Returns data allows you to unlock substantial value in your ecommerce business.
You have the power to reduce your ecommerce return rate by identifying products that are underperforming as well as why they are underperforming.
In fact, some merchants have reduced their return rate by 10%.
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But all of this starts with good data - particularly in terms of return reasons.
Return reasons are a list of pre-determined reasons a shopper can choose to indicate why she is returning a product.
Return reasons are established by the merchant when starting with a returns management solution.
It may seem like a trivial part of the process, but your return reasons dramatically impact what you can learn from your returns.
With good return reasons, you'll be able to quickly summarize the causes of returns, setting yourself up to make improvements.
Below are the priorities in terms of return reasons, so you can capitalize on your returns data and reduce your return rate.
Your return reasons should encompass nearly all reasons why a shopper may wish to return a product.
In a general sense, this may include quality / defect, sizing (too big or too small), didn't match description, received the wrong item, ordered the wrong item, and didn't like it.
Of course, the optimal reasons are bound to differ across product categories, but this is a good place to start.
You can also skim through return comments to see if there are any other core issues that are not reflected in your return reasons.
In order to gather insights from return reasons, they need to be specific.
Try to capture particular concerns and avoid vague reasons, when possible.
For example, “Too Big" and “Too Small" are much more informative than “Sizing."
And for more vague return reasons, such as “Didn't Like It" or “Changed Mind," consider requiring open-ended return comments so that you can investigate these returns in greater detail.
The value of return reasons is the ability to summarize returns; specificity is key to ensuring informative insights.
Be sure that there is minimal overlap between your return reasons.
For instance, if your return reasons include “Too Big," “Too Small," and “Sizing," then none of the three are really interpretable.
Overlap between reasons will also confuse shoppers, creating decision fatigue and friction in the returns experience.
Actionability is always a priority in data. If it's not actionable, it's not valuable.
So, while you want your return reasons to be fully comprehensive, consider the actions you might take as a result of each reason.
If a particular reason wouldn't trigger any changes to a product, its description, or fulfillment, reconsider its place on the list.
Again, you could require comments to extract more information from such reasons.
Last, but most definitely not least, is simplicity.
It's easy to think about return reasons from the perspective of the merchant, but we need to consider how shoppers will interact with them.
Some merchants implement ten, twelve, or even more return reasons. The goal of the returns experience is to keep it simple and frictionless for shoppers.
They do not want to be tasked with choosing from a long, complex list of reasons.
And remember that return reasons will not be useful to you if they are not meaningful to your shoppers.
If shoppers cannot delineate between two reasons, or if they do not understand one or more of the reasons, then your returns data will be messy.
So, keep it simple. Anywhere from five to eight return reasons is typically optimal. And avoid technical jargon that may confuse shoppers.
Returns data holds substantial value in ecommerce.
It enables you to reduce your return rate, and can inform future product lines.
Learn more about Returns Optimization
But all improvements start with the collection of quality data. Keep these best practices for return reasons in mind, and you'll be well on your way to driving bottom line growth.
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