For many companies, reverse logistics (or returns) can quickly become a significant cost. This is because items that are returned often have limited re-sell value, and sometimes cannot be resold at all. This means you lose the value of that product. This is especially problematic for online sales, with nearly 30% of e-commerce products being returned, compared to less than 9% of in-store purchases. Smooth return processes can help to reduce the cost of reverse logistics, but overall the best way to reduce these costs is to reduce the number of returns overall. Here are some tips on reducing returns.
What are your customers thinking?
An incredible way to reduce returns is to allow customers to read and give honest reviews of the products they purchase. Return rates significantly decrease when real and honest reviews are available prior to purchasing. Additionally, provide surveys to customers who have completed a return to get more details as to why they chose to return the product. This way you can identify issues which you can improve to manage return rates.
Give the best price
Have you ever bought a product online, and had some buyers remorse when it arrived? Us too! Most often, customers have buyer remorse when a product is more expensive than they think it should be, leading to likely returns. Companies who optimize prices, and offer the best possible price from the start, are less likely to experience remorseful returns.
Show them what they get
Customers buying items online need a clearly defined idea of what they are buying since they can't see the product in front of them. Use detailed, high-quality pictures with the option to zoom in. Show the product from many angles, and use size comparisons if applicable, like with clothing or accessories. Customers also benefit from detailed descriptions include the size, color, flavor, weight, or the number of units for products, where applicable. This way, customers know what they are buying when they place their order and are less likely to return products.
Use your data
When you suggest products to customers, either through emails, advertisements, or a "you might also like..." section on your webpage, it's important to recommend products a customer might actually want. Using data from past purchase history, and purchase history from other customers, you can determine what items to recommend to your customers, providing them with the best possible recommendations, and reducing the likelihood of returns.