Computational visualization of customer mood using affective space model approach
Customers' mood information is acquired to facilitate marketers' understanding in order to tailor the marketing strategies for positive outcomes optimization. Mood can be reasonably hypothesized as one of the factor that influences customers' decision in buying the products or servi...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IOS Press
2017
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/63005/13/63005-Computational%20visualization%20of%20customer%20mood%20using%20afective%20space%20model%20approach.pdf http://irep.iium.edu.my/63005/2/63005%20Computational%20Visualization%20of%20Customer%20Mood%20Using%20Affective%20%20SCOPUS.pdf http://irep.iium.edu.my/63005/ http://ebooks.iospress.nl/volumearticle/47573 |
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| Summary: | Customers' mood information is acquired to facilitate marketers' understanding in order to tailor the marketing
strategies for positive outcomes optimization. Mood can be reasonably hypothesized as one of the factor that
influences customers' decision in buying the products or services offered. There have been many researchers reporting the correlation between moods and buying decision. However, to date, there is no such method that can exactlyquantify the customer's mood. Typically, a questionnaire is given to the participant to gauge their mood on the
focused product or services. The drawback from such approach is that participants can fake, exaggerate or suppress
their mood resulting to questionable inference. Hence, a new method of data acquisition is needed to be able to
visualize the dynamics of the customers' mood for more accurate analysis. In this paper, the customer brain signal is
captured using electroencephalogram (EEG) to track and record brain wave patterns in regard to their emotional
states. The sequence of emotion is then used to identify their mood. A computational visualization technique is
adopted to facilitate understanding of one minute emotion transition that assembling mood. The experimental
results show that such approach is feasible and can be extended to study mood in a more comprehensive manner. It
is envisaged that this work will be the catalyst for large mood data analysis tool that can help researchers in the near
future to look at mood and buying decision for the improvement of comprehensive customer understanding in a more accurate manner. |
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