Moncler
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.