Timeline
6+ weeks, 2024
Role
Product Designer
Responsibilities
Desk/User Research
UI Design
Prototyping
User Testing
Tools
Figma
Figjam
Midjourney
Overview
Women in their 30s often need to purchase new clothing for various reasons. However, their busy professional and personal lives often leave little time for shopping. Moreover, due to different styles and standards, finding suitable items can be time-consuming. Therefore, there is a demand for a service that can help them discover clothes matching their preferences.
"It saves a lot of time because if you already have set preferences, it'll constantly show you based on those preferences, so you don't have to keep typing it."
Approach
Desk Research
User Research
Affinity Diagram
User Persona
User Journey Map
Problem Prioritization
Brainstorming
Idea Sketch
Mid-fi Wireframes
User Feedback
Hi-fi Design
Prototype Design
Usability Test
Empathize
To identify market demands, I researched online forums where women discuss their experiences with online shopping.
The findings are categorized into four main areas:
Shop less than in their 20s
More purposeful with specific preferences
Focusing on quality
This desk research suggests that women in 30s have a need for purposeful shopping over frequent shopping, driven by well-defined size and style preferences and a focus on a high quality.
With the insights gathered from desk research, I conducted in-depth user interviews with two professional women in their 30s. I was focusing on discovering their real-world challenges when shopping for clothes online.
Here are the main challenges they mentioned:
Time-consuming process
"It shouldn't take that long because when I find something I like, it's often sold out."
Difficulty in comparison
“It’s hard to see and buy one item from bunch of different websites on the phone.”
Discrepancy between image and reality
“Sometimes you feel cheated when you buy something and it was expensive but the quality isn't.”
Size and fit uncertainty
“Not being able to trust the sizing for the place that you're shopping at is the main challenge.”
Hard to find items matching preferences and standards
“I know what I like, but finding it is another story.”
Through interviews, I confirmed that the needs identified in the desk research such as purposeful shopping with specific criteria are not being met in actual shopping experience.
Define
To synthesize and categorize the insights identified from the user interviews, I created Affinity Diagram.
The challenges were categorized as below:
Choice Overload
The abundance of options online can be overwhelming.
Shopping Fatigue
Users feel that shopping takes longer than it should.
Assessment Challenge
Evaluating size, fit, quality and other product details is difficult.
Limitation of Recommendation
Recommendations often fail to align with users' style or intent.
Fragmented Shopping Experience
Users' preferred items are scattered across multiple websites, making the process inefficient.
Budget and Value Concerns
Users struggle to balance their budgets with their quality standards.
The affinity diagram showed that challenges were interrelated and partially causing each other. Moreover, most of them seem to originated from one main problem.
By analyzing the relationship between the challenges, I was able to identify a root issue to address:
Ideate
To design a user-centric solution, I refer to retailer’s sales techniques:
Providing excellent customer service:
For example, offering reliable product information.
Personalizing the customer experience:
For example, recommending products based on their size, preferences, or shopping intent.
Based on these insights, I focused on the following aspects to shorten users’ exploring and comparing process:
Identifying users’ buying preferences and shopping intention.
Recommending available items and best deals along with detail information aligned with existing user’s criteria and goals.
Personal Preference Onboarding
By collecting users' preferences and sizes during onboarding, the platform can effectively showcase and recommend products aligned with their tastes and sizes.
Option 1
Easy, clear to select options.
Needs so many steps to input all personal information.
Option 2
Users can quickly select a large amount of information based on categories in one page.
It might feel overwhelming and boring due to a lot of options.
Option 3
Users can select information by browsing through categories.
It might be cumbersome to visit each categories individually.
Intent Identification
By asking users questions, the platform can narrow down their intention for visiting, enabling more relevant product recommendations.
Option 1
Ask intention from top area when user first open the app.
Users may not feel that they need to answer the question.
Option 2
Ask intention while they are shopping by displaying questions in between products on the browsing page.
Users may not feel that they need to answer the question.
Option 3
Ask intention through the bottom sheet from all the area.
The options in bottom sheet list could be limited.
Product Image Based Virtual Try-on
Providing a 3D virtual try-on based on the user's body size, helping them visualize how the products will fit.
Option 1
Size recommendation through text.
Size recommendation based on user’s body size.
It might not be guaranteed that the recommendation is accurate.
Option 2
Virtual try-on with avatar as a product image based on user’s body size.
It will help user to quickly estimate clothing fits while browsing.
It needs accurate user data.
Option 3
Recommendation comments below the product detail.
This can help users assess whether the item matches their preferences.
Without visual cues, it may still be difficult for users to accurately gauge size and fit.
After evaluating the pros and cons of each idea sketch, I decided to prioritize the most intuitive options. This decision aligns with my primary goal of reducing shopping fatigue for the target user.
Prototype
With selected ideas, I proceeded to the prototyping. During the workflow mapping process, I identified an additional need: a Personal Preference Settings feature. It would allow users to manage and update their preferences beyond the initial onboarding process. Recognizing its importance, I added this new feature on the wireframes.
Test
From the user test, I was able to gather feedbacks on design options. Based on these insights, I improved the designs.
Balancing Detail and Simplicity
The initial designs had challenges:
Option A was overwhelming with all details on one page, while Option B required excessive back-and-forth navigation despite its simplicity.
Preference Settings
The user suggested adding the ability to input preferred brands to enhance personalization.
Final Designs
Personal Preference Onboarding
Like a sales representative, the feature collects user’s personal preferences to provide recommendations closely aligned with their preferences.
Chatbot-Assisted Intent Identification
When users are unsure of what they want, the chatbot asks about their shopping intentions based on context, narrowing down options to better match their needs.
Product Image-Based Virtual Try-on
Users can see how products fit and look on their own body, providing both visual and text-based size information, reducing size issues.
Style Profile Settings
Beyond basic preferences, users can set highly detailed criteria such as texture, personal values for even more personalized recommendations.
I gathered the opinions about the final design to the user and these are the key points:
Positive Onboarding Experience
Users found the onboarding process to be straightforward which provided them with a clear understanding of the service features and set appropriate expectations for personalized recommendations.
Personalized Shopping Assistant
Users found the shopping assistant feature to be useful, as it allowed them to refine their search and get more recommendations.
Engaging directly with users provided deeper insights into their needs. For example, while they were frustrated by long shopping times, they also enjoyed exploring new and diverse products. Additionally, user test shows unexpected behaviors, such as users skipping the onboarding process designed for them. This highlighted the importance of observing user actions with an open mind. This experience allowed me to rethink UX strategies to ensure users engage with features instead of skipping them.