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En Forme

GRAD SCHOOL PROJECT • RESEARCH • UX DESIGN

Roles and Responsibilities

  • User Research: Competitive Analysis, User Interviews, Storyboarding, Persona Mapping, Surveys, Empathy Maps

  • UX Design: Sketches, Prototyping, Wireframing, Usability Testing, 

Project Context

  • Fall 2018

  • Grad School Project

  • 3 Product Designers

Tools

  • Figma

  • Miro

  • Sketch

  • Notion

America and fast fashion

American fast fashion industry generates $5.5 trillion annually

Americans return 16.6% of all clothing merchandise they purchase.

Between November and December, $158 billion worth clothings are returned

Fashion industry contributes to 8% of CO2 emission, more than aviation and shipping, combined

A $761 billion problem

As of 2020, 76% of Americans prefer to shop online and at least 96% of US adults between the age of 18 and 29 have smartphones. To stay competitive in a 5.5 trillion economy, free shipping  and returns has become a standard to make customer’s lives easier. Returns are becoming more and more seamless and Americans return almost $761 billion worth of merchandise every year.

Choice Conundrum

If you search for Summer clothes women on a famous fast fashion website, it delivers over “394,026” options. Research shows that “when there is, consumers are less likely to buy anything at all, and if they do buy, they are less satisfied with their selection and may potentially return them”.

Time Conundrum

Overall, 57% of employed Americans confessed to shopping while on the clock, the personal finance site said, totalling some 234 million hours a day browsing the internet. On average, workers spend about 1.7 hours a day browsing the internet, a study finds. 

Money Conundrum

Americans return $761 billion worth of merchandise every year. The cost associated with returning and the cost to the environment are not taken into account in this estimate. 16.6% of purchased goods are returned back 

Problem worth solving

How might we solve a problem that can target all the problems listed above? If we try to address each of these problems separately, the result would be diminishing returns (pun intended). Looking closely, an ideal situation would be consumers being satisfied with their choice and retailers not receiving the goods they sold.

Solving for women, first.

Women’s shopping behaviour differs from men. Women comparatively shop more than men. They women also engage more while shopping, partly because they have more options to shop from. So we decided to focus on prioritizing women’s shopping experience first and then expand those solutions to the men’s world.

We targeted female users within the age range of 20–40​ who are either working professionals and/or university students. We screened around 21 participants and selected 7 participants who fell within our target population for contextual interview.

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We focused our conversation around the following:

  1. Talking about their past shopping experiences

  2. Understanding their pain points and needs.

 

We also shadowed their shopping journey to uncover a few behavioural actions that we couldn’t capture in the contextual interview session.

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From our interview feedback, we were able to put together empathy maps for visualising the interview data better and help us organically form the user needs.

Core user needs

From our contextual interview session and shadowing sessions, we consistently saw two prominent themes.

  1. Information representation

  2. Personal fit needs

Armed with the research and competitive analysis, we dwelled into the brainstorming session.

Talking about the problem out loud

We came up with 28 different ideas that closely aligned with our user needs. Our ideas ranged from small features to a full blown application. As we tried to converge our ideas, we were able to come up with two primary solutions.

  1. Designing an app to help women find attires that fit them

  2. A chat bot for personalised recommendations

Designing for navigation

We wanted to make sure that we got the flow correct, including the onboarding process. We wanted to make sure the onboarding process is quick and easy to complete. This is vital because the recommendations are based on the information provided by our users. Hence we wanted to make sure they don’t lose interest by answering a lot of questions in the beginning.

Next, we designed information representation. This is our core idea to crowdsource clothing items from different websites of the user's choice. 

Our user were excited but had some feedback too

When we tested our first iteration, we received really good feedback on the concept. Thanks to user scenarios, we were able to identify gaps in the user journey during the usability testing. 

 

For the first iteration of testing, we conducted think-aloud usability study to identify the shortcomings of our app. Some key insights were:

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  • The users were confused about the working of the avatar feature.

  • They found the body scanning and adding the measurement feature too cumbersome.

  • Users wanted a more personalized home feed based on their onboarding.

  • They wanted better and intuitive signifiers and icons.

Incorporating user feedback - v2.

Based on the findings for our sketches we made a more flushed out low fidelity prototype to have the users interact with specific features of our app and see how well they understand the functionality.

Testing low fi one more time

As a group, our team started out by conducting in-field usability testing with 10 potential users to find out their pain points and what they think of the current prototype. The tasks were focused on the in-app registering, onboarding process, creating the avatar and browsing for the items. I also asked users to complete a few tasks after providing them with proper scenarios to find out the reason why they use the product, what do they use it for, and what their pain points are:

What we learned

  • The onboarding process needs to be more refined to incorporate the users’ choices

  • The avatar feature was confusing to users as they thought that it was a cartoon and it would not give them an accurate representation of fitting

  • Technical concerns with the avatar feature — app overload and accuracy

Designing En Forme

Satisfied with the functionality, our focus shifted to the form. We wanted to make our product visually appealing. We wanted to make it fun by choosing bright personal colours like yellow and purple.

Onboarding Process

The first time user has five optional steps to get started with the app and set up their personal preferences 

Personalized Home Page

Based on the user inputs during onboarding the home page is personalized to according to user preference. 

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Personal Fit Needs

User can also add more information about their body dimensions and sizes to better curate the app for them. The product page provides cues to the user in order to guide them and choose a proper fitting product. User can create an account to save their preferences for future use. 

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 Crowdsourcing clothes

The application crowdsources items from various popular clothing brands based on their preference making it convenient for the user to view all the brands from one app instead of browsing on different platforms. The app also sends timely notifications about the users orders. 

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Meet Coco

We named our assistant Coco (after Coco Chanel- a fashion icon). We decided on including the chatbot as it would make personalization easier for the users. Users would not have to drill down to find what they need, they can easily just ask the chatbot for recommendations and the products they want.
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We wanted the chatbot to process natural language so that the user does not need to use very technical terms or feel like they would rather find what they need by drilling down in the app instead. We wanted them to feel they’re talking to a real human who is also their shopping assistant.

Learnings and takeaways

Our MVP turned out to be a success in the end with tremendous scope. We learned that things may not always go as planned and the user needs to be put in front of your own personal desires.
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We wanted to have a body scan and avatar feature but all users were not very fond of the idea as they found it time-consuming. Hence, we needed to pivot from it entirely.
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The app focused heavily on onboarding and we tried to design in a way that it is as seamless as possible.
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We also learned that good user data collection and analysis will help solve the problem and not tend to one or two users’ needs.
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Feedback and iteration is the key to making a product great!

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