Product

Product

E-commerce | B2C

E-commerce | B2C

E-commerce | B2C

My Role

My Role

UX Designer

UX Designer

UX Designer

Main Goal

Main Goal

Help users mix and match Zalando purchases
to create personalised stylish outfits

Help users mix and match Zalando purchases
to create personalised stylish outfits

Help users mix and match Zalando purchases
to create personalised stylish outfits

Plan your looks. Save time. Elevate your style. Meet your virtual
Mix & Match Zalando assistant.

About

Zalando is a leading European e-commerce platform for fashion and lifestyle, offering a wide selection of clothing, shoes, accessories, and beauty products from top brands, with a focus on convenience, personalisation, and seamless shopping experiences.

Zalando is a leading European ecommerce platform for fashion and lifestyle, offering a wide selection of clothing, shoes, accessories, and beauty products from top brands, with a focus on convenience, personalisation, and seamless shopping experiences.

ERPNext is an open-source ERP platform that helps businesses manage operations like procurement, sales, accounting, inventory, and more in one integrated system. Widely adopted in over 140 countries, ERPNext is consistently recognised as one of the top open-source ERP solutions globally.

Context

Have you ever stared at a closet full of clothes and still felt like you had nothing to wear — or spent way too much time putting together the right outfit?

Challenge

Over 4 weeks, I developed a feature for the Zalando app that helps users combine their past purchases into ready-to-wear outfits, making daily outfit decisions easier and faster.


The app also recommends complementary items to complete each look, enhancing the styling experience while increasing user engagement and potential upsell opportunities.

DISCOVER
Secondary research

To design a meaningful and user-focused outfit planning feature, I first needed to understand:


  • Who are Zalando’s core users?

  • What are their shopping behaviours and outfit planning habits?

  • What pain points do they face when choosing what to wear from their existing wardrobe?


For the MVP, I focused on Zalando’s core demographic, primarily users between 25 and 40 years old, aligning with the platform’s largest visitor age group (25–34).

Key personas include:

  • Millennials (25–40):
    Regularly shop for work-wear, casual-wear, and occasion-based outfits. They value convenience and style versatility.

  • Gen Z (18–24):
    Trend-driven and fashion-conscious, often seeking creative inspiration and outfit variety.

  • Gender Split:
    Based on recent user data, Zalando’s audience is approximately 55% women and 45% men.

To design a meaningful and user-focused outfit planning feature, I first needed to understand:


  • Who are Zalando’s core users?

  • What are their shopping behaviours and outfit planning habits?

  • What pain points do they face when choosing what to wear from their existing wardrobe?


For the MVP, I focused on Zalando’s core demographic, primarily users between 25 and 40 years old, aligning with the platform’s largest visitor age group (25–34).

Key personas include:

  • Millennials (25–40):
    Regularly shop for work-wear, casual-wear, and occasion-based outfits. They value convenience and style versatility.

  • Gen Z (18–24):
    Trend-driven and fashion-conscious, often seeking creative inspiration and outfit variety.

  • Gender Split:
    Based on recent user data, Zalando’s audience is approximately 55% women and 45% men.

To design a meaningful and user-focused outfit planning feature, I first needed to understand:


  • Who are Zalando’s core users?

  • What are their shopping behaviours and outfit planning habits?

  • What pain points do they face when choosing what to wear from their existing wardrobe?


For the MVP, I focused on Zalando’s core demographic, primarily users between 25 and 40 years old, aligning with the platform’s largest visitor age group (25–34).

Key personas include:

  • Millennials (25–40):
    Regularly shop for work-wear, casual-wear, and occasion-based outfits. They value convenience and style versatility.

  • Gen Z (18–24):
    Trend-driven and fashion-conscious, often seeking creative inspiration and outfit variety.

  • Gender Split:
    Based on recent user data, Zalando’s audience is approximately 55% women and 45% men.

DISCOVER
Competitor analysis

By analyzing key competitors — Whering, A Closet, and Save Your Wardrobe — I examined how similar apps approach digital outfit creation and address user pain points.
My focus was on understanding:

  • 🧭 User flows for outfit creation and saving

  • 🎨 UI patterns for mixing and matching items

  • 📦 Wardrobe setup methods (manual vs. auto-import)

  • 💡 Feature scope and personalization

A Closet

A Closet

A Closet

Whering

Whering

Whering

Save Your Wardrobe

Save Your Wardrobe

Save Your Wardrobe

Whering

Save Your Wardrobe

DISCOVER
Customer journey map

With limited time and no direct user feedback, I relied on secondary research and competitive analysis to identify user pain points, such as decision fatigue and lack of time. I used a customer journey map to visualize these frustrations and quickly identified opportunities for improvement.

DEVELOP
Prototyping

Once the wireframes were in place, I built a comprehensive interactive prototype covering both primary flows—like mixing items, comparing looks, and saving outfits by occasion—as well as key edge cases. This ensured a smoother, more realistic user experience and allowed me to test how the feature holds up under different user behaviors.

DEFINE
User Flow & Information Architecture

DEFINE
User Flow &
Information Architecture

During the design process, I was faced with the temptation to include a range of exciting features — AI-driven outfit suggestions, smart calendar integration, and even a style mood board.


But after conducting user research and analysing competitors, I realised the key user pain points were clear: time constraints, decision fatigue, and a desire for simplicity.


To address these, I scoped the MVP to focus on the essentials:
✅ Mix & match items using a single method — drag-and-drop.
✅ Compare outfits side-by-side
✅ Save and label looks by occasion (e.g. “Work,” “Vacation”)

This helped me stay focused on creating real value, fast.

During the design process, I was faced with the temptation to include a range of exciting features — AI-driven outfit suggestions, smart calendar integration, and even a style mood board.


But after conducting user research and analysing competitors, I realised the key user pain points were clear: time constraints, decision fatigue, and a desire for simplicity.


To address these, I scoped the MVP to focus on the essentials:
✅ Mix & match items using a single method — drag-and-drop.
✅ Compare outfits side-by-side
✅ Save and label looks by occasion (e.g. “Work,” “Vacation”)

This helped me stay focused on creating real value, fast.

During the design process, I was faced with the temptation to include a range of exciting features — AI-driven outfit suggestions, smart calendar integration, and even a style mood board.


But after conducting user research and analysing competitors, I realised the key user pain points were clear: time constraints, decision fatigue, and a desire for simplicity.


To address these, I scoped the MVP to focus on the essentials:
✅ Mix & match items using a single method — drag-and-drop.
✅ Compare outfits side-by-side
✅ Save and label looks by occasion (e.g. “Work,” “Vacation”)

This helped me stay focused on creating real value, fast.


* complete flow, later scoped down for the MPV version

* complete flow, later scoped down for the MPV

DEFINE
User Flow

One of the key challenges was finding the right home for the feature. I explored several placement options: should it live within the user’s owned items, appear as a new tab, or be linked to past purchases?

Balancing discoverability and existing navigation patterns, I evaluated each option through the lens of user habits and mental models. The final decision: a dedicated "Outfits" section within the ‘Items You Own’ area—accessible from the Account page, where users already manage personal wardrobe-related content.

DEVELOP
Wireframing

DEVELOP
Wireframing

DEFINE
User Flow &
Information Architecture

With a clear MVP in mind, I mapped out a focused user flow to ensure a smooth and intuitive outfit creation experience. This set the stage for the first wireframes, where I explored how users could mix and match items, compare outfits side by side, and save their looks—tagged by context like "Work", "Home", or "Travel" for quick access later.

DEVELOP
Usability Testing

To validate the core functionality and usability of the new “Outfits” feature, I’m currently conducting usability testing with 6 users. The focus is on observing how easily users can:

  • Navigate the interface and access the feature

  • Mix and match owned items

  • Compare outfit combinations and save looks for different occasions.

Mid-Fi Wireframes

Mid-Fi Wireframes

Hi-Fi Wireframes

Hi-Fi Wireframes

Low-Fi Wireframes

Low-Fi Wireframes

DEVELOP
Prototyping

Once the wireframes were in place, I built a comprehensive interactive prototype covering both primary flows—like mixing items, comparing looks, and saving outfits by occasion—as well as key edge cases. This ensured a smoother, more realistic user experience and allowed me to test how the feature holds up under different user behaviors.

I redesigned both ERP interfaces to improve collaboration between suppliers and buyers.
•Supplier Interface: Enabled suppliers to create shipping notices, keeping buyers informed about order progress.
•Buyer Interface: Provided access to shipment details, allowing buyers to track orders efficiently and respond proactively to potential delays.


Started with an initial design, I transitioned to Balsamiq wireframes. This allowed me to focus on core functionality.

Scenario:

Imagine you’ve received an email from Zalando about a new feature that allows you to create outfits using previously bought items. This feature lets you mix and match clothes to create outfits. You decide to check it out and see how it works.

Task 1:
Can you find this feature in the interface?

PROTOTYPING
Buyer identifies delayed orders
and analyses shipment insights to determine next steps

Some of the User Tasks:

Shipments Overview
Buyer's interface

DELIVER
Performance Evaluation & Metrics


In a live project scenario, my next step would be to track key metrics to measure the design’s impact:

  1. User Engagement:

    I'd track user engagement with the "Outfits" feature, including interaction frequency, time spent, and actions like adding or saving items.

  2. Retention Rate:

    I'd track the percentage of users who return to the "Outfits" section after their first use, over time (e.g., week 1, week 2).

  3. Conversion Rate:

    I’d track how the feature impacts business goals, such as the percentage of users who save outfits and purchase or add recommended complementary outfits to the cart.

  4. Interaction Patterns:

    I’d analyse user interactions, focusing on frequent actions and common pathways to identify pain points and engagement areas.


These findings will guide future updates to optimise the user experience.


FINAL DESIGN

DEVELOP
USABILITY TESTING

To validate the core functionality and usability of the new “Outfits” feature, I’m currently conducting usability testing with 6 users. The focus is on observing how easily users can:

  • Navigate the interface and access the feature

  • Mix and match owned items

  • Compare outfit combinations and save looks for different occasions

FINAL DESIGNS

Some of the Tasks:


Scenario:

After discovering the new feature, you decide to plan your outfit for tomorrow using clothes you've already purchased from Zalando. You’re wondering what looks better with your jeans.

Task 2:
Create an outfit using your owned jeans, green shirt, and black sandals.

Scenario :

Now you’re thinking maybe the pullover and sneakers would fit better with jeans.
Task 3:
Try a different combination using the same jeans, but this time with the red pullover and beige GANT sneakers to see how they fit together.

PROTOTYPING
Supplier informs the client about the remaining shipment

Scenario:

Scenario 3

(for Buyers):

Imagine you’ve received an email from Zalando about a new feature that allows you to create outfits using previously bought items. This feature lets you mix and match clothes to create outfits. You decide to check it out and see how it works.


Task 1:
Can you find this feature in the interface?

As a buyer managing orders from multiple suppliers, you need to keep track of delayed shipments and decide how to handle the situation.


Task 3:
Find out which shipments are delayed and check what might be causing the delay.

Scenario:

Scenario 1

(for Suppliers):

After discovering the new feature, you decide to plan your outfit for tomorrow using clothes you've already purchased from Zalando. You’re wondering what looks better with your jeans.


Task 2:
Create an outfit using your owned jeans, green shirt, and black sandals.

After discovering the new feature, you decide to plan your outfit for tomorrow using clothes you've already purchased from Zalando. You’re wondering what looks better with your jeans.


Task 2:
Create an outfit using your owned jeans, green shirt, and black sandals.

Scenario:

Scenario 2

(for Buyers):

Now you’re thinking maybe the pullover and sneakers would fit better with jeans.

Task 3:
Try a different combination using the same jeans, but this time with the red pullover and beige GANT sneakers to see how they fit together.

Now you’re thinking maybe the pullover and sneakers would fit better with jeans.

Task 3:
Try a different combination using the same jeans, but this time with the red pullover and beige GANT sneakers to see how they fit together.

Usability Test Results
* currently underway. Findings will be used to inform the next iteration.

Usability Test Results
*currently underway

DELIVER
Performance Evaluation & Metrics

DELIVER
Performance Evaluation
& Metrics

In a live project scenario, my next step would be to track key metrics to measure the design’s impact:

  1. User Engagement:

    I'd track user engagement with the "Outfits" feature, including interaction frequency, time spent, and actions like adding or saving items.

  2. Retention Rate:

    I'd track the percentage of users who return to the "Outfits" section after their first use, over time (e.g., week 1, week 2).

  3. Conversion Rate:

    I’d track how the feature impacts business goals, such as the percentage of users who save outfits and purchase or add recommended complementary outfits to the cart.

  4. Interaction Patterns:

    I’d analyse user interactions, focusing on frequent actions and common pathways to identify pain points and engagement areas.