Understanding Player Engagement with a Mobile Game Mechanic
Combining behavioral data and user research to investigate why players were engaging with a game mechanic less than expected.
Role: Sole UX Researcher
Research: Evaluative / Mixed Methods
Methods: Unmoderated Usability Study · Survey
Analysis: Behavioral Observation · Qualitative Synthesis
Platform: PlaytestCloud
Participants: 5 players
Timeline: ~4 weeks
*Client, product, and specific research findings have been anonymized to protect confidential information.
01 – Challenge
Behavioral data from a mobile game showed that players were engaging with a game mechanic less than expected.
The data revealed what players were doing, but the team didn’t know why. Lower engagement could potentially reflect how players understood the mechanic, how and when they encountered it, or other priorities shaping their decisions during gameplay.
The research focused on a key question:
What factors influence players’ engagement with the game mechanic?
The goal was to understand possible explanations for the observed behavior.
02 – Approach
Move From “What” to “Why”
Behavioral data revealed a pattern of low usage of the game mechanic, prompting the team to investigate why this behavior was occurring. To explore the behavior in context, an unmoderated usability study was proposed to explore the behavior in context, observing players interacting with the game while using a think-aloud protocol to capture their reasoning and decision-making.
A post-session survey provided an additional perspective on players’ understanding, experience, and perceptions.
This approach connected observed product behavior with players’ experiences and reasoning, helping investigate why the pattern seen in the behavioral data might be occurring.
03 – Working Within Research Constraints
Initially it’s recommended to recruit 8-10 participants, but the project had budget constraints associated with participant recruitment.
After negotiating with the team, the study proceeded with five participants, a sample size considered sufficient for identifying meaningful qualitative patterns while remaining within the available recruitment budget.
The research focused on identifying recurring behavioral and comprehension patterns and plausible explanations that could help the team interpret the existing behavioral data.
The findings provided insight into possible reasons for the observed behavior within this group of players, while offering hypotheses that could be further investigated in the broader player population.
04 – From Behavior to Explanation
Analysis brought together three perspectives:
The analysis considered multiple perspectives to establish context and settings for understanding the observed behavior – lower utilization.
05 – Outcome
The research suggested that lower engagement could reflect multiple factors.
There were indications that some aspects of the mechanic could be better understood, but players’ priorities and the context in which they encountered the mechanic could also influence whether they chose to engage with it.
Based on these findings, I recommended introducing contextual, just-in-time guidance to help players understand the mechanic when it became relevant during gameplay.
The recommended guidance was subsequently incorporated into the next update of the game.
06 – Reflection
Balance Research Confidence With Practical Constraints
The five-participant sample was useful for identifying potential behavioral and comprehension patterns, but additional participants would have increased confidence in the findings and provided stronger evidence about how broadly those patterns applied.
The research also resulted in a design change, but no follow-up evaluation was conducted after implementation to measure its impact.
In retrospect, the research plan should have budgeted for a follow-up evaluation from the outset, allowing the team to use behavioral data or another study to determine whether the new guidance actually improved players’ understanding and engagement with the mechanic.