Where are we eating?

Hungry but don’t know where to eat? Get a personalized restaurant recommendation

User interviews, wireframing, prototyping, usability studies, design iteration, user testing, data persistence, functional database creation, use of APIs

Skills Utilized

Decision fatigue when deciding where to eat and what new restaurant to try.

Problem

Goal

Create an application that makes it easy for users to find new restaurants to try based on their preferences.

• saves time for users

• consolidates research process

• allows local businesses to gain exposure

Why is this worth addressing?

1. Busy users who do not have time to research restaurants to find where to eat

2. Users who want to try a new restaurant but don’t know where to start

3. Users who want to try a new restaurant based on their specific preferences (previous restaurants they liked, their chosen cuisine, their preferred price point, etc.)

Pain Points

• Individuals who need quick recommendations for their next meal

• Individuals who love trying new restaurants

Primary Users

Shreya is a grad student who lives in Boston. She loves supporting local restaurants and trying new food, but with grad school, she doesn’t have the time to research new restaurants and wishes there was an easier way for her to get personalized recommendations.

“After a long day, the last thing I want to do is spend a lot of time figuring out where to order dinner from.”

Shreya

User Mapping

Mapping Shreya’s user journey showed that it would be convenient to allow users to add filters if they want; however, if they do not utilize filters, the application will use data from the restaurants that Shreya has saved and/or added to her favorites in order to provide a personalized recommendation. 

A key part of my design process was creating digital wireframes. These digital frames addressed the key user pain points. Through the process of designing these wireframes, I focused on ease of use and regularly received feedback on my iterations.

Digital Wireframes

Low-Fidelity Prototype

You can view the WaWe low-fidelity prototype here.

During my design process, I regularly received user feedback. Both rounds of significant feedback allowed me to update my low-fidelity prototype and final mockup.

Usability Studies

• Users wanted a separate screen for their filters

• Users wanted a slider for the distance filter instead of a dropdown

Round 1

• Users wanted a cleaner way to view restaurants in their saved lists

• Users wanted more information on the restaurant view of the application

Round 2

After conducting my usability studies, I updated the item view for individual restaurants. I included more photographs on the screen and created a button that would take users to Google Maps to allow them to receive directions to the restaurant.

Mockups

Other Key Mockups