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Game Analytics - industry perspectives

Game Analytics - industry perspectives. Agenda. Paradigm shift: F2P Challenges in Game Analytics A call for research: what the problems are. Paradigm shift. Free-to-Play/online games Casual market AAA-market Serious games . Piracy. F2P model eliminates game piracy

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Game Analytics - industry perspectives

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  1. Game Analytics - industry perspectives

  2. Agenda • Paradigm shift: F2P • Challenges in Game Analytics • A call for research: what the problems are

  3. Paradigm shift • Free-to-Play/online games • Casual market • AAA-market • Serious games

  4. Piracy • F2P model eliminates game piracy • All transactions controlled • No solid evidence on whether piracy is causing revenue losses or gains

  5. Retail • The retail link adds 60% overhead to price • Publishers also pay retail for placement etc. • Online distribution removes the retail link

  6. Resale • Re-sale of used games • 47.3 mio people US • 8.7 bio revenue US • 42% GameStop´s 2008 profit • 45% vs. 25% profit margin for retail • Most used games sold via retail stores • Costs publishers revenue (?) • F2P model removes re-sale issue • Unless developer open up for this • Re-sale of virtual items (Diablo 3, World of Warcraft, etc.)

  7. Platform agnostic • No need to worry about console/PC – unless client install • No expensive ports between platforms through tightly controlled platform holders • Servers driving the load minimizes requirements for client hardware

  8. Content friendly • Easy to field new content • Proliferation in DLC – also stand-alone titles • Deus Ex 3, Mass Effect 3, Diablo 3 going online • Extend lifetimes and increases revenue channels • All consoles already supporting DLC

  9. Easy to distribute • Cuts off the physical distribution network • Easy to expand to new markets • Platforms ready: Steam, Origin ... • Zynga, Wooga ... have own platforms

  10. Technical capacity • Technical innovation makes it possible to field fairly advanced graphics in browsers • More broadband coverage, more mobile phones • Installed clients for high-end graphics – Tera, Battlefield ...

  11. Revenue driver • Proven driver of revenue– not casual - alsoAAA • TF2 -> F2P -> 12* revenue • hats!

  12. Analytics • Lastly and importantly: Game Analytics • Allows monetization in F2P business model • Unparalleledinsights into user behavior

  13. Definitions • Analytics • Game Analytics • Game Telemetry • Game Metrics

  14. Analytics • The process of discovering and communicating patterns in data towards solving problems in business • Supporting enterprise decision management • Driving action • Improving performance • Or for purely frivolous and artistic reasons!

  15. Game Analytics • A specific domain of analytics: game development and game research • The game as a product: user experience, revenue … • The game as a project: the process of developing the game • The game as performance: the technical infrastructure, the support of persistence

  16. Game telemetry • Any amount of quantitative, unprocessed data obtained over any distance, which pertain to game development or game research. • Describes attributes about objects • Many sources: Installed clients, game servers, mobile units, user testing/playtesting

  17. Game metrics • Interpretable, quantitative measure of one or more attributes of one or more objects – operating in the context of games • Object: virtual item, player, user, process, developer, forum post .... • Attribute: an aspect of the object • Context: tied to process, performance or users of games.

  18. Why game analytics? ”Never before have so few known so much about so many”

  19. Why game analytics? • Evidence-drivensupport for decision making at operational, tactical and strategic levels • User-oriented analytics the most common today • BI methods support all areas of a business: • Management • Design • Production • Marketing • Customer support • Etc.

  20. User analytics • Games live and die by engagement/UX • Users are alpha and omega for game success • Analyzing how users interact with games provides a measure of success • Hence industry focus on user analytics

  21. "You are no longer an individual, you are a data cluster bound to a vast global network" –

  22. User metrics

  23. User analytics • 1) Strategic analytics, which target the global view on how a game should evolve based on analysis of user behavior and the business model. • 2) Tactical analytics, which aims to inform game design at the short-term, for example an A/B test of a new game feature. • 3) Operational analytics, which targets analysis and evaluation of the immediate, current situation in the game. For example, informing what changes should be made to a persistent game to match user behavior in real-time.

  24. Why analytics? • Examples of game metrics or analyses and how they are useful to developers

  25. Current challenges

  26. 1. Lots´n lots of data • Even a mid-size game can generate TBs of data per week –> storage/processing • 100+ features, millions of users, game system • Reporting needs to be fast -> rapid analysis • Bandwidth vs. data coverage -> feature selection • Coverage vs. speed -> sampling

  27. 2. Lack of knowledge and best practices

  28. 3. Unique beasts • Games are not websites • Goal of games: user experience – not selling running shoes (virtual shoes maybe) • Games can be immensely complex information systems • Hundreds of possible user interactions • Extended periods of user-game interaction • From 1 to lots of people interacting in-game • Hard to import methods from other IT-fields – adaptation needed

  29. 4. Analysts • We need analysts! • But what skills should they have?

  30. 5. Capacity gap • Game analytics requires expertise • Big companies have the advantage • Hard for indies

  31. 6. Knowledge transfer • What is going on? • Minimal knowledge flow about methods, algorithms, ideas • No dedicated conferences or workshops • Presentations at events high level • Not oriented towards application • More ”bragging” than helping ...

  32. 6. Knowledge transfer • Analytics is business intelligence – holds direct monetary value • A strong predictive algorithm can make a game • Therefore kept confidential • Problem: re-inventing the deep platter • Everybody benefits from knowledge transfer

  33. 6. Knowledge transfer • Huge untapped potential in dozens of fields/sectors: • Human behavior analysis • Spatial analytics • Behavioral economics • Insurance, banking and finance • Social and community research • Ecology and large-scale biological modeling

  34. 7. The ethics dilemma

  35. 8. Knowledge gulf • Knowledge gulf: academia – industry • Academia provides a strong partner in analytics • 1000´s of specialists in dozens of fields • Can do explorative/blue sky research • This is a call for research!

  36. Science to the rescue!

  37. Current needs • Descriptive analytics and standard metrics in place -> mainly a knowledge dissemination issue • Help with the ”hard” methodological problems • Prediction of user behavior (playtime, monetization) • Clustering/classification – ideally dynamic • Sequence mining

  38. Current needs • Infering UX from behavior/finding design/UX problems • Spatial analytics • Adaptive games/agents using real-time analytics • Monitoring and using changes in playstyle/learning

  39. Current needs • Synchronizing and meshing data sources • Methods for using process and performance metrics to guide development • Analytics for supporting Game User Research • Standardizedmeasures across games

  40. Current needs • Finding/analyzing revenue drivers and player loyalty • Pushing the ”light side” of analytics: games for education, impact, change ... - combining analytics with design and new business models • Innovation: What can we do which we are not doing?

  41. Mention a couple of examples • Clustering and how it is useful • Etc.

  42. Game data mining • 4 high-potential areas of game data mining: • Prediction: inform about future behavior of users • When will the user stop playing? Buy something? • Behavioral clustering: making high-dimensional behavior datasets accessible • What are the main behaviors our users adapt?

  43. Game data mining • Association and sequence: finding the patterns and associations in how games are played • If people buy the Axe of Mayhem, do they also buy the Cape of Awesomeness +5? • Spatio-temporal analytics • Does not reduce the dimensions of game metrics data - deals with the actual dimensions of play. • Trajectory analysis - how do users navigate the environment? What assets are under-/overused?

  44. Thank you • ”Game Analytics – maximizing the value of player data” • 50+ experts from industry and research • 400,000 words (!) • Will be out in the Fall 2012 from Springer Publishers (ebook version also available) • For a sneak preview on a chapter on Game Data Mining: mail me! • Game Analytics: www.gameanalytics.com(WE ARE HIRING!) • Blog: blog.gameanalytics.com • Slides from presentation will be available on: www.andersdrachen.wordpress.com • Contact: anders@gameanalytics.com or christian@gameanalytics.com

  45. What lies beneath?

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