Happie | 2022
A restaurant app that aids decision-making
We all know the following scenario; it’s a Friday night and you’re having some drinks with a couple of friends, around 7 someone starts asking “what do you guys want to do for dinner?”. Everyone starts to shout their favourite restaurants and the endless search for the “perfect” restaurant begins. On Google Maps you scroll through the infinite amount of restaurants in your area and after 20 minutes of disagreeing you start to get frustrated.
My graduation project, called Happie, is a concept application that I’ve created to solve the frustrating and unnecessarily complicated problem above.
The Ask
For my graduation project I wanted to end my 4 year design studies on a special note. I wanted to focus on a project where I could connect some of my heritage and one of my deep passions with digital design. Being brought up by restaurant owners and constantly being involved in the kitchen, I’ve always had an intrinsic interest in food and dining. Dining out, experimenting in the kitchen, trying new cuisines, and connecting with others over a good plate of food has been an integral part of my life.
My goal was to think of a frequent problem in the realm of gastronomy and food which everyone experienced on all levels. That’s when I realised that I personally suffered quite frequently from a problem when trying to find a restaurant with friends. Which is “not being able to decide where you want to eat”, due to a variety of reasons such as different individual preferences and lack of insight on quality of restaurants. After confirming with the people around me (of all ages and demographic backgrounds, not just friends) that this is indeed an undesirable problem, I decided to commit my graduation project to solving this problem.
My project goals were simple. Identify why exactly people were incapable to concede on a single choice of restaurant and use these insights to create new concept platform which would aid decision-making (assuming the current platforms are doing so insufficiently).
Research
My research was primarily focused on gathering qualitative data because I needed to know the ‘why’s’ of this problem. Why are people experiencing trouble making choices of where to eat? With this as my central research question I went on to conduct multiple user interviews within the target audience that I defined. Diners in Amsterdam between the age of 20 and 30. During these interviews I had a clear objective of what I wanted to know of the participant.
Interview objectives
Confirm whether the target audience consider this a undesirable problem (so I know if the problem is worth solving)
Try to understand the deeper emotional motives of why the target audience goes out to restaurants
A walkthrough of the last time they could remember experiencing the inability to pick a restaurant
So I could quantify how frequent this problem occurs by taking the average of all participants
So I could understand in which context the problem occurs the most frequently (such as breakfast, lunch, diner, groups of 2, 3, 4 or more?)
So I could understand which tools the target audience uses to solve this problem
Below follows an illustration with the key insights that I mostly gathered from conducting user interviews and a survey, in Dutch.
Research sources
🎙 6 qualitative interviews
Qualitative interviews that with participants from the target audience, mapping the current situation and identifying pain points.
👓 2 participant observations
During these observations I would observe how participants searched for restaurants, to identify solutions they currently used.
👔 2 qualitative interviews
Qualitative interviews through which I could gain the perspective of restaurant owners on this problem.
📝 48 responses to survey
The survey contained questions that helped quantifying the severity of the multiple pain points and problems. This data served as directional input.
✍🏻 1 co-creation
During this co-creation I invited a participant to co-create a customer journey map with me. This map would document the journey, pain points, and possible opportunities.
⌨️ Desk research
The conducted desk research evolved around the available online literature surrounding this problem. E.g the psychology of decision making and information overload.
Discovery
What I discovered through conducting the user interviews was that the problem, of not being able to pick a restaurant, could have different kinds of conditions. So in the scenario of a group not being able to pick a restaurant, that could be caused by a variety of conditions. For example; each individual in the group wanted to eat something else or some individuals know what they wanted to eat but not at which restaurant. Identifying these varying conditions that caused the problem helped me understand why the target audience was having trouble deciding and which solutions could potentially solve this.
The discovery of these different conditions was crucial, because what it really showed was the different kind of ways people were searching. E.g when a group can agree on the cuisine they would like to eat and cannot agree on where to eat it, they are already much further in the search funnel and show a refining & narrowing search behaviour. Meaning that you should only present them restaurants in a certain domain (the cuisine they want to eat). Oppose to a group that hasn’t decided on a cuisine yet. They are showing an explorative search behaviour and should be presented restaurants in all domains.
In the illustration below you can see a visual explanation of these conditions that caused the problem of participants not being able to decide on which restaurant to pick.
Key insights
👥 Search difficulty and group size
The literature, interviews and survey conclude that group size plays a big role in search difficulty.
⏰ Last minute diners are the hardest
The interviews and surveys show that the target audience finds it the hardest to pick a restaurant for spontaneous last minute diners.
🚫 The problem is (indeed) undesired
Participants confirmed in both the survey and interviews that ‘not being able to find a restaurant’ is indeed an undesired and common problem.
Validating insights and assumptions
When nearing the end of my research phase I was left with lots of insights, assumptions and some starting ideas. Before diving nose deep into ideation I thought it would be wise to validate whether how I envision the journey (based on the user interviews) was truly accurate. I did this by sending out a survey and by using a method that I learned during my time at Momkai; they taught me a great method to test these assumptions you have based on your conducted research and align them with how the target audiences experiences it. Through a co-creative customer journey session. So that’s what I did.
I invited one of the participants from the user interviews to co-create a customer journey map with me. With mostly the participant’s input we would map out how a journey would look like when a group of friends are unable to pick a restaurant. So where this would be, when that trigger would start, how they would solve the problem, which pain points they’d experience, etc.
The participant’s journey and the pain points she stated were marker earlier in my research. Confirming the research contained conclusions that aligned with the participants’ experience and journey.
Ideas
Once the research concluded and gave sufficient insight into the users’ experience and journey, some ideas and concepts were already coming to mind. Because I already had a strong sense on which features endproduct should contain; I decided to take a pragmatic two-step method on mapping and generating ideas. The first step was mapping soft requirements that the endproduct would have to meet and think of patterns accordingly. You can see this process in the left image below, with the green post-its illustrating the requirements and yellow post-its illustrating the patterns.
The second step was to turn these collections of post-its into lightweight concepts. On the right image you can see the post-its which contained names/short descriptions of concepts that I later developed into light-weight prototypes for peer reviews. By letting peers; fellow students and teachers, test my lightweight prototypes I could gain feedback and insight into the usability and desirability of these concepts.
The feedback from the peer reviews later indicated that the ‘searching through your preferences’ concept was the most favourable route to keep pursuing.
Iterations
The feedback from the peer review combined with the research gave me enough direction to continue iterating on one of the concepts. The searching through preferences’ concept was considered the best concept to continue developing. Peers and participants from the target audience deemed this concept as most useful and viable. During each iteration I would conduct small tests with target users and peers to find flaws, improve and sometimes add new features. This entire process took about 8 iterations until I landed on an endproduct that I (and the user) was satisfied with.
Happie - The app to help you find and pick restaurants
After weeks of iterating I’ve come up with Happie! The easy and quick restaurant app that helps you find and pick restaurants. A great solution to those who find themselves stuck in indecisive moments with others whenever they have to choose a place to eat. Happie shows you the best options that suit your palate and the ones of who are dining with you. The app will also help you decide, by showing you limited (but great) dining options in your area, you’ll find it much easier to make a decision. And if you’re still unable you can spin the Happie Roulette wheel. This will pick a random option within your preferences.
With Happie ‘not being able to decide where to eat’ is a problem of the past and you can spend more time on what really matters. Dining out with the people you love.
