Volta

Find the perfect café based on your needs and preferences.

Summary
Due to the COVID-19 pandemic, local businesses are losing customers to larger, corporate businesses as they were forced to close down.

As a team, our mission was to design a Machine Learning (ML) powered application that provides users with tailored café recommendations based on their coffee preferences and desired atmosphere.

Duration
July - August 2023

Team
Anya Jain (Designer)
Sabrina Lao (Researcher)

My Role
User Journey Mapping, Wireframing, Prototyping, User Interviews

Background

Cafés are losing business due to COVID-19

Design Prompt
As a term-wide project for the course BET 350: Customer Experience Design, my team was prompted to identify an industry, a local business, or a customer segment that is struggling due to COVID-19 and identify a white-space opportunity to design a brand-new business solution.

My group decided to focus on local coffee shops and cafés. We wanted to create a product that would encourage people to try locally owned cafés in their community, rather than large coffee shop chains.

Research

Find areas for improvement in the customer experience.

To understand our users’ experience with finding and visiting new cafés, we created a customer journey map. Through this, we were able to highlight areas that could be improved during the process. We used external research as well as user interviews to determine each stage of the process.

Image of the request manager dashboard from the first iteration of designing.
Customer Journey Map
takeaway 01
The process of comparing menus, prices, and ratings between cafés is stressful and confusing.
takeaway 02
If wanting to try something new, asking the barista for recommendations can be intimidating.

Surveys and Interviews

We conducted an online survey and user interviews to help us understand our users' habits when visiting a café. Here is some examples of the questions we asked:

takeaway 01
Not many people are loyal to a single coffee shop or franchise, which begs the question: how can we get people to explore other coffee shops?  
takeaway 02
Students enjoy studying in cafés, but they avoid trying new ones in fear that they won't have specific requirements such as wifi, suitable seating, or outlets.

The Problems

After conducting external research and user interviews, we were able to identify two main pain-points that users face when trying to find a new café and trying menu items that match their taste.  

It’s difficult to find information and compare cafés that match the user’s preferences.

People are indecisive and tend to have doubts when trying new menu items.

Ideation

How might we improve café experiences for users with specific preferences and how can we encourage them to explore other cafés?

Our Solution

Create an Machine-Learning (ML) based application, which provides users with tailored local café recommendations based on their coffee preferences and desired atmosphere.

Requirements & Constraints

To ensure that the team was on track to finish the project on time, we determined the requirements and constraints of the project.  

requirement 01
Use machine learning to recommend local cafés and coffee shops users.
requirement 02
Display information such as peak hours, seating availability, and aesthetic.
requirement 03
Allow users to save listings and view them later for future reference.
constraint 01
the high-fidelity prototype must be complete within a month.

Wireframes and Explorations

Potential User Journey and Initial Wireframes

Due to the short time-frame, we focused on 3 main pages that we believed to be the most important; the home page, saved page, and the listing page. We decided that if we had time, we would also include a settings page and history page to let users track the cafés that they visited. Our project also required us to design an onboarding user journey.

I quickly sketched some wireframes of the journey between each page so that the team could move on to mid-fidelity designs.

Design

When users onboard the app, they are prompted to enter their coffee preferences, and qualities that they seek in a café. The user-entered preferences are used to improve the machine learning algorithm used in the home page to make personalized recommendations.

Image of the request manager dashboard from the first iteration of designing.
Image of the request manager dashboard from the first iteration of designing.

Next Steps + Reflection

Determine and specify your scope as early as possible.

As the prompt for this project was quite vague, it was important that the team determined a scope for this project straight-away. We found that many of us had great ideas, but we simply did not have enough time to work through all of them. Identifying the scope early in the design process can keep the whole team on-track, help to understand requirements, and to ensure that the project is done on time.

Consistent feedback is important.

Throughout the design process, the team often encountered situations where we had multiple ideas but were uncertain about which one would be most “beneficial” for the users. This is when input from fellow designers, stakeholders, and peers becomes crucial. Speaking to potential stakeholders helped us to prioritize certain features over others and helped to drive the design process in the correct direction.