Nordstrom Trunk Club
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After creating an account, users were dropped into the app where the Messages tab would be for existing customers. The screen prompted them to request a Trunk, but we saw significant number of users go to Discover or Profile instead.
Also users could get into a 'limbo' state if they created an account on web and then downloaded the iOS app, where they could send messages into the abyss because they were not yet assigned to a stylist.
For users who did follow the happy path and entered the request Trunk flow, they were immediately asked to provide a shipping address and credit card. Unsurprisingly, this is where we saw the highest drop-off in the flow.
We were presenting the highest friction steps that requested sensitive customer data without building momentum, excitement or demonstrating value.
We also heard a variety of common customer confusion points:
For users who made it through, the next step displayed data science generated outfits, internally referred to as "express preview." Users could like or dislike the outfit items. Liked items would appear in the Trunk for the stylist, disliked items would be removed. Customers knew these were not picked by a human based on how quickly they were generated. They expressed disappointment and were already suspicious a human would not be packing their Trunk.
Stylists hated express preview as well. They didn't know why these items were chosen and placed in the Trunk before they had started packing. They would often remove those items before packing a Trunk. It also didn't give good direction. Stylist didn't know why items were liked or disliked.
Customers were able to enter specific needs but found the process uninspiring, especially if they were unsure what they wanted.
There were also usability issues. Customers thought the suggested pills were already added to their request. There was no limit to the amount of item requests or indication of how many items fit in a Trunk. Stylists would get requests they couldn't fill because there was simply not room in the Trunk.
Customers expressed general confusion about the service. They didn't understand what a preview entailed, when to expect that, or when to expect the Trunk to arrive. They weren't sure if they could request for a certain date. When or how much they would be charged.
Some customers knew exactly what they were looking for and wanted to explain that in detail and request specific items. Other customers were unsure and wanted the stylist to decide.
Stylists struggled to pack Trunks that would convert if there was no guidance on current needs/goals.
Customers often had a "trigger" occasion or situation in mind where they wanted to improve their look.
Stylists would frequently ask "is this trunk for work, weekend, a special event, or something else?" to narrow the focus of the Trunk. It helped them to pack a thoughtful, curated selection when there was a theme.
Customers wanted to see some options and get inspired. "Blank slate", open ended forms made them feel like they need to put in a lot of effort.
If a customer had a specific event or wanted to ensure they'd be home, they wanted to be able to schedule when the Trunk would be delivered.
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One of the pain points for stylists in the old experience was that customers would tend to add too many requests or none at all. We also heard from customers that they felt overwhelmed by the existing request form.
I wanted to simplify the task and narrow the customer's focus by guiding them to just add top three items they'd like to see. I thought if we could nail those three items, we could drive additional Trunk requests to meet additional unmet needs.
Customers gave very positive feedback during testing, especially around the suggestions and search. However, stakeholders and stylists felt it might be too restrictive.
Another pain point we heard from stylists is they didn't have enough direction at the individual item request level. Some customers knew exactly how to describe what they wanted, while others struggled.
This concept allowed customers to add notes at the item level but also let them browse and add visual inspiration.
Customers loved the browse inspiration feature, however some felt adding notes to each item would be time consuming (notes were not required, but not clear enough in prototype). Ultimately there were technical constraints in our internal styling tool with item level feedback, so I had to explore alternative solutions.
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We knew some users would not be ready to request a Trunk, would have questions or want to explore. Also a small percentage of our customers stated they just wanted to talk to someone first. I wanted to keep the main navigation consistent whether a customer had requested a Trunk or not. So I designed empty states for the Messages tab and the Trunks tab. The primary CTA on the Messages page encouraged our more high-touch users to ask questions and start a chat. On the Trunks page, the primary CTA encouraged customers to request a Trunk. It also included a "How it Works" section that explained the process in three steps.
While we were working on this project, another product squad was working on redesigning the main navigation to support a Home and My Style section. The Home section included a dynamic Trunk status card which we were able to leverage as an additional callout for new customers to request their first Trunk.
The new flow leads with an interstitial screen that callouts key value props and clarifies questions about the service that we frequently heard during user testing. The goal was to simplify the "next steps" information to a few key points and guide the user to the CTA.
In the previous architecture, this CTA to request a Trunk lived within the main app navigation where the messages tab would normally be. I chose to use model instead to focus attention and limit options for the user. Users could still close out of the model and drop into the main nav where they would see empty states on Messages and Trunks that would explain the process and guide them back into the Request a Trunk flow.
Some customers want to schedule their Trunks for specific time, especially for those who want to be styled for a specific occasion. However based on testing and user feedback, the majority of new customers wanted a Trunk as soon as possible. To reduce cognitive load, I designed this step to default to ASAP and hide the calendar until the customer engaged with the "schedule" option.
This step was also an opportunity to clarify confusion and set expectations. The ASAP option includes secondary text that explained the customer could expect a preview of their Trunk within 24 hours.
The core concept of this project was that guiding users to focus on a specific occasion would help reduce choice paralysis, set better expectations, and give stylists a more clear, actionable Trunk request to pack.
The focus options were based on common themes identified after reviewing dozens of stylist conversations. I worked with our marketing team to develop imagery and copy for each Trunk focus. Our team then used Contentful as our CMS to populate the content so it would be easy for content stakeholders to switch out seasonality.
This step went through the most iterations. It was tricky balancing simplicity and customization. Some customers had very specific, detailed requests for their Trunks, while others just wanted to see what the stylist would pack.
We ultimately landed on allowing up to 6 category requests and a free form note. In my initial designs for this approach, customers could search and browse categories to add and see recommendations based on their preferences and behavior. I also created a lower effort option review with the team where customers could quickly type to add shopping list items. Our team decided to go with the lower effort option to reduce scope for the initial release.
Adding a payment method is now shown at the end of the flow, after a customer has a better understanding of what they're getting and what to expect. I also included a number of copy improvements to address the common customer confusion points we heard in feedback sessions. Specifically: