The last time you returned something, what reason did you note for the return? Did you hastily check “other,” or did you search for the real answer among three or four other options? In our work with retailers, we’ve noticed that return reason codes are one of the most under-utilized data collection sources when it comes to identifying the root cause of returns.
Some retailers may have the data but aren’t sure how to make it intelligible and actionable, and many retailers don’t capture the information at all. If you’re thinking about re-strategizing your return reason codes, consider these top 7 reasons customers are returning your product:
In November, I purchased the perfect booties online from a brand-who-shall-not-be-named. When they arrived the following week, I was excited to try them on only to unbox and find a completely different pair of booties. I contacted customer service to initiate an exchange, but the original shoes I ordered were out of stock… Overall, not a great experience!
Ongoing studies show that more than 20% of product returns are due to “wrong item shipped.” This is likely due to pick and pack errors during fulfillment, and it has a big impact on customer experience and customer churn.
Another 20% of product returns happen because the item arrived damaged. Having the knowledge of which items are being damaged in transit helps to identify opportunities for improvement in quality, packaging, and carriers.
If you’re looking to laugh, and cringe, try Googling “shopping expectations vs reality.” While these examples are the worst-case scenarios, “product did not match the website description” is one of the top reasons customers return. Why such a disconnect between customer expectations and description/photography?
Size and fit could mean…
…And the list goes on… “Size and fit” is such a catch-all for many potential garment issues. And while shopping for the correct “Size and Fit” is particularly difficult when shopping online, new pandemic practices like closing dressing rooms will likely contribute to rising return rates in stores as customers will need to buy clothes in order to try them on at home.
“Bracketing” is the practice of buying a number of different sizes or colors of an article of clothing with the intention to return most, if not all of the item. In order to overcome consumer distrust of purchasing clothing online, retailers introduced fast, free, and frictionless returns. With so much variation in sizing, customers realized they can turn their bedrooms into a fitting room by buying multiple sizes and styles and seeing which they preferred. Frankly, it’s a brilliant strategy for a customer looking to find their perfect fit, but it’s a significant expense for retailers.
Quality is one of the most difficult return reasons to decipher: when every customer’s expectations for quality is different, how do you distinguish subjective from constructive feedback? Luckily, Natural Language Processing (NLP) technology has made it possible to isolate actionable feedback from noise and determine things such as: Did the customer expect a higher quality because of marketing or price? Or did quality miss the mark due to product lifecycle challenges like factory changes mid-season or skipping QA to meet the speed to market?
A recent report that revealed that losses from return fraud increased by 35% from 2018 to 2019, totaling to about $27 billion. Unfortunately, not all shoppers are honest, and many take advantage of today’s lax return policies and bend-over-backward customer service. Retailers who don’t require a receipt or proof of purchase are more vulnerable, but no one is immune to fraud. Return fraud takes on many forms, some notable examples include:
I’ll end with this: “Other” is never the reason for a return and it won’t get you any closer to understanding the root cause of your returns. But we understand: striking the right balance between capturing needed details without demanding too much of your customer’s time is challenging—you don’t want to create friction in the returns process.
At Newmine we help our customers identify which return reason codes will fit their business best, and Chief Returns Officer’s natural language processing algorithm analyzes Voice of the Customer data to fill in the gaps.