Considering transportation and unit costs to determine the feasibility of a return is no longer the best practice for any business. While returning goods may seem like a straightforward process, it can be complex, time-consuming and expensive. However, by using analytics, businesses can optimize the reverse logistics process and minimize associated costs.
Analytics can be used to track and predict customer patterns in returns data. This information can then be used to establish guidelines for processing returns more efficiently, shape return policies, and even improve products. For instance, if a given product is consistently being returned, businesses can proactively identify the reason and address it to reduce future returns.
Additionally, analytics can help businesses to detect problem areas in their supply chain that may be causing an increase in returns. Businesses can avoid costly delays further down the line by addressing these issues early on.
Overall, using analytics to optimize the reverse logistics process can help businesses to save time and money. To learn more about other way return analytics can improve your business, read this article (see pages 22 - 23).