Abstract
Over the last few decades, consumers’ preferences and lifestyles have changed significantly. In such market, the number of products and services aims to mass marketing have become less effective. Therefore, personalization tailored to individual characteristics, certain segments, and one-to-one marketing focusing on individuals are becoming important. Therefore, customer relationship management, retaining existing customers and costs for acquiring new customers are important for both of academic and business field. However, for that purpose, it is essential to grasp customer behavior in more detail and use it for analysis. Therefore, in this study, we estimate the potential clusters of customers and customers’ purchase behavior. Concretely, we use pLSA and XGBoost which become popular machine learning methodologies. In this study, we set the number of store visits per month for objective variable and show a prediction model of it. Then we compare the model that incorporates the result of predicting the probability of the latent cluster as an explanatory variable with the model that incorporates a general explanatory variable.
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Acknowledgement
This work was supported by JSPS KAKENHI Grant Number 19K01945.
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Saito, R., Otake, K., Namatame, T. (2021). Customer Visit Prediction Using Purchase Behavior and Tendency. In: Meiselwitz, G. (eds) Social Computing and Social Media: Applications in Marketing, Learning, and Health. HCII 2021. Lecture Notes in Computer Science(), vol 12775. Springer, Cham. https://doi.org/10.1007/978-3-030-77685-5_10
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DOI: https://doi.org/10.1007/978-3-030-77685-5_10
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