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Player-centric Game design: Adding UX Laddering to the Method Toolbox for Player Experience Measurement. A Poker case study

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Player-centric Game design: Adding UX Laddering to the Method Toolbox for Player Experience Measurement. A Poker case study

  1. 1. Player-Centric Game Design:Adding UX Laddering to theMethod Toolbox for Player Experience Measurement A poker case study Bieke Zaman CUO, KULeuven – iMinds http://www.linkedin.com/in/biekezaman Presentation at Measuring Behaviour Conference 2012
  2. 2. Measuring player experiences Informing game designUser eXperience Laddering
  3. 3. Overview methods
  4. 4. Physiological data Playtesting e.g. RITE Critical Facet metrics Playtest InitialExperience Playtest Deep Gameplay
  5. 5. Physiological data Playtesting Benchmark e.g. RITE Initial Critical FacetExperience Playtest Playtest Deep Gameplay Qualitative
  6. 6. Physiological dataPlaytesting e.g. RITE Critical Facet Benchmark Playtest LADDERIN G Mixed-
  7. 7. When to use which
  8. 8. PIII-approach
  9. 9. Marketing final productWhen to use
  10. 10. http://www.flickr.com/photos/pensiero/100754831/ UX
  11. 11. OriginsMeans-End Chain TheoryHow do specific features of a product relate topersonal values?
  12. 12. People choose a product because it containsattributesthat are instrumental to achieving the desiredconsequencesand fulfilling values
  13. 13. People choose a product because it containsattributes (the means)that are instrumental to achieving the desiredconsequences and fulfilling values (the ends)
  14. 14. Means-End Chain Theory inspiredGame eXperience ModelInsight into1. Player2. Game system3. Game context
  15. 15. Laddering?One particular method for interviewing and datatreatment within Means-End TheoryOrigins: Popular in consumer researchCurrent use: broader research domainsrelevance for user profiling, revealing personalbenefits of product use, supporting the redesignprocess, supporting marketingcampaigns, product benchmarking, ...
  16. 16. What is UX Laddering?
  17. 17. UX Laddering refers to BOTH the Lenient Laddering interviewAND the data analysis approach
  18. 18. Example
  19. 19. Real participants!
  20. 20. Product Choice Situation
  21. 21. 1 Product Interaction
  22. 22. 2 Preference Ranking
  23. 23. 3 Lenient Laddering
  24. 24. 4 Data analysis Qualitative & Quantitative
  25. 25. Real moves Game speed Arrow keys5 keyboard Cuddly toy interaction game Output Hierarchical Value Map
  26. 26. Real example
  27. 27. What are the motivations to playonline poker (i.c. Poker Stars & FBZynga)?What are the differences betweenamateur, semi-pro and a professionalplayer, if there are any?How does the design of the onlinepoker website influence the game playexperiences and website preferences?
  28. 28. n=18  6 amateur  6 semi-pro  6 pro18-28 year olds17 men, 1 womanBelgium, higher education
  29. 29. Preference RankingI: “You’ve been playing both onlinepoker games. If you had thechoice, which one would you prefer?”R: “Pokerstars” Interview 6 – semi- professional
  30. 30. Which attributes top of mind? DirectelicitationI: “You usually play poker onFacebook, euhm, now that I asked you toplay poker on PokerStars, which one wouldyou prefer?”R: “Yes, now I actually prefer PokerStarsbecause I find it clearer and more user-friendly than Facebook poker.” Interview 15 - amateur
  31. 31. Lenient LadderingProbing why these attributes areimportant• I: “Why do you play 6 tables at a time?”• R: “Eh, it is just a matter of being able to play more hands an hour so that you can earn more. It is a matter of playing so many tables so that you think you can always play your best game.”• I: “It is maybe a stupid question but why do you want to play better or be more focused?”• R: (laughing) “Well euh, yes, I want to earn more money.” Interview duration: 6 minutes – 47 minutes
  32. 32. Qualitative Data analysisTranscribing the interviewsCoding & categorizingSecond coder ICR (n=6/n =18, k=.934) total
  33. 33. Concrete Attributes: – Extra features (time bank, search function, multi table, filters, hand history…) – Stand alone software – Real money –… CA
  34. 34. Abstract Attributes: – User friendly – Serious game play – Compatibility – Large user base – Legal –… AA
  35. 35. Functional Consequences: – Being more focused – Play quicker – Playing more hands an hour – Profit maximalization – Earn more money – …. FC
  36. 36. Psycho-Social Beliefs: – Challenge – Trust – Playing amongst friends – Fun – Better life –… PSB
  37. 37. Quantitative Data analysisScore MatrixLadderux.com Avg. ladders/resp= 7.8 Avg. elements/ladder=3.7
  38. 38. Quantitative Data analysisImplication MatrixLadderux.com
  39. 39. HVM – Amateur
  40. 40. HVM – Semi-pro
  41. 41. HVM – Professional player
  42. 42. Challenges
  43. 43. Duration and effort of data gathering andanalysis – Interviewing, transcribing, coding…Research aim – Can it successfully feed the design?Products studied – Not always existing, hence fewer ladders, no values?
  44. 44. Bieke Zaman Kristof Geurden master student, poker study KU Leuven, Belgium Vero Vanden Abeele Ladderux.comQuestions? Thanks!

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