Abstract
The user has requested enhancement of the downloaded file. 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.
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CITATION STYLE
Deligiannis, A., Argyriou, C., & Kourtesis, D. (2020). Building a Cloud-based Regression Model to Predict Click-through Rate in Business Messaging Campaigns. International Journal of Modeling and Optimization, 26–31. https://doi.org/10.7763/ijmo.2020.v10.742
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