Case study: Moncler A/B Testing

UX/UI Design  |   Data analysis   |  Testing

role & responsibilities:

Working on data analytics, information architecture, 

wireframes, and prototypes, to find insights, test and 

optimise different areas of the existing digital experience 

of the company.

tools used:

Figma

Kameleoon

Contentsquare

Google analytics

A/B testing is used to help designers make decisions based on data. By exposing users to different versions of a design we can determine which version works better for the goal and help confirm or reject or hypothesis. 

 

Ex. this diagram showcases the process of A/B testing

Observation

Currently, most of the users on mobile when
landing on the homepage directly tap on the
menu, without exploring / scrolling the page.

This means low engagement in the homepage
and no inspirational navigation.

Hypothesis

Giving the users an alternative way of
browsing the homepage may increase the
exploration of some specific contents, leading
to a possible higher conversion.

Problems

FLP has a high exit rate

Low engagement

FLP is not so scrolled

Possible solution

Insert new modules with carousels with recommended products 

 

 

Goals

Increase the engagement 

on the FLP

Make the user navigate 

through more pages

Increase the number 

of PDP visited

Variations

Currently, most of the users on mobile when
landing on the homepage directly tap on the
menu, without exploring / scrolling the page.

This means low engagement in the homepage
and no inspirational navigation.

Hypothesis

Giving the users an alternative way of
browsing the homepage may increase the
exploration of some specific contents, leading
to a possible higher conversion.