The function of AI inside a retail business is often associated with the work of an assistant or partner, and when used inside a returns system, the technology does exactly that. As a return  is processed through point of sale or online, AI analyzes a shopper’s history — seeking any suspicious behavior — and assists retail associates with a recommendation on whether a return should be processed or not. 

The AI aide helps identify fraudulent and abusive returns. But there’s an entire other layer of AI and returns for businesses to consider that goes beyond the returns counter: generating data-driven insights. As a return is processed through the retailer, AI models that analyze returns data can help executives and supporting retail business teams uncover root causes of why returns are happening in the first place, find product issues, spotlight operational errors, alert store-level discrepancies, and more.  

Returns data and insights can reveal a lot about what’s happening throughout a retail business, and retailers can leverage AI to dig deeper to find ways to save money and regain profits. 

Demonstrating Data-Driven Returns Analytics at Work

To start with an example, consider an omnichannel home furnishings retailer with hundreds of stores across the country. At the store level, a company may not understand why a specific line of lamps continues to be returned. Each store or online customer service team processes the returns as they come in, but they view each return as a one-off transaction.  

Processing the return of a lamp, the teams follow their returns policy, using AI to analyze each transaction. AI reviews a consumer’s transaction history anonymously, looking for fraudulent behavior, or if the consumer frequently returns items. The AI, as a co-pilot, then helps by suggesting whether a return should be accepted, declined, or in some cases flagged for further review. 

However, with those same AI analytics, the home furnishing retailer can also take time to study each return. In the case of the lamps, loss prevention practitioners, buyers, and business teams can see that there’s been a rash of returns. In this example, peeling back layers of the AI insights, the retailer could learn that the ceramic base is arriving cracked after delivery. Gathering that insight, organizations can investigate how the item is being packed for shipping or work closely with their supplier on strengthening the product to reduce returns. 

This level of insight leads retailers to solve a business issue that has been costing them significant profits. In fact, research shows total returns rate as a percentage of sales can come to more than 13%. Returns heavily impact retailers’ profits, and AI can help them get to the bottom of their returns problem.  

Uncovering the Root Cause of Returns and Consumer Behavior Patterns

To be sure, retailers are swimming in data. However, a major challenge is gleaning insights from that wealth of knowledge. Returns insights support retailers in many ways, including: 

Returns policy adjustments. If a retailer wants to review whether its restrictive returns policy is impacting business, the company can look at returns data. The retailer can analyze receipted and non-receipted returns, trends in buy online return in-store (BORIS) and buy online return online (BORO) transactions, as well as frequent returns claims, to see where consumer interactions can be improved to potentially reduce returns. 

Trends assessments. Granular returns insights enable retailers to look at locations, consumer demographics and time periods, in order to see where returns are happening more frequently, and where action can be taken throughout the shopping and logistics process to reduce returns in those defined stores and regions. 

Consumer behavior learnings. Because AI models are producing real-time insights, retailers can analyze the data with AI-driven solutions to identify patterns that will optimize merchandising or determine whether merchandising will be more susceptible to a return.  

Operational errors, and fraud and abuse. Whether fraud happens internally or externally, returns insights help protect a retailer’s revenue by identifying loss drivers early in the process to prevent fraud and abuse, operational errors, or organized retail crime (ORC) incidents. 

To retailers, every return equals a dollar lost, while every return stopped equals a dollar back. Using insights to uncover consumer behaviors or trends in returns can build a company’s bottom line. 

Returns will always be an issue at retail, but companies that tighten their strategies to reduce returns as much as possible can bring new life to their businesses. AI and returns insights dig deeper into a retailer’s operations to find the real story behind each return. Adding AI and returns analytics to the returns process arms retailers with more protection and intelligence into why shrink, returns fraud, and frequent returns are happening.  

Pete Barker is the director of product at Appriss Retail.