Book-a-Bite

A mobile app designed to provide Ann Arbor's busy residents and students with real-time updates on restaurant wait times and offerings, enhancing their dining experience through time-efficient choices.

Background

A mobile app designed to provide Ann Arbor’s busy residents and University of Michigan students with a centralized hub for real-time restaurant wait times, reservations, and dining options. By streamlining access to up-to-date information, Book-a-Bite simplifies restaurant discovery, reduces wait-time uncertainty, and empowers users to make time-efficient dining choices.

Task

The goal was to design an app prototype that serves as a centralized hub for Ann Arbor residents and University of Michigan students, simplifying the process of finding real-time restaurant wait times and making reservations seamlessly.

I focused on:

Reducing uncertainty by consolidating restaurant listings, wait times, and reservation options into a single, easy-to-navigate platform.
Enhancing usability and accessibility to help users efficiently plan their dining experience with real-time updates, digital waitlist integration, and personalized recommendations.

Action

Research and Problem Framing

Conducted user interviews and surveys with Ann Arbor residents and University of Michigan students to uncover key pain points related to restaurant wait times and dining experiences.
Synthesized insights into a clear problem statement: “As a busy Ann Arbor resident or University of Michigan student, I need a reliable way to check restaurant wait times and book reservations so I can efficiently plan my dining experience without uncertainty.”

Ideation & Feature Prioritization

Brainstormed Features: Explored potential features including real-time wait time tracking, digital waitlists, restaurant discovery, reservation booking, and integration with nearby attractions.
Prioritized Features: Focused on user needs, feasibility, and value to ensure a streamlined prototype, emphasizing real-time wait times, a seamless reservation system, and personalized restaurant recommendations.
Key design elements included:
- Search & Discovery: Users can find restaurants by location, cuisine, and price range.
- Real-Time Updates: Displays current wait times and peak hours.
- Reservations & Waitlist: Digital queue system for seamless booking.
- Dining Time Estimates: Helps users gauge expected meal duration.
- Integrated Recommendations: Suggests nearby attractions and dining spots.

Result

The prototype received positive feedback from testers, who noted that the app simplifies restaurant discovery, reduces uncertainty around wait times, and enhances the dining experience.

Key Outcomes:

Enhanced decision-making: The centralized platform provided one-stop access to restaurant availability, dining duration estimates, and reservation options, making it easier for users to choose where and when to eat.
Improved dining efficiency: Users reported that this would reduce frustration with long wait times and greater ease in planning meals around their schedules.

Device Compability and Technical Considerations

Cross-Platform Development: Designed for both iOS and Android with a single codebase.
Web Accessibility Standards: Ensured usability for individuals with disabilities.
Cloud-Based & Responsive Design: Adaptable interface across different screen sizes.
API Integration: Connected with restaurant databases for live updates.
Security & Data Protection: Implemented SSL/TLS encryption to safeguard user data.

Community Values and App Impact

Designed with the Ann Arbor community in mind, addressing the needs of:
- Students seeking efficient dining solutions.
- Residents looking for a seamless reservation system.
- Local restaurants wanting to optimize customer flow.
The app enhances the dining experience by reducing uncertainty, improving time management, and ensuring accessibility to diverse food options.

Data Collection and Protection

Collected user-generated and restaurant-fed data to enhance functionality:
- User Data: Age, food preferences, free time, crowdsourced reviews.
- Restaurant Data: Wait times, menu pricing, open hours, food supply availability.
Ensured data privacy by implementing:
- Anonymous ratings and recommendations.
- Clear, concise privacy policies with user control over data (edit, delete, opt-out options).

Data Storage and Analysis

Secure Data Storage: Implemented SQL-based authentication and structured databases for efficient organization.
Machine Learning Integration: Used cluster analysis to group similar user preferences and content-based analytics to refine restaurant recommendations.
Crowdsourced Data Utilization: Optimized real-time updates on restaurant wait times based on user contributions.

Conclusion

Problem Addressed: The app successfully tackled the issue of unpredictable restaurant wait times and reservation difficulties.
Effective Solution: By combining crowdsourced data and restaurant-fed information, the app provided users with accurate and up-to-date wait times.
Key Differentiator: The gamification of user contributions (discount incentives, reviews) and personalized dining recommendations set the app apart from competitors.
Successful Outcome: The prototype demonstrated high engagement and usability, proving its potential for real-world implementation.

Get in Touch!

If you would like to connect about an opportunity or design talk, please feel free to reach out using the links below.

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