Dr H.Y. (Hang-Yee) Chan

Dummy
King's Business School
King's College London
Former ERIM PhD Candidate
Field: Marketing
Affiliated since 2014

PhD Track Decoding the consumer’s brain: Neural representations of consumer experience

Understanding consumer experience – what consumers think about brands, how they feel about services, whether they like certain products – is crucial to marketing practitioners. ‘Neuromarketing’, as the application of neuroscience in marketing research is called, has generated excitement with the promise of understanding consumers’ minds by probing their brains directly. Recent advances in neuroimaging analysis leverage machine learning and pattern classification techniques to uncover patterns from neuroimaging data that can be associated with thoughts and feelings. In this dissertation, I measure brain responses of consumers by functional magnetic resonance imaging (fMRI) in order to ‘decode’ their mind. In three different studies, I have demonstrated how different aspects of consumer experience can be studied with fMRI recordings. First, I study how consumers think about brand image by comparing their brain responses during passive viewing of visual templates (photos depicting various social scenarios) to those during active visualizing of a brand’s image. Second, I use brain responses during viewing of affective pictures to decode emotional responses during watching of movie-trailers. Lastly, I examine whether marketing videos that evoke similar brain responses among consumers turn out performing better in the market. These three studies show how analysis of brain responses uncovers nonverbal and ephemeral experiences of consumers. While mindful of the technical and ethical challenges, this dissertation hopefully lays the groundwork for the expansion of consumer neuroscience from resolving ‘how consumers use their mind’ to focusing on ‘what consumers have in mind’.

Keywords
Consumer experience, branding, emotion, consumer neuroscience, neuromarketing, neuroimaging, pattern analysis, machine learning
Time frame
2014 - 2020
2020
March
13

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