Retailers can solve their returns problem – and the answer lies in existing data

The growing number of returned items is having a disastrous impact on many retailers’ profits. But tools and insights are becoming available to help solve this challenge

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Returned items have become a painful problem for retailers, but many don’t yet know how to tackle it. Return rates in fashion and clothing, for example, range from 40% to an eye-watering 70%, according to 2022 research from AlixPartners. This costs retailers billions and has destroyed profits for many.

Rocketing return rates are a symptom of the shift to online shopping, which moves the changing room from store to bedroom. Customers now routinely order multiple sizes, colours and fits online, keep one item and return the rest. Online shopping may allow shoppers to capitalise on recent trends, such as wardrobing – wearing an item once then returning it – and staging, whereby an individual posts a picture wearing an item on social media before returning it.

Retailers must foot the huge bills for servicing and reselling all these returned items. And, while acutely aware of it, many have yet to tackle the deeper systemic issues that cause high return levels. But more sophisticated data use can repair much of the damage. Retailers mostly have this data already, and it doesn’t require massive investment in technology.

Why returns are so destructive

Brian Kalms, digital partner and managing director, Europe retail sector, at consultancy AlixPartners says: “The ability to return items gives customers confidence to buy online and change their mind when they receive the product. But retailers struggle to make that cost-effective. They assumed going online would save money. But profits have fallen due to shipping and returns costs complexity and range expansion.”

Returns destroy profits because they involve extra inventory, warehousing, shipping, packaging, labour and refurbishing. Kalms says, in his experience, only around 75% of returned items make it to resale, and many of those are discounted because they are no longer in fashion or season. This level of waste can be disastrous for retailers’ profit margins and carbon emission targets too.

What retailers can do

Removing return policies would make retailers uncompetitive. So, the challenge is to reduce and recondition return behaviours without annoying customers or depriving them of choice.

“Some have responded by tightening the returns window, or even charging for returns,” says Kalms. “But those options feel like they are punishing the customer and should be a last resort. Others have tried partial solutions, such as technologies that identify worn or washed items, accelerate refurbishment, speed the returns process, or identify high-returning or fraudulent customers.”

Some have also built solutions that give them post-season information such as return rates by size, style or region.

“Post-season analysis is useful but better results come from those who take a more disruptive approach and undertake cross-functional initiatives to better understand what’s happening and act while the product is live,” says Kalms. “Few retailers have built systems with this in mind. Most have focused technology investment on forward logistics – moving goods from manufacturer to customer.

“But identifying the correlations between returns and product, geography, pricing, and customer and supplier behaviour – these are complex data problems retailers are yet to solve.”

Such analysis can reveal many subtleties, such as:

  • The difference between a stager posting to a few friends and one who is a popular influencer promoting the product.
  • A certain size returned often in one region can be redirected automatically, via the return label, to another area, where it is selling quickly.
  • One fabric returned often can be removed from sale automatically while you investigate the quality.
  • A customer who regularly orders three sizes but always sends back smaller ones can be discouraged or make smaller ones unavailable to them.

Another approach is giving premier customers free returns, while non-premier customers pay for them. But not many companies scientifically test and compare effectiveness for those two options, or other variants.

The role of stores and omnichannel

For an omnichannel retailer, stores often become a place for people to see and try items before buying them online; and to click-and-collect or return items. Stores may also create a brand halo effect for passing customers.

“Evaluating stores’ overall contribution to profits in the omnichannel environment requires a better understanding of how these factors integrate with other information, such as customer profiles in that region,” says Kalms. “Answering all these questions often requires sophisticated information, which is a challenge. Typical reporting systems do not usually take all this into account.

“But, in our experience, some questions are not that complex—the data is mostly there, and retailers just struggle to pull it together. They need to access, blend and analyse the data in a way that answers these questions and provides a more complex understanding of store economic value, for example.”

The roles of customer segmentation and AI

Such an approach also drives a much clearer understanding of product performance and customer behaviour. It ideally generates a profitability score for each customer – accounting for all factors – buying a specific product, such as a shirt. You can then segment each customer by this score. Barring low-profit customers is usually not possible. So, the aim is to improve their profitability by tweaking individualised experiences.

This requires huge data pools, especially for retailers with thousands of products and thousands or even millions of customers. Such companies have battled to see the trends and react quickly enough.

Fortunately, artificial intelligence (AI) can come to the rescue by helping you understand the interrelations between relevant factors much more quickly, says Kalms. AI platforms such as Palantir Foundry are built to identify trends in huge datasets and automate responses, he says.

AlixPartners, in partnership with Palantir, is already helping some of the world’s largest retailers optimise their returns operations in this way.

An available source of profit

Returns have a massive impact on your profit and loss, and the C-suite needs to manage them, says Kalms. “Greater understanding of these interrelationships should help improve your bottom line,” he says. “It may not seem an easy source of profit, but it is available without having to grow your top line, find new products and markets, or redesign your supply chain. So if your profits are underwhelming, you should be going at this right now.”

AlixPartners is working with senior executives at leading retailers around the world to solve these problems. “We are helping build the tools and insights that enable more informed decisions,” says Kalms.

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