OVERVIEW

Big corporates have become more aware of diversity, equity and inclusion (DEI). Yet, how can we create an environment where employees feel comfortable and supported at work whilst continuously developing their careers?

MY ROLE

I was the only UX Designer in the team. In my team, there are 3 software developer (Jino John, Nathaly Reyes and Manvinderjit Singh), 1 machine learning specialist (Drew Anderson) and 1 product manager (Jennifer Blunt).

PROBLEM

GOALS

• Reduce mismatched mentors and mentees
• Help mentees increase their satisfaction towards the mentorship program
• Optimize the time and effort in matching mentors and mentees

IMPACT

• More than 50% of interviewees reported that they would have greater confidence in being matched with a good mentor under this design
• Reduced the time of booking a mentorship session by 30%

PERSONA

USER INTERVIEWS

In order to understand the users better, I conducted user interviews with the existing employees in the company to learn more about the current mechanism regarding the mentorship propgram. Below are several insights gained from the interviews:

• Long list: Employees are often matched with 500+ mentors, it takes a long time for the mentees to go through mentors’ profile one-by-one in order to select “the most desirable / compatible” mentors;
• Mentor’s availability: It can be tricky to cope with mentor’s availability. Sometimes it’s either the mentor is too occupied or it’s hard to arrange a meeting as there’s timezone difference.
• Notes keeping: Some mentors are mentoring multiple mentees at the same time, however, there’s no handy space for mentors to save a profile of the mentees on e.g. the conversation, the follow-up tasks, etc.

PRODUCT FEATURES

From the research, I realized that matching a right mentor for the mentee is crucial as it will help mentee to visualize the motivation of staying in the company and understand their prospect in a more clearer way.

• Use machine learning to increase the compatibility of the match:
  ‍Instead of pushing 500+ profiles to the mentees, our product will use machine learning to match users with a similar background, areas of interests, aspirations, etc., which will significantly reduce the possibility of matching mentees with a “wrong” mentor;
• Narrow down options to save time:
  ‍The algorithm will pre-select the best 3 options for the mentees to reduce mental load;
• Book mentorship session right away:
  To avoid delay due to unavailability and misinterpretation in the timezone, the system will allow users to book a mentorship session right away.

IDEATION

FINAL DESIGNS

KEY LEARNINGS

• Identify the focal point within the problem space:
  ‍Our team found it very challenging to narrow down the scope of the problem space given the time constraint in the hackathon. If I were to do it again, I would begin with a round of research first, followed by immediately reaching out to stakeholders to identify the actual problems.
• Collaboration between cross-functional teams:
  ‍Our close communication between team members had sped up the process of ideation and iteration. I believe that for any good design to take place, collaborative efforts from each team member are required.

FUTURE STEPS

• Redo the ideation process:
  that I still have plenty of ideas regarding the problem space now that are yet to be validated by research findings. I’d like to redo the brainstorming process and see if there is any blind spot that has yet to be discovered.
• Continue to talk to people:
  ‍I firmly believe that great designs are for people, not for users. I’d like to gain insights from and understand the pain points of the persona through talking to more people.