隨著電子商務近年的蓬勃發展，許多形形色色的創新服務應運而生。本研究針對具指標性的線上餐飲訂位平台---EZTable(簡單桌)進行分析，對於EZTable而言，前來訂位的客戶是十分重要的，因為高客戶忠誠度能為其帶來更多市佔率。因此，本研究將會針對以下兩點問題進行研究分析：(1)為了能夠準確預測顧客回訪率，那些因素與客戶回訪率是高關聯性的？如訂位者、餐廳、地理位置等資訊。(2)應如何建構並驗證高準確度的預測模型？根據以上問題，本研究使用廣義加成模型(GAM)、決策樹(decision tree)、套袋抽樣(bagging)、隨機森林(random forest)等模型訓練方法，搭配EZTable大量的訂位資料，建構不同的預測模型來預測客戶回訪率。以EZTable的資料而言，本研究發現比起訓練模型的方法，模型變數的選擇更明顯影響了預測表現，而關於訂位本身的資訊，如訂位狀態，能夠大幅度提升預測準確度。這些發現能夠幫助如EZTable等服務提供者，了解哪些變數對於顧客忠誠度是相當重要的；再者，公司能夠透過這些資訊，為有較高回訪率的會員量身打造適合的促銷活動。透過將行銷資源集中在特定的客戶上，這些提供服務公司的行銷成本也能夠因此減少。 / Electronic commerce still grows rapidly in the recent years and innovative services are introduced in the recent years accordingly. This research analyzes a representative online reservation service provider – EZTable. Loyalty of customer is crucial because EZTable can obtain more market share with high customer loyalty. Therefore, we expect to answer the following research questions: (1) What are relevant and useful consumer, restaurant, and demographic factors to predict customer retention? (2) How do we develop and determine effective predictive models? We apply generalized additive model, decision tree, bagging, and random forest, to a large volume of operational data from EZTable and develop a set of predictive models. Instead of model complexity, identifying critical variables from the research context determines predictive performance. Transaction-dependent factors could substantially enhance predictive performance. Our findings enable companies like EZTable to understand what predictors are critical to customers’ loyalty. Further, the company can design effective promotions for customers with higher return probability. Our modeling effort could help those service providers reduce advertising cost by allocating limited resources to customers with higher probability to place orders again.