作業價值管理 (AVM) 與顧客終身價值之結合 – 傳統方法與AI預測方法之比較

本研究以作業價值管理(Activity Value Management, AVM)產出之顧客資訊為基礎，運用傳統方法及 AI 預測方法計算出顧客終身價值(Customer Lifetime Value, CLTV)。瞭解顧客之未來價值將有助於企業提升顧客關係管理之能力。近年來因為市場需求變化快速，競爭越趨激烈，使得企業更為重視其顧客價值，除了掌握顧客之現有資訊，如何找出未來最具潛力之顧客實為值得探討之議題。因此本研究將說明 AI 於管理會計之應用，說明其與傳統方法之差異，並探討此資訊對於企業之長期效益。本研究採用個案研究方法，以國內之美妝保養品貿易公司為研究對象，設計 CLTV 之計算模組。此模組將說明如何運用 AVM 之顧客資訊，以傳統方法及 AI 預測方法計算顧客未來價值，並探討兩種方法之差異。最後分析個案公司顧客之結果資訊，並給予相關之顧客關係管理建議。期望能協助公司有效管理顧客，將資源投入於最具價值之顧客，長期提升企業之競爭優勢。

關鍵字

顧客終身價值；作業價值管理制度；顧客關係管理； AI預測；機器學習

並列摘要

Based on the customer information produced by Activity Value Management (AVM), this research uses traditional method and AI prediction method to calculate Customer Lifetime Value (CLTV). Knowing the future value of customers would help companies improve their customer relationship management capabilities. In recent years, due to the rapid changes in market demand and increasingly fierce competition, companies pay more attention to their customer value. In addition to grasping the existing information of customers, how to figure out the most potential customers in the future is indeed a topic worthy of discussion. Therefore, this research will explain the application of AI in management accounting, and explore the long-term benefits of this information to enterprises. This research adopts the case study method, takes domestic beauty and skincare products trading companies as the research object, and designs the calculation module of CLTV. This module will explain how to use AVM's customer information to calculate the future value of customers with traditional method and AI prediction method, and explore the differences between two methods. Finally, analyze the results of the company's customers, and give relevant customer relationship management suggestions. Hoping to help the company manage customers effectively, invest resources in the most valuable customers, and enhance the company's long-term competitive advantage in the long run.