Abstract—The goal of the research presented here is to describe an innovative approach to predicting the impact of a business messaging campaign, by estimating the percentage of message recipients who will engage with a message. The motivation is to facilitate business marketers to address the problem of estimating the return on investment coming from a potential messaging campaign. The presented solution relies on the processing of large scale business data, taking into account state-of-the-art predictive algorithms, GDPR compliance requirements, and the challenge of increased data security and availability. In this paper we discuss the design of the core functional components of a system that could make this possible, which encompasses predictive analytics, data mining and machine learning technologies in a cloud computing environment.
Index Terms—Marketing automation, predictive analytics, cloud computing, business messaging, data privacy, GDPR, machine learning, XGBoost, regression, IBM Watson Studio, SPSS Modeler Flow.
The authors are with the Research & Development Department, Apifon, Greece (e-mail: a.deligiannis@apifon.com, c.argyriou@apifon.com, dk@apifon.com).
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Cite: Alexandros Deligiannis, Charalampos Argyriou, and Dimitrios Kourtesis, "Building a Cloud-based Regression Model to Predict Click-through Rate in Business Messaging Campaigns," International Journal of Modeling and Optimization vol. 10, no. 1, pp. 26-31, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0).
