Adding an AI-driven personalized stylist feature to ASOS e-commerce

Online fast fashion retailer ASOS reported a $316M pre-tax loss in 2022, largely driven by a nearly 50% return rate, with many returned items never resold. To help customers find clothes that truly suit them and reduce returns, I designed an AI stylist for ASOS. It uses personalized avatars and tailored outfit suggestions based on body type, skin tone, style preferences, occasions, and virtual try-on experience.

Methods
Interviews,
Moderated usability testing

Duration
Jun 2024 (3 weeks)

Tools
Figma/FigJam, Google form

My Role
UX/UI designer

ASOS, an online-only retailer, faces a nearly 50% return rate. Without in-person fittings, online shoppers struggle to gauge fit, style, and color on them—leading to high return rates and missed opportunities for brand loyalty.

Problem

The ASOS AI Stylist built into the ASOS site, solves these challenges by integrating personalization and AI-driven recommendations across three core features:

  • Personalized avatars for virtual try-on

  • AI outfit recommendations

  • Virtual fitting experience

Solution

The fast fashion industry faces high return rates

Research

I conducted a competitive analysis of fast-fashion brands sharing similar target customers with ASOS—Zara, H&M, Uniqlo, and Forever 21—to explore how they tackle return issues and use technology.

    • Fast fashion leader known for rapid trend adaptation and sleek, minimalist designs.

    • Approximately 30–50%, influenced by "bracketing" behavior where customers order multiple sizes or styles with the intention of returning some items.

    • Charges a return fee of £1.95 in the UK and $4.95 in the US for online returns via drop-off points; in-store returns remain free.

    • Limited public information on AI stylist features; primarily relies on traditional online shopping interfaces.

    • Zara uses an AR app that displays virtual models wearing new collections when users scan store displays or packages.

    • Focuses on functional, high-quality basics with an emphasis on innovation and timeless design.

    • Specific figures not publicly disclosed; however, the brand's strict return policies may contribute to a lower return rate.

    • Does not accept in-store returns for online purchases; customers must mail returns at their own expense, and the process is often considered cumbersome. Reddit

    • Introduced "StyleHint," an app that provides outfit suggestions based on user-uploaded photos and preferences.

    • Targets young, budget-conscious consumers with trendy, fast-fashion offerings.

    • Specific figures not publicly available; however, the brand's broad product range and pricing strategy suggest a moderate return rate.

    • As of April 2025, all sales are final due to the company's bankruptcy proceedings; no returns or exchanges are accepted. New York Post

    • No known AI stylist features implemented; relies on traditional e-commerce browsing experiences.

    • Offers affordable fashion with a growing emphasis on sustainability and conscious collections.

    • Estimated between 20–30%, consistent with industry averages for online apparel retailers. Business of Fashion

    • Implements a £1.99 return fee for online purchases in the UK, waived for members of their loyalty program.

    • Limited information available; no prominent AI stylist features reported.

    • H&M has explored AR through pop-up books and holographic experiences, notably in collaborations and its Monki brand.

Key findings:

• These brands have average 20%-50% returning rate.

• Competitors had Augmented Reality try-on or AI-related feature but not widespread.

• None of these competitors have implemented AI stylist feature.

I also studied existing AI stylist apps (Aiuta, Style.ai, Acloset, Style DNA, presented in this order below) to learn from their user feedback and design approaches.

    • Aiuta is an AI-powered personal styling app that offers real-time fashion advice and personalized outfit recommendations, targeting busy professionals and fashion enthusiasts seeking convenient, tailored style solutions.

    • Personalized outfit recommendations based on user input (occasion, preferences, weather).

    • Styling tips and color matching suggestions.

    • Users start by creating a profile and providing style preferences, occasion types, and basic wardrobe info; then they interact with the AI stylist via chat to get immediate outfit recommendations.

    • Lacks deep wardrobe integration and long-term user engagement beyond single recommendations.

    • Style.ai serves as a virtual fashion assistant providing personalized styling feedback, wardrobe optimization, and style evolution tracking, catering to users of all fashion experience levels aiming to refine their personal style.

    • Virtual fashion assistant that gives personalized recommendations based on style preferences, trends, and existing wardrobe.

    • Wardrobe management tools with AI-driven suggestions for outfit combinations.

    • Trend analysis to help users stay stylish and on-trend.

    • Community engagement features like sharing looks and receiving feedback.

    • Onboarding involves uploading photos or inputting wardrobe items, answering style preference questions, and receiving initial AI feedback to set up a personalized style profile.

    • Limited personalization depth; no strong focus on sustainability or AI body/fit analysis.

    • Some users note the app recycles styles and lacks a distinct aesthetic eye. Google Play

    • Acloset is a South Korean fashion tech startup offering an AI-powered digital closet platform that allows users to digitize their wardrobe, receive personalized outfit recommendations, and manage their clothing efficiently, primarily attracting Gen Z women in Europe and the U.S.

    • AI-powered digital closet that digitizes and organizes your wardrobe.

    • Personalized outfit recommendations based on weather, style, and calendar.

    • Style analytics offering insights into wardrobe usage and shopping habits.

    • Secondhand selling integration to encourage sustainable fashion.

    • Users digitize their wardrobe by uploading photos of clothing items, categorize them, and input style preferences and calendar events to receive outfit recommendations.

    • Primarily focused on closet management; lacks emotional/psychological style insights and personalized shopping guidance.

    • Style DNA is an AI-driven personal stylist app that creates personalized style profiles from user selfies, offering tailored fashion advice and promoting mindful shopping habits, appealing to individuals seeking sustainable and personalized fashion experiences.

    • AI-driven style profile creation from user selfies.

    • Color analysis and body shape detection for personalized advice.

    • Personalized shopping recommendations that match the user’s fashion style and DNA (skin tone and body shape).

    • Sustainable fashion focus with tips for mindful consumption.

    • Users take selfies and answer questions about body shape, color preferences, and lifestyle; the AI analyzes these inputs to generate a personalized style profile and shopping recommendations.

    • Heavily focused on static style profiles; less adaptive to changing fashion trends, moods, and evolving user needs.

Key takeaways:

  • These apps onboarded users by having them choose fashion keywords, upload outfit inspiration, clothing photos, or selfies to create personalized style profiles or digital wardrobes.

  • These apps offer outfit suggestions based on occasion, weather, and style preferences, some of them based on calendar, skin tone, and body shape too.

  • Personalized outfit recommendations and digital closet management are trending features.

Primary Research

Shoppers need outfit recommendations, diverse body & skin representation without trying on in person

To better understand the real-world challenges, habits, and unmet needs of online clothing shoppers, I conducted a survey (21 respondents) and 6 structured interviews (2 fashion enthusiasts, 3 busy professionals, 1 student). I aimed to learn:

Shopping’s Role in Daily Life

How does clothing shopping fit into shoppers’ professional and personal life?

Experiences and pain points

Online clothing shoppers’ shopping and styling experience/pain points?

Key Insights

Affinity map was performed to synthesize key insights. This partial view shows how I got the insights. Click the link for the full report and the image to enlarge.

After the interview, I found the following key findings:

  • Frustration with limited body shape and skin tone representation (5/6)

  • Strong desire for outfit recommendations (6/6)

  • A trial-and-error approach to personal styling (2/6)

Diverse body types and skin tones of models needed

Outfits recommendations for different situations needed

I identified two distinct groups, from interview and survey, and created personas to keep their traits and needs in mind throughout my design process: 1. Fashion enthusiasts value meticulous style details. 2. Busy professionals seek personal style development amidst time constraints.

Our target customers

“ How might we help online shoppers visualize outfit fit and receive personalized recommendations without being in-store? “

Building the foundation: onboarding for a personalized AI stylist with avatar-based fit and style guidance

User Flows

Armed with these insights, I set out to design an AI Stylist onboarding flow that tackled these challenges directly while making the process engaging, user-friendly, and efficient. Through a conversational quiz, I balanced guidance and flexibility while gathering key details such as body measurements, skin tone, and style preferences. I included:

  • Designing a flow that balances conversational with traditional onboarding process to gather necessary information.

  • “Skip” and “Surprise me” options to prevent fatigue.

  • Visual aids to help users choose styles.

AI stylist onboarding user flow

Click the image to see a full size image

Low-Fidelity Wireframes

Building on the onboarding flow, I began sketching low-fidelity wireframes to explore layout options, formats and icon placements, and visual hierarchy.

ASOS Design System Updates

To maintain brand consistency, I designed new components within the existing design system—product cards, interactive select states, a status bar, and modal open/close tabs.

1st Usability Test

Shoppers needed an even more flexible, shorter, and guided onboarding process

Once I had a working solution, I tested it with users to evaluate the visual and functional experience of the onboarding process—and understand the perceived value of this feature within the website—I conducted moderated usability tests with 5 users via Zoom. The key findings are:

AI stylist icon on the ASOS site:

  • All 5 users highly valued AI stylist feature.

  • Average icon search time was 5 seconds, suggesting good visibility.

AI stylist onboarding process:

  • Average total onboarding completion was 5 minutes 52 seconds, but 1 - 3 minutes is common for smooth experiences, based on NNG.

  • 4/5 users found intent-driven question for body type queries confusing.

  • All 5 users needed a more flexible onboarding process, allowing skip without losing provided information

I organized the findings using an affinity map to uncover patterns in user feedback. These insights highlighted key areas for improvement in the onboarding experience and informed the following design priorities:

Key Insights

Users need shorter onboarding process

Users require more pre-built databases & skippable steps

Guided onboarding is favored over intent-driven ones

Flexible process

Shorter process

Guided process

Iteration

Initially, I thought key questions couldn't be skipped. However, I realized I needed to be more empathetic, finding ways to reduce the user's load while still collecting the necessary information.

1. Users need the flexibility to skip or choose from presets

BEFORE

AFTER

Break the AI stylist flow into two separate tasks: 'My Avatar' and 'My Fashion Preferences.' Users can save and edit their information and preferences, allowing them to return anytime to make adjustments for greater flexibility.

2. Break one long onboarding process down to two shorter tasks

BEFORE

AFTER

3. Change from “open-ended questions” to “guided body type questions”

During the usability test, I found that users felt more comfortable with guided questions, as they wanted the avatar to be accurately built. They felt lost with open-ended questions and were unsure how to provide accurate information without guidance.

BEFORE: ONE OPEN-ENDED QUESTION

AFTER: MULTIPLE GUIDED QUESTIONS

2nd Usability Test

only two people they all value the idea and think it can resolve the problem

Once I had a working solution, I tested it with users to evaluate the accessibility and experience of the onboarding process—and understand the perceived value of this feature within the website—I conducted moderated usability tests with 5 users via Zoom. The key findings are:

AI stylist onboarding process:

  • Average total onboarding completion was 5 minutes 52 seconds, but 1 - 3 minutes is common for smooth experiences, based on NNG.

  • 4/5 users found intent-driven question for body type queries confusing.

  • All 5 users needed a more flexible onboarding process, allowing skip without losing provided information

I organized the findings using an affinity map to uncover patterns in user feedback. These insights highlighted key areas for improvement in the onboarding experience and informed the following design priorities:

Key Insights

Personalized avatar creation for virtual try-on

Basic body measurements

Basic body shape

Avatar building with tailored skin tone

Avatar body shape fine-tune

AI stylist powered by fashion quiz for personalized outfit recommendation

Style keyword quiz with image support

Outfit upload for style preference building

Refine style preferences

Indicate the outfit's occasion and purpose

Suggested outfit with expandable item details and more options

Prototype

Final Thoughts

What I accomplished

  • I designed an AI Stylist feature for ASOS, enabling personalized avatars for virtual try-on and AI-powered outfit suggestions.

  • This feature was designed to resolve online shoppers’ challenges in matching outfits to their body types, skin tones, unique fashion styles, and occasions.

  • A seamless onboarding process balances AI intelligent adaptability and user guidance. It offers flexible image options, text-image explanations, auto-saving steps, and manageable tasks while gathering sufficient information.

Next Steps and Reflection

  • Balancing conversational inputs and traditional questionnaires:

    Reflecting on my ASOS AI stylist project, I recognized the importance of guiding users through the onboarding process to ensure their comfort and trust in the platform's accuracy. Key lessons learned included avoiding standalone open-ended questions when integrating AI functionality, the effectiveness of a text-image questionnaire over text-only or image-only options, and the value of combining multiple-choice and open-ended questions. This approach offered clearer guidance for users while encouraging them to express their unique needs and preferences.

  • Ethical artificial intelligence insights in style tech:

    Simultaneously, I acknowledged the pressing need to delve into AI ethics within the fashion world. By promoting ethical integration and usage of AI technology, I sought to contribute positively to the industry and foster responsible innovation. Enhancing avatar accuracy and harnessing the swap function for feedback-driven outfit suggestions also became crucial components of my vision to blend technology and ethics harmoniously.

  • Adding delightful, user-centric functionalities:

    Looking ahead, I’m eager to expand the AI stylist’s capabilities based on insights from competitor research and user interviews. I see potential in integrating the AI stylist with a user’s calendar to provide outfit suggestions for specific events, while also offering a closet management system to help users visualize and organize their wardrobe. These features aim to reduce decision fatigue and make the AI stylist a truly personalized, proactive companion in users’ daily lives—enhancing not just style, but also confidence and convenience.

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