Being a first-of-a-kind solution, IBM Travel Concierge involved managing multiple technical teams and business units. We closely aligned with our Proof-of-Concept client, the IBM Sales team and our Research team. Managing all expectations from a design standpoint was not a trivial task. It involved recurring check-ins and playbacks with stakeholders that fell in tandem with Design & Agile Sprints. We instilled the approach wherein every design decision fell back on research. This allowed for every conversation to be grounded on as much information from users as possible, which alleviated team members’ needs to base decisions off of personal preference.
From a craft standpoint, devising the UX was a challenge. The application hinged on the IBM Watson Assistant platform, and the conversational component relied on a back-end database and recommendation engine. Ultimately this meant understanding each technical call and how that might impact the UX. With every new preference gleaned from user conversation, recommendations on the main UI would evolve to reflect new information. As a result, providing ample user feedback became critical and failing to do so would translate to a poor user experience.
The first working prototype of IBM Travel Concierge was revealed at the IBM Think event in Las Vegas in 2018. We collaborated with United Airlines to produce a fully functioning working prototype. The experience hinged on a chat window being the primary mode of interaction, accompanied by a traditional shopping experience. As users talked to the agent like they would to any travel agent, the agent would start to capture preferences and characteristics in order to build out a profile. Furthermore, while chatting, recommendations would appear on the other half of the screen, allowing users to explore photos and read more information. From a UX standpoint, my team and I had to manage expectations: how would the experience handshake from conversation to traditional GUI? We devised the solution wherein conversational triggers would update the GUI and GUI interactions would cause the agent to respond to them.
In order to encourage interaction through conversation, the first interaction a user has of the prototype is mediated through a modal that nudges the user to chat.
Upon iteracting, the modal dismisses itself and the resulting experience provides two avenues for interaction: one through conversation and the other through a more conventional user experience.
After the user interacts with a destination card, they have the ability to find more about the locale.
Many of the data elements on this screen, such as weather predictions and attraction information, are powered through machine learning, particularly collaborative filtering.
Whether the user decides to advance the experience through conversation or by more conventional interaction paradigms, the end goal is to book a flight.
When the user reaches this step, they will be presented with a bar chart that shows them a breakdown of price points for different dates of interest.
Recording of the working prototype prior to demo at IBM Think 2018
The prototype was successfully delivered to our first set of pilot clients in mid-2018. After this time, the United's usability and development teams took on the work of running alpha testing with a select group of their MileagePlus customers. Despite having left shortly after this project was handed off, the client signed on for additional work to be completed on this alpha release pending testing results. All that said, early qualitative feedback from customers has been positive and our NPS score tracked in the low 60s with this select group of testing customers.