Dinner Dice
An application to assist in the sometimes chaotic nature of planning group dinners. We want to remove the barriers to togetherness.
Problem Background
The age old questions of “what do you want to eat” can be a haunting scream into the void sometimes. This may sound dramatic, but reflect back on times you’ve tried to arrange a dinner out between more than 2 people. Geographic distance, what’s fair, and who likes to eat what, are all questions that can drive indecision or cause friction.
We forge some of our fondest memories meeting with friends over food. Historically the sharing of meals has brought humans together. In our recent human collective memory we’ve experienced times where meeting with friends, or dining out were not possible.
During our research all (100%) of our respondents indicated that indecision is a real problem when they are planning on where to eat. This impacts negatively on their decision making process when planning outings with friends. The difficulty of this process can cause hesitancy in respondents for planning and meeting with friends. 100% of respondents indicated that within their core friend group there are members who are geographically dispersed. This adds increased complexity and points of friction within the surveyed group as trying to find a restaurant that meets dietary as well as travel needs can present an obstacle. Finding a restaurant that meets the group's dietary preferences can also be tough as only 40% of those surveyed indicated they had similar taste to their friends.
Our research demonstrates a fairly common problem. How does one decide on a group dinner that can satisfy disparate tastes, different schedules and that is a fair travel distance for those involved, and limits the need for one person to make a decisive choice. There is no current solution for this situation.
Goals
We want to create an intuitive process to plan group dinners. One that takes into account the blockers presented above, Distance, Dietary Preferences and Decision making (Three D’s).
By collecting the users location data and their dietary preferences we want to present a curated list of restaurants that meet the following criteria, 1) exist in a fair travel distance for all participating and 2) restaurants are weighed on most common dietary preference, using a utilitarian approach (the needs of the many).
- Remove indecision from the dinner outing planning process - decrease the duration it takes to plan an outing.
- Create a list of restaurants that will increase user happiness with the decision making process.
- Reduce the travel time and distance for users to agree upon a restaurant.
- Increase the frequency at which dinner outings are planned and booked by users.
- Provide a clean UI and intuitive process for this
User Stories
As a user, I want to make planning dinners with groups of friends easier, so i am more motivated to plan these get togethers
As a user, I want to find a restaurant that most closely meets the dietary preferences of the group.
As a user, I want to find a restaurant that is a fair travel distance for the group.
Problem Statement:
How can we make the experience of planning group dinners intuitive and conflict free so that this process is less stressful and encourages more frequent human connection over food?
Proposed Solution:
We want to start with a small and narrow focus to ensure we address the 3 main blockers that we’ve discovered exist in the group dinner planning process.
Dietary Preference: Users will be prompted to select from a list of restaurant / food types, ie Pizza, middle eastern, sushi, Italian. Each user's list will be used to create the final list of suggested restaurants with weighting favouring common choices.
Distance: Users location data, either taken from their device, or entered in their profile, will be used to establish a radius. This will influence the location of the restaurants in the final list presented to the group weighted to provide a fair travel distance for users in the group.
Decision: The above 2 parameters will result in a list of restaurants that the user group can use to make their decision. A decision made by an application and not one person which should reduce the friction, personal feelings, and indecision.
Scenarios
User Story #1: As a user, I want to make planning dinners with groups of friends easier, so i am more motivated to plan these get togethers
Acceptance Criteria:
- User is generated a list of restaurants that meets their groups needs
- Transparent planning process, all members in app group see same info and get same notifications
- User can see previous group dinners planned in the application
User Story #2: As a user, I want to remove conflict from the dinner planning process, as my friend group has disparate tastes in food.
Acceptance Criteria:
- User can create a weighted list of their dietary preferences
- Users lists are used to create final list presented to group
- Logic weighs choice based on utilitarian ethics, “what is best for the group”. Ie the most popular choices taken from each users lists
User Stay #3: As a user, I want to remove resentment some of my friends feel due to distance they travel, this will ensure these friends are more likely to attend
Acceptance Criteria
- Users are able to set their address, or have their device location used when creating a profile.
- Location data is stored and used to calculate a mean travel distance
- Users location are used to determine the geographical area the restaurant suggestions will be pulled from
Measuring Success
By Demo date, we would like to be able to generate a restaurant list that meets our criteria of
1) Food preferences
2) geographical Distance
Product Success Metrics
- Track the number of active users - aim for 100 within the first quarter of operation
- Track the number of created lists - 80% completion rate of create lists to users
- Track the prevailing food preferences - aggregate and display trends
- Track location data - use to tweak algorithms and effectiveness of travel time
- Understand gaps in this simplistic approach to inform future features
- Understand if users follow through and use a restaurant from the generated lists- no booking tracking at the moment, this is a future goal - will be tracked (conversion rate)
These metrics will allow us to identify if our application is providing its intended value. They will also help us identify gaps and other use cases that are currently not supported but could be a future feature.
Learnings
Product Manager Learnings:
Andrew Core
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
&
As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
&
As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
&
Developer Learnings:
Maurquise Williams
&
- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
&
All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
&
I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.