As a retailer, you already know how much fun managing returns can be, especially this time of year. Ok, so it's not that fun. You need to make sure it's easy for your customers to return products. You need to have a system in place to expedite the returns process. And you need to understand why the return is happening in the first place so that you can prevent future returns. This takes a lot of effort across many departments and systems, and most retailers we've spoken to are still in the dark about one or more of these areas, especially a process for preventing returns.
The 3 Stages of Returns Prevention Maturity
While the retail industry has focused on mitigating the cost of returns by seeking better secondary market opportunities, the root cause of returns remains largely unknown, and as a result, unaddressed.
The reality is, many retailers are not even at stage one in the returns prevention maturity curve. That means they are squandering a significant opportunity to gain insights to prevent returns. Here is what the returns maturity curve looks like:
Manually Managing Returns Reason Codes. Many leading ecommerce shopping carts lack functionality to capture return codes when customers notify the retailer that they want to return a product. Instead this data comes in via individual emails, or on paperwork when the item arrives at the collection center. Sometimes this data gets manually entered into spreadsheets, but sorting through it is tedious and time consuming. Few retailers find real value since the data is now dated.
Integrating CRM Data with Returns Data. More advanced retailers have automated the collection of returns data, such as using a customer support platform (like Zendesk or FreshDesk) to manage returns. Through interaction between the customer and Service Rep, the retailer collects better quality data – sometimes even photos – earlier in the returns process. But it still often ends up in a spreadsheet, subject to manual analysis processes.
Using Weighted Return Data to Gain Prevention Insights. In the most mature approach, retailers not only automatically capture higher quality data on return reasons as soon as a return is initiated, but the back end is automated as well. Via returns prevention analytics, they can automatically weight returns data to quickly identify meaningful trends and take action to prevent them, such as correcting a mis-slotting in the warehouse or pulling and replacing a damaged SKU. According to Chain Store Age, 65% of returns are due to retailer error, not consumer fault.
Reducing return rates is a primary initiative for retailers we've talked to recently, as it provides a bottom line benefit. If it's not a priority at your organization yet, it should be. Make this year the year you focus on a solid returns prevention process.