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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/136102


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/136102


    Title: Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning
    Authors: 莊皓鈞
    Chuang, Howard Hao-Chun
    Chou, Ping
    Chou, Yen-Chun
    Chou, Ting-Pengx
    Contributors: 資管系
    Keywords: Virtual governance;Relational governance;Institutional distance;Environmental dynamism;Global supply chain;Emerging market
    Date: 2021-04
    Issue Date: 2021-07-21
    Abstract: Predicting customer repurchase propensity/frequency has received broad research interests from marketing, operations research, statistics, and computer science. In the field of marketing, Buy till You Die (BTYD) models are perhaps the most representative techniques for customer repurchase prediction. Those probabilistic models are parsimonious and typically involve only recency and frequency of customer activities. Contrary to BTYD models, a distinctly different class of predictive models for customer repurchase is machine learning. This class of models include a wide variety of computational and statistical learning algorithms. Unlike BTYD models built on low-dimensional inputs and behavioral assumptions, machine learning is more data-driven and excels at fitting predictive models to a large array of features from customer transactions. Using a large online retailing data, we empirically assess the prediction performance of BTYD modeling and machine learning. More importantly, we investigate how the two approaches can complement each other for repurchase prediction. We use the BG/BB model given the discrete and non-contractual problem setting and incorporate BG/BB estimates into high-dimensional Lasso regression. In addition to showing significant improvement over BG/BB and Lasso without BG/BB, the integrated Lasso-BG/BB provides interpretability and identifies BG/BB predictions as the most influential feature among ∼100 predictors. The lately developed CART-artificial neural networks exhibit similar patterns. Robustness checks further show the proposed Lasso-BG/BB outperforms two sophisticated recurrent neural networks, validating the complementarity of machine learning and BTYD modeling. We conclude by articulating how our interdisciplinary integration of the two modeling paradigms contributes to the theory and practice of predictive analytics.
    Relation: European Journal of Operational Research
    Data Type: article
    DOI 連結: https://doi.org/10.1016/j.ejor.2021.04.021
    DOI: 10.1016/j.ejor.2021.04.021
    Appears in Collections:[資訊管理學系] 期刊論文

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