| 書目名稱 | Temporal Modelling of Customer Behaviour | | 編輯 | Ling Luo | | 視頻video | http://file.papertrans.cn/903/902967/902967.mp4 | | 概述 | Nominated as an outstanding Ph.D. thesis by the University of Sydney, Australia.Presents innovative machine learning techniques for modelling dynamic customer purchasing behaviour.Reviews cutting-edge | | 叢書名稱 | Springer Theses | | 圖書封面 |  | | 描述 | .This book describes advanced machine learning models – such as temporal collaborative filtering, stochastic models and Bayesian nonparametrics – for analysing customer behaviour. It shows how they are used to track changes in customer behaviour, monitor the evolution of customer groups, and detect various factors, such as seasonal effects and preference drifts, that may influence customers’ purchasing behaviour. In addition, the book presents four case studies conducted with data from a supermarket health program in which the customers were segmented and the impact of promotional activities on different segments was evaluated. The outcomes confirm that the models developed here can be used to effectively analyse dynamic behaviour and increase customer engagement. Importantly, the methods introduced here can also be used to analyse other types of behavioural data such as activities on social networks, and educational systems. . | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | Customer Behaviour Analysis; Tracking Customer Behaviour; Temporal Aspects of Customer Behaviour; Custo | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-18289-2 | | isbn_ebook | 978-3-030-18289-2Series ISSN 2190-5053 Series E-ISSN 2190-5061 | | issn_series | 2190-5053 | | copyright | Springer Nature Switzerland AG 2020 |
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