Dinner Dice

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.