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UberEats Redesign Concept.

Design Exercise

My Role
Interaction Designer

6 Days

Pen + Paper, Sketch, Invision, Principle

"Order food together with joy, efficiency and convenience."

Design Problems & Outcome

People love ordering food together on Uber Eats. But based on the user research, it requires them a lot of time and effort to decide on a restaurant, collect everyone’s orders and pay their bills with a group. Therefore, I proposed a solution which enables users to decide, order and pay as a group conveniently and efficiently.


Collabative workspace to get good food as a group.

You and your friend can make a workspace on Uber Eats to decide, order and split the bill, all on your own devices.


No need to compromise! Pick a good restaurant that everyone likes.

Users can facilitate their discussion with recommendations especially for the groups. Also, they can directly pick a restaurant together on the app by matching everyone's preferences.


Order and pay with convenience and efficiency.

Users could order with useful information to help them pick the cuisines and keep pace with the group. Also, more flexibility for payment is provided to help users avoid troublesome money issues.


My Challenges in This Project

The time is very limited for this project since it's a design exercise. Therefore, I applied the agile development flow of rapid research, prototyping and tests. Also, the context for this problem is really complex. Different groups do ordering under different scenarios so I need to consider the user flow flexibility a lot.



Why UberEats?

UberEats is one of my favorite applications. Both the online experience as well as its delivery/pick-up service are really enjoyable (and have cultivated my laziness in cooking). As a designer, I really love how they present the information and make the most of the algorithms with a really smooth workflow.

However, it is not a perfect application. Eating is never a personal experience, like many people, me and my friends love ordering food from one restaurant together when we hang out. This enables us to save delivery fees/tips and avoid the trouble of fetching the delivery so many times or picking up at different places. But, having experienced a lot of pains in such a group ordering experience, I started my redesign with an assumption that other people also often encounter frustrations and inconvenience when trying to order food with others. Therefore, I did an investigation on how people order in groups on Uber Eats.

So, What's Current Experience Like?

I recruited 7 users who had experience in ordering food with others on Uber Eats. With semi-structured interviews, I tried to understand users’ goals, current problems and interactions. With my primary design assumption, my interview questions are focused on several aspects:

  • Primary goals and scenarios to order food with others
  • Typical flows, mindsets and pain points when ordering food with others on Uber Eats
  • People’s dynamic to finish the order
  • With many interesting findings, I verified that people did encounter many frustrations and inconvenience when trying to order food with others. What’s more, I also created a storyboard that demonstrate an annoying experience on Uber Eats, based on a true story.
    Let's see what happened to Henry:

    Identifying Users and Their Goals

    Henry's experience is quite annoying and inconvenient, right? Apart from that, I also found a variety of common scenarios where people tried to order food with others at Uber Eats. Correspondingly, I identified different users’ goals and needs under certain scenarios. Based on my research findings, I created personas to help with the following design process.

    Competitive Analysis: See from Both Product and Business Side

    After my investigation of target users and their primary problems, I did domain research on the competitors in the market. Surprisingly, I found that some of them already have “group ordering” features. This really stimulated my curiosity - Have they already solved user problems?
    Therefore, I researched on their solutions and experience by:

  • Walking through the “group ordering” flow on Doordash / Postmates with 2 users and asking them to think aloud to get their feedback & experience.
  • An analysis and critique on their features with my identified user profiles.
  • Based on my research, I found that even though their solutions do have some advantages, NONE of them can help any of our personas to finish their goal well. I summarized the key pros/cons below.

    With these takeaways, I got many inspirations and also understood better on the current gaps. What's more, I investigated on why these competitors launch such features: Not only for a better product UX, but also for a business side: improving such experience would increase the chance for people to order more and possibly help the app to get more users in such a social context. Therefore, I planned to help Uber Eats to provide a better solution and experience than its competitors to win in the market.


    Deciding, ordering, and paying in groups w/ Uber Eats requires too much time and effort.

    Define the Design Goals

    Based on the user needs and the gaps in existing products, I summarized the higher level goals I hope to achieve with the redesign. These also served as guidance for me to start the ideation.

    Design Principles to Guide My Design

    Based on the essential user goals, I created 3 design principles as the goals to guide the following design.

  • Efficiency first: Users enjoy eating food, not ordering it. So the product should let users order quickly and get the food as soon as possible.
  • Flexible: With different goals and interactions, different groups would prefer different ways to finish an order. So the product should provide flexible workflows to accommodate.
  • Warm & Joyful: Ordering with others is definitely a kind of collaboration. So the product should be warm and joyful which could help people bond together.


    How to help groups better make mutual decisions on Uber Eats?

    To start with, what are the mutual decisions that people make? I listed all the mutual decisions that people in groups make in the whole journey. From the research, most frustrations are from ‘deciding the restaurant’, so I selected it as the design focus and investigated more.

    How Groups Decide on a Restaurant?

    How do groups make decisons? I did a deep dive on what’s actually happening when choosing restaurants. I found the group's decision making process is not as I thought before. Usually people narrow down from a bunch of restaurants first, then pick one from the options. This is a quite reasonable and natural startegy, so I planned to follow these 2 stages and see how I can help them.

    Why does deciding on a restaurant so hard?

    I tried to find the reasons from each stage during the decisio-making process. I summarized the key problems of each stage that causes the difficulty to guide my ideation and the following design.

    How to Better Narrow Down on the Restaurants?

    With such insights, I started to think about how to help people better narrowing down their discussion on the restaurants first. I did a brainstorm session to explore a good way to do that. I referred to Group Decision Theory and how we make other decisions in life as inspirations. Then I got 3 design alternatives, using ways from simplest to most complicated.

    I walked through each concept of wireframes with 2 users and did an analysis on the pros/cons for each design concept. There are 2 important insights that influenced my design decision:

  • Unlike making important decisons, users prefer not to precisely calculate the pain and gain when try to decide on a restaurant. Therefore, a very scientific group-decision making is of no necessity for them.
  • Group recommendation may not be applicable when most group members are new to Uber Eats, also former order data cannot represent what users want at this moment.
  • Based on the feedback and analysis, I planned to go with the option 2. But one problem left is that maybe not all the restaurants in the list are ok for all. Therefore, I started to think about how to help groups really pick one restaurant that fit for all.

    How to Help Groups Pick the One?

    From all the recommended restaurants of their common preference, groups will need to pick one restaurant to start their order. When they feel hard to pick the one for the group, or group memebers are not sitting together (on the way, at different rooms, can't discuss together, etc.), how can we help them? Therefore I started to approach the solution with 2 key principles:

  • Ensure the picked restaurant is ok with ALL the group members.
  • Provide options that let users avoid being responsible for the group's choices.
  • Based on these principles and former design for "narrowing down", I come up with 2 design options.

    I again, walked through each concept of wireframes with 2 users and did an analysis on their pros/cons. People really love the Tinder-like feature! I talked about the concern that they cannot see all common likes for design option 2, both of the test takers feel they would just want to find one restaurant as long as it fits for all. Also, option 2 is more efficient which is really important according to the design principles.

    Based on the analysis I decided to go with the design option 2. Also, I designed for the detailed interactions among the users.

    A Reflection on the Decison-Making Flow

    I got another interesting feedback during my test for "Tinder-Like" feature, which is: why not let users just match the restaurants without voting for the preference first? This really made me think back and reflect on the whole flow, then 2 facts helped me to validate the design decision.

  • UberEats usually have at least 100+ restaurants at urban places. It will require a lot of effort for users to have a match if we don’t narrow down on the restaurants.
  • "Narrowing down" first gives users a chance to reach a consensus at an early stage, especially when they can sit together to discuss. So that they don’t need to make any effort on matching.
  • Based on all these analysis, I decided to go with this logic of "Narrowing Down First, then Picking the One logic". Also, I provided an option for users to just start ordering after "narrowing down".



    How to provide efficient and convenient workflows for users to finish their goal?

    After I finished the design to help users make decisions on restaurants, I started to think about people's whole journey when ordering in groups. Then an important fact emerged: finishing a group order needs both collaborative and individual effort in different stages. Therefore, to provide a suitable workflow for users, we should build a workspace that enables group members to decide on restaurants collaboratively, but order/pay individually on their own phones.

    Design For Efficient & Convenient User Flows

    Based on the insights and my previous design decisions, I created the core user flow where group members could collaborate as well as finish their own tasks with their own phone. Also, I also created muliple options at different stages to accommendate different group's need and characteristics.

    Wireframes & Low Fidelity Iterations

    After I finished the user flows, I created a very low-fidelity wireframes and presented it to 2 users and 2 design experts. With a lot of feedback received, I found some problems existing in the interfaces. Therefore, I did some iterations on the features and user flows to make users’ workflow more efficient and convenient.

    User Psychology: How to Guarantee the Group’s Efficiency?

    Besides these iterations, I also refined the interfaces with a consideration about user psychology. During the testing, I realized an important issue that may highly impact the group’s efficiency: The total time needed only depends on the last group member to finish matching/ordering/paying.

    In the interviews, such a scenario is mentioned for many times: one group member is really casual and indecisive, they spend so much but unnecessary time picking the food, flavors, etc. For the reason of politeness, usually other group members will not urge him to finish the order quickly.

    To avoid this kind of situation, I tried to find solutions based on user psychology:
    What motivates us to be efficient in a team?

    Based on the 2 essential motivations, I came up with 2 solutions to guarantee the group’s efficiency. Then I analyzed and summarized 3 facts that help me with my design decision:

  • Notifying a group's progress rather than just setting a time limit provides flexibility for groups to accommodate their time.
  • Countdowns may cause some stress in the process.
  • Excluding users for their efficiency is like a punishment, which may cause the loss of order, and even users.
  • Therefore, I decided to go with design option 2. Also, to motivating users along the whole process, I decided to push such notification not only when all others have paid the bill, but also when all others have chosen the category preference, or anyone in the group has finished voting for all restaurants druing the "matching".


    High-Fidelity Interfaces & Refined User Story

    With all these refinements, I created high-fidelity interfaces. Let's see a refined user story of Henry, checking how my design could help his group have a better experience.

    Finalized User Flows

    Here are all the user flows with high-fidelity design. Some use cases are not included in the story above.


    Future Steps

    1. Design validation.

    This redesign is definitely not perfect. Even though I showed the wireframes and mockups to several users along the way, they are not representative enough (mostly my friends and roommate). I would definitely want to finish all the screens in high fidelity and test this design to more people and get more feedback.

    2. A more comprehensive UX thinking.

    In this redesign, I don’t have time to think and prototype out the edge cases, such as what if one group member doesn't want to order in the halfway, how can they quit without affecting others? So for the next step of this project, I would consider all possibilities of users' flows. Then make interfaces of different status such as: nothing / loading / none / one / too many / incorrect / done.

    3. Investigate more on the technical part.

    I had some assumptions on Uber’s capacity in data and machine learning, if I have more time, I would do more research on Uber’s user data matrix as well as restaurant tags - those may help me provide a better experience.

    4. More potential in group ordering.

    Another use case that I considered but did not have time to finish, is "sharing the food", such as ordering one pizza and share together. It would be interesting to design how they could decide on the flavors to pick and help them split the bills in an easy way.




    Nemo the Talking Fish