double- or triple-handle a product that ultimately never gets sold?
  • What is the true end-to-end cost of a return and does this vary by category?

These are meaty operational questions that cut to the core of the challenge. For example, nailing down the true end-to-end cost of a return (including the environmental cost) requires a deep understanding of the cross-functional data that supports the returns journey. However, the journey is surely worth the effort if you are able to take data-based decisions with a solid understanding of the impact.

The solution we use with our clients, developed in partnership with Palantir, drives this type of real-time decision-making regarding returns, and is laser-focused on the bottom line. However, digital tools shouldn’t be seen as the panacea for this problem. For a challenge – or opportunity – this big, why aren’t we seeing more focus on operating model solutions? Shouldn’t a prize this big warrant a cross-functional focus, rather than, at best perhaps, a part-time position alongside other warehousing or distribution responsibilities?

The richness of the data that you can uncover around returns and subsequently distill into new operating practices will make your business a more intelligent organisation, not just a transactionally efficient one.

Ignoring this data is unforgivable, given the size of the problem that it could solve. Making customers pay for your reluctance to tackle it is not a good look either and many will let you know by taking their business elsewhere. But wouldn’t you like to know how many of them the data says you should happily be waving goodbye to?

The personalised touch will win out in the end – for business and consumer – but that relationship can only be deepened through data. In the complex world of returns, all customers are not equal, and nor should they be treated that way.