Looking Fashion Forward: 5 Best Practices for Fashion + Apparel Retailers
As digital transformation takes over, department stores are particularly susceptible to the retail apocalypse. The proliferation of online marketplaces has beauty brands essentially competing with themselves. And of course, every retailer, particularly in the sporting and outdoor space, has to be mindful of the Amazon effect.
We’ve written several best practices guides focusing on the unique challenges faced in various verticals. On the surface, fashion and apparel sounds like a broad enough category that it’s easy to think they don’t have their own obstacles. Instead, fashion and apparel brands have the other retailers’ struggles on steroids because they face all of them.
Department store discounts have hurt full-price fashion, resulting in apparel brands leaving Macy’s en masse or closing down their own flagships instead. Meanwhile, Amazon is doubling down on fashion, poised to be the largest apparel retailer in the U.S. as they aggressively invest in private labels. Consumers are already buying more clothes on Amazon than anywhere else, including individual retailers’ websites.
This means that fashion and apparel brands have no choice but to make their own shopping experiences as perfect as possible. As consumers seamlessly navigate between brick and click, these retailers must do the same, recommending products in a more sophisticated manner and diversifying their trigger messaging, just to name a few.
How can fashion and apparel brands get — and stay — ahead in the increasingly complicated retail landscape? Our best practice guide answers that question, featuring use cases from Rent the Runway, Urban Outfitters, JustFab, Sephora and Adidas.
Look Beyond Collaborative Filtering Like Urban Outfitters
Shirts, pants, shoes, accessories: Fashion and apparel brands have large, varied inventories. On the one hand, that’s great because it means you most likely have something for everyone. However, that could also make your website, app, and email marketing somewhat overwhelming for customers. The handbags tab on Bloomingdales.com has nine subsections for different styles and five more for different sizes. And that’s not even counting wallets.
Collaborative filtering is one way to help guide people in the right direction, particularly if they’re new customers. But if someone is a repeat customer, you should know enough about them to take your recommendations a step further.
Interest-based recommendations allow fashion retailers to tailor the experience based on what a specific buyer prefers. Depending on the season, you can target her with recommendations based on her preference for strappy sandals or clutches. The approach works across seasons. Once winter comes, show her your new jackets if your data demonstrates that she is interested in dark, downy winter coats year after year.
Predictive recommendations offer an intent-based approach. If you can predict what a customer will buy, her shopping experience should reflect that. Put those products front and center on the homepage or app home screen.
From the first Retail Personalization Index to the second, no brand improved as much as Urban Outfitters. And the brand’s sophisticated recommendation strategy is a huge reason why. Collaborative filtering is just one type of recommendation Urban Outfitters use. The retailer also uses customer behavior, such as browsing history and interest, to fuel those items in the “Just for you” section.
To learn more about predictive modeling, trigger messages and more, download Looking Fashion Forward: 5 Best Practices for Fashion+ Apparel Retailers here!