For every product a D2C merchant sells, about 19% will end in a return. 

In itself, that’s not great news. But what’s the bright side?

By building a solid returns strategy, you can use customer returns as an avenue for gathering new insights about your business. You can ask questions and track user behavior, which will help you understand why customers are returning products. 

You can use those data insights to build processes for optimizing your inventory, product descriptions, and return policies. Ultimately, this will help you lower the number of returns, increase the number of exchanges, preserve customer relationships, and maintain more recurring revenue.

In this article, we’ll look at how you can gather detailed customer insights from returns to help you shape a strategy for retaining revenue.

Ask the right questions

When a customer processes a return, you should provide them with a questionnaire around “return reasons” that includes a multiple choice section, as well as an open box where they can add any specific comments.

What options should you include? 

Make sure that you include options that apply to the most common return reasons across all product categories. According to a SaleCycle survey, these include:

  • Item damaged or broken (80.2%)
  • Do not match descriptions (64.2%)
  • Don’t like the item (37.2%)
  • Poor value (7.5%)
  • Delivered late (7%)

However, there are return reasons that may be applicable to certain product categories as well, so make sure that you include relevant return reasons for each specific product category. With apparel or shoes, you should make sure to include options related to fit, along with other common return reasons. 

For example, Amazon lists these options for returning a pair of jeans:

  • Too small/short
  • Too large/long
  • Poor condition/presentation
  • Style not as expected
  • Fabric/material not as expected
  • Color/pattern not as expected
  • Wrong item was sent
  • Item arrived too late
  • Inaccurate website description
  • No longer needed
  • Defective item
  • Product and shipping box both damaged
  • Better price available

When starting a return, your customer should need to simply check the relevant box to choose a reason. Then, if desired, they can fill out additional information. For example, if the product “doesn’t match description,” they may want to add information about the color or fabric being different from what they expected. 

By providing simple multiple choice options for processing return reasons, you’ll be able to easily collect survey data that your brand can view at a glance and easily analyze. Gathering more detailed long-form answers helps you gain additional context in deciding whether to grant a return or understanding your customers’ issues.

Depending on the size of your brand, you may wish to use customer sentiment analysis tools to automatically analyze your customers’ written comments. These tools can help you understand trends and automatically action next steps, whether that includes manual review or not. Using machine learning can help you quickly process and categorize comments, so you’ll see trends in the customer feedback without needing to read each comment in detail. 

For example, two customers who both had issues with a product not matching its image in the product photography might say things “looked red on the website, but it was pink,” or “I didn’t like the color in real life.” All of these comments can be extracted from their response forms and grouped in categories, so your customer service team can review trends in aggregate, rather than looking through each individual return form.

You’ll be able to compile both the multiple-choice responses and the sentiment analysis into detailed reports, which can be sortable based on product category, customer, brand/merchant, and individual item, among other categories. These detailed analytics reports can then be used to help you evaluate trends in your customer returns patterns so that you’ll be able to improve your performance in the future.

When reviewing your data, you should be able to understand trends in analytics that will help you see flaws in your process or issues with individual merchants or items.

For example, in fashion, many returns tend to occur because of a poor fit. A size 6 in one brand may equate to a size 8 in another brand, and customers don’t know that until they try the item on. 

However, armed with good data, you can take steps to prevent this from happening. For example, if you get a lot of returns for a certain brand of sweater, with the feedback “too small,” you can recommend that customers purchase the next size up when they’re viewing the item online. Compiling this feedback on a brand-by-brand basis will help you to offer the right fit recommendations for all of the products for that brand, so you can lower return frequency for fit-related reasons.

If returns come in under the reason “didn’t like style,” think about how your products are being portrayed online. Are you simply displaying the items independently or on a size-0 model? Often, customers won’t be sure about an item until they know how it looks on people like them. 

Consider taking a more size-inclusive approach to modeling clothing, with options to see models of different sizes wearing an item. You can also enable customers to submit their own photos of themselves wearing the item in question. By giving a more realistic picture of how your apparel will look on your customers themselves, they’ll be more likely to be satisfied with the purchases they make.

Returns that are due to genuine problems with the item, like missing or defective parts, can generally be attributed to problems in the manufacturing process. In these types of scenarios, it’s important to pay attention to what trends are arising in your return comments and track the problems down to your supply chain or manufacturer. If you notice that a particular product is receiving many complaints, it may even be necessary to put a recall out for that product or to preemptively offer your customers an exchange or upgrade. If your ecommerce store carries products from multiple brands, and you notice one brand consistently receiving return requests due to faulty products, you might consider discontinuing stocking products from that brand.

As long as a customer return request falls into your return window and policy allowances, there’s no need to make customers feel bad about requesting returns. Simply use each one as a learning experience, which you can use to build a better customer experience and a stronger relationship with your customers.

Learning from your customer return data

By asking the right questions and analyzing your data, you’ll be able to understand which types of products are most frequently returned and why. 

You can use these insights to revamp your product presentation and information. Proactively let customers know what to expect by providing them with details on how a piece of apparel is likely to fit them or offer them helpful troubleshooting tips on setting up a new device. Taking these steps upfront will help you minimize the number of returns that come in due to a product not meeting customer expectations.

As for the returns that are a result of production errors, you can use this data as an opportunity to audit your sourcing and manufacturing process and find ways to optimize your quality assurance process to reduce the number of faulty items that are sent to customers.

Turning returns into a profit center

That said, all the data in the world won’t help you eliminate returns in their entirety. Some customers simply prefer to try things out before making a commitment to them and may purchase multiple items with plans to return at least a portion of them.

So rather than focusing on bringing your return rate to zero, focus instead on increasing the number of returns that can be transformed into exchanges. 

When a customer wants to initiate a return, find out the reason, and then look for ways to turn that into an exchange opportunity. For example, if a customer is returning a too-small item, you can offer an exchange for the next size up. 

Consider incentivizing exchanges by offering bonus credit to exchange instead of return. When a customer makes a return, that may be the end of that relationship—but when they do an exchange instead, that’s a continuation of the customer relationship, which can lead to a lifetime of recurring customer revenue.

To learn more about how to optimize your returns process, contact our team.