How Data Addresses Unavoidable and Avoidable Returns

returns team illustration feature

Ecommerce became the mainstay of consumer purchasing in 2020, and after looking back at this past peak season, that trend continues. According to the National Retail Federation (NRF), online and other non-stores sales grew 23.9% in November and December vs. 2019, to $209 billion, on top of a 10.9% lift in October sales due to consumers shopping earlier than ever.

Unfortunately for retailers, what ships out often comes back. CBRE predicts that ecommerce returns could hit $70.5 billion for the 2020 holiday season, a jaw-dropping 73% increase from the previous five-year average, testing reverse logistics operations like never before. This will significantly impact bottom lines, but lost revenue from crediting customers only represents part of the cost.

Ensuring those orders coming back ship efficiently and effectively can keep shipping costs low and protect margins. Some companies even deploy strategies to avoid returns altogether for a variety of reasons, in favor of giving customers refunds and the unwanted products.

With the imperative to reduce these shipping costs, it’s no wonder fulfillment teams of large and midsize shippers increasingly look for data trends. Patterns can help identify recurring fulfillment errors, which can be eliminated to help reduce avoidable returns. But as the stakes continue to rise, merchants increasingly want to know more about the “unavoidable” returns. If the customer received exactly what they ordered in a timely fashion, does that really mean the return was unavoidable?

Using Analytics to Reduce Avoidable Returns

Reducing the number of avoidable returns serves as a key money-saving strategy. Analytics and business intelligence can play a pivotal role in uncovering a variety of cost-saving opportunities. Analytics can identify weak links in fulfillment by tracing wrong shipments to certain distribution centers, pickers or procedural problems. This allows teams to correct inventory and warehouse management processes or take other resolution steps to ensure customers get what they ordered.

While many teams put this business intelligence to good use in correcting fulfillment errors, not all are sharing these insights with other departments. As a result, many retailers’ views of avoidable returns may be too narrow and limiting. Taking a second look at these processes can often help organizations further reduce their rate of returns.

Evaluating Data Beyond Fulfillment to Address Unavoidable Returns

When fulfillment errors don’t explain patterns of recurring returns, merchants increasingly understand that other steps can be taken to improve the bottom line. Analytics can point to potential merchandising problems, for example, and answer questions like:

  • Which products are most returned and what can be done about it?
  • Should we consider discontinuing product lines or only selling certain products in store?
  • Are many returns originating from the same product or product category?

Identifying routinely returned or problematic SKUs gives you a chance to take proactive steps to address the issues. It could be something as simple as an incorrect size listing or something more serious, such as a manufacturing defect, that prompts regular returns. Even when fulfillment confirms it did its job correctly, repeating returns may still be avoidable if insights make it to the right teams.

Consumers often return products because they lack some information at checkout. Examine your ecommerce website and descriptions related to returned products to identify missing or misleading product data, asking questions like:

  • Are the images low resolution?
  • Is the product description accurately explaining features like dimensions, or how to use it?
  • Are products being returned because colors appear differently online than in person?
  • Are people ordering clothes that are too small due to an inconsistent size chart?
  • Are certain products being returned more frequently than others?

With proper visibility into returns data and a structured approach to extract insights across departments, you can streamline operational workflows and more effectively reduce returns rather than react to them. Any of these approaches can pay dividends when proactively applied. But the first step in determining which model is right is analyzing data to see where opportunities exist for returns optimization.

Leveraging Technology to Optimize Returns

Even as their share narrows, there will always be some unavoidable returns, and merchants should apply a multi-carrier rate shopping approach just as you they do for outbound shipments. Technology can help you determine the best returns policy and carrier services by automatically rate shopping across carriers, selecting the most appropriate services based on established business rules. You can also print paperwork and labels, track inbound shipments and audit the entire shipping and returns process to identify cost-saving opportunities throughout the fulfillment lifecycle.

For most merchants, returns will always peak after the holidays, and an inability to maximize efficiency in reverse processes will routinely lead to higher costs. Fortunately, they increasingly find correctable errors when they identify patterns in returns and work throughout the organization to identify and correct the underlying causes. While some returns are truly unavoidable, examine the dollars and sense of looking more closely at data analytics to continue shrinking that slice of the pie.

Ken Fleming is president of Logistyx Technologies