Crave: Streamlining the Dining Experience
Crave: Streamlining the Dining Experience
Role
Product Designer
Role
Product Designer
Project Type
Academic
Project Type
Academic
Team
Individual
Team
Individual
Timeline
Oct 2025
(2 weeks)
Timeline
Oct 2025
(2 weeks)
Tools
Figma
Notion
Tools
Figma
Notion
Skills
UX Research
UX Design
User Testing
Mobile Design
Prototyping
Design Systems
AI Integration
Skills
UX Research
UX Design
User Testing
Mobile Design
Prototyping
Design Systems
AI Integration
What is Crave?
What is Crave?
Crave is a concept for an all-in-one application where users can reserve, discover, and rate restaurants. Crave includes recommendations based on the current party, live waiting times, alternate restaurants, and a social ecosystem to overcome barriers to picking and dining at a restaurant, especially in major cities. The application uses readily available data and is feasible considering current infrastructure.
Crave is a concept for an all-in-one application where users can reserve, discover, and rate restaurants. Crave includes recommendations based on the current party, live waiting times, alternate restaurants, and a social ecosystem to overcome barriers to picking and dining at a restaurant, especially in major cities. The application uses readily available data and is feasible considering current infrastructure.
Project Context
Project Context
72% of diners would not wait for more than 30 minutes, but most popular restaurants in major cities have waits over one hour AND 50% of people do not make reservations before dining out. On top of that, 79% of Americans have difficulty choosing what to eat.
Spontaneous diners struggle to find reliable, real-time information about restaurant wait times and availability which leads to frustration and difficult decision making. Users experience stress from long or uncertain waits, difficulty coordinating group decisions, and challenges finding alternative options when plans change.
72% of diners would not wait for more than 30 minutes, but most popular restaurants in major cities have waits over one hour AND 50% of people do not make reservations before dining out. On top of that, 79% of Americans have difficulty choosing what to eat.
Spontaneous diners struggle to find reliable, real-time information about restaurant wait times and availability which leads to frustration and difficult decision making. Users experience stress from long or uncertain waits, difficulty coordinating group decisions, and challenges finding alternative options when plans change.
User and Field Research
User and Field Research
I interviewed 5 avid diners across 3 major US cities and conducted field research on 5 restaurants to study wait time and dining behaviors. Secondhand research was conducted using LLMs to further approve of the problem space.
I decided to conduct a field study on top on user research to really confirm whether dining in major cities is as ambiguous and confusing as statistics show. From the field research, the difference of wait times posted online and actual wait times were on average around 1 hour longer. From the user interviews, I confirmed 6 main user painpoints and user needs.
I interviewed 5 avid diners across 3 major US cities and conducted field research on 5 restaurants to study wait time and dining behaviors. Secondhand research was conducted using LLMs to further approve of the problem space.
I decided to conduct a field study on top on user research to really confirm whether dining in major cities is as ambiguous and confusing as statistics show. From the field research, the difference of wait times posted online and actual wait times were on average around 1 hour longer. From the user interviews, I confirmed 6 main user painpoints and user needs.
User Painpoints
User Painpoints
Long waits with no visibility
Long waits with no visibility
Inaccurate info on the web
Inaccurate info on the web
Difficulty finding alternatives
Difficulty finding alternatives
No personalization
No personalization
Binding reservations
Binding reservations
Poor group coordination
Poor group coordination
User Needs
User Needs
Real time availability
Real time availability
Unified platform
Unified platform
Personalization
Personalization
Social coordination
Social coordination
Flexible waiting experience
Flexible waiting experience
Spontaneity friendly features
Spontaneity friendly features
Competitor Analysis
Competitor Analysis
I looked at 5 competitors across navigation and food functions to see if these competitors complete the user needs
I looked at 5 competitors across navigation and food functions to see if these competitors complete the user needs
After comparing the user needs to each app's functionalities, NO apps fulfilled all user needs
After comparing the user needs to each app's functionalities, NO apps fulfilled all user needs
Ideation
Ideation
I started rapid ideation by doing crazy 8s, exploring various physical and digital solutions. In the end, I narrowed it down by considering:
Feasability
User needs and wants
Current data availability
I started rapid ideation by doing crazy 8s, exploring various physical and digital solutions. In the end, I narrowed it down by considering:
Feasability
User needs and wants
Current data availability
Low and Mid Fidelities
Low and Mid Fidelities
Taking into account user wants and feasibility, Crave is a full app instead of a plug-in into an existing app. The reason for this is because Crave carries a multitude on new features such as party taste matching, real time wait times, taste-matched suggestions, and personalized recommendations. Current apps only hold one or less of these features, so introducing a multilayered concept to existing platforms would take away from their business vision. With Crave being its own app, users will no longer have to jump between apps, as the current dining experience is highly fragmented.
Taking into account user wants and feasibility, Crave is a full app instead of a plug-in into an existing app. The reason for this is because Crave carries a multitude on new features such as party taste matching, real time wait times, taste-matched suggestions, and personalized recommendations. Current apps only hold one or less of these features, so introducing a multilayered concept to existing platforms would take away from their business vision. With Crave being its own app, users will no longer have to jump between apps, as the current dining experience is highly fragmented.
I started by creating an information hierarchy to envision how the data of the app will be shaped. The image on the left show low fidelity mockups of how the app will look. I used standard UI practices (nav bar, search bar, map) to ensure a low barrier of entry for users.
I started by creating an information hierarchy to envision how the data of the app will be shaped. The image on the left show low fidelity mockups of how the app will look. I used standard UI practices (nav bar, search bar, map) to ensure a low barrier of entry for users.
A/B Testing
A/B Testing
Since Crave would have social and discovery features, I conducted A/B testing to see if users preferred a social forward or discovery forward platform. Users prefer a discovery forward platform because a food oriented app should be catered towards discovery. Thus, the map page became the landing page.
Since Crave would have social and discovery features, I conducted A/B testing to see if users preferred a social forward or discovery forward platform. Users prefer a discovery forward platform because a food oriented app should be catered towards discovery. Thus, the map page became the landing page.
Social forward (0 votes)
Social forward (0 votes)
Discovery forward (5 votes)
Discovery forward (5 votes)
Usability Testing
Usability Testing
I tested the ability of users to navigate through the app with these tasks:
Find an alternate restaurant with no wait time
Add people to your party
See friend's ratings
I tested the ability of users to navigate through the app with these tasks:
Find an alternate restaurant with no wait time
Add people to your party
See friend's ratings
Task 1:
100% success rate
Task 1:
100% success rate
Task 2:
80% success rate
Task 2:
80% success rate
Task 3:
100% success rate
Task 3:
100% success rate
After usability testing, users had additional complaints about the lack of information on the restaurant widgets, incomplete filters for food discovery, and confusion in seeing friends rating. With these results in mind, I continue to create the final prototypes.
After usability testing, users had additional complaints about the lack of information on the restaurant widgets, incomplete filters for food discovery, and confusion in seeing friends rating. With these results in mind, I continue to create the final prototypes.
Design System
Design System
To ensure scalability and consistent UI, I created my own mini design system with visual elements in mind. The main colors of the app are targeted towards the feeling of warmth and hunger stimulation. I also used LLMs to look for industry standard UI practices to build my own mini design system. The mini design system consists of:
Widget components
Brand colors
Scalable logo
Font
Button components
To ensure scalability and consistent UI, I created my own mini design system with visual elements in mind. The main colors of the app are targeted towards the feeling of warmth and hunger stimulation. I also used LLMs to look for industry standard UI practices to build my own mini design system. The mini design system consists of:
Widget components
Brand colors
Scalable logo
Font
Button components
‘Crave’ High Fidelities
‘Crave’ High Fidelities
After rounds of feedback and iterations, the final prototype boasts a 4 main pages: discovery, friends, profiles, and food. As an all-in-one dining app, users are able to do all steps in the dining experience within this app. The features to highlight is taste matching (using personal and party preferences to calculate restaurant's %match) and live waiting times.
After rounds of feedback and iterations, the final prototype boasts a 4 main pages: discovery, friends, profiles, and food. As an all-in-one dining app, users are able to do all steps in the dining experience within this app. The features to highlight is taste matching (using personal and party preferences to calculate restaurant's %match) and live waiting times.
Discovery
Discovery
Friends
Friends
Profiles
Profiles
Food
Food
Discovery
Discovery
The discovery page is the main page where users can discover, search and choose places to eat. In this page, multiple new features are available to streamline the dining experience
Real time wait times
Search functionality
Add friends to party
Filter current party's cravings
Recommendations based on current party
Recommendations based on popularity
Recommendations based on distance
The discovery page is the main page where users can discover, search and choose places to eat. In this page, multiple new features are available to streamline the dining experience
Real time wait times
Search functionality
Add friends to party
Filter current party's cravings
Recommendations based on current party
Recommendations based on popularity
Recommendations based on distance
Friends
Friends
The friends page is where users are able to see what friends have eaten and see their honest ratings. Based on the people you are friends with, this page will also populate recommendations. Within each recommendation widget, it will show the %match to your current party set on the discovery page
The friends page is where users are able to see what friends have eaten and see their honest ratings. Based on the people you are friends with, this page will also populate recommendations. Within each recommendation widget, it will show the %match to your current party set on the discovery page
Profiles
Profiles
Aside from following standard UI practice, the profiles page allows users to customize their preferences for food, view active waitlists and reservations, see friends, bookmarks, and see personal ratings. The feature to highlight is preferences; for example, if a user has a nut allergy and is added to someone else's party, the restaurants filtered will all be nut allergy friendly.
Aside from following standard UI practice, the profiles page allows users to customize their preferences for food, view active waitlists and reservations, see friends, bookmarks, and see personal ratings. The feature to highlight is preferences; for example, if a user has a nut allergy and is added to someone else's party, the restaurants filtered will all be nut allergy friendly.
Food
Food
When a widget is clicked on, the food page will show comprehensive information about the restaurant alongside the ability to save, rate, reserve, or add to waitlist. When scrolling further down, the user will also be able to see alternate choices of restaurants incase the current restaurant has too long of a wait time. Of course, this is all relative to the %match to the current party.
When a widget is clicked on, the food page will show comprehensive information about the restaurant alongside the ability to save, rate, reserve, or add to waitlist. When scrolling further down, the user will also be able to see alternate choices of restaurants incase the current restaurant has too long of a wait time. Of course, this is all relative to the %match to the current party.
‘Crave’ Final Prototype
‘Crave’ Final Prototype
Design Feasabilities
Design Feasabilities
Upwards of 70% restaurants use systems like OpenTable and Resy that can monitor live wait times, restaurants will be incentivized through marketing opportunities and increase in consumers if data is shared to Crave
Monetization will be possible after initial launch stage by charging fees to participating restaurants and the use paid boosts for restaurants that want to promote their business
The use of AI in workflows will enable taste profiles and taste matches to be done
Upwards of 70% restaurants use systems like OpenTable and Resy that can monitor live wait times, restaurants will be incentivized through marketing opportunities and increase in consumers if data is shared to Crave
Monetization will be possible after initial launch stage by charging fees to participating restaurants and the use paid boosts for restaurants that want to promote their business
The use of AI in workflows will enable taste profiles and taste matches to be done
Final Reflections
Final Reflections
I was able to explore what it is like to design a fully functional app using my own design kit within a design sprint of 2 weeks. These are the valuable things I learned throughout the process:
How to design a cohesive design kit relevant to business needs
The importance of conducting user testing to validate design decisions
Constant iterating and feedback is crucial to the design process
By using LLM models during the research and standardization phase, I was able to streamline the process of designing and ensuring that my UI is up to standards.
I was able to explore what it is like to design a fully functional app using my own design kit within a design sprint of 2 weeks. These are the valuable things I learned throughout the process:
How to design a cohesive design kit relevant to business needs
The importance of conducting user testing to validate design decisions
Constant iterating and feedback is crucial to the design process
By using LLM models during the research and standardization phase, I was able to streamline the process of designing and ensuring that my UI is up to standards.
Special thanks to Prof Sana and participating users for the guidance and help throughout this quick design sprint!
Special thanks to Prof Sana and participating users for the guidance and help throughout this quick design sprint!
