Personalizing the theme park:Psychometric profiling and physiological monitoring

Stefan Rennick EgglestoneUniversity of Nottingham

1. The theme park as a target for UMAP

2. Proof-of-concept profiling study in a theme park

3. Questions for future research

Overview

• More than 100 million visits per year

• Little published research

• Few operators (but all large scale)

• Substantial investment in innovation

Why the theme park?

Challenges

Challenge one: The theme park recommender system

Challenge two: Personalised rides

Challenges

A common approach involves building a user profile!

• Information collected before the visit– Psychometric personality profiling

• Information collected during the visit– Physiological monitoring

Research into profile design

Personality profiling overview

Sensation Seeking Scale40 questions

Thrill seeking: 8/10Experience seeking: 7/10Disinhibition: 8/10Boredom susceptibility: 7/10

Big 538 questions

Openness to experience: 9/10Conscientiousness: 6/10

Extraversion: 9/10Agreeableness: 5/10Neuroticism: 5/10

Questionnaire on entry or during on-line ticket purchase?

• Heart-rate

• Skin conductance

• Breathing rate

Physiological monitoring

Affective computing: Analysis of physiological data reveals emotional response

What does physiological monitoring reveal about ride experience?

Research questions

Can personality profiles predict experiences on rides?

1. Negotiate access to local theme park

2. Choose single ride

3. Recruit cohort of participants

4. Profile: Personality tests on entry to theme park

5. Profile: Heart-rate response recorded on single ride

6. Profile: Participant quantifies their experience on the ride

7. Analysis: Relationships between profile and experience

Approach

Oblivion @ Alton Towers

Arousal: How much do you feel alert, with your body pumped up and buzzing, ready for action? (1,9)

Valence:How positive or negative do you feel? (-4,+4)

The circumplex model

Self-report data

Arousal Valence

During drop

What does heart-rate reveal?

0 50 100 150 200

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Median heart-rate during the before phase

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• Correlations between personality dimensions and self-reports at various places on the ride (r~0.3, all at p=0.001)

– Thrill seeking– Extraversion– Openness to Experience

• Two different ways of using these dimensions to cluster participants into groups who report similar experience

– Thrill seeking– Extraversion and Openness to Experience

Results – personality profiling

• Potential use of heart-rate as a measure of whether visitors are excited or relaxed

• Evidence exists for the efficacy of personality profiling in predicting experience

Conclusions profile design

• Extend analysis to multiple rides

• Consider other attractions in theme park

• How to make recommendations for groups?

• Different physiological measures

• Different psychometric measures

• Considering patterns of queuing?

Further work – theme park

• When is personality modelling applicable in profiling?

• Trade-off between accuracy of model and time taken to fill out questionnaire

• High-value applications?– E.g internet dating

• Issues of multiple identity / personality

• Cross-cultural issues

Personality profiling