RFM模型(Recency, Frequency, Monetary)已長期被廣泛使用於行銷領域，對消費者行為模式具有良好的預測能力和分群的能力，本研究主要探討以超商零售業銷售資料預測顧客行為的模型與方法，並以Recency、Frequency指標之經典模型Pareto/NBD(Schmittlein, Morrison, & Colombo, 1987)為基礎進行延伸，加入ITTs(Inter Transaction Times)指標之Pareto/GGG模型(Platzer & Reutterer, 2016)進行分析，以馬可夫鏈蒙地卡羅法進行參數模擬，對以ITTs估計出Regularity指標k進行分析，並使用機器學習之演算法以估計出之參數為特徵值，對會員到店天數之預測做監督式學習，優化預測結果，對行銷策略提供更好的方向。

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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