與電商平台顧客流失與顧客貢獻度有關之主要變數分析與對策

Abstract

歸功於科技的進步降低了數位化轉型阻礙，企業能夠輕易地捕捉和儲存顧客活動資訊，建構龐大數據庫，包含人口變數、行為數據與心理特徵，並透過數據分析深入了解顧客需求與期望，以提供客製化體驗，進而成為競爭優勢。同時，顧客也擁有了更便捷的方式探索資訊，在不同的企業與商品之間進行選擇。 一直以來，顧客之於企業都是不可或缺的存在，且顧客一旦流失，便需要耗費大量的資源來獲取新顧客。所以，如何讓企業的既有顧客盡可能地留存下來，並且提高每一位顧客的貢獻程度，是讓企業得以長久經營之關鍵所在。 因此，本研究旨在探討與顧客流失以及顧客貢獻度攸關之變數，各自運用三種機器學習模型分析哪些企業或顧客行為會影響顧客流失與貢獻度。除此之外，也研究每一個攸關變數之背後可能原因，並針對該原因提出對應的建議對策，以協助企業進行後續改善措施的規劃。

With the advancement of technology, the barriers to digital transformation have been significantly eliminated. Consequently, corporations are able to easily collect and store vast amounts of customer data. By deeply analyzing this data, corporations will learn the demands and expectations of customers, enabling them to provide a personalized experience for each customer and gain competitive advantages in the market. Meanwhile, customers now have more convenient ways to access information, allowing them to switch between different companies and products easily. Customers have always been indispensable to businesses. Once an existing customer is lost, it takes a lot of resources to attract a new customer. Therefore, for companies aiming to operate sustainably, it is crucial to retain as many customers as possible and to enhance the contribution of each customer. Thus, in this study, three machine learning models are employed to analyze variables related to customer churn and customer contribution. In addition, possible explanations of each relevant variable and strategic recommendations are provided in order for enterprises to plan subsequent improvements.