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• W3093479215 abstract "This study conducts a dynamic rolling comparison between the Pareto/NBD model (parametric model) and machine learning algorithms (observation-driven models) in customer base analysis, which the literature has not comprehensively investigated before. The aim is to find the comparative edge of these two approaches under customer base analysis and to define the implementation timing of these two paradigms. This research utilizes Pareto/NBD (Abe) as representative of Buy-Till-You-Die (BTYD) models in order to compete with machine learning algorithms and presents the following results. (1) The parametric model wins in transaction frequency prediction, whereas it loses in inactivity prediction. (2) The BTYD model outperforms machine learning in inactivity prediction when the customer base is active, performs better in an inactive customer base when competing with Poisson regression, and wins in a short-term active customer base when competing with a neural network algorithm in transaction frequency prediction. (3) The parametric model benefits more from a short calibration length and a long holdout/target period, which exhibit uncertainty. (4) The covariate effect helps Pareto/NBD (Abe) gain a better predictive result. These findings assist in defining the comparative edge and implementation timing of these two approaches and are useful for modeling and business decision making." @default.
• W3093479215 created "2020-10-29" @default.
• W3093479215 creator A5037902795 @default.
• W3093479215 date "2020-10-19" @default.
• W3093479215 modified "2023-09-26" @default.
• W3093479215 title "COMPARATIVE MODELS IN CUSTOMER BASE ANALYSIS: PARAMETRIC MODEL AND OBSERVATION-DRIVEN MODEL" @default.
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• W3093479215 doi "https://doi.org/10.3846/jbem.2020.13194" @default.
• W3093479215 hasPublicationYear "2020" @default.
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