Summary

• Retail returns and costs for businesses to process returns are increasing.
• High return rates lead to increased costs, decreased profits, and damaged reputations for retailers. 
• The National Retail Federation reported $816 billion in lost sales for U.S. retailers in 2022 due to returns, emphasizing the need for effective returns management.
• Return rates are notably higher for online purchases, especially within the fashion sector, with rates reaching 30% to 40%.
• Understanding why products are being returned, who is returning these products, and where these products are purchased is integral to improving your return value. 
• Utilizing AI-driven data analytics for effective returns management will help retailers lower return rates, ensuring profitability and customer satisfaction.

The World of Returns Is Changing

You may have noticed changing return policies at some of your favorite stores as of late. As the volume of returns, and the cost for businesses to process returns, increases, many retailers are opting for shorter return periods, fees instead of free returns, or free returns in-store only. While these adjustments help retailers cut costs, they make for a less ideal shopping process for customers, and may result in lower customer satisfaction. 

The National Retail Federation shared in its Consumer Returns Report that total returns accounted for $816 billion in lost sales for U.S. retailers in 2022. With this much money on the line, it is imperative that retailers understand the importance of implementing returns management software and tracking return rates to uncover key insights into why returns are happening and where they can make adjustments to win sales moving forward. Utilizing data analytics to effectively manage return rates will help retailers ensure profitability and customer satisfaction as they navigate the changing retail landscape.

The Impact of Returns Management on Retailers

Return rates vary depending on the industry, but tend to be higher for online versus brick and mortar retailers. Gartner authors Jonathan Kutner and Tom Nolan highlight that, “The impact of mismanaged consumer returns is most severely felt within fashion retailers. This is especially true of products bought online, with current return rates as high as 30% to 40% returned to the retailer.”

High return rates can lead to increased costs for processing and restocking returned items, decreased profits, and damage to a retailer’s reputation. Alternatively, low return rates suggest that customers are satisfied with their purchases and that the products are meeting customer expectations. Tracking returns is important as this data provides retailers with valuable insights into customer behavior and preferences. By analyzing return rates, retailers can identify the most frequently returned products, and understand why.

Benefits of Tracking Return Rates Through AI Returns Management

Tracking return rates is imperative to business success as you are able to understand the “why” behind what is happening in your stores. Are there particular products or categories where returns are more prevalent? Are you seeing an influx of home changing room returns? By analyzing the data behind returns through AI-driven returns management, retailers can take actionable steps to lower return rates. This might look like improving descriptions for a certain product or offering better sizing guides to reduce the rate of customers buying multiple sizes to only keep one. Additionally, tracking return rates helps retailers identify key patterns in customer behavior. For example, you can see which customers are more likely to return items and develop targeted strategies such as running personalized promotions with products that match an individual’s purchase history, therefore, increasing customer satisfaction and reducing return rates accordingly.

Tools for Returns Management in Retail

There is a lot of data to sift through and understand when analyzing returns – this is where AI merchandising software comes into play. EDITED’s AI merchandising experience platform provides tools and features that focus specifically on returns analysis. Our software pulls your internal data and data from your partners to track order value, returned units, and return rates for data across omnichannel, web, and store. 

By connecting data from web analytics with return data, EDITED allows you to anticipate problems that might be occurring within your business. You can: 

1. Spot drivers of returns. 
2. See return rates for categories, products, or specific SKUs. 
3. Compare an item’s return data to its peers and pinpoint items that have much higher return value or returned units.
4. Uncover who is returning. 
5. Identify if you are sending excessive traffic to products with high return issues.

With access to these metrics through EDITED, you can isolate exactly where your issue lies and adjust quickly to improve your rate of return and returns value. EDITED even offers a feature where you can create alerts to notify teams about what is going on in terms of high returned products, so you have the right person on your team looking into the issue immediately.

Conclusion

At a time when high return rates are eroding the profitability of retail businesses, it’s imperative to understand what is going wrong faster and adjust accordingly. Retailers who opt to utilize AI-driven returns management software to track and monitor returns data are able to make informed decisions about which products to stock and how to market them more accurately. They can also quickly identify quality control issues, make improvements to specific products or categories, and personalize the shopping experience for individual customers, all reducing the likelihood of returns.  

Learn more about how to manage returns and increase profitability with EDITED’s AI-driven returns management tools here.