Creating Simplicity From Complexity: Managing Returns Through SaaS, AI & Machine Learning

0aaaJoAnn Martin JDA

Consumer behavior is reshaping retail, ultimately challenging merchants to create enhanced solutions to their increasing demands. Managing returns without compromising the experience for customers or retailers alike is one of these challenges.

But 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, optimize store operations and even strengthen customer engagement. To do this, however, retailers must recognize the value of integrating data-driven intelligent software into their return management strategies.

With the cost of returns estimated to be more than $550 billion by 2020, retailers need to take control of their return management. This does not come without a catch, however. Retailers need to meet the demands of consumers wanting what they want, when they want it and, on their terms, despite the challenges this presents to merchants.


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

How SaaS, AI And Machine Learning Are Reshaping Return Management

Taking a closer look at the benefits of SaaS, AI and machine learning, it’s important to recognize the value this trio delivers for retailers in their return 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 merchants. Combined with AI, retailers benefit from real-time decision making based on operational and external data that optimizes all probabilistic predictions and outcomes. When aligned with a retailer’s corporate strategy, thanks to machine learning, individualized 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, optimizing 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 merchants to strengthen sales, reduce costs and improve efficiency at rates never thought possible. Additional factors to consider when simplifying return management include:

  • SaaS, AI and machine learning-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;
  • 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; and
  • Personalized 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.  

Retailers Should Embrace Returns

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

As retailers aim to strengthen their return management, they must also consider who this experience is being optimized for. The retailers’ business should always stay top of mind, yet they should not lose sight of the customer along the way. With the National Retail Federation reporting that 92% of customers will shop again with a retailer if their return process is easy, it’s critical to make the return process one that customers can appreciate.


JoAnn Martin brings 20+ years of retail, merchandising and supply chain expertise to JDA. In her current role of Global VP of Retail Industry Strategy, she counsels global retailers on industry-leading best practices and technology advancements and collaborates with JDA’s product development and innovation teams to align on addressing the challenges of today’s retailers. Prior to JDA, Martin was Senior Director at the Luxottica Group but spent most of her career at Designer Shoe Warehouse (DSW), where she was VP of Planning and Merchandise Operations. She has deep expertise in softlines and JDA solutions in that vertical. Martin’s expertise covers retail strategy, omni-channel, digital commerce, merchandise financial planning, assortment planning and change management. JoAnn is a graduate of Miami University, where she received her Bachelor of Arts degree in Finance and Marketing.

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