ABOUT SPACE APE
Space Ape's hit real time strategy game, Samurai Siege, has been played by over 11m people and generated over $50m in revenue since it's launch in October 2013. The game was built by a team of 12 over 6 months.
Samurai Siege has sustained in the grossing charts where many come and go in no small part because of the team's focus on live operations. Every week new content is pushed live, marketing strategies are refreshed and the game is optimised based on a combination of player research and analytics.
ABOUT THIS PRESENTATION
This presentation shows the evolution of the Samurai Siege analytics stack and some of the applications of the data by Space Ape's product, marketing and community teams.
The stack started as a simple MVP but evolved over time as the game matured and the competitive landscape changed. It is now a fully functioning service that was easily replicated to support the launch of their next game Rival Kingdoms (currently in public Beta). As such, the presentation will be of interest to smaller games studios who are figuring out how to prioritise investment in data as well as established studios who might be re-thinking their legacy systems and figuring out how to bring the data focus needed to succeed in the modern free to play games business.
This presentation was made by Space Ape's analyst Richard Reyes and shared with local game developers at the Great British Big Data Game Show & Tell in London on 25 February 2015.
For more on Space Ape's Live Ops and Analytics stacks see
https://tech.spaceapegames.com/2016/12/07/space-ape-live-ops-boot-camp/
4. Mobile real time strategy, iOS, Android
Developed in 6 months by team of 12
Live October 2013
11M Installs / 1.2M MAU / 250K DAU
$23M in Gross Revenue to Date
11. โ Automated and Run Daily
through an SQL Runner
โ Delta Process. Clear and
Insert Data for Past 3 Days
โ Process completed in
Minutes not Hours
19. โ Automated and Run
Daily through an SQL
Runner
โ Recreated in Full every
day
โ Based on Daily
Summary Table
โ Process completed in
Minutes not Hours
DAILY
SUMMARY
27. โ Segment Users
โ Assign Appropriate Curve
based on Segmentation
โ Calculate pLTV based on
different time periods
โ Automated and Run Daily
through an SQL Runner
32. Retention
Average Revenue Per User
Average Revenue Per Paying User
Daily/Monthly Active Users
New Users
Session Length
Average Sessions Per User
Spend Metrics
36. Set up A/B test in
SWRVE + basic results
More advanced metrics
analysis in DWH
37. Army composition for castle 9 and 10 ? in Japan, during a
event, ...More dimensions, aggregation flexibility
โ Monthly / weekly revenue
โ Full device split
โ Event type
โ ...
To be applied on a lot of information
โ IAPs , diamonds spending
โ Economy movements
โ Event performance
โ Honor movement
โ Behavioural changes (eg army
composition)
โ Community stats (time to first response,
CSAT, per value tier)
โ ...