Commentary

Opinion: SaaS, AI and machine learning – the key to effective returns management

JoAnn Martin, VP Retail Strategy at JDA Software, looks at how new technologies can solve the problems of returns management.

Consumer behaviour is reshaping retail, ultimately challenging retailers to create enhanced solutions to address their increasing customer demands. Managing returns without compromising the customer experience is certainly one of these challenges.

Returns do not have to be perceived as an obstacle. Retailers should instead look at this historically complicated process as an opportunity to ease reverse logistics, optimise store operations and even strengthen customer engagement. To do this, retailers need to recognise the value of integrating data-driven intelligent software into their returns management strategies.

With the cost of online returns estimated to rise to £5.6bn over the next five years according to GlobalData, retailers need to take control of their returns management. However, this does not come without a catch. Retailers should meet the demands of consumers wanting what they want, when they want it and, on their terms, despite the challenges this presents.

There are ways to support returns without compromising retailer or consumer expectations. This includes incorporating software as a service (SaaS) based solutions that can analyse large amounts of external and internal data to solve return problems. Additionally, artificial intelligence (AI) and machine learning (ML) can positively impact the bottom line, while decreasing the touch points and frustrations that returns can present to retailers and customers alike.

Reshaping Returns Management

Taking a closer look at the benefits of SaaS, AI and ML, it’s important to recognise the value this trio delivers for retailers in their returns management process. A cognitive and connected SaaS platform links everything together, even beyond a retailer’s extended supply chain, including data, systems, trading partners, inventory availability, machines and networks. Through the intelligence collected via SaaS, retailers can be more precise and more proactive in their decisions, ultimately strengthening the domino effect of details that returns have on them.

Combined with AI, retailers benefit from real-time decision-making based on operational and external data that optimises all probabilistic predictions and outcomes. When aligned with a retailer’s corporate strategy, thanks to ML, individualised scenarios can be quickly identified and resolved during the return process in response to the automated collection of data that is then applied to make real-time return decisions.

Whether through reallocating inventory, optimising open-to-buy management, reducing inventory markdowns or even enhancing workload efforts among store staff, the collective outcome delivered from SaaS, AI and ML is undeniable. Having quicker and more strategic reactions to returns allows retailers to strengthen sales, reduce costs and improve efficiency at rates never thought possible. There are three key areas to consider when simplifying returns management:

  1. SaaS, AI and ML-powered returns reduce the time it takes to process these transactions, helping to meet the demands of today’s modern consumers who prefer fast and convenient shopping experiences
  2. AI can help detect fraudulent returns in real time by using internal and external intelligent data, ultimately preventing store shrinkage, while increasing a merchant’s profit opportunity
  3. Personalised customer care can be heightened during the return process as a direct result of SaaS, AI and machine learning capabilities, reinforcing the demands of consumers who want enhanced customer service

Creating Competitive Advantage Through Returns

Retailers looking to gain a competitive advantage in an increasingly competitive retail marketplace should embrace returns as an opportunity versus a challenge. Understanding consumer buyer behaviours, return histories, internal inventory data, external consumer factors and even pricing strategies based on these collective details can help retailers achieve this. Optimising their response to returns should be a top priority, but this cannot be achieved without implementing SaaS, AI and ML.

As retailers aim to strengthen their returns management, they must also consider who this experience is being optimised for. The retailers’ business should always stay top of mind, yet they should not lose sight of the customer along the way. In fact, this year online fashion giant ASOS increased the returns policy from 28-45 days.

Although the day of free returns are coming to an end, retailers are still finding ways to improve the returns experience. Whether that’s increasing the period people can return things, or offering a quality tracking service, it’s never been more critical to make the return process one that customers can appreciate. But the question to ask is, how long until other retailers follow suit?

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