Using spatial analytics to evaluate design decisions in a mobile game
Polvi, Teemu (2018)
2018
Johtaminen ja tietotekniikka (Pori)
Talouden ja rakentamisen tiedekunta - Faculty of Business and Built Environment
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Hyväksymispäivämäärä
2018-06-06
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201805241862
https://urn.fi/URN:NBN:fi:tty-201805241862
Tiivistelmä
While working on a mobile game called Safari Kart, research was made into using spatial analytics in order to detect possible gameplay issues and validate design decisions. Emphasis was put on making the gameplay support a flow experience and to generate a sense of control for the player over their rate of advancement.
Safari Kart is a racing game for mobile devices. During development, Game User Research (GUR) methods, mostly playtesting sessions, were used for improving the game-play and for detecting frustration-inducing segments. After running these sessions, it became obvious that using only qualitative research methods would be too labor intensive and expensive. Additional research was made into different case studies of blending quantitative and qualitative data. From these, a decision was made to employ game analytics, and especially spatial game analytics in the project. Spatial analytics uses different visualizations to show where certain events have happened in the game.
Many of the existing case examples relied heavily on tools developed in-house. Because of our limited resources and time, we explored a range of different third-party analytics tools that could be utilized. We ended up using Unity Analytics Heatmaps, which integrated well into the Unity editor, which we used for developing the game.
We recruited testers to play the game on their own mobile devices. Their gameplay data was automatically sent to a backend cloud service, from where it was then retrieved to the editor for visualization. In our case, we used heatmaps for drawing different colored cubes where a significant number of users had either hit an obstacle or run off the track. It had been these two events that had been determined as the most frustration-inducing, based on previous observations during playtesting sessions.
From the visualizations we were able to effectively recognize the previously selected problematic gameplay events and to fix a significant number of them. We then proceeded on to a second testing round to validate that these fixes were effective.
Our study of other cases combined with our own findings, shows that game analytics can provide significant insight for finding gameplay problems, without conducting a major qualitative study. By using third party tools, the actual development load for an experienced team is minimal when compared to the benefits.
Safari Kart is a racing game for mobile devices. During development, Game User Research (GUR) methods, mostly playtesting sessions, were used for improving the game-play and for detecting frustration-inducing segments. After running these sessions, it became obvious that using only qualitative research methods would be too labor intensive and expensive. Additional research was made into different case studies of blending quantitative and qualitative data. From these, a decision was made to employ game analytics, and especially spatial game analytics in the project. Spatial analytics uses different visualizations to show where certain events have happened in the game.
Many of the existing case examples relied heavily on tools developed in-house. Because of our limited resources and time, we explored a range of different third-party analytics tools that could be utilized. We ended up using Unity Analytics Heatmaps, which integrated well into the Unity editor, which we used for developing the game.
We recruited testers to play the game on their own mobile devices. Their gameplay data was automatically sent to a backend cloud service, from where it was then retrieved to the editor for visualization. In our case, we used heatmaps for drawing different colored cubes where a significant number of users had either hit an obstacle or run off the track. It had been these two events that had been determined as the most frustration-inducing, based on previous observations during playtesting sessions.
From the visualizations we were able to effectively recognize the previously selected problematic gameplay events and to fix a significant number of them. We then proceeded on to a second testing round to validate that these fixes were effective.
Our study of other cases combined with our own findings, shows that game analytics can provide significant insight for finding gameplay problems, without conducting a major qualitative study. By using third party tools, the actual development load for an experienced team is minimal when compared to the benefits.