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| Title: | 雲端服務中銷售員支援之研究
A study on sales force support in cloud service |
| Authors: | 翁玉麟 |
| Contributors: | 曾淑峰
翁玉麟 |
| Keywords: | 線上分析處理
資料採礦
客戶關係管理
銷售員自動化
客戶推薦
Online analytical processing
Data mining
Customer relationship management
Sales force automation
Customer recommendation |
| Date: | 2015 |
| Issue Date: | 2018-06-01 17:38:04 (UTC+8) |
| Abstract: | 客戶關係管理（Customer Relationship Management, CRM）藉由各種資訊技術來留住客戶，以產生更多的商業價值。然而，許多文獻指出，CRM系統的失敗率很高，尤其是CRM主要的核心能力--銷售員自動化（Sales Force Automation, SFA）。研究指出改善的方式包含更好的管理支援、培訓、系統易用性和強烈的使用動機等等。接續此建議，本文提出了一個銷售員支援（Sales Force Support, SFS）系統，藉由線上分析處理（Online Analytical Processing, OLAP）、資料採礦（Data Mining, DM）和雲端服務（Cloud Service）等技術，協助彙整及提供支援銷售員的客戶推薦 (Customer Recommendation)和自我績效評估(Self Evaluation)功能，以刺激更好的銷售能力、滿足客戶與管理。可望提高系統的易用性和業務人員的使用動機，藉以橋接銷售員和管理人員之間的差異。為了評估推薦功能之適用性，本論文也發展一套驗證指標，並採用一套隨機數學模型（Stochastic Mathematical Model），作為強化推薦預測之嘗試。
Customer Relationship Management (CRM) adopts various information technologies to retain and attain customers in order to generate more business values. However, the earlier studies indicate the failure rate for CRM systems is high and it’s even higher for Sales Force Automation (SFA), a major core in CRM. They usually suggest the enhancement in better management support, more training, user friendliness, and usage motivation, and so on. Following the suggestions, this research proposes a Sales Force Support (SFS) system to integrate technologies like OLAP (Online Analytical Processing), Data Mining (DM), and cloud service, etc. to provide supporting information in customer recommendation and self-evaluation, in order to better stimulate sales and satisfy customer and management. The objectives can be achieved by enhancing the user friendliness and usage motivation, and bridging the differences between sales force and management. To evaluate the fitness of recommendation function, a set of validation measures is also developed. In addition, a stochastic mathematical model is also attempted to enhance the recommendation prediction. |
| Reference: | 中文文獻：
可樂旅遊（2017年3月）。得獎榮耀。可樂旅遊。取自http://www.colatour.com.tw/webDM/Portal/intro/award.html。
江逸之（2014年10月）。建立參謀制度 營收五年翻倍。天下雜誌，559。取自https://www.cw.com.tw/article/article.action?id=5062107
交通部觀光局（2010）。中華民國98年國人旅遊狀況調查，臺北市：中華民國政府出版品。
交通部觀光局（2012）。中華民國100年國人旅遊狀況調查，臺北市：中華民國政府出版品。
交通部觀光局（2014）。中華民國102年國人旅遊狀況調查，臺北市：中華民國政府出版品。
唐偉展（2012年12月9日）。四川綿陽市設立台北旅遊服務中心，康福雀屏中選 副市長親授牌。TTN旅報，755，53。取自https://issuu.com/valaissuu/docs/755
梁任瑋（2012年5月）。王文傑打造雄獅百億旅遊王國的傳奇。今週刊，0806，85-88。取自https://www.businesstoday.com.tw/article-content-80408-94544-%E7%8E%8B%E6%96%87%E5%82%91%E6%89%93%E9%80%A0%20%E9%9B%84%E7%8D%85%E7%99%BE%E5%84%84%E6%97%85%E9%81%8A%E7%8E%8B%E5%9C%8B%E7%9A%84%E5%82%B3%E5%A5%87
蔡瑞、齊佳音、屈啟興（2013）。刻畫客戶行為特徵的概率分布選擇研究，北京郵電大學學報（社會科學版）， 15(2)，79-85。
蘇占東、游福成、楊炳儒（2004）。關聯規則的綜合評價方法研究與實例驗證，計算機應用，24(10)，17-20。
英文文獻：
Accenture. (2012). Connecting the dots on sales performance: Leveraging the 2012 sales performance optimization study to inform sales effectiveness initiatives. Retrieved from http://www.accenture.com/usen/Pages/insight-connecting-dots-sales-performance.aspx
Adomavicius, G., & Tuzhilin, A. (1999, August). User profiling in personalization applications through rule discovery and validation. In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 377-381). ACM.
Agrawal, R., Imielinski, T., & Swami, A. (1993). Database mining: A performance perspective. IEEE transactions on knowledge and data engineering, Special Issue on Learning and Discovery in Knowledge Based Databases, 5(6), 914-925.
Agrawal, R., & Srikant, R. (1994, September). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB (Vol. 1215, pp. 487-499).
Ahearne, M., Jelinek, R., & Rapp, A. (2005). Moving beyond the direct effect of SFA adoption on salesperson performance: Training and support as key moderating factors. Industrial Marketing Management, 34(4), 379-388.
Apicella, M., Mitchell, K., & Dugan, S. (1999). Customer relationship management: Ramping up sales service. InfoWorld, 21(33), 68-80.
Baars, H., & Kemper, H. G. (2008). Management support with structured and unstructured data—An integrated business intelligence framework. Information Systems Management, 25(2), 132-148.
Balabanović, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communications of the ACM, 40(3), 66-72.
Barker, R. M., Gohmann, S. F., Guan, J., & Faulds, D. J. (2009). Why is my sales force automation system failing?. Business Horizons, 52(3), 233-241.
Becker, J. U., Greve, G., & Albers, S. (2009). The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. International Journal of Research in Marketing, 26(3), 207-215.
Block, J., Golterman, J., Wecksell, J., Scherburger, K., & Close, W. (1996). Building blocks for technology enabled selling. Gartner Group Research Report R-100-104. Stamford, CT: Gartner Group.
Blodgett, M. (1995). Vendor tries to simplify sales force automation. Computerworld, 30(1), 62-62.
Brey, E. T., So, S. I. A., Kim, D. Y., & Morrison, A. M. (2007). Web-based permission marketing: Segmentation for the lodging industry. Tourism Management, 28(6), 1408-1416.
Brin, S., Motwani, R., Ullman, J. D., & Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. Acm Sigmod Record, 26(2), 255-264.
Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (Eds.). (2011). Handbook of markov chain monte carlo. CRC press.
Buehrer, R. E., Senecal, S., & Pullins, E. B. (2005). Sales force technology usage—Reasons, barriers, and support: An exploratory investigation. Industrial Marketing Management, 34(4), 389-398.
Buhalis, D., & Law, R. (2008). Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research. Tourism management, 29(4), 609-623.
Bush, A. J., Moore, J. B., & Rocco, R. (2005). Understanding sales force automation outcomes: A managerial perspective. Industrial Marketing Management, 34(4), 369-377.
Chase, P.R. (2000). Why CRM implementations fail….and what to do about it. Scribe Software Corporationn, Manchester, NH, USA.
Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM) People, process and technology. Business process management journal, 9(5), 672-688.
Cheng, A. J., Chen, Y. Y., Huang, Y. T., Hsu, W. H., & Liao, H. Y. M. (2011, November). Personalized travel recommendation by mining people attributes from community-contributed photos. In Proceedings of the 19th ACM international conference on Multimedia (pp. 83-92). ACM.
Cheyne, J., Downes, M., & Legg, S. (2006). Travel agent vs internet: What influences travel consumer choices?. Journal of Vacation Marketing, 12(1), 41-57.
Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert systems with Applications, 23(3), 329-342.
Choi, C., Cho, M., Kang, E. Y., & Kim, P. (2006, February). Travel ontology for recommendation system based on semantic web. In Advanced Communication Technology, 2006. ICACT 2006. The 8th International Conference (Vol. 1, pp. 624-627). IEEE.
Collins, R. J. (1997). Better business intelligence: How to learn more about your competitors. Management Books 2000, Chalford.
Delgado, J. A., & Davidson, R. (2002). Knowledge bases and user profiling in travel and hospitality recommender systems. In Proceedings of the ENTER 2002 Conference (pp. 1-16). Innsbruck, Austria.
Elmuti, D., Jia, H., & Gray, D. (2009). Customer relationship management strategic application and organizational effectiveness: An empirical investigation. Journal of Strategic Marketing, 17(1), 75-96.
Eng Koh. (2012, December 10). What is Sales Force Automation?. Retrieved from http://konsultanseojakarta.com/what-is-sales-force-automation.php
Eraker, B. (2001). MCMC analysis of diffusion models with application to finance. Journal of Business & Economic Statistics, 19(2), 177-191.
Fesenmaier, D. R., Ricci, F., Schaumlechner, E., Wöber, K. & Zanella, C. (2003). DIETORECS: Travel advisory for multiple decision styles. In Information and communication technologies in tourism 2003: The Proceedings of the International Conference of ENTER 2003 (pp. 232-241). Helsinki, Finland.
Fu, X., Budzik, J., & Hammond, K. J. (2000, January). Mining navigation history for recommendation. In Proceedings of the 5th international conference on Intelligent user interfaces (pp. 106-112). ACM.
Gamerman, D., & Lopes, H. F. (2006). Markov chain Monte Carlo: stochastic simulation for Bayesian inference. Chapman and Hall/CRC.
Gavalas, D., Konstantopoulos, C., Mastakas, K., & Pantziou, G. (2014). Mobile recommender systems in tourism. Journal of network and computer applications, 39, 319-333.
Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pat. Anal. Mach. Intel, 6, 721–741.
Gohmann, S. F., Guan, J., Barker, R. M., & Faulds, D. J. (2005). Perceptions of sales force automation: Differences between sales force and management. Industrial Marketing Management, 34(4), 337-343.
Greene, R. M. (1966). Business intelligence and espionage. Homewood: Dow Jones-Irwin.
Hair, J., Anderson, R., Mehta, R., & Babin, B. (2008). Sales management: Building customer relationships and partnerships. Nelson Education.
Hemert, J. V., & Baldock, R. (2007). Mining spatial gene expression data for association rules. Bioinformatics Research and Development Lecture Notes in Computer Science, 4414, 66–76.
Honeycutt Jr, E. D., Thelen, T., Thelen, S. T., & Hodge, S. K. (2005). Impediments to sales force automation. Industrial Marketing Management, 34(4), 313-322.
Hsu, K. C., & Li, M. Z. (2011). Techniques for finding similarity knowledge in OLAP reports. Expert Systems with Applications, 38(4), 3743-3756.
Huang, C. L., & Huang, W. L. (2009). Handling sequential pattern decay: Developing a two-stage collaborative recommender system. Electronic Commerce Research and Applications, 8(3), 117-129.
Iyer, B. and Henderson, J. (2010). Preparing for the future: Understanding the seven capabilities of cloud computing. MIS Quarterly Executive, 9(2), 117–131.
Jannach, D., Lerche, L., Kamehkhosh, I., & Jugovac, M. (2015). What recommenders recommend: An analysis of recommendation biases and possible countermeasures. User Modeling and User-Adapted Interaction, 25(5), 427-491.
Jelinek, R. (2013). All pain, no gain? Why adopting sales force automation tools is insufficient for performance improvement. Business Horizons, 56(5), 635-642.
Jobber, D., & Lancaster, G. (2006) Selling and Sales Management, 7th ed. Prentice Hall, Harlow.
Jones, E., Sundaram, S., & Chin, W. (2002). Factors leading to sales force automation use: A longitudinal analysis. Journal of Personal Selling & Sales Management, 22(3), 145-156.
Korb, K. B., & Nicholson, A. E. (2010). Bayesian artificial intelligence. CRC press.
Kurashima, T., Iwata, T., Irie, G., & Fujimura, K. (2010, October). Travel route recommendation using geotags in photo sharing sites. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 579-588). ACM.
Lallich, S., Teytaud, O., & Prudhomme, E. (2007). Association rule interestingness: Measure and statistical validation. In Quality measures in data mining (pp. 251-275). Springer, Berlin, Heidelberg.
Lee, C. K. H., Choy, K. L., Ho, G. T., Chin, K. S., Law, K. M. Y., & Tse, Y. K. (2013). A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Systems with Applications, 40(7), 2435-2446.
Lenca, P., Meyer, P., Vaillant, B., & Lallich, S. (2008). On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid. European journal of operational research, 184(2), 610-626.
Li, L., & Mao, J. Y. (2012). The effect of CRM use on internal sales management control: An alternative mechanism to realize CRM benefits. Information & management, 49(6), 269-277.
Liao, S. H., Chen, Y. J., & Deng, M. Y. (2010). Mining customer knowledge for tourism new product development and customer relationship management. Expert Systems with Applications, 37(6), 4212-4223.
Lucas, J. P., Luz, N., Moreno, M. N., Anacleto, R., Figueiredo, A. A., & Martins, C. (2013). A hybrid recommendation approach for a tourism system. Expert Systems with Applications, 40(9), 3532-3550.
Luhn, H. P. (1958). The automatic creation of literature abstracts. IBM Journal of Research and Development, 2(2), 159-165.
Mahani, A. S., & Sharabiani, M. T. (2015). SIMD parallel MCMC sampling with applications for big-data Bayesian analytics. Computational Statistics & Data Analysis, 88, 75-99.
Mehta, D., Sharma, J. K., & Mehta, N. K. (2010). A Study of Customer Relationship Management Practices in Madhya Pradesh State Tourism Services. Theoretical and Applied Economics, 17(5), 73-80.
Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. Retrieved from http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087-1092.
Mindtree. (2013, November 20). Sales Force Automation Solution / MSFA Solution/ mSales –Mindtree. Retrieved from http://www.mindtree.com/industries/consumer-packaged-goods/sales-force-automation
Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on web usage mining. Communications of the ACM, 43(8), 142-151.
Mobasher, B., Dai, H., Luo, T., Sun, Y., & Zhu, J. (2000, September). Integrating web usage and content mining for more effective personalization. In International Conference on Electronic Commerce and Web Technologies (pp. 165-176). Springer, Berlin, Heidelberg.
Morgan, A. J., & Inks, S. A. (2001). Technology and the sales force: Increasing acceptance of sales force automation. Industrial Marketing Management, 30(5), 463-472.
Power, D. J. (2007). A brief history of decision support systems. DSSResources. COM, World Wide Web, http://DSSResources. COM/history/dsshistory. html, version, 4.
Prat, N., Comyn-Wattiau, I., & Akoka, J. (2011). Combining objects with rules to represent aggregation knowledge in data warehouse and OLAP systems. Data & Knowledge Engineering, 70(8), 732-752.
Reinartz, W., Krafft, M., & Hoyer, W. D. (2004). The customer relationship management process: Its measurement and impact on performance. Journal of Marketing Research, 41(3), 293-305.
Ricci, F. (2002). Travel recommender systems. IEEE Intelligent Systems, 17(6), 55-57.
Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook (pp. 1-35). Springer US.
Ricci, F., & Werthner, H. (2001). Case base querying for travel planning recommendation. Information Technology & Tourism, 4(3-1), 215-226.
Rigby, D. K., Reichheld, F. F., & Schefter, P. (2002). Avoid the four perils of CRM. Harvard business review, 80(2), 101-109.
Rivers, L. M., & Dart, J. (1999). Sales Technology Applications: The acquisition and use of sales force automation by mid-sized manufacturers. Journal of Personal Selling & Sales Management, 19(2), 59-73.
Rudolph, J. L., Jones, R. N., Levkoff, S. E., Rockett, C., Inouye, S. K., Sellke, F. W., Lewis, A. L., Basel, R., Sidney, L., & Marcantonio, E. R. (2009). Derivation and validation of a preoperative prediction rule for delirium after cardiac surgery. Circulation, 119(2), 229-236.
Ryding, D. (2010). The impact of new technologies on customer satisfaction and business to business customer relationships: Evidence from the soft drinks industry. Journal of retailing and consumer services, 17(3), 224-228.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000a, October). Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce (pp. 158-167). ACM.
Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000b). Application of dimensionality reduction in recommender system-A case study (No. TR-00-043). Minnesota Univ Minneapolis Dept of Computer Science.
SAS Education. (2011). Applied Analytics Using SAS Enterprise Miner Course Notes. SAS Institute. Inc., Cary, North Carolina, USA.
Scott, S. L., Blocker, A. W., Bonassi, F. V., Chipman, H. A., George, E. I., & McCulloch, R. E. (2016). Bayes and big data: The consensus Monte Carlo algorithm. International Journal of Management Science and Engineering Management, 11(2), 78-88.
Sell, D., Cabral, L., Motta, E., Domingue, J., & Pacheco, R. (2005, August). Adding semantics to business intelligence. In Database and Expert Systems Applications, 2005. Proceedings. Sixteenth International Workshop on (pp. 543-547). IEEE.
Sellers-Rubio, R., & Nicolau-Gonzálbez, J. L. (2009). Assessing performance in services: The travel agency industry. The Service Industries Journal, 29(5), 653-667.
Shen, L., Liu, S., Chen, S., & Wang, X. (2012). The application research of OLAP in police intelligence decision system. Procedia Engineering, 29, 397-402.
Sigala, M. (2011). eCRM 2.0 applications and trends: The use and perceptions of Greek tourism firms of social networks and intelligence. Computers in Human Behavior, 27(2), 655-661.
Sigala, M. (2012). Exploiting Web 2.0 for new service development: Findings and implications from the Greek tourism industry. International Journal of Tourism Research, 14(6), 551-566.
Silbermann, T., Bayer, I., & Rendle, S. (2013, October). Sample selection for MCMC-based recommender systems. In Proceedings of the 7th ACM conference on Recommender systems (pp. 403-406). ACM.
Sorensen, D., & Gianola, D. (2007). Likelihood, Bayesian, and MCMC methods in quantitative genetics. Springer Science & Business Media.
Speier, C., & Venkatesh, V. (2002). The hidden minefields in the adoption of sales force automation technologies. Journal of Marketing, 66(3), 98-111.
Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1(2), 12-23.
Sultan, N. (2010). Cloud computing for education: A new dawn?. International Journal of Information Management, 30(2), 109-116.
Sun, X., Kong, F., & Chen, H. (2005, October). Using quantitative association rules in collaborative filtering. In International Conference on Web-Age Information Management (pp. 822-827). Springer, Berlin, Heidelberg.
Tan, P. -N., Steinbach, M. and Kumar, V. (2005). Introduction to Data Mining. Addison-Wesley, Pearson International Edition, Boston, MA, USA.
Tseng, S. F., & Won, Y. L. (2016). Integrating multiple recommendation schemes for designing sales force support system: A travel agency example. International Journal of Electronic Business, 13(1), 1-37.
Wang, H. C., & Guo, J. L. (2013). Constructing a water quality 2.0 OLAP system in Taiwan. Journal of cleaner production, 40, 40-45.
Wang, Y. F., Chuang, Y. L., Hsu, M. H., & Keh, H. C. (2004). A personalized recommender system for the cosmetic business. Expert Systems with Applications, 26(3), 427-434.
Winds SFA. (2013, October 10). On Demand Sales Force Automation Tool SFA / Sales Force CRM Software / Software for Sales Manag. Retrieved from http://windssfa.com/sfa_Service.php
White, C. J. (1999). The business intelligence software Solution Version 3. CA: Database Associate International.
Wright, A., McCoy, A., Henkin, S., Flaherty, M., & Sittig, D. (2013). Validation of an association rule mining-based method to infer associations between medications and problems. Applied clinical informatics, 4(1), 100-109.
Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y. (2015). Petuum: A new platform for distributed machine learning on big data. IEEE Transactions on Big Data, 1(2), 49-67.
Yang, W. S., & Hwang, S. Y. (2013). iTravel: A recommender system in mobile peer-to-peer environment. Journal of Systems and Software, 86(1), 12-20.
Zhou, B., Hui, S. C., & Chang, K. (2004, December). An intelligent recommender system using sequential web access patterns. In Cybernetics and Intelligent Systems, 2004 IEEE Conference on (Vol. 1, pp. 393-398). IEEE. |
| Description: | 博士
國立政治大學
資訊管理學系
94356505 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0094356505 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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