Zara

New Zara app provides personalized styling suggestions every day and curated shopping experience.

2019

Individual Project

Timeline 3 Weeks

Role User Research, UX Design, Visual Design, Prototyping, and User Testing

Personalized, from top to bottom.

Your favorite style, the weather, today's schedule, you name it. We've got the style, sophisticatedly personalized for you.

Get the most out of your closet

Say goodbye to the struggle to get ready every morning. Zara will set you up for the perfect style based on the items that are actually in your wardrobe!

Kick Off

1. Initial Insight

Choosing an outfit is harder than we think.

2. Setting up goals

Users

Quickly find a nice style that is appropriate in weather and occasion setting and speaks my personality well

Design

Create an effortless and delightful experience that helps users to find a look for a day and a style that suits them

Business

Build a strong omni-channel shopping experience that can attract younger generation users into the Zara ecosystem

Discovery

1. Who is the user?

18 - 34

Demographic

Generation cohorts spanning millennials and generation Z

Platform

Mobile is the new primetime for our target users

The cool factors

Our users want a product that is personalized to them

Interest

Users have high expectation of technology. Also they are looking for fun and new experience

2. The persona

Anna, 27, MBA student

"A Style Is Important, But I Have A Million Other Things To Manage In My Life."

Needs

1. Quickly find nice and stylish outfits for different occasions in the hectic morning.

2. Affordable personal shopper to pick my clothes on behalf of me

3. What are the pain points?

Styling takes time

A styling process takes too long for a hectic morning. Users often spend over 30 minutes just to decide what to wear.

Fashion is difficult

Target user base is not familiar with the style that suits them. They ask opinions to friends or search the social media to find an inspiration.

Limited use of the items

Users tend to end up being wearing the same style over and over again

4. What do users want?

Personalization

Recommend me what to wear today out of the items that I have.

Smart suggestion

Check the weather and my schedules, and suggest me the right outfit.

Curation Service

Show me the items and styles that I might like.

Define

1. Reframing pain points

"How might we help a user's style decision?"

2. Metadata for personalization

Style

Personality, individuality, a mood, preference and taste

Weather

Temperature, Chance of rain or snow, and other weather conditions.

Functionality

Materials, Comfort, performance of clothes and fabrics

Schedule

Special events or everyday styling. Time, Place, and Occasion of today’s event.

3. User journey mapping

4. How does the recommendation work?

Recommendation algorithm uses an input of style specialists, a trend data based on age and gender group as a data set.

User preference data will be continually refined by user input including browse and purchase history, and manual preference input(shake to refine, rating recommendation)