ASOS rang in the new year with a change to its returns policy, saying it would eliminate return fees for consumers with a lower return rate, writes Paul Gardner, VP of product at loss prevention consultants Appriss Retail.

The retailer said shoppers can also monitor their rate of returns directly within the ASOS app.

The policy change is considerable, and it also awakens the conversation on why retailers need to personalise returns policies in order to manage margins and maintain loyalty and consumer happiness.

Currently, three out of four UK retail businesses charge for returns.

Consumers can’t be happy with that trend. Yet, retailers are attempting to fix tight profit margins by charging for returns, all at the expense of the customer.

This is where a more personalised approach to returns can help.

Retailers keep loyal consumers happy while targeting abusive or fraudulent returners, chains get more personal through policy changes, but also by leveraging retail analytics and shopper data.

Returns cost millions

Returns come with baggage. Frictional costs caused by returns – such as paying to resend items and spending money and resources to repackage them for resale (often at a heavy discount) – compound the loss.

Factor in fraud and abuse, and returns are consistently a leader in shrink.

Recent University of Sheffield research found roughly three-quarters of the 60 million UK online shoppers have a retailer’s returns policy in mind when shopping and will abandon a cart if the policy is not explicit in how it handles multiple returns.

The research alludes to shoppers wanting to buy a bunch of products freely to test at home and then return most of those products.

However, this process, also called ‘wardrobing‘, might be something that consumers don’t realise devastates a retailer’s bottom line.

And, in turn, it should be understandable why retailers would need to charge for returns. Direct fees tend to be around £3, but retailers also can mask fees inside subscriptions or membership costs.

Ultimately, retailers want to keep their most loyal shoppers happy, and being more transparent in the returns process and adding personalisation can improve loyalty.

Aggressive returns policies can cost consumers

By being more open and flexible with returns, retailers can also avoid enforcing overly strict, blanket policies such as “no receipt, no return”.

An aggressive policy like that can cause a retailer to frustrate a high-spending and valuable shopper for life, all over a short-term return.

A recent consumer survey from Appriss Retail found 55% of consumers decided not to buy from a retailer because their returns policy was too strict, and 31% said they stopped shopping with a retailer entirely because of a negative returns experience.

Transparency around a retailer policy can also ease consumers into reducing their returns behaviour.

In the case of the updated ASOS policy, the retailer will waive its £3.95 fee for shoppers who have a history of returns extending beyond 70% of the total value of their past orders and who are returning less than £40 worth of products from an order.

With this approach, ASOS is enabling consumers to follow their returns history in their mobile app, bringing more transparency to the returns process overall. Shoppers gain more understanding of why there are fees, and they have a chance to control their return rate.

But personalisation can go one step further.

Reducing fraud

Retailers that embed a process of using retail analytics at the point of each return can treat each transaction as a unique, custom return.

For instance, companies can leverage AI to anonymously review each return to suggest whether a return should be denied.

The process removes a store worker from the equation, allowing the staff member to rely on the system to communicate to a shopper that their return has been flagged and is not to be accepted.

From there, they can give the shopper a phone number to call for more information.

On the other hand, and more commonly, AI can anonymously review a shopper’s history of returns, loyalty information, purchase history, and more, helping the retailer to be informed if they are a loyal shopper with an honest history of returns.

The system might even recommend a way to incentivise or reward that shopper at the return, or issue a simple warning to help a consumer understand that they might be doing something abusive that they didn’t realise.

The goal is to turn a borderline unprofitable consumer into a profitable one

Lastly, unified retail data, streamlined across in-store, online, and customer call centres, can be analysed by AI and find unusual patterns, like the use of multiple credit cards and shipping locations, that leads to a potential incident of returns fraud.

Personalisation means treating each return on its merit and using retail analytics and AI to protect a retailer’s profits.

Protecting profits

Loyal shoppers shouldn’t have to pay for a few bad returners, and retailers shouldn’t have to charge fees for all returns when they treat each return individually.

AI and retail analytics, combined with transparency and more personalised returns policies, can help retailers put their best customers first.

In return, shoppers gain more trust in the retailer, and companies will build more loyal shoppers.

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