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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140982


    Title: 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
    The Integration of Activity Value Management and Customer Lifetime Value – The Comparison between Traditional Method and AI Prediction Method
    Authors: 李佳璇
    Lee, Chia-Hsuan
    Contributors: 吳安妮
    Wu, Anne
    李佳璇
    Lee, Chia-Hsuan
    Keywords: 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
    Customer lifetime value
    Activity value management
    Customer relationship management
    AI prediction
    Machine learning
    Date: 2022
    Issue Date: 2022-08-01 17:06:37 (UTC+8)
    Abstract: 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。
    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.
    Reference: 中文部分：

    尤啟鴻，2013，B2B 策略性顧客資本之管理及評價-以食品業為例，國立政治大學會計學系未出版之碩士論文。
    吳安妮，2007，確立管理方向設計專屬 ABC－作業基礎成本制之發展與整合，會計研究月刊，第 263 期 ：60-74。
    吳安妮，2012，策略性智慧資本評估管理模組介紹及個案解析，會計研究月刊，第 314 期 ：100-113。
    吳安妮，2018，策略形成及執行：以BSC為核心，為企業創造「利」與「力」，台北，臉譜出版社。
    吳安妮，2019，企業策略的終極答案：用「作業價值管理 AVM」破除成本迷 思，掌握正確因果資訊，做對決策賺到「管理財」，台北，臉譜出版社。
    李昀，2020，作業價值管理(AVM)對通路管理之影響：以 Y 進口保養品代理商為例，國立政治大學會計學系未出版之碩士論文。
    周世玉、蕭登泰，2005，顧客交易資料庫之探勘－以網路電話公司之非契約型顧客為例，資訊管理學報，第 2 期 ：183-199。
    林宜靜，2016，探討 AVM 與顧客關係管理結合-巨量資料分析，國立政治大學會計學系未出版之碩士論文。
    徐佳炾，2004，用 ABM 正確算出成本以 CVM 精準衡量獲利－運用顧客價值管理衡量與管理顧客獲利，會計研究月刊，第 225 期 ：86-94。

    英文部分：

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    Chen, P. P., Guitart, A., del Río, A. F. and Periánez, A. (2018). Customer lifetime value in video games using deep learning and parametric models. Paper presented at the 2018 IEEE international conference on big data (big data).
    Cheng, C.-J., Chiu, S., Cheng, C.-B. and Wu, J.-Y. 2012. Customer lifetime value prediction by a Markov chain based data mining model: Application to an auto repair and maintenance company in Taiwan. Scientia Iranica 19 (3):849-855.
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    Hwang, H., Jung, T. and Suh, E. 2004. An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications 26 (2):181-188.
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    Description: 碩士
    國立政治大學
    會計學系
    109353017
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109353017
    Data Type: thesis
    DOI: 10.6814/NCCU202200726
    Appears in Collections:[Department of Accounting] Theses

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