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題名 機器學習在顧客關係管理之應用－以汽車服務個案為例
A case study on machine learning for customer relationship management in service industry
作者 何元君
Ho, Yuanchun
貢獻者 尚孝純
Shari S. C. Shang
何元君
Ho, Yuanchun
關鍵詞 行動研究
顧客關係管理
資料探勘
機器學習
服務業
Action research
Customer relationship management
Data mining
Machine learning
Service industry
日期 2018
上傳時間 2-Feb-2018 15:31:51 (UTC+8)
摘要 隨著科技進步，基於機器學習技術的資料分析工具在顧客關係管理領域已被廣為使用。過去的相關研究文獻大多著重於高交易頻次、與客戶互動頻繁的產業，諸如金融、電信、零售業等，但對於具有相反產業特性的服務業等則是缺乏著墨。本研究希望透過案例研究的方式，完整呈現企業如何實際將基於機器學習技術的資料分析工具應用於顧客關係管理業務的過程，以及這些新技術如何幫助企業提升顧客關係管理的成效。本案例使用行動研究方法來歸納、分析、整理整個專案的過程與結果，文末總結本案例於作業、管理以及策略層面的管理意涵與建議。本研究使用的資料來源為台灣一間大型汽車經銷商的資訊部門與其旗下的服務廠，總共包含了約273萬筆資料。利用於微軟Azure平台上的決策樹模型分析資料，產出高購買機率的顧客推薦名單，服務廠的技師可以針對名單上的顧客推銷，不僅能有效提高推銷的成功率，節省第一線技師的時間，還能夠提升技師以及顧客的滿意度。最後本研究的結果顯示，運用機器學習技術產出的推薦顧客名單，確實能夠幫助本案例公司於顧客區隔以及顧客發展，並達成更有效的顧客關係管理。
Data-mining tools and machine-learning techniques have long been used in customer relationship management (CRM), including for customer retention in the financial, retail, and telecommunications industries. However, research on machine learning for CRM in service industries remains rare. Accordingly, this paper uses action research to arrive at a holistic understanding of the process of applying machine learning-based data mining in a specific service-sector business, and whether, how, and how much these novel techniques can help it improve its customer relationships. Key areas of interest include operational, managerial and strategic decision-making processes. Based on approximately 2.73 million rows of data collected from a large car dealership’s IT department and its vehicle-maintenance plants, Microsoft Azure’s boosted decision-tree model generated lists of recommended customers. Such lists could be used by the company to increase the success rates of its promotional activities and to decrease both the overall duration and frequency of technicians’ involvement with promotion. This in turn could lead to more efficient and effective frontline operations, and increased satisfaction not only among customers but also among technicians. In short, machine learning-based recommended-customer lists helped the company achieve more effective CRM through better customer segmentation and customer development.
參考文獻 Ali A., Morteza S., & Zahra J. (2012). An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank. Expert Systems with Applications, 39, 12358–12370.
Almotairi, M. (2008, May). CRM success factors taxonomy. European and Mediterranean Conference on Information Systems, Dubai, UAE.
Alpaydin, E. (2004). Introduction to machine learning. London, England: MIT Press.
Alt, R., & Puschmann, T. (2004, January). Successful practices in customer relationship management. Proceedings of the 37th Hawaii International Conference on System Sciences 2004, Big Island, Hawaii, USA.
Baroudi, R. (2014). KPIs: Winning tips and common challenges. Performance, 6(2), 36-43.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602-613.
Bose, I., & Mahapatra, R. K., (2001). Business data mining – a machine learning perspective. Information & Management, 39, 211-225.
Bryman, A., & Bell, E. (2011). Business research methods (3rd ed.). Oxford, England: Oxford University Press.
Campbell, A. J. (2003). Creating customer knowledge competence: Managing customer relationship management programs strategically. Industrial Marketing Management, 32, 375-383.
Chen, Q., & Chen, H.-m. (2004). Exploring the success factors of eCRM strategies in practice. Database Marketing & Customer Strategy Management, 11(4), 333-343.
Coghlan, D., & Brannick, T. (2001), Doing action research in your own organization. London: Sage.
Coughlan, P., & Coghlan, D. (2002). Action research for operations management. International Journal of Operations & Production Management, 22(2), 220-240.
Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27-36.
Croteau, A.-M., & Li, P. (2003). Critical success factors for CRM technological initiatives. Canadian Journal of Administrative Sciences, 20(1), 21-34.
El-Zehery, A. M., El-Bakry, H. M., & El-Kasasy, M. S. (2013). Applying data mining techniques for customer relationship management: A survey. International Journal of Computer Science and Information Security, 11(11), 76-82.
Faggella, D. (2016). What is machine learning? https://www.techemergence.com/what-is-machine-learning/, accessed 30 September 2017.
Fürnkranz, J., Gamberger, D., & Lavrač, N. (2012). Foundations of rule learning. Berlin Heidelberg: Springer.
Gualtieri, M., & Curran, R. (2015). A machine learning primer for BT professionals. Cambridge, England: Forrester Research.
Ince, T., & Bowen, D. (2011). Consumer satisfaction and services: Insights from dive tourism. Service Industry Journal, 31(11), 1769-1792.
Iriana, R., & Buttle, F. (2007). Strategic, operational, and analytical customer relationship management: Attributes and measures. Journal of Relationship Marketing, 5(4), 23-42.
Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72-85.
Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34.
King, S. F. &, Burgees, T. F. (2008). Understanding success and failure in customer relationship management. Industrial Marketing Management, 37, 421-431.
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249-268.
Kracklauer, A. H., Mills, D. Q., & Seifert, D. (2004). Customer management as the origin of collaborative customer relationship management. Collaborative Customer Relationship Management - taking CRM to the next level, 3–6.
Krishna, G., & Vadlamani, R. (2016). Evolutionary computing applied to customer relationship management: A survey. Engineering Applications of Artificial Intelligence, 56, 30-59.
Larivie, B., & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29, 472-484.
Lewin, K. (1946). Action Research and Minority Problems. Journal of Social Issues, 2(4), 34-46.
Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491-502.
Maestrini, V., Luzzini, D., Shani, A. B., & Canterino, F. (2016). The action research cycle reloaded: Conducting action research across buyer-supplier relationships. Journal of Purchasing & Supply Management, 22, 289-298.
Martens, D., Vanthienen, J., Verbeke, W., & Baesens, B. (2011). Performance of classification models from a user perspective. Decision Support Systems, 51(4), 782-793.
Malik, F. (2013). Application of data mining in changing times and its role in future. Indian Journal of Commerce & Management Studies, 4(1), 73-77.
Microsoft Azure (2017). Two-class boosted decision tree. Retrieved October 14, 2017, from https://msdn.microsoft.com/en-us/library/azure/dn906025.aspx
Mitchell, T. M. (1997). Machine learning. New York City, United States: McGraw-Hill.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. Cambridge, MA and London: MIT Press.
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. London, England: MIT Press.
Ngai, E.W.T., Xiu, L, & Chau, D.C.K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
Olafsson, S., Li, X., & Wu, S. (2008). Operations research and data mining. European Journal of Operational Research, 187, 1429-1448.
Özden Gür Ali, & Umut Arıtürk. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41, 7889–7903.
Pan, Z., Ryu, H., & Baik, J. (2007, August). A case study: CRM adoption success factor analysis and six sigma DMAIC application. Fifth International Conference on Software Engineering Research, Management and Applications, Busan, South Korea.
Parvatiyar, A., & Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic and Social Research, 3(2), 1-34.
Patidar, P., & Tiwari, A. (2013). Handling missing value in decision tree algorithm. International Journal of Computer Applications, 70(13), 31-36.
Prinzie, A., & Van den Poel, D. (2008). Random forests for multiclass classification Random MultiNomial Logit. Expert Systems with Applications, 34, 1721-1732.
Samuel, Arthur L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 3(3), 210-229.
SAS (2017). Machine learning: What it is and why it matters. Retrieved September 14, 2017, from https://www.sas.com/it_it/insights/analytics/machine-learning.html
Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies. Upper Saddle River, NJ: Prentice Hall PTR.
Wei, J.-T., Lee, M.-C., Chen, H.-K., & Wu, H.-H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40, 7513-7518.
Chiang, W. Y. (2012). To establish online shoppers’ markets and rules for dynamic CRM systems: An empirical case study in Taiwan. Internet Research, 22(5), 613-625.
West, P. M., Brockett, P. L., & Golden, L. L. (1997). A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Science, 14(4), 370-391.
Xiao, S. H., & Nicholson, M. (2011). Mapping impulse buying: A behaviour analysis framework for services marketing and consumer research. Service Industry Journal, 31(15), 2515-2528.
描述 碩士
國立政治大學
資訊管理學系
105356003
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356003
資料類型 thesis
dc.contributor.advisor 尚孝純zh_TW
dc.contributor.advisor Shari S. C. Shangen_US
dc.contributor.author (Authors) 何元君zh_TW
dc.contributor.author (Authors) Ho, Yuanchunen_US
dc.creator (作者) 何元君zh_TW
dc.creator (作者) Ho, Yuanchunen_US
dc.date (日期) 2018en_US
dc.date.accessioned 2-Feb-2018 15:31:51 (UTC+8)-
dc.date.available 2-Feb-2018 15:31:51 (UTC+8)-
dc.date.issued (上傳時間) 2-Feb-2018 15:31:51 (UTC+8)-
dc.identifier (Other Identifiers) G0105356003en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/115776-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 105356003zh_TW
dc.description.abstract (摘要) 隨著科技進步，基於機器學習技術的資料分析工具在顧客關係管理領域已被廣為使用。過去的相關研究文獻大多著重於高交易頻次、與客戶互動頻繁的產業，諸如金融、電信、零售業等，但對於具有相反產業特性的服務業等則是缺乏著墨。本研究希望透過案例研究的方式，完整呈現企業如何實際將基於機器學習技術的資料分析工具應用於顧客關係管理業務的過程，以及這些新技術如何幫助企業提升顧客關係管理的成效。本案例使用行動研究方法來歸納、分析、整理整個專案的過程與結果，文末總結本案例於作業、管理以及策略層面的管理意涵與建議。本研究使用的資料來源為台灣一間大型汽車經銷商的資訊部門與其旗下的服務廠，總共包含了約273萬筆資料。利用於微軟Azure平台上的決策樹模型分析資料，產出高購買機率的顧客推薦名單，服務廠的技師可以針對名單上的顧客推銷，不僅能有效提高推銷的成功率，節省第一線技師的時間，還能夠提升技師以及顧客的滿意度。最後本研究的結果顯示，運用機器學習技術產出的推薦顧客名單，確實能夠幫助本案例公司於顧客區隔以及顧客發展，並達成更有效的顧客關係管理。zh_TW
dc.description.abstract (摘要) Data-mining tools and machine-learning techniques have long been used in customer relationship management (CRM), including for customer retention in the financial, retail, and telecommunications industries. However, research on machine learning for CRM in service industries remains rare. Accordingly, this paper uses action research to arrive at a holistic understanding of the process of applying machine learning-based data mining in a specific service-sector business, and whether, how, and how much these novel techniques can help it improve its customer relationships. Key areas of interest include operational, managerial and strategic decision-making processes. Based on approximately 2.73 million rows of data collected from a large car dealership’s IT department and its vehicle-maintenance plants, Microsoft Azure’s boosted decision-tree model generated lists of recommended customers. Such lists could be used by the company to increase the success rates of its promotional activities and to decrease both the overall duration and frequency of technicians’ involvement with promotion. This in turn could lead to more efficient and effective frontline operations, and increased satisfaction not only among customers but also among technicians. In short, machine learning-based recommended-customer lists helped the company achieve more effective CRM through better customer segmentation and customer development.en_US
dc.description.tableofcontents Acknowledgement i
摘要 ii
Abstract iii
Contents iv
Table vi
Figure vii
Chapter 1 Introduction 1
1.1. Background: Business use of machine learning and data mining 1
1.2. Research motivation 2
1.3. Research objective 2
Chapter 2 Literature Review 3
2.1. Defining machine-learning techniques 3
2.2. Machine learning, data analytics, and customer relationship management 3
2.3. Data analysis for CRM 5
2.4. Critical success factors for CRM implementation 7
Chapter 3 Research Methodology 8
3.1. Research approach 8
3.2. Research process 8
3.3. Research analysis 10
Chapter 4 Case Study 11
4.1. T-Company and its maintenance service: Background 11
4.2. Data gathering 11
4.3. Data feedback 12
4.4. Data analysis: Regression method 12
4.5. Data analysis: Classification method 13
4.6. Action planning 13
4.7. Implementation 14
4.8. Evaluation 15
Chapter 5 Managerial Implications 18
Chapter 6 Conclusion and Recommendations 20
References 22
Appendix A. Information Attribute Inputted for Decision Tree Model 26
Appendix B. Important Variables from Decision Tree Regression Results 31		zh_TW
dc.format.extent 745011 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356003en_US
dc.subject (關鍵詞) 行動研究zh_TW
dc.subject (關鍵詞) 顧客關係管理zh_TW
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 服務業zh_TW
dc.subject (關鍵詞) Action researchen_US
dc.subject (關鍵詞) Customer relationship managementen_US
dc.subject (關鍵詞) Data miningen_US
dc.subject (關鍵詞) Machine learningen_US
dc.subject (關鍵詞) Service industryen_US
dc.title (題名) 機器學習在顧客關係管理之應用－以汽車服務個案為例zh_TW
dc.title (題名) A case study on machine learning for customer relationship management in service industryen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Ali A., Morteza S., & Zahra J. (2012). An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank. Expert Systems with Applications, 39, 12358–12370.
Almotairi, M. (2008, May). CRM success factors taxonomy. European and Mediterranean Conference on Information Systems, Dubai, UAE.
Alpaydin, E. (2004). Introduction to machine learning. London, England: MIT Press.
Alt, R., & Puschmann, T. (2004, January). Successful practices in customer relationship management. Proceedings of the 37th Hawaii International Conference on System Sciences 2004, Big Island, Hawaii, USA.
Baroudi, R. (2014). KPIs: Winning tips and common challenges. Performance, 6(2), 36-43.
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for credit card fraud: A comparative study. Decision Support Systems, 50, 602-613.
Bose, I., & Mahapatra, R. K., (2001). Business data mining – a machine learning perspective. Information & Management, 39, 211-225.
Bryman, A., & Bell, E. (2011). Business research methods (3rd ed.). Oxford, England: Oxford University Press.
Campbell, A. J. (2003). Creating customer knowledge competence: Managing customer relationship management programs strategically. Industrial Marketing Management, 32, 375-383.
Chen, Q., & Chen, H.-m. (2004). Exploring the success factors of eCRM strategies in practice. Database Marketing & Customer Strategy Management, 11(4), 333-343.
Coghlan, D., & Brannick, T. (2001), Doing action research in your own organization. London: Sage.
Coughlan, P., & Coghlan, D. (2002). Action research for operations management. International Journal of Operations & Production Management, 22(2), 220-240.
Coussement, K., Lessmann, S., & Verstraeten, G. (2017). A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry. Decision Support Systems, 95, 27-36.
Croteau, A.-M., & Li, P. (2003). Critical success factors for CRM technological initiatives. Canadian Journal of Administrative Sciences, 20(1), 21-34.
El-Zehery, A. M., El-Bakry, H. M., & El-Kasasy, M. S. (2013). Applying data mining techniques for customer relationship management: A survey. International Journal of Computer Science and Information Security, 11(11), 76-82.
Faggella, D. (2016). What is machine learning? https://www.techemergence.com/what-is-machine-learning/, accessed 30 September 2017.
Fürnkranz, J., Gamberger, D., & Lavrač, N. (2012). Foundations of rule learning. Berlin Heidelberg: Springer.
Gualtieri, M., & Curran, R. (2015). A machine learning primer for BT professionals. Cambridge, England: Forrester Research.
Ince, T., & Bowen, D. (2011). Consumer satisfaction and services: Insights from dive tourism. Service Industry Journal, 31(11), 1769-1792.
Iriana, R., & Buttle, F. (2007). Strategic, operational, and analytical customer relationship management: Attributes and measures. Journal of Relationship Marketing, 5(4), 23-42.
Jones, S., Johnstone, D., & Wilson, R. (2015). An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes. Journal of Banking & Finance, 56, 72-85.
Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1-2), 3-34.
King, S. F. &, Burgees, T. F. (2008). Understanding success and failure in customer relationship management. Industrial Marketing Management, 37, 421-431.
Kotsiantis, S. B. (2007). Supervised machine learning: A review of classification techniques. Informatica, 31, 249-268.
Kracklauer, A. H., Mills, D. Q., & Seifert, D. (2004). Customer management as the origin of collaborative customer relationship management. Collaborative Customer Relationship Management - taking CRM to the next level, 3–6.
Krishna, G., & Vadlamani, R. (2016). Evolutionary computing applied to customer relationship management: A survey. Engineering Applications of Artificial Intelligence, 56, 30-59.
Larivie, B., & Van den Poel, D. (2005). Predicting customer retention and profitability by using random forests and regression forests techniques. Expert Systems with Applications, 29, 472-484.
Lewin, K. (1946). Action Research and Minority Problems. Journal of Social Issues, 2(4), 34-46.
Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491-502.
Maestrini, V., Luzzini, D., Shani, A. B., & Canterino, F. (2016). The action research cycle reloaded: Conducting action research across buyer-supplier relationships. Journal of Purchasing & Supply Management, 22, 289-298.
Martens, D., Vanthienen, J., Verbeke, W., & Baesens, B. (2011). Performance of classification models from a user perspective. Decision Support Systems, 51(4), 782-793.
Malik, F. (2013). Application of data mining in changing times and its role in future. Indian Journal of Commerce & Management Studies, 4(1), 73-77.
Microsoft Azure (2017). Two-class boosted decision tree. Retrieved October 14, 2017, from https://msdn.microsoft.com/en-us/library/azure/dn906025.aspx
Mitchell, T. M. (1997). Machine learning. New York City, United States: McGraw-Hill.
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2012). Foundations of machine learning. Cambridge, MA and London: MIT Press.
Murphy, K. P. (2012). Machine learning: A probabilistic perspective. London, England: MIT Press.
Ngai, E.W.T., Xiu, L, & Chau, D.C.K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
Olafsson, S., Li, X., & Wu, S. (2008). Operations research and data mining. European Journal of Operational Research, 187, 1429-1448.
Özden Gür Ali, & Umut Arıtürk. (2014). Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Systems with Applications, 41, 7889–7903.
Pan, Z., Ryu, H., & Baik, J. (2007, August). A case study: CRM adoption success factor analysis and six sigma DMAIC application. Fifth International Conference on Software Engineering Research, Management and Applications, Busan, South Korea.
Parvatiyar, A., & Sheth, J. N. (2001). Customer relationship management: Emerging practice, process, and discipline. Journal of Economic and Social Research, 3(2), 1-34.
Patidar, P., & Tiwari, A. (2013). Handling missing value in decision tree algorithm. International Journal of Computer Applications, 70(13), 31-36.
Prinzie, A., & Van den Poel, D. (2008). Random forests for multiclass classification Random MultiNomial Logit. Expert Systems with Applications, 34, 1721-1732.
Samuel, Arthur L. (1959). Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. 3(3), 210-229.
SAS (2017). Machine learning: What it is and why it matters. Retrieved September 14, 2017, from https://www.sas.com/it_it/insights/analytics/machine-learning.html
Swift, R. S. (2001). Accelerating customer relationships: Using CRM and relationship technologies. Upper Saddle River, NJ: Prentice Hall PTR.
Wei, J.-T., Lee, M.-C., Chen, H.-K., & Wu, H.-H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40, 7513-7518.
Chiang, W. Y. (2012). To establish online shoppers’ markets and rules for dynamic CRM systems: An empirical case study in Taiwan. Internet Research, 22(5), 613-625.
West, P. M., Brockett, P. L., & Golden, L. L. (1997). A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Science, 14(4), 370-391.
Xiao, S. H., & Nicholson, M. (2011). Mapping impulse buying: A behaviour analysis framework for services marketing and consumer research. Service Industry Journal, 31(15), 2515-2528.		zh_TW