Machine learning algorithms are innovatively transforming the field of business, attracting deepened interest from researchers. In this project, we review marketing research and develop machine learning methods useful in building marketing strategies. We provide an overview of machine learning methods, and compare them with statistical methods that marketing researchers have traditionally used. Machine learning methods can be used to process large-scale data, providing flexible analysis models and yielding solid predictive performance. We present an integrative conceptual framework to extract insights from large-scale tracking, and network data to represent descriptive, causal, and inferential analyses. Customer purchase journeys with decision support capabilities can connect the machine learning methods to marketing theories and human insights. The specific applications of machine learning methods in many marketing segments and their contribution for marketing sectors have been validated. The proposed methods can be applied to analyze dynamic mechanism of marketing data with diverse customer features.
저자
Minho Sun [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
Seung Woo Kim [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
Jai Woo Lee [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
