| Summary: | 碩士 === 國立政治大學 === 資訊管理學系 === 106 === With outstanding prediction and segmentation performance, the RFM(Recency, Frequency, Monetary) model has been widely used in various business area. Based on the classic Pareto/NBD(Schmittlein et al., 1987) model, the Pareto/GGG model(Platzer & Reutterer, 2016) proposes a new concept ITTs(Inter Transaction Times) including a new parameter k which describes the regularity of purchase behaviors. With 120 thousands transaction record of a leading convenience store in Taiwan, this research analyzes the predictive performance of the Pareto/GGG model. Additionally, using parameter estimated from Markov chain Monte Carlo as input features, we conduct supervised learning on customer purchase frequencies to improve forecast accuracy of customer shopping behaviors.
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