本研究探討機器學習應用於零售通路顧客之回購機率預測，利用RFM(最近消費日
期、總消費次數、總消費金額，Recency, Frequency, Monetary)等特徵建立顧客回購
預測模型，建立研究理論基礎以及研究方法依據；再比較兩間不同商業模式零售商模
型預測之成效。其中一間零售商以實體通路為主，另一間零售商以網路平台為主，我
們比較各特徵與不同演算法對於模型在兩家零售商預測表現的影響，並且透過檢驗顧
客的消費行為模式來探討顧客行為對預測效果以及公司營業收入帶來的影響，以及最
後歸納機器學習在何種商業模式下，進行精準行銷較能獲得良好的預測結果。

This study investigates the application of machine learning to predict the repurchase rate of retail channel customers by examining the literature related to RFM (Recency, Frequency, Monetary) and precision marketing to establish the theoretical foundation and methodological basis of the study. Then comparing two retailers with very different business models using machine learning methods. One retailer focuses on the physical channel and the other on the online platform, and the differences in the results of precision marketing are investigated. Finally, we conclude which machine learning algorithm is suitable for which business model to conduct precision marketing and obtain good prediction results.

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