Product Design | Visual Design | Wireframe Iterations
banner image of minu page, showing screen mockup, awarded 2nd place in adobe creative jam, duration of 2 weeks and teammates names.
title of the page - minu
A product concept that eliminates the stress of choice overload & decision paralysis while ordering food at a restaurant.
Swiping food for likes & dislikes as done on Tinder
AI Training for food personalization
Showing 3 full menu options for making faster choices
Choose a full menu under 3 taps
Saving foodlists, just how you would save music on spotify
Save food choices based on mood and food type
Jump to Solution
"Fabulous idea, concept and presentation..."
"...like oh my god, where can I download this. ”
"...different from the competitors..."

Context

College + The Futur Creative Jam is an annual competition that tasks participants to create a product pitch that has a clear goal, value proposition and to envision the impact of the concept.

The Problem

Ordering food at a restaurant or at home through apps has become more exhaustive & anxiety-inducing than the convenience-filled experience it’s supposed to be.

People often experience decision paralysis thanks to a range of choices of restaurants, dish types & customization options .

Process Framework

We followed the design thinking framework to make sure we eliminated biases while ideating for the solution to this challenge.

Our objective was to let the research-driven data guide us towards the solution.

design thinking process framework

My Role

1. Product USP and Product flow (IA/ID)
2. Brainstorming
3. User research
4. Wireframing & prototyping with iterations

Brainstorm & Sketch

The first step was to narrow down on the problem we wanted to tackle. We decided we wanted to focus on food ordering decision-paralysis, and the natural next steps during our sessions were to get more information on:

  1. Stakeholders in the hospitality business
  2. Research Plan
whiteboarding session 1 to brainstorm on ideastimelapse of the team working through discussion and whiteboardingteammates brainstorming on research plan

User Research

After conducting primary and secondary research, we found many people shared the sentiment of confusion and unease while ordering food at restaurants or on online apps due to the presence of “too many options”.

We listed 3 main assumptions that had to be validated during our research:

  1. Decision paralysis stems from the fear of making a wrong decision.
  2. The more options people have, the more prominent the feeling of making the wrong decision. In the end, they’re likely to choose something that they trust and is a safe choice.
  3. Customers taking unreasonably long to order in restaurants can hamper the business during rush hour or happy hours.
Participants explain how quick, slow or difficult their food decision making process usually is.

User Scenarios

Based on a market analysis and user research, we wanted to consider the scenarios that will make this experience more personalized to each individual. Here are the user scenarios we considered:

Persona 1, who knows where to eat but don't want to spend time deciding what to eat
Persona 2, who has entered a restaurant but doesn't know what to eat there
Persona 3, who doesn't know where to eat or what to eat
Scenarios for future consideration to make a more comprehensive use case

Insights

The key insights from our research were:
  1. Multiple Choices - While restaurants feel it’s a good practice to give their patrons plenty of choices, for some customers this leads to an information overload and is thus stressful.

  2. Value for Money - If people feel dissatisfied with their order, one of their first thoughts is around making a bad investment. Their trust in the restaurant declines if they make the wrong choice.

  3. Balance - Flexibity and balance is key for people to have control over their choices and still receive assistance.

Branding & Design System

brandbook with logo design, color palette and typeface
Typography design systemButton and component library

Wireframing (Low-Fidelity)

The following were the navigation and hierarchy styles we considered, tried implementing or eliminated:

low fidelity screens exploring different navigation and hierarchies of screen layout

High Fidelity & Screen Flow

screen flow based on the information architecture. All screens are high fidelity and shows a clear flow from onboarding, homepage, surprise me, and ordering food quickly

The Downside!!

After some guerilla user testing, we found our solutions had MANY FLAWS! While there were many improvements to fix, we had to prioritize in the timeline we were bound with.
Here are some key issues we definitely wanted to fix:

Whiteboard thought dump on food ordering through online vs physical channels.

Navigation too confusing!!🥴

No way to go back to scan another QR code 😒

Doesn't really work as Homepage, as we initially assumed 😭

No hierarchy in icons, cards or buttons 😫

(L) Snehal and I (R) mapping out some ideas based on the research insights.

What happens when the QR code doesn't work?🥴

Not Accessibility compliant 😒

What happens if the user exits the app?😭

(L) Snehal and I (R) mapping out some ideas based on the research insights.

How's it different from "suggestions"?🤨

How does the AI learn about food choices?😶

What's the next step to personalization?🧐

Information Architecture

Based on the problems we saw in our final designs, we wanted take a step back to improve the IA/ID flow.
Some key questions we asked and wanted to answer through wireframes:

Version 1

information architecture of the site flow

Version 2

information architecture of the site flow

Wireframing iteration 2 (Low-Fidelity)

For this iteration, we started wireframing while following the iOS guidelines. As of September 2021, iOS had 59.48% of the market share, which is why we wanted to prioritize iOS over Android.

low fidelity wireframes, version 2, following iOS guidelines

Solution

Homepage screen with a minimized menu of that restaurant

Concise options generated by AI

Three options on the homepage based on learned behavior and previous choices.

AI will learn your food choices based on mood, time of the day and suggest your choice of comfort food, healthy food, etc.

screen showing a surprise me feature

Auto-choose

If you’re not in the mood to choose an entire meal for yourself, then Minu can curate a food-list for you (much like a Spotify playlist), based on the app’s understanding of your behavior and moods.

curated list of an entire meal screen

Fun Onboarding

Train the AI and also self-reflect on your food preferences.

The app continues to learn the more you use it, and every recommendation improves with usage.

Swipe match for AI training module
filters and dietary restrictions screen

Next Steps

Some of our next steps that we’d like to explore at some point in the future would be:

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