Trunk Club - Active Response Design

Automatic message through "Active Response"

The first message is an automated message from the stylist to the customer. If the customer responds, then the stylist is prompted to message the customer back.

Understanding the user's needs

The stylist follows a dynamic script based on tested conversation flows to understand the user's sense of style and ultimately what they are looking for.

Setting expectations and underestanding price point

While the landing page explains the pricing of the service, many potential customers would get to the point of purchase and not understand price.

The stylist reiterates pricing expectations, but also has data from the user's signup flow to understand how to position this conversation. Ultimately, this part of the script would confirm expectations before showing product.

Adding payment, shipping, and laying out logistics

The stylist will send a card through the messenger to prompt users to add payment and shipping. The convenience of this interaction was much preferred to the previous method of asking users to update their profile with these details.

The stylist will then explain logistics of the trunk delivery process and ask the user if they had any specific questions at that point.

Viewing product and confirming the trunk

Once the stylist chooses product within their catalog application, they can drop a product card into the messenger.

The user can then view their upcoming trunk, request replacements, and confirm the trunk to be shipped.

TRUNK CLUB - MESSAGING SERVICE (ACTIVE RESPONSE)

Trunk Club is an online styling service that connects users with their personalized apparel stylist. In 2016, I helped design a messaging experience that allowed users to immediately chat with a stylist via desktop and mobile messaging application. This experience was called "Active Response".

What problem were we trying to solve?

After new users completed the onboarding flow, they would have to wait a maximum of a few days to be matched with a stylist. During this period, the user's interest in the service would wane, and she would drop off.

What were our users saying?

Through user interviews, we understood pain points to be the following:

"The sign up process was tedious and then I had to wait even longer to get my clothes."
"I got matched with my stylist after a few days and she never reached out to me."
"I reached out to my stylist through the app and it took them forever to respond."
"It takes too long to see clothes. The stylist asks me even more questions after I answered a bunch of questions to sign up. And many of them were the same."

What pain points were we trying to solve?

Three pain points:

Long response time
Poor stylist conversation experience
Long time before being presented product

What did we design?

Through rapid prototyping and utilizing existing infrastructure, we created a chat experience where the stylist would immediately send a message to the user after sign-up. The user would then be able to chat with their stylist and be shown product within the chat experience.

How did we do it?

I designed a system to send an automated message to the user 10 seconds after they sign up. The message prompted users to respond ("Hi --- Let's get started! What are you looking for? Clothes for work, fun, or a special event?"). If a user chooses to answer the question, they would be connected to a stylist on shift to chat. If they don't respond, a stylist would not be notified to respond to the user.

Our product design team tested multiple chat scripts to see what would be the best converting conversation experience. We tested a few "first messages" to see what would prompt a response, and then a few subsequent conversation flows to see what would best lead to a sale.

How did it turn out?

After training stylists to serve users, we noticed nearly double the response rate with our stylists. These stylists would also have a response time under a minute. If stylists followed the prescribed conversation flows, conversion would nearly double as well.