Reading the player: game and player analytics
How data informs design: the game analytics value chain, telemetry events, quantitative and qualitative data, engagement and retention (DAU/MAU, D1/D7/D30, churn), the player lifecycle, progression metrics and funnels, heatmaps, economic, social and technical metrics, A/B testing, predictive analytics, tools, and the ethics of collecting player data. In the practical you analyse a launch week of synthetic telemetry from Pip's Peak.
Slides: use the arrow keys or swipe; press F for full screen and Esc for an overview.
- Explain what game analytics is for, where telemetry fits in the analytics value chain, and why it needs qualitative data beside it.
- Read engagement, retention and churn, progression, economic, social and technical metrics.
- Turn a number into a design change: evidence, hypothesis, change, measurement and an A/B test.
- Collect player data responsibly, with privacy, consent and minimisation, and without turning prediction into manipulation.
Finish your three Pip's Peak findings (evidence, hypothesis, change, measurement, playtest question). Optional: read the GDnD wiki pages on game analytics and the player lifecycle.
Activity files
- Read the climb: analytics task (PDF)PDF
- Read the climb: analytics task (Word)DOCX
- Pip's Peak telemetry, synthetic (Excel)XLSX
- Pip's Peak players, synthetic (CSV)CSV
- Pip's Peak level attempts, synthetic (CSV)CSV
Key reading
El-Nasr, M. S., Drachen, A. and Canossa, A. (eds) (2013) Game Analytics: Maximizing the Value of Player Data. London: Springer.
Clark, O. (2014) Games as a Service: How Free to Play Design Can Make Better Games. Burlington, MA: Focal Press.
Su, Y., Backlund, P. and Engström, H. (2021) 'Comprehensive review and classification of game analytics', Service Oriented Computing and Applications, 15, pp. 141–156.
Thompson, C. (2007) 'Halo 3: how Microsoft Labs invented a new science of play', Wired, 15(9).