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Introduction 👋

I am currently working as an assistant professor of Data Analytics at the Marketing, Innovation, and Organization Department (Research Group Data Analytics) at Ghent University. Besides that, I am also Visiting Professor at the University of Namur. Prior to joining Ghent University, I worked as an assistant professor at the University of Edinburgh Business School and postdoctoral researcher at the KU Leuven. I studied Business Engineering and received a PhD in Business Economics at Ghent University in 2018. I have taught a wide range of data analytics courses ranging from basic statistics and database management to advanced predictive analytics and social media and web analytics. My research focuses on applications of descriptive, predictive and prescriptive analytics in social media, customer relationship management, hospitality and sports. My research has been published in several well-known international journals such as the European Journal of Operational Research, Omega, Decision Sciences, among others.

📫 Get in touch:

• E-mail: [email protected]
• LinkedIn: www.linkedin.com/in/matthias-bogaert-79a28148
• Twitter (all expressed opinions are my own): https://twitter.com/matthbogaert

Research 📄

Selected journal publications

• NEW: Janssens, B., Bogaert, M., Bagué, A., & Van den Poel, D. (2022). B2Boost: Instance-dependent profit-driven modelling of B2B churn. Annals of Operations Research, 1-27. (LINK)
• Janssens, B., Bogaert, M., & Maton, M. (2021). Predicting the next Pogacar: a data analytical approach to detect young professional cycling talents. Forthcoming. (LINK)
• Janssens, B., Bogaert, M., & Van den Poel, D. (2021). Evaluating the influence of Airbnb listings’ descriptions on demand. International Journal of Hospitality Management, 99, 103071. (LINK)
• Bogaert, M., Ballings, M., Van den Poel, D., & Oztekin, A. (2021). Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms. Decision Support Systems, 147, 113517. (LINK)
• PhD Dissertation

Selected conference proceedings

• Janssens, B., & Bogaert, M. (2021). Imputation of non-participated race results. 8th Workshop on Machine Learning and Data Mining for Sports Analytics, ECML/PKDD 2021 Workshop.(LINK)

For a full list of all my publications, you can view my CV here.

General overview

My area of research lies at the interface of IT (databases), algorithms (machine learning, AI, optimization, and econometrics models) and business applications. Hence, my research focusses on applying descriptive, predictive and prescriptive analytics to business-related problems. I believe that the main goal of data analytics should be to use data to gain insight and eventually increase business performance.

My methodological interests include:

• Big data
• Predictive modeling
• Ensemble learning
• Text mining
• Natural Language processing
• Recommender systems
• Deep learning
• Reinforcement learning.

My theoretical interests include, but are not limited to:

• Social media
• CRM (acquisition, churn and retention management, cross-selling)
• Hospitality (e.g., reviews)
• Finance
• Sports

Teaching 🎓

Current courses:

• Social Media and Web Analytics (Course Specifications)
• Advanced Predictive Analytics (Course Specifications)
• Predictive and Prescriptive Analytics (Course Specifications)
• Business Analytics and Big Data (Course Specifications)

Previous courses:

• Business Research Methods (at the University of Edinburgh)
• Predictive Analytics and Modeling of Data (at the University of Edinburgh)
• Data Mining (at the University of Edinburgh)
• Principles of Database Management (at the KU Leuven)

Software 💻

• AggregateR (CRAN link / Github link)
• DecorateR (CRAN link / Github link)

Research group 📈

I work at the Research Group Data Analytics at the Faculty of Economics and Business Administration of Ghent University.The research group Data Analytics engages in teaching and research on the use of data to improve and optimize business processes. This research is based on techniques such as statistics, datamining and machine learning (e.g. deep learning and reinforcement learning) and big data. The research group focuses on methodological as well as technical innovations and applications in a large number of application areas.

Below you can see a picture of the members in our research group. Currently, there are 3 professors: Prof. Dr. Dries Benoit (at the back, far left), Prof. Dr. Dirk Van den Poel (at the back, far right), and yours truly (at the back, second from left).