Machine Learning and Unstructured Data Analytics for Digital Marketing
| dc.contributor.advisor | Han, Zhu | |
| dc.contributor.committeeMember | Pan, Miao | |
| dc.contributor.committeeMember | Fan, Lei | |
| dc.contributor.committeeMember | Overgoor, Gijs | |
| dc.contributor.committeeMember | Chen, Ming | |
| dc.contributor.committeeMember | Nguyen, Hien Van | |
| dc.creator | Xie, Wen | |
| dc.date.accessioned | 2024-01-27T17:02:02Z | |
| dc.date.created | December 2023 | |
| dc.date.issued | 2023-12 | |
| dc.date.updated | 2024-01-27T17:02:03Z | |
| dc.description.abstract | 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. 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. | |
| dc.description.department | Electrical and Computer Engineering, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Xie, Wen, Mi Hyun Lee, Ming Chen, and Zhu Han. "Understanding Consumers’ Visual Attention in Mobile Advertisements: An Ambulatory Eye-Tracking Study with Machine Learning Techniques." Journal of Advertising (2023): 1-19. | |
| dc.identifier.uri | https://hdl.handle.net/10657/16225 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Unstructured Data Analytics, Machine Learning, Marketing, Brand Management, Social Media, Advertising, Diversity | |
| dc.title | Machine Learning and Unstructured Data Analytics for Digital Marketing | |
| dc.type.dcmi | text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2025-12-01 | |
| local.embargo.terms | 2025-12-01 | |
| thesis.degree.college | Cullen College of Engineering | |
| thesis.degree.department | Electrical and Computer Engineering, Department of | |
| thesis.degree.discipline | Electrical Engineering | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |