Defining my goals

To begin, I outlined my key questions:

1. Who are the people visiting NYC museums at this time?

2. What drives museum-goers’ decisions of museum choice and visiting time?

3. What is the level of concern towards COVID and trust in institutions’ safety guidelines?

4. How have the museums’ reservation tools succeeded so far?

5. In what ways have museums’ processes frustrated or confused them?

Uncovering users’ behavior and paint points

I then interviewed 5 people who recently attended museums in New York since the reopening. While I had my own opinions from my experiences, I needed to understand users’ expectations and experiences with the museums’ websites and a museum visit during a pandemic in general.

Identifying the biggest problems facing users

Lack of clarity around timing

• Interviewees complained about the lack or obfuscation of information around the meaning of their time slot.

Need for flexibility

• The participants did not book their tickets more than a week in advance.

• They are social and want to enjoy the city’s offerings after the lockdown, and desire flexibility to adjust their schedules as they go

Unclear expectations of their visit

• Users were unsure what their experience in the museum would be like.

• Unsure what they had access to with their tickets.

Safety assurances

• All had high expectations of museums and trusted that they would have proper safety protocols in place.

• However, there remained questions around guidelines and impact on their visit.

Building a persona to ground design decisions

To ground my decision-making, I created a persona to represent my participants and an affinity map to illustrate her behaviors and attitudes.

Mapping the users’ journey

With the parameters in place, I explored the flow a user should take to complete their tasks of planning their trip and reserving their tickets. There emerged two distinct problems the tool needed to solve for:

Museum-Based Reservation

The user already knows the museum they want to go to. They should be able to easily select the museum, view its information, and find an available time slot.

Time-Based Reservation

This user has a general idea of the time they’d like to visit a museum, but is not sure which museum to choose. They should be able to compare their options easily and make a decision accordingly.

Sketching out solutions

I decided to focus on the journey for users who needed to solve for both variables. Following the user journey, I began quickly ideating the flow and site content with hand-drawn sketches of potential layout options.

Ideating and testing a rough prototype

After reviewing my sketches, I made some design decisions and moved forward with building a low fidelity prototype to test with real users.

Addressing user feedback

Following the test, I made some key adjustments to the prototype, including:

1. Streamlining the process of picking the museum, time and ticket quantity by combining these actions into one module.

2. More clearly incorporating special exhibit reservations into the general admission booking process

3. Adding a filter tool on the lefthand side for ease of adjusting search parameters

4. Following advice of the museum professional, I accounted for more flexibility certain types of guests who qualified for non-standard prices (seniors, children, students, etc.)

Creating a brand identity

After deciding on a creative direction, I fleshed out the brand identity including logo and color palette.

Applying the design system

With the visual identity in place, I revisited my wireframes and applied the new design system. I aimed to create a modern and clean experience with plenty of white space, bold colors, images of art, and large typography.

Validating the user journey and visual design

I tested the hi-fidelity prototype again among a set of museum-goers and art enthusiasts to review the UI and overall aesthetic, as well as one final sweep for any missing information or features.

Adjusting designs for a minimum viable product

The final round of testing provided more learnings, but given the time constraints I had to prioritize the biggest problems with the tool. The rest I would leave for further iterations and more testing.