Player-Centric Game Design:

Adding UX Laddering to the Method Toolbox for Player

Experience MeasurementA poker case study

Bieke ZamanCUO, KULeuven – iMinds

http://www.linkedin.com/in/biekezamanPresentation at Measuring Behaviour Conference

2012

Measuring player experiences

Informing game designUser eXperience

Laddering

Overview methods

Physiological data

Initial Experience

Playtest

Critical Facet Playtest

Playtestinge.g. RITE

metrics

Deep Gameplay

Quantitat

ive

Physiological data

Initial Experience

Playtest

Critical Facet Playtest

Playtestinge.g. RITE

Benchmark

Deep Gameplay

Qualitative

Physiological data

LADDERING

Critical Facet Playtest

Playtestinge.g. RITE

Benchmark

Mixed-method

When to use which method?

PIII-approach

When to use Laddering?

Marketing final product

http://www.flickr.com/photos/pensiero/100754831/UX

Laddering

Origins

Means-End Chain Theory

How do specific features of a product relate to personal values?

People choose a product because it contains attributes

that are instrumental to achieving the desired consequences

and fulfilling values

People choose a product because it contains

attributes (the means)

that are instrumental to achieving the desired consequences and fulfilling values (the ends)

Means-End Chain Theory inspiredGame eXperience Model

Insight into1. Player2. Game system3. Game context

Laddering?

One particular method for interviewing and data treatment within Means-End Theory

Origins: Popular in consumer research

Current use: broader research domainsrelevance for user profiling, revealing personal benefits of product use, supporting the redesign process, supporting marketing campaigns, product benchmarking, ...

What is UX Laddering?

UX Laddering refers to BOTH

the Lenient Laddering interview AND the data analysis approach

Example

Real participants!

Product Choice Situation

1Product Interaction

2 Preference Ranking

3 Lenient Laddering

4 Data analysisQualitative & Quantitative

5 OutputHierarchical Value Map

keyboard Cuddly toy interaction game

Arrow keys

Game speed

Real moves

Real example

What are the motivations to play online poker (i.c. Poker Stars & FB Zynga)?

What are the differences between amateur, semi-pro and a professional player, if there are any?

How does the design of the online poker website influence the game play experiences and website preferences?

?

n=18 6 amateur 6 semi-pro 6 pro

18-28 year olds17 men, 1 womanBelgium, higher education

1

Preference Ranking

I: “You’ve been playing both online poker games. If you had the choice, which one would you prefer?”

R: “Pokerstars”

Interview 6 – semi-professional2

Which attributes top of mind? Direct elicitation

I: “You usually play poker on Facebook, euhm, now that I asked you to play poker on PokerStars, which one would you prefer?”

R: “Yes, now I actually prefer PokerStars because I find it clearer and more user-friendly than Facebook poker.”

Interview 15 - amateur

Lenient LadderingProbing why these attributes are important

• 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.” 3

Interview duration: 6 minutes – 47 minutes

Qualitative Data analysisTranscribing the interviewsCoding & categorizingSecond coder ICR (n=6/ntotal=18, k=.934) 4

Concrete Attributes:–Extra features (time bank, search

function, multi table, filters, hand history…)–Stand alone software–Real money–…

CA

Abstract Attributes: –User friendly–Serious game play–Compatibility–Large user base–Legal–…

AA

Functional Consequences:–Being more focused–Play quicker–Playing more hands an hour–Profit maximalization–Earn more money–….

FC

Psycho-Social Beliefs:–Challenge–Trust–Playing amongst friends–Fun–Better life–…

PSB

Quantitative Data analysisScore Matrix

Ladderux.com Avg. ladders/resp= 7.8Avg. elements/ladder=3.7

Quantitative Data analysisImplication Matrix

Ladderux.com

5HVM – Amateur

HVM – Semi-pro

HVM – Professional player

Challenges

Duration and effort of data gathering and analysis– Interviewing, transcribing, coding…

Research aim –Can it successfully feed the design?

Products studied–Not always existing, hence fewer

ladders, no values?

Questions?

Bieke Zaman

Kristof Geurden master student, poker study

KU Leuven, Belgium

Vero Vanden AbeeleLadderux.com

Thanks!