Returns cause friction for everyone in the fashion and apparel industry. From repackaging hassles to excess inventory and lost revenue, the process impacts us all.
And while consumers expect the convenience of returns, ultimately, they just want to find the perfect fit. This is especially true as they browse the digital shelf, where satisfaction is harder to predict.
In this article, we’ll explore several ways to minimize apparel returns — from product data fixes to strategies driving the future of fashion.
4 product data fixes for apparel returns
There’s $761 billion of merchandise that hits reverse logistics —and apparel accounts for about 12.2%. From that perspective, there really is no such thing as a free return.
It’s not hard to argue that retailers have a returns problem. However, the likely root cause is a data problem. Many returns, especially in fashion and apparel, correlate with missing or bad product data.
With that in mind, here are four places to consider a product data fix.
-
Product data accuracy
Sizing data and other attributes are crucial to buying decisions. Product data also drives features like search and filter, which means inaccuracies often lead customers down the wrong path.
Avoiding errors is incredibly challenging with thousands of attributes, hundreds of relationships, and even millions of records. That’s why a baseline for returns prevention is a single view of product data — achievable with PIM.
A PIM platform helps you deliver accurate, up-to-date product information consistently across all channels your shoppers use.
-
Gaps in supplier data
Poor supplier collaboration causes issues when there’s a change to fabric, materials, sourcing, or design. A lack of transparency can mislead customers and cause more returns.
When it comes to supplier relationships, updating product data can feel out of your hands. But by establishing a supplier portal, you gain control over onboarding data from suppliers — quickly and in a standardized format.
-
The perfect image
High-quality images and sizing charts are essential for apparel shopping — especially on the digital shelf. But because new fashion products are constantly released, digital assets can get lost in the shuffle. Teams waste time on duplicate effort, and sometimes publish product pages without the ideal mix of assets.
That’s when it’s time to consider digital asset management (DAM). DAM can help you centralize, link, and locate all the assets you need for a product launch.
-
Helping indecisive customers
Let’s say your customer has perfect product descriptions, helpful reviews, and plenty of images…and they still can’t decide. You can help by showing their options. Optimizing product relationships allows you to provide relevant recommendations, and even cross-sell and upsell in a personalized way. An informed customer is less likely to make a return. Do all you can to aid their research and discovery.
Fashion-forward strategies to manage returns
Getting product data in order is the first step to preventing returns. And once you have PIM, you can also consider fashion-forward strategies to reduce returns.
Tap into the #ThriftUp trend
Interest in pre-owned or resale items has increased 12% year-on-year. We expect that trend to continue, especially as inflation squeezes consumer budgets.
The resale trend is working. Take Patagonia, for example. Instead of disposing their used and returned merchandise, the retailer resells these products on their Worn Wear marketplace. They see great success with this approach, capitalizing on a trend that’s popular with younger demographics, price-conscious consumers, and shoppers concerned with sustainability.
Selling returned items may impact your product data. For example, cycling a product back through your inventory may require additional attributes, such as “condition,” e.g., new, used, or new with tag. You may need to track inventory down to the individual SKU level, since the condition of each item is unique. But with a flexible data model, you can easily make these updates.
Join the augmented shopping revolution
Deloitte calls augmented shopping the “quiet revolution,” noting that 3D and AR experiences will change the future of clothing retail. By giving consumers a realistic and personalized product experience, augmented shopping also has the potential to lower the rate of returns. Major retailers like Zara, Gap, Sephora, and ASOS have already launched AR features, with many others expected to follow suit.
Offer AI-powered size and style recommendations
Fashion retailers and digital-only companies like Stitch Fix use AI and machine learning to provide personalized size and style recommendations. Data drives these recommendations, including purchase history, product data, browsing behavior, and customer feedback. By helping customers find products that fit well and align with their style, retailers can make huge strides in reducing the rate of returns.
Analyze data and make real-time improvements
By analyzing returns data with AI, you can make quick decisions that impact returns. For example, if you see a high rate for a particular product, you can adjust your promotion strategy or list it lower on the digital shelf.
Another benefit? By tracking returns, product, and supply chain data, you can gather insights to help educate customers. We all have a part to play in reducing the environmental impact of returns, and consumers do care. But they want retailers to start the conversation.
How will you handle apparel returns?
Reducing returns requires a multifaceted approach that includes accurate product data, innovative strategies, and a commitment to customer education and engagement.
Whatever the approach, your data strategy and processes must be ready. Your ability to easily centralize, enhance, enrich, and syndicate product data will make all the difference.
To learn more about reducing returns and meeting customer demand, download our free resource, presented with Salsify, How to Win on the Digital Shelf.