Gijs Overgoor Headshot

Gijs Overgoor

Assistant Professor

Department of MIS, Marketing, and Analytics
Saunders College of Business

585-475-7114
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Gijs Overgoor

Assistant Professor

Department of MIS, Marketing, and Analytics
Saunders College of Business

Bio

Gijs Overgoor is an Assistant Professor of Marketing at the Department of MIS, Marketing, and Analytics in the Saunders College of Business at Rochester Institute of Technology.

He completed his Marketing PhD at the University of Amsterdam, under the supervision of Professor Willemijn van Dolen and Professor Bill Rand. He holds a Masters degree in Econometrics with a specialization in Big Data and Business Analytics. Gijs Overgoor spent most of his time during his PhD in the United States as a visiting scholar at Poole College of Management at NC State University.

In his research, Gijs Overgoor adopts a quantitative approach to marketing. He aims to solve marketing problems by applying techniques from AI and Econometrics. Gijs’ work has been published in International Journal of Research in Marketing and California Management Review and he was the finalist for the EMAC-AiMark Doctoral Dissertation Award 2022.

His research has received international press coverage from Business Insider and NPO. Gijs is also regularly invited to speak on podcasts such as Today in Digital Marketing or UpNext.

Select Scholarship

Published Conference Proceedings
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Xie, W., Overgoor, G., Lee, H., & Han, Z. (2023). Automated Detection of Skin Tone Diversity in Visual Marketing Communication. Hawaii International Conference on System Sciences.
Bollam, P., Mestri, R., Overgoor, G., & Rand, W. (2022). Text vs. Image: An application of unsupervised multi-modal machine learning to online reviews. Hawaii International Conference on System Sciences.
Overgoor, G., Mestri, R., & Rand, W. (2021). In the Eye of the Reviewer: An Application of Unsupervised Clustering to User Generated Imagery in Online Reviews. Hawaii International Conference on System Sciences.
Overgoor, G., Rand, W., van Dolen, W., & Scholte, H. (2020). The Champion of Images: Understanding the role of images in the decision-making process of online consumers. Hawaii International Conference on System Sciences.
Overgoor, G., Mazloom, M., Worring, M., Rietveld, R., & van Dolen, W. (2017). A Spatio-Temporal Category Representation for Brand Popularity Prediction. The Annual ACM International Conference on Multimedia Retrieval.
Invited Article/Publication
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences. . .
Overgoor, G., Rand, W., Van Dolen, W., & Mazloom, M. (2021). Simplicity is not Key: Understanding Firm-Generated Social Media Images and Consumer Liking. International Journal of Research in Marketing. . .
Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the Computers Take Over: Using AI to Solve Marketing Problems. California Management Review. . .

Currently Teaching

MKTG-365
3 Credits
Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI). Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize wasted online and offline marketing dollars. It also provides marketers with the information necessary to help support company investment in marketing strategy and tactics. This course provides the participant with the necessary knowledge and practical insights that will help a marketing manager get more out of available data and take strategic advantage of the analysis. This interactive, participatory course is designed to answer key questions: “What is marketing analytics, how can marketing analytics improve my marketing efforts and how can I integrate marketing analytics into my business?
MKTG-430
3 Credits
This course introduces the student to the general theories of Social Media Marketing and its relevance and importance as a Marketing tool. The student will learn how to create campaigns and the strategies and tactics in the most popular social media platforms, as generate reports and actions based on social media analytics.
MKTG-768
3 Credits
This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

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