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題名 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作者 李佳璇
Lee, Chia-Hsuan貢獻者 吳安妮
Wu, Anne
李佳璇
Lee, Chia-Hsuan關鍵詞 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日期 2022 上傳時間 1-Aug-2022 17:06:37 (UTC+8) 摘要 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.參考文獻 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：Berger, P. D. and Nasr, N. I. 1998. Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing 12 (1):17-30.Blattberg, R. C., Glazer, R. and Little, J. D. 1994. Marketing information revolution, Harvard Business School Press.Blattberg, R. C., Glazer, R. and Little, J. D. 1994. Marketing information revolution, Harvard Business School Press.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.Cooper, R. and Kaplan, R. S. 1988. Measure costs right: make the right decisions. Harvard business review 66 (5):96-103.Di Benedetto, C. A. and Kim, K. H. 2016. Customer equity and value management of global brands: Bridging theory and practice from financial and marketing perspectives: Introduction to a Journal of Business Research Special Section. Journal of Business Research 69 (9):3721-3724.Gupta, S. and Lehmann, D. R. 2003. Customers as assets. Journal of Interactive Marketing 17 (1):9-24.Gupta, S., Lehmann, D. R. and Stuart, J. A. 2004. Valuing customers. Journal of marketing research 41 (1):7-18.Helgesen, Ø. 2007. Customer accounting and customer profitability analysis for the order handling industry—A managerial accounting approach. Industrial Marketing Management 36 (6):757-769.Hwang, H. 2015. A dynamic model for valuing customers: a case study. Adv. Sci. Technol. Lett 120:56-61.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.LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. nature 521 (7553):436-444.Mittal, V., Sarkees, M. and Murshed, F. 2008. The right way to manage unprofitable customers. Harvard business review 86 (4)Monica, T. 2012. Customer Lifetime Value (CLV) Estimation–Case Study. Romanian name of international volume: Progrese în teoria deciziilor economice în condiţii de risc şi incertitudine. Volume number 17:75-81.Nenonen, S. and Storbacka, K. 2016. Driving shareholder value with customer asset management: Moving beyond customer lifetime value. Industrial Marketing Management 52:140-150.Niraj, R., Gupta, M. and Narasimhan, C. 2001. Customer profitability in a supply chain. Journal of marketing 65 (3):1-16.Payne, A. and Frow, P. 2006. Customer relationship management: from strategy to implementation. Journal of marketing management 22 (1-2):135-168.Peppers, D. and Rogers, M. 1993. The one to one future: Building relationships one customer at a time, Currency Doubleday New York.Peppers, D. and Rogers, M. 1993. The one to one future: Building relationships one customer at a time, Currency Doubleday New York.Ramos, P., Santos, N. and Rebelo, R. 2015. Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and computer-integrated manufacturing 34:151-163.Reinartz, W., Krafft, M. and Hoyer, W. D. 2004. The customer relationship management process: Its measurement and impact on performance. Journal of marketing research 41 (3):293-305.Rosset, S., Neumann, E., Eick, U. and Vatnik, N. 2003. Customer lifetime value models for decision support. Data mining and knowledge discovery 7 (3):321-339.Suhermi, N., Prastyo, D. D. and Ali, B. 2018. Roll motion prediction using a hybrid deep learning and ARIMA model. Procedia computer science 144:251-258.Sun, Y., Chen, Y., Wang, X. and Tang, X. 2014. Deep learning face representation by joint identification-verification. Advances in neural information processing systems 27Thakkar, A. and Chaudhari, K. 2021. A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert systems with applications 177:114800.Van Calster, T., Baesens, B. and Lemahieu, W. 2017. ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing 60:775-785.Verhoef, P. C. and Lemon, K. N. 2013. Successful customer value management: Key lessons and emerging trends. European Management Journal 31 (1):1-15.Yong, B. X., Abdul Rahim, M. R. and Abdullah, A. S. 2017. A stock market trading system using deep neural network. Paper presented at the Asian simulation conference. 描述 碩士
國立政治大學
會計學系
109353017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109353017 資料類型 thesis dc.contributor.advisor 吳安妮 zh_TW dc.contributor.advisor Wu, Anne en_US dc.contributor.author (Authors) 李佳璇 zh_TW dc.contributor.author (Authors) Lee, Chia-Hsuan en_US dc.creator (作者) 李佳璇 zh_TW dc.creator (作者) Lee, Chia-Hsuan en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:06:37 (UTC+8) - dc.date.available 1-Aug-2022 17:06:37 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:06:37 (UTC+8) - dc.identifier (Other Identifiers) G0109353017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140982 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 109353017 zh_TW dc.description.abstract (摘要) 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。 zh_TW dc.description.abstract (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. en_US dc.description.tableofcontents 第壹章 緒論 1第一節 研究動機及目的 1第二節 研究問題 3第三節 論文架構 5第貳章 文獻探討 7第一節 顧客關係管理之相關文獻 7第二節 作業價值管理與 CLTV 結合之相關文獻 15第三節 CLTV 之計算方法相關文獻 24第四節 CLTV 之 AI 預測方法相關文獻 35第五節 研究延伸 46第參章 研究方法 48第一節 個案研究法 48第二節 研究流程 49第肆章 個案公司介紹 50第一節 產業介紹 50第二節 個案公司介紹 52第伍章 作業價值管理與 CLTV 之結合 54第一節 個案公司之 AVM 與顧客關係管理現況 54第二節 顧客相關資料前處理及說明 61第三節 CLTV 之傳統方法與 AI 預測方法 64第四節 傳統方法與 AI 預測方法之比較及長期效益 84第陸章 結論與建議 89第一節 研究結論 89第二節 研究限制 93第三節 研究建議 94參考文獻 96 zh_TW dc.format.extent 2652072 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109353017 en_US dc.subject (關鍵詞) 顧客終身價值 zh_TW dc.subject (關鍵詞) 作業價值管理制度 zh_TW dc.subject (關鍵詞) 顧客關係管理 zh_TW dc.subject (關鍵詞) AI預測 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) Customer lifetime value en_US dc.subject (關鍵詞) Activity value management en_US dc.subject (關鍵詞) Customer relationship management en_US dc.subject (關鍵詞) AI prediction en_US dc.subject (關鍵詞) Machine learning en_US dc.title (題名) 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 zh_TW dc.title (題名) The Integration of Activity Value Management and Customer Lifetime Value – The Comparison between Traditional Method and AI Prediction Method en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 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：Berger, P. D. and Nasr, N. I. 1998. Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing 12 (1):17-30.Blattberg, R. C., Glazer, R. and Little, J. D. 1994. Marketing information revolution, Harvard Business School Press.Blattberg, R. C., Glazer, R. and Little, J. D. 1994. Marketing information revolution, Harvard Business School Press.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.Cooper, R. and Kaplan, R. S. 1988. Measure costs right: make the right decisions. Harvard business review 66 (5):96-103.Di Benedetto, C. A. and Kim, K. H. 2016. Customer equity and value management of global brands: Bridging theory and practice from financial and marketing perspectives: Introduction to a Journal of Business Research Special Section. Journal of Business Research 69 (9):3721-3724.Gupta, S. and Lehmann, D. R. 2003. Customers as assets. Journal of Interactive Marketing 17 (1):9-24.Gupta, S., Lehmann, D. R. and Stuart, J. A. 2004. Valuing customers. Journal of marketing research 41 (1):7-18.Helgesen, Ø. 2007. Customer accounting and customer profitability analysis for the order handling industry—A managerial accounting approach. Industrial Marketing Management 36 (6):757-769.Hwang, H. 2015. A dynamic model for valuing customers: a case study. Adv. Sci. Technol. Lett 120:56-61.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.LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning. nature 521 (7553):436-444.Mittal, V., Sarkees, M. and Murshed, F. 2008. The right way to manage unprofitable customers. Harvard business review 86 (4)Monica, T. 2012. Customer Lifetime Value (CLV) Estimation–Case Study. Romanian name of international volume: Progrese în teoria deciziilor economice în condiţii de risc şi incertitudine. Volume number 17:75-81.Nenonen, S. and Storbacka, K. 2016. Driving shareholder value with customer asset management: Moving beyond customer lifetime value. Industrial Marketing Management 52:140-150.Niraj, R., Gupta, M. and Narasimhan, C. 2001. Customer profitability in a supply chain. Journal of marketing 65 (3):1-16.Payne, A. and Frow, P. 2006. Customer relationship management: from strategy to implementation. Journal of marketing management 22 (1-2):135-168.Peppers, D. and Rogers, M. 1993. The one to one future: Building relationships one customer at a time, Currency Doubleday New York.Peppers, D. and Rogers, M. 1993. The one to one future: Building relationships one customer at a time, Currency Doubleday New York.Ramos, P., Santos, N. and Rebelo, R. 2015. Performance of state space and ARIMA models for consumer retail sales forecasting. Robotics and computer-integrated manufacturing 34:151-163.Reinartz, W., Krafft, M. and Hoyer, W. D. 2004. The customer relationship management process: Its measurement and impact on performance. Journal of marketing research 41 (3):293-305.Rosset, S., Neumann, E., Eick, U. and Vatnik, N. 2003. Customer lifetime value models for decision support. Data mining and knowledge discovery 7 (3):321-339.Suhermi, N., Prastyo, D. D. and Ali, B. 2018. Roll motion prediction using a hybrid deep learning and ARIMA model. Procedia computer science 144:251-258.Sun, Y., Chen, Y., Wang, X. and Tang, X. 2014. Deep learning face representation by joint identification-verification. Advances in neural information processing systems 27Thakkar, A. and Chaudhari, K. 2021. A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions. Expert systems with applications 177:114800.Van Calster, T., Baesens, B. and Lemahieu, W. 2017. ProfARIMA: A profit-driven order identification algorithm for ARIMA models in sales forecasting. Applied Soft Computing 60:775-785.Verhoef, P. C. and Lemon, K. N. 2013. Successful customer value management: Key lessons and emerging trends. European Management Journal 31 (1):1-15.Yong, B. X., Abdul Rahim, M. R. and Abdullah, A. S. 2017. A stock market trading system using deep neural network. Paper presented at the Asian simulation conference. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200726 en_US
