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<div><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 15px; font-weight: 400; color: rgb(36, 36, 36);"><img alt="Dissertation Defense Announcement at the Cullen College of Engineering" width="600" height="174" style="width: 600px; height: 174px; margin: 0px;" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/thesis1.png"></span></div>
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<p style="line-height: normal; margin: 0px; font-family: Helvetica;"><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 24px; color: rgb(200, 16, 46);"><b>Machine Learning and Unstructured Data Analytics for Digital Marketing</b></span></p>
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<div style="line-height: 20px; margin: 0px 0px 5px;"><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 18px; color: rgb(36, 36, 36);"><b>Wen Xie</b></span></div>
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<span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(36, 36, 36);">November 30, 2023; 9:30 AM - 11:30 AM (CST)<br>
Location: ECE large conference room D N328<br>
Zoom: </span><span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(200, 16, 46);"><a href="https://urldefense.com/v3/__https://www.google.com/url?q=https:**Auh-edu-cougarnet.zoom.us*j*91275322202*pwd*3DakhuU1hncVFsZWFYUVRWTEZXOTZZQT09&source=gmail-html&ust=1700068572695000&usg=AOvVaw0SMhW3TwJeSMso9MDcGGPr__;Ly8vLz8l!!LkSTlj0I!EBbWUfXqekWGa59jErWolJFlaPjRX3a8HDuOsXvU7bRd8LqfD0qauKGrGbRaqj90iikTwTFFKxfr1WotG494AaKN$" target="_blank" id="OWA6e1f3c9d-a1e1-bd5c-6ae7-f7f831f7794d" class="OWAAutoLink" rel="noreferrer noopener" data-auth="NotApplicable" data-linkindex="0" style="margin: 0px; color: rgb(200, 16, 46);" data-loopstyle="linkonly">https://uh-edu-cougarnet.zoom.us/j/99146061274</a></span></p>
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<span style="letter-spacing: normal; font-size: 14px; color: rgb(36, 36, 36);"><b>Committee Chair:</b></span><span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(36, 36, 36);"><br>
Zhu Han, Ph.D.</span></p>
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<span style="letter-spacing: normal; font-size: 14px; color: rgb(36, 36, 36);"><b>Committee Members:</b></span><span style="letter-spacing: normal; font-size: 14px; font-weight: 400; color: rgb(36, 36, 36);"><br>
Miao Pan, Ph.D. | Ming Chen, Ph.D. | Hien Nguyen, Ph.D. | Gijs Overgoor, Ph.D.</span></p>
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<span style="letter-spacing: normal;"><b>Abstract</b></span></p>
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<span style="letter-spacing: normal; font-weight: 400; color: rgb(36, 36, 36);">The internet has been a fertile ground for content creation, with people generating vast amounts of data daily. The proliferation of unstructured data can hold significant potential
for practitioners and managers, as it may reflect the public's interests and behaviors. Analyzing this data, however, presents challenges for traditional marketing and business researchers. The emergence and evolvement of machine learning (ML), data science
(DS), and sophisticated computational tools has given rise to novel approaches that can address these challenges. This dissertation investigates three prominent ML and DS applications within marketing research, demonstrating their capacity to effectively
process and interpret complex unstructured data—including images and videos—for business insights and consumer welfare.<br>
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The first study explores the integration of object detection algorithms with eye-tracking technology to dissect the consumer shopping experience online and on mobile platforms. This combination of techniques offers fresh perspectives on consumers' visual attention,
yielding practical insights for digital advertising strategies. The second study employs image segmentation alongside Bayesian analysis to explore skin-tone representation in brand visuals on social media. The proposed approach refines the strategic management
of marketing communications. In the final study, I apply rigorous data science methods to scrutinize user engagement with advertising on a short-video social platform. Informed by processing fluency theory, this analysis reveals nuanced patterns of consumer
behavior under various contexts, providing essential insights to enhance algorithms for ad ranking and recommendation systems. Collectively, these studies demonstrate the significant capacity of ML and engineering techniques to transform marketing research
by offering deeper, data-driven insights into unstructured data within the digital landscape.</span></p>
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<td><span style="letter-spacing: normal; font-family: Arial, sans-serif; font-size: 15px; font-weight: 400; color: rgb(36, 36, 36);"><img alt="Engineered For What's Next" width="600" height="82" style="width: 600px; height: 82px; margin: 0px;" src="https://www.egr.uh.edu/sites/www.egr.uh.edu/files/enews/2022/images/dissertation2.png"></span></td>
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