AboutWork
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About
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Role
UX Researcher
Duration
May - Aug 2023
Domain
Service Design, Social Dynamics, Gaming, Live Service Platform, User Research
Overview
This case study walks through my process—from initial user research to designing a matchmaking system that encourages understanding of player motivations.
[Estimated Reading Time: 8 minutes]
Research Objectives
Research shows that toxic gaming environments reduced player engagement and revenue by up to 54%. I researched ways to mitigate harassment in gaming by designing a  framework that foster positive social dynamics.
Key Takeaways
Inclusive Design
SEL framework aimed to boost player engagement by 15%.
Toxicity Insights
Actionable data bridges a 54% spend gap, promoting healthier gameplay.
Persona Mapping
User data reveal how social-emotional traits drive in-game behavior.
“How might we foster a understand of intentions and reduce toxicity?”
User Research
What requires more research?
Behavioral Frameworks
ABOUT THE PLAYERS
Investigate how to improve players' emotional intelligence, relationships, and decision-making skills by understanding how their needs and expectation impact their gaming experience.
Behavioral Frameworks
By understanding these player expectations, we can tailor game features and community management strategies to better meet the needs of diverse player groups, ultimately enhancing their experience and reducing the likelihood of harassment and toxicity.
Initial Personas
AUDIENCE SPECIFICS
I started by developing a categorization of video game players based on their motivations, preferences, and playing styles.
User Research
WHAT ARE CURRENT EXPECTATIONS
I researched the community’s current sentiment through user interviews, surveys, and observation play sessions to understand values informing design advocacy.
User Research
In my research, I analyzed player perceptions of in-game behavior to identify optimal social environments.

Participants defined negative interactions as uncommunicative behavior, name-calling, and blaming, while positive interactions centered on respect and a good attitude.
Observational DATA
WHAT ARE CURRENT EXPECTATIONS
The survey data identified optimal environments for observing player behavior, which I tracked by documenting social interactions during typical play sessions.
Surprise Findings
How does this align with others?
New Personas
More ways to play
Through research and ideation, we identified six new player personas tailored to League of Legends users' needs and expectations.

Using these insights, we designed a feature set to address key behavioral challenges within this context.
Design System
What can we change and why?
Intervention
“ViBE” MATCHMAKING
This new matchmaking system lets players express their needs and expectations before entering a League of Legends match.

By showing what other players are in the mood for, it provides behavioral context for their actions and motivations.
Resolution
What are our results, and what's next?
Prototype testing
Testing IMPRESSIONS
Initial player feedback revealed key pain points: newer players preferred a relaxed, pressure-free space for experimentation, while experienced players desired a ranked-like environment for warm-ups and build testing.

This highlighted the need to simplify features, as design complexity disrupted natural social interactions and uncovered diverse player personas.
Advanced Features Integration:

Explore integrating machine learning algorithms for personalized real-time price comparisons.
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Ecosystem Expansion: 

Scale community-centric features and collaborate with local organizations to expand service reach.

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Ongoing Support:

Enhance customer service channels for continuous user support and system updates.


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