The Netflix of Fashion
Tote Swap is the core customer experience for Le Tote’s clothing rental subscription service. The feature lets users browse clothing and accessories available to rent and decide what they would like shipped to them. Tote Swap is also the primary feature that differentiates Le Tote from competitors like Stitch Fix and Trunk Club, which don't allow users to customize their shipments.
Platforms: DESKTOP, MOBILE WEB, IOS
In 2017 I led the cross-platform redesign of Tote Swap. My responsibilities included user research, concept development, product strategy, aligning stakeholders on product goals, information architecture, designing user flows, visual design, interaction design, prototyping, user testing, and quality assurance.
How the Subscription Model Works
Le Tote customers subscribe to a monthly plan that allows them to rent a set number of clothing and accessory items per shipment, i.e. “tote”. They can have one tote out at a time, which they may exchange for a new tote as often as they want. Le Tote’s proprietary styling algorithm uses data from onboarding and from items the user likes (i.e. “adds to her Closet”) to populate the initial items in a user’s tote. Once a tote has been styled by the algorithm, the user can accept or override these selections within a 48 hour window, by entering Tote Swap.
When one user rents a particular item, that item is no longer in circulation - therefore unlike traditional e-commerce, when a user browses items in Tote Swap, the inventory they see constantly varies. This presents challenges and opportunities that are unique to the rental subscription model.
Defining the Problem
Circulating inventory has created the impression that it is difficult for users to find specific items they want to receive. The current browsing experience in Tote Swap is also impersonal and cumbersome, forcing users to sift through a lot of inventory that doesn’t appeal to them. Additionally there is no transparency about Le Tote's fit and style recommendations, which are based on machine learning.
1. Use data to surface dynamic & personalized recommendations
2. Help users quickly find what they're looking for
3. Improve the overall perception of Le Tote's service & brand
EXISTING USER FLOW
Previously, if a user wanted to customize her tote, she would first have to select which item to remove, then browse for its replacement. This assumed she was already certain about her decision. Once she swapped the item into her tote, she would return to the beginning of this process. Because each item in her tote had a separate entry point into browse, her browsing history was not saved, forcing her to navigate past several products she'd already seen.
Revamped User Flow
The old UX flow was oriented around the user completing a single task. I designed the new user flow to encourage discovery and engagement with Le Tote's inventory. Now users can browse available inventory without committing to removing a specific item from their tote.
The Improved Tote Swap
Now she can easily toggle between Collections, Clothing, Accessories, and her Closet. When she taps on an item for more detail, her browsing history is saved, eliminating the friction of having to start her search from scratch each time.
With the new Collections tab, browsing finally feels personalized. Each collection is dynamically updated with available inventory in her size, and features data-driven recommendations, items she’s added to her closet, and items she’s worn previously and rated highly.
Filters & Sorts
Now she can sort and filter search results by best match for her fit and style, newest, highest rated, item category, weather, and color.
Easy Tote Customization
Now she can browse for items she likes without committing to any changes to her tote. If she finds an item she likes, she can easily swap it into her tote without losing her browsing history.