My presentation from Digital Dragons 2014 conference on organization of online gaming company with high level coverage of development process, technology, data analytics and human factors.
Text of It's not a product, it's a service!
1. Its not a product, its a service! Maciej Mrz CTO, Ganymede firstname.lastname@example.org
2. Focused on synchronous multiplayer games for most of our existence Office in Krakw city center Online gaming company 60+ people and growing Social casino, arcad e and skill games Online gaming company About Us Founded 10+ years ago Office in Krakw city center 2 3. Social casino Casual Multiplayer Social casino Casual multiplayer Multiple platforms Web Social networks Mobile Our Products 4. 4 More than game development Community management Server operations Data analytics Marketing Web development 4 5. Making it work 5 Development Process Data Technology People 6. Development Process Lean startup approach 6 Validate your game idea first Everything else usually can be deferred MVP is probably half of what you think it is Every game is a learning opportunity Be prepared for the unexpected Careful about future proofing by developers 7. Team dedicated to a game may actually grow post release Users expect new content/features to be delivered on regular basis A lot of effort spent on optimization A/B testing experiments Exploratory analytics Successful games are serving players for many years Development Process Release is only the beginning... 7 8. Development Process Scrum in game production 8 9. Development Process Scrum in game production 9 Does not apply to prototyping process (ad-hoc 2-3 person teams) Dedicated Product Owners and Scrum Masters External training/coaching absolutely worth it Getting it right is very challenging 10. Development Process Scaling game development/operations 10 Game teams are independent of each other Shared technology GitHub-like development model Some oversight necessary Teams handle big part of operations 11. Data Analytics = process + tools 11 Leaning more to the process side Tools are a solvable problem Collection/storage is cheap Analysis is a significant investment Data science know-how takes a lot of time to develop and is hard to acquire Third party tools are getting better 12. Data Scientific approach to product development 12 Dont guess if you dont have to: The answer may be in the data you already have Or in the data you can easily get Guessing is fine, but: State your assumptions Validate afterwards 13. Data Information flow 13 Well defined common KPIs: Accessible to anyone in game team within