NomsAway
Reduce decision fatigue, group conflict and find your “most-fit” restaurant!
Overview
Role & Team
I worked with Harshitha Shivaraju, Alice Nie, Vijaya Nukala for a 4 week project.
Goal
Create a new service that streamlines group decision making processes and reduce fatigue.
What I did
Market & user research, UX/UI design, prototyping testing, team resolution, pitching.
What I delivered
PESTLE / POG, User personas, paper prototypes, wireframes, lo/mid/hi screens.
Get personal with your group restaurant suggestions!🍔
Filter and decide on a top restaurant choice with a group voting feature based on near by cuisines, type of food and user specific preferences for easy group decision making!
Process
Problem
The question of: “Where are we eating?” is a costly affair.
Did you know that 997 million minutes per year on average is spent on deciding where to eat? That translates to around 15 billion dollars as time = money! Especially when traveling in a foreign location, travel groups typically make last minute decisions in regards of food. Instead of enjoying their travels, they fixate, argue and stress!
“How might we facilitate on-the go group decision making while accounting for each members preferences and restrictions?”
Solution
On the go group decision making without the conflict!
💡Our project aim:
Reduce group conflict and decision fatigue by simplifying group decision making while satisfying needs and preferences of all members.
Quick & Informative On-Boarding Experience.
Hassle free registration
Brief overview & outline of app’s goals
Intuitive user graphics
Personalized character types
Understanding who they are traveling with
Traveling habits
Getting personal with the traveller.
Access to multiple recommendation sources.
Recommendations pulled from multiple sources.
Recommendations tailored to specific users.
Location tracking allows on-the-go recommendations
Intuitive, informative and condensed information about any location suggested
Get recommendations along the way.
Access to multiple recommendation sources.
Recommendations pulled from multiple sources.
Recommendations tailored to specific users.
Research
Through this project, we found the value of rapid research… this helped us pivot 3 different times!
Our first initial concept was called “Tip Wise” we are still exploring around the restaurant / food industry however it was more focused on the idea and social construct of eliminating “Guilt Tipping”. Rapid initial research was done through PESTLE / SET analysis.
Economical
Rising inflation caused a “cash crunch” in middle-class (72% consumers income short of spending)
CPI is on the incline at 6.5%; making everything unaffordable.
Sources: CBO Report 2023
To validate the TipWise idea, we selected participants to implement 2 types of tests… a prototype-driven test and personalized account of experiences.
We created a “Tipping Guide” prototype to simulate a realistic environment and took participants to local restaurants / coffee shops. It was an insightful experience to see first-hand how participants interacted with the guide on the spot.
Our goal was to determine if participants felt more comfortable, confident and less “awkward” when utilizing the guide compared to their standard ways of tipping.
Flow 1
Pivot 2:
New Target Users
Target audience are now travelers (travel / vacation based)
Location & Crowd-Source Service
MVP 2 is now location-based crowd source service that notifies the user
Flow 2
Not only Tipping
Not only tips - expanded to restaurants and all things uncertain (hidden expenses, must-haves/avoids, allergies, etc).
A new user flow is proposed, showcasing sample screens that integrates major changes to our service, based on previous MVP insights.
Flow 3
To validate this new iteration, we utilized our sketches and flash cards to implement experience prototyping…
Experience prototyping helps to simulate a realistic environment while following our proposed screens. The goal is to test which specific information users want to see on the app when selecting restaurants. We ran tests through the following:
Insight
Guilt tipping is an “in-the-moment” niche problem, hence, they don’t want to pay for the service
Insight
Customers get accustomed to tipping uncertainties overtime and hence, don’t require a guide in the long term
Users don’t prefer pulling out an app and checking recommended tip, they feel more awkward.
Insight
Technological
Point of Sales systems have pushed tipping prompts to consumers. This increased discomfort regarding tipping practices as it became on the spot and unavoidable.
Sources: The Motley Fool
Conducted sessions lead by participants including international students to hear about their personal experiences and interactions with tipping.
We selected 2 major groups, firstly participants who are customers that suffer from “awkward” tipping practices and also people of the service industry. This allows us to have a perspective from the user receiving the service and the provider.
Through Infinity Clustering we realize TipWise needs to be pivoted into something more impactful for our users…
TipWise
Social
Americans tipped 16% more in 2022. 62% felt pressure to tip.
100k monthly Google searches of "How To's" and generic tipping guides.
Sources: CNBC 2022 survey, Google
Pivot 1:
TipWise- MVP 2
Based on findings and gaps from MVP 1 validation, we knew we had to pivot our idea and expand; tipping is too niche of a problem frame. In MVP 2, we also decided to pivot our target user and this opened an entirely new market and possibilities of features.