Statistical Analysis of Online Eye and Face-Tracking Applications in Marketing
| dc.contributor.advisor | Wedel, Michel | en_US |
| dc.contributor.author | Liu, Xuan | en_US |
| dc.contributor.department | Mathematics | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2015-06-26T05:37:38Z | |
| dc.date.available | 2015-06-26T05:37:38Z | |
| dc.date.issued | 2015 | en_US |
| dc.description.abstract | Eye-tracking and face-tracking technology have been widely adopted to study viewers' attention and emotional response. In the dissertation, we apply these two technologies to investigate effective online contents that are designed to attract and direct attention and engage viewers emotional responses. In the first part of the dissertation, we conduct a series of experiments that use eye-tracking technology to explore how online models' facial cues affect users' attention on static e-commerce websites. The joint effects of two facial cues, gaze direction and facial expression on attention, are estimated by Bayesian ANOVA, allowing various distributional assumptions. We also consider the similarities and differences in the effects of facial cues among American and Chinese consumers. This study offers insights on how to attract and retain customers' attentions for advertisers that use static advertisement on various websites or ad networks. In the second part of the dissertation, we conduct a face-tracking study where we investigate the relation between experiment participants' emotional responseswhile watching comedy movie trailers and their watching intentions to the actual movies. Viewers' facial expressions are collected in real-time and converted to emo- tional responses with algorithms based on facial coding system. To analyze the data, we propose to use a joint modeling method that link viewers' longitudinal emotion measurements and their watching intentions. This research provides recommenda- tions to filmmakers on how to improve the effectiveness of movie trailers, and how to boost audiences' desire to watch the movies. | en_US |
| dc.identifier | https://doi.org/10.13016/M2X05X | |
| dc.identifier.uri | http://hdl.handle.net/1903/16620 | |
| dc.language.iso | en | en_US |
| dc.subject.pqcontrolled | Statistics | en_US |
| dc.subject.pqcontrolled | Marketing | en_US |
| dc.subject.pquncontrolled | Bayesian ANOVA | en_US |
| dc.subject.pquncontrolled | Bayesian joint model | en_US |
| dc.subject.pquncontrolled | eye-tracking | en_US |
| dc.subject.pquncontrolled | face-tracking | en_US |
| dc.title | Statistical Analysis of Online Eye and Face-Tracking Applications in Marketing | en_US |
| dc.type | Dissertation | en_US |
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