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題名 客戶回訪預測模型應用於線上預約服務
Predictive modeling of customer retention in online reservation services作者 林煥禹
Lin, Huan Yu貢獻者 周彥君
Chou, Yen Chun
林煥禹
Lin, Huan Yu關鍵詞 電子商務
線上服務
回訪率
機器學習
廣義加成模型
E-commerce
Online services
Return rate
Machine learning
Gam日期 2017 上傳時間 3-七月-2017 14:36:02 (UTC+8) 摘要 隨著電子商務近年的蓬勃發展，許多形形色色的創新服務應運而生。本研究針對具指標性的線上餐飲訂位平台---EZTable(簡單桌)進行分析，對於EZTable而言，前來訂位的客戶是十分重要的，因為高客戶忠誠度能為其帶來更多市佔率。因此，本研究將會針對以下兩點問題進行研究分析：(1)為了能夠準確預測顧客回訪率，那些因素與客戶回訪率是高關聯性的？如訂位者、餐廳、地理位置等資訊。(2)應如何建構並驗證高準確度的預測模型？根據以上問題，本研究使用廣義加成模型(GAM)、決策樹(decision tree)、套袋抽樣(bagging)、隨機森林(random forest)等模型訓練方法，搭配EZTable大量的訂位資料，建構不同的預測模型來預測客戶回訪率。以EZTable的資料而言，本研究發現比起訓練模型的方法，模型變數的選擇更明顯影響了預測表現，而關於訂位本身的資訊，如訂位狀態，能夠大幅度提升預測準確度。這些發現能夠幫助如EZTable等服務提供者，了解哪些變數對於顧客忠誠度是相當重要的；再者，公司能夠透過這些資訊，為有較高回訪率的會員量身打造適合的促銷活動。透過將行銷資源集中在特定的客戶上，這些提供服務公司的行銷成本也能夠因此減少。
Electronic commerce still grows rapidly in the recent years and innovative services are introduced in the recent years accordingly. This research analyzes a representative online reservation service provider – EZTable. Loyalty of customer is crucial because EZTable can obtain more market share with high customer loyalty. Therefore, we expect to answer the following research questions: (1) What are relevant and useful consumer, restaurant, and demographic factors to predict customer retention? (2) How do we develop and determine effective predictive models? We apply generalized additive model, decision tree, bagging, and random forest, to a large volume of operational data from EZTable and develop a set of predictive models. Instead of model complexity, identifying critical variables from the research context determines predictive performance. Transaction-dependent factors could substantially enhance predictive performance. Our findings enable companies like EZTable to understand what predictors are critical to customers’ loyalty. Further, the company can design effective promotions for customers with higher return probability. Our modeling effort could help those service providers reduce advertising cost by allocating limited resources to customers with higher probability to place orders again.參考文獻 Au, W., Chan, K., & Yao, X., (2003). A novel evolutionary data mining algorithm with applications to churn prediction., IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, 532–545. Coussement, K., & Van den Poel, D., (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameters selection techniques., Expert Systems with Applications, Vol. 34, 313–327. Coussement, K., Benoit, D. F., & Van den Poel, D. (2010). Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Systems with Applications, Vol. 37, No.3, 2132-2143. Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A., & Joshi, A., (2008). Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th international conference on Extending Database Technology: Advances in database technology, ACM, 697–711. Difference between glm and splines, Retrieved June 20 2016, from http://stats.stackexchange.com/questions/115245/what-is-the-difference-between-glm-and-splines Eastin, M. S. (2002). Diffusion of e-commerce: an analysis of the adoption of four e-commerce activities., Telematics and informatics, Vol. 19, No. 3, 251-267. eCommerce Industry Outlook 2015, Retrieved August 15 2016, from http://www.criteo.com/media/1432/criteo-ecommerce-industry-outlook-2015.pdf Emmanouilides, C., & Hammond, K. (2000). Internet usage: Predictors of active users and frequency use., Journal of Interactive Marketing, Vol. 14, No. 2, 17–32. From Functional Data to Smooth Functions, Retrieved June 19 2016, from http://www.psych.mcgill.ca/misc/fda/downloads/FDAtalks/smooth_talk.pdf Fawcett, T. (2006). An introduction to ROC analysis., Pattern recognition letters, Vol. 27, No.8, 861-874. GAM: The Predictive Modeling Silver Bullet, Retrieved June 20 2016, from http://multithreaded.stitchfix.com/blog/2015/07/30/gam/ Ganesh, J., Arnold, M., & Reynolds, K., (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, Vol. 64, No. 3, 65–87. Generalized additive model, Retrieved June 17 2016, from https://en.wikipedia.org/wiki/Generalized_additive_model H€aubl, G., & Trifts, V., (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids., Marketing Science, Vol. 19, No. 1, 4–21. Hastie, Trevor J., & Robert J. Tibshirani., (1990). Generalized additive models., CRC press, Vol. 43. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning., New York: springer, Vol. 6. Jarvis, L.P. & E.J. Mayo, (1986) Winning the Market-Share Game, Cornell Hotel Restaurant Administration Quarterly, Vol. 27, No. 3, 72-79. Lemmens, A., & Croux, C., (2006). Bagging and boosting classification trees to predict churn., Journal of Marketing Research, Vol. 43, No. 2, 276–286. Lemon, Katherine N., Tiffany Barnett White, & Russell S. Winer., (2002). Dynamic customer relationship management: Incorporating future considerations into the service retention decision., Journal of marketing, Vol. 66, No.1, 1-14. Luarn, Pin, & Hsin-Hui Lin., (2003). A Customer Loyalty Model for E-Service Context., J. Electron. Commerce Res, Vol. 4, No. 4, 156-167. Moe, W. W., & Fader, P. S. (2004). Capturing Evolving Visit Behaviour in Clickstream Data., Journal of Interactive Marketing, Vol. 18, No. 1, 5-19. Moe, W. W., & Fader, P. S. (2004). Dynamic Conversion Behaviour at e-Commerce Sites., Management Science, Vol. 50, No. 3 , 326-335. Montgomery, A.L., (2001). Applying quantitative marketing techniques to the internet., Interfaces, Vol. 31, No. 2, 90–108. Morrisonn, A. M., Jing, S., O`Leary, J. T., & Cai, L. A. (2001). Predicting usage of the Internet for travel bookings: An exploratory study., Information Technology & Tourism, Vol. 4, No.1, 15-30. Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry., IEEE Transactions on Neural Networks, Vol. 11, No. 3, 690–696. Neslin, S., Gupta, S., Kamakura, W., Lu, J., & Mason, C., (2006). Detection defection: Measuring and understanding the predictive accuracy of customer churn models., Journal of Marketing Research, Vol. 43, No. 2, 204–211. Reichheld, F.F. & P. Schefter, (2000). E-Loyalty: Your Secret Weapon on the Web, Harvard Business Review, Vol. 78, No. 4, 105-113. ROC curve, Retrieved June 17 2016, from http://estat.pixnet.net/blog/post/61795603-roc%E6%9B%B2%E7%B7%9A-(receiver-operating-characteristic-curve) Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share., Journal of Retailing, Vol. 69, No. 2, 193–215. Shankar, V., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online and offline environments., International journal of research in marketing, Vol. 20, No.2, 153-175. Shmueli, G., (2010), To explain or to predict?, Statistical Science, Vol. 25, No. 3, 289-310. Shim, S., Eastlick, M. A., Lotz, S. L., & Warrington, P. (2001). An online prepurchase intentions model: The role of intention to search: Best Overall Paper Award—The Sixth Triennial AMS/ACRA Retailing Conference, 2000☆ 11☆ Decision made by a panel of Journal of Retailing editorial board members., Journal of retailing, Vol. 77, No.3, 397-416. Sismeiro, C., & Bucklin, R. E. (2004). Modeling purchase behavior at an e-commerce web site: A task-completion approach., Journal of marketing research, Vol. 41, No. 3, 306-323. Smoothing spline, Retrieved June 17 2016, from https://en.wikipedia.org/wiki/Smoothing_spline Smooth Splines: Advanced Methods for Data Analysis, Retrieved June 20 2016, from http://www.stat.cmu.edu/~ryantibs/advmethods/notes/smoothspline.pdf Van den Poel, Dirk, & Wouter Buckinx., (2005). Predicting online-purchasing behaviour., European Journal of Operational Research, Vol. 166, No.2, 557-575. Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests., Expert Systems with Applications, Vol. 36, No.3, 5445-5449. 描述 碩士
國立政治大學
資訊管理學系
104356001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104356001 資料類型 thesis dc.contributor.advisor 周彥君 zh_TW dc.contributor.advisor Chou, Yen Chun en_US dc.contributor.author (作者) 林煥禹 zh_TW dc.contributor.author (作者) Lin, Huan Yu en_US dc.creator (作者) 林煥禹 zh_TW dc.creator (作者) Lin, Huan Yu en_US dc.date (日期) 2017 en_US dc.date.accessioned 3-七月-2017 14:36:02 (UTC+8) - dc.date.available 3-七月-2017 14:36:02 (UTC+8) - dc.date.issued (上傳時間) 3-七月-2017 14:36:02 (UTC+8) - dc.identifier (其他 識別碼) G0104356001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/110654 - 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) 104356001 zh_TW dc.description.abstract (摘要) 隨著電子商務近年的蓬勃發展，許多形形色色的創新服務應運而生。本研究針對具指標性的線上餐飲訂位平台---EZTable(簡單桌)進行分析，對於EZTable而言，前來訂位的客戶是十分重要的，因為高客戶忠誠度能為其帶來更多市佔率。因此，本研究將會針對以下兩點問題進行研究分析：(1)為了能夠準確預測顧客回訪率，那些因素與客戶回訪率是高關聯性的？如訂位者、餐廳、地理位置等資訊。(2)應如何建構並驗證高準確度的預測模型？根據以上問題，本研究使用廣義加成模型(GAM)、決策樹(decision tree)、套袋抽樣(bagging)、隨機森林(random forest)等模型訓練方法，搭配EZTable大量的訂位資料，建構不同的預測模型來預測客戶回訪率。以EZTable的資料而言，本研究發現比起訓練模型的方法，模型變數的選擇更明顯影響了預測表現，而關於訂位本身的資訊，如訂位狀態，能夠大幅度提升預測準確度。這些發現能夠幫助如EZTable等服務提供者，了解哪些變數對於顧客忠誠度是相當重要的；再者，公司能夠透過這些資訊，為有較高回訪率的會員量身打造適合的促銷活動。透過將行銷資源集中在特定的客戶上，這些提供服務公司的行銷成本也能夠因此減少。 zh_TW dc.description.abstract (摘要) Electronic commerce still grows rapidly in the recent years and innovative services are introduced in the recent years accordingly. This research analyzes a representative online reservation service provider – EZTable. Loyalty of customer is crucial because EZTable can obtain more market share with high customer loyalty. Therefore, we expect to answer the following research questions: (1) What are relevant and useful consumer, restaurant, and demographic factors to predict customer retention? (2) How do we develop and determine effective predictive models? We apply generalized additive model, decision tree, bagging, and random forest, to a large volume of operational data from EZTable and develop a set of predictive models. Instead of model complexity, identifying critical variables from the research context determines predictive performance. Transaction-dependent factors could substantially enhance predictive performance. Our findings enable companies like EZTable to understand what predictors are critical to customers’ loyalty. Further, the company can design effective promotions for customers with higher return probability. Our modeling effort could help those service providers reduce advertising cost by allocating limited resources to customers with higher probability to place orders again. en_US dc.description.tableofcontents 1. Introduction 4 2. Literature Review 7 2.1 Online purchase behavior 7 2.2 Churn analysis in industries 7 3. Data 9 3.1 data resource 9 3.2 Measures 9 4. Method 18 5. Empirical result 22 5.1 GAM 22 5.2 Tree-Based Learning 27 6. Discussion 35 7. References 37 zh_TW dc.format.extent 1811904 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104356001 en_US dc.subject (關鍵詞) 電子商務 zh_TW dc.subject (關鍵詞) 線上服務 zh_TW dc.subject (關鍵詞) 回訪率 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 廣義加成模型 zh_TW dc.subject (關鍵詞) E-commerce en_US dc.subject (關鍵詞) Online services en_US dc.subject (關鍵詞) Return rate en_US dc.subject (關鍵詞) Machine learning en_US dc.subject (關鍵詞) Gam en_US dc.title (題名) 客戶回訪預測模型應用於線上預約服務 zh_TW dc.title (題名) Predictive modeling of customer retention in online reservation services en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Au, W., Chan, K., & Yao, X., (2003). A novel evolutionary data mining algorithm with applications to churn prediction., IEEE Transactions on Evolutionary Computation, Vol. 7, No. 6, 532–545. Coussement, K., & Van den Poel, D., (2008). Churn prediction in subscription services: An application of support vector machines while comparing two parameters selection techniques., Expert Systems with Applications, Vol. 34, 313–327. Coussement, K., Benoit, D. F., & Van den Poel, D. (2010). Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Systems with Applications, Vol. 37, No.3, 2132-2143. Dasgupta, K., Singh, R., Viswanathan, B., Chakraborty, D., Mukherjea, S., Nanavati, A., & Joshi, A., (2008). Social ties and their relevance to churn in mobile telecom networks. In: Proceedings of the 11th international conference on Extending Database Technology: Advances in database technology, ACM, 697–711. Difference between glm and splines, Retrieved June 20 2016, from http://stats.stackexchange.com/questions/115245/what-is-the-difference-between-glm-and-splines Eastin, M. S. (2002). Diffusion of e-commerce: an analysis of the adoption of four e-commerce activities., Telematics and informatics, Vol. 19, No. 3, 251-267. eCommerce Industry Outlook 2015, Retrieved August 15 2016, from http://www.criteo.com/media/1432/criteo-ecommerce-industry-outlook-2015.pdf Emmanouilides, C., & Hammond, K. (2000). Internet usage: Predictors of active users and frequency use., Journal of Interactive Marketing, Vol. 14, No. 2, 17–32. From Functional Data to Smooth Functions, Retrieved June 19 2016, from http://www.psych.mcgill.ca/misc/fda/downloads/FDAtalks/smooth_talk.pdf Fawcett, T. (2006). An introduction to ROC analysis., Pattern recognition letters, Vol. 27, No.8, 861-874. GAM: The Predictive Modeling Silver Bullet, Retrieved June 20 2016, from http://multithreaded.stitchfix.com/blog/2015/07/30/gam/ Ganesh, J., Arnold, M., & Reynolds, K., (2000). Understanding the customer base of service providers: An examination of the differences between switchers and stayers. Journal of Marketing, Vol. 64, No. 3, 65–87. Generalized additive model, Retrieved June 17 2016, from https://en.wikipedia.org/wiki/Generalized_additive_model H€aubl, G., & Trifts, V., (2000). Consumer decision making in online shopping environments: The effects of interactive decision aids., Marketing Science, Vol. 19, No. 1, 4–21. Hastie, Trevor J., & Robert J. Tibshirani., (1990). Generalized additive models., CRC press, Vol. 43. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning., New York: springer, Vol. 6. Jarvis, L.P. & E.J. Mayo, (1986) Winning the Market-Share Game, Cornell Hotel Restaurant Administration Quarterly, Vol. 27, No. 3, 72-79. Lemmens, A., & Croux, C., (2006). Bagging and boosting classification trees to predict churn., Journal of Marketing Research, Vol. 43, No. 2, 276–286. Lemon, Katherine N., Tiffany Barnett White, & Russell S. Winer., (2002). Dynamic customer relationship management: Incorporating future considerations into the service retention decision., Journal of marketing, Vol. 66, No.1, 1-14. Luarn, Pin, & Hsin-Hui Lin., (2003). A Customer Loyalty Model for E-Service Context., J. Electron. Commerce Res, Vol. 4, No. 4, 156-167. Moe, W. W., & Fader, P. S. (2004). Capturing Evolving Visit Behaviour in Clickstream Data., Journal of Interactive Marketing, Vol. 18, No. 1, 5-19. Moe, W. W., & Fader, P. S. (2004). Dynamic Conversion Behaviour at e-Commerce Sites., Management Science, Vol. 50, No. 3 , 326-335. Montgomery, A.L., (2001). Applying quantitative marketing techniques to the internet., Interfaces, Vol. 31, No. 2, 90–108. Morrisonn, A. M., Jing, S., O`Leary, J. T., & Cai, L. A. (2001). Predicting usage of the Internet for travel bookings: An exploratory study., Information Technology & Tourism, Vol. 4, No.1, 15-30. Mozer, M. C., Wolniewicz, R., Grimes, D. B., Johnson, E., & Kaushansky, H. (2000). Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry., IEEE Transactions on Neural Networks, Vol. 11, No. 3, 690–696. Neslin, S., Gupta, S., Kamakura, W., Lu, J., & Mason, C., (2006). Detection defection: Measuring and understanding the predictive accuracy of customer churn models., Journal of Marketing Research, Vol. 43, No. 2, 204–211. Reichheld, F.F. & P. Schefter, (2000). E-Loyalty: Your Secret Weapon on the Web, Harvard Business Review, Vol. 78, No. 4, 105-113. ROC curve, Retrieved June 17 2016, from http://estat.pixnet.net/blog/post/61795603-roc%E6%9B%B2%E7%B7%9A-(receiver-operating-characteristic-curve) Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share., Journal of Retailing, Vol. 69, No. 2, 193–215. Shankar, V., Smith, A. K., & Rangaswamy, A. (2003). Customer satisfaction and loyalty in online and offline environments., International journal of research in marketing, Vol. 20, No.2, 153-175. Shmueli, G., (2010), To explain or to predict?, Statistical Science, Vol. 25, No. 3, 289-310. Shim, S., Eastlick, M. A., Lotz, S. L., & Warrington, P. (2001). An online prepurchase intentions model: The role of intention to search: Best Overall Paper Award—The Sixth Triennial AMS/ACRA Retailing Conference, 2000☆ 11☆ Decision made by a panel of Journal of Retailing editorial board members., Journal of retailing, Vol. 77, No.3, 397-416. Sismeiro, C., & Bucklin, R. E. (2004). Modeling purchase behavior at an e-commerce web site: A task-completion approach., Journal of marketing research, Vol. 41, No. 3, 306-323. Smoothing spline, Retrieved June 17 2016, from https://en.wikipedia.org/wiki/Smoothing_spline Smooth Splines: Advanced Methods for Data Analysis, Retrieved June 20 2016, from http://www.stat.cmu.edu/~ryantibs/advmethods/notes/smoothspline.pdf Van den Poel, Dirk, & Wouter Buckinx., (2005). Predicting online-purchasing behaviour., European Journal of Operational Research, Vol. 166, No.2, 557-575. Xie, Y., Li, X., Ngai, E. W. T., & Ying, W. (2009). Customer churn prediction using improved balanced random forests., Expert Systems with Applications, Vol. 36, No.3, 5445-5449. zh_TW
