Please use this identifier to cite or link to this item:
標題: 利用邏輯斯迴歸方法建構銷售管道預測分析模型-以健身器材產業為例
Sales Pipeline Prediction and Analysis by Using Logistic Regression: A Case of Fitness Equipment Business
作者: 王汎嫈
Fan-Ying Wang
關鍵字: 大數據;預測分析;需求預測;銷售管道;客戶關係管理;銷售漏斗;銷售業績預測;big data;predictive analysis;sales pipeline;CRM;sales funnel;sales prediction
引用: 1.王家英(2009),看見隱藏的價值- 變革管理IBM把業績變利潤了 ,哈佛商業評論。 2.郭芳宜(2013),顧客關係管理系統與企業績效關係之研究,義守大學資訊管理研究所碩士學位論文。 3.陳文華. (2000),顧客關係管理成功關鍵-高品質的顧客資料.,能力雜誌。 4.陳德富(2013),顧客關係管理,滄海書局。 5.高倩, 安英博, & 吴凤祥. (2011),基于 Logistic 回归的落叶松毛虫预测模型研究, 河北农业大学学报, 34(6), 108-110. 6.葉凱莉、林怡孜. (2012).,從關係行銷的觀點探討顧客參與對產品創新的影響。 7.湯宗義(2008),顧客關係管理-導論與應用,全華圖書股份有限公司。 8.谢花林, & 李波,(2008). 基于 logistic 回归模型的农牧交错区土地利用变化驱动力分析地理研究。 9.謝邦昌/宋龍華/李紹綸(2017 ),大數據分析SQL Server 2016與R全方位應用,碁峰。. 10. Agarwal, Anupam, Keeping Pipeline Insights Actionable, Customer Relationship Management; Oct 2007; 11, 10; ProQuest pg. 48 11. Cefkin, M. (2007, October). Numbers may speak louder than words, but is anyone listening? The rhythmscape and sales pipeline management. In Ethnographic Praxis in Industry Conference Proceedings (Vol. 2007, No. 1, pp. 187-199). Blackwell Publishing Ltd. 12. Chase, C. W. (2016). Next generation demand management: People, process, analytics, and technology. John Wiley & Sons. 13. Danny Estrada, Your Sales Pipeline Can Make You or Break You, CUSTOMER RELATIONSHIP MANAGEMENT | AUGUST 2017, 36, 14. Heroman, W. M., Davis, C. B., & Farmer Jr, K. L. (2012). Demand forecasting and capacity management in primary care. Physician executive, 38(1), 30. 15. pipeline-management/ 16. Jackson, B. B., & Bund, B. (1985). Winning and keeping industrial customers: The dynamics of customer relationships. Free Press. 17. Kapelianis, D., & Guesalaga, R. (2015). A Two-Stage Model of Sales Opportunity Outcomes. In Ideas in Marketing: Finding the New and Polishing the Old (pp. 639-642). Springer, Cham. 18. Khirallah, K. (1999). Should retail banks race toward the one-to-one future. Bank Technology News, 12(4), 41. 19. Kotler, P., Rackham, N., & Krishnaswamy, S. (2006). Ending the war between sales and marketing. Harvard Business Review, 84(7/8), 68. 20. Lapide, L. (2004). Sales and operations planning part I: the process. The Journal of business forecasting, 23(3). 21. Lowe, D. J., & Parvar, J. (2004). A logistic regression approach to modelling the contractor's decision to bid. Construction Management and Economics, 22(6), 643-653. 22. Moon, M. A., Mentzer, J. T., & Thomas Jr, D. E. (2000). Customer demand planning at Lucent Technologies: a case study in continuous improvement through sales forecast auditing. Industrial Marketing Management, 29(1), 19-26. 23. Moon, M. A., Mentzer, J. T., Smith, C. D., & Garver, M. S. (1998). Seven keys to better forecasting. Business Horizons, 41(5), 44-52. 24. Nick T. Thomopoulos, Demand Forecasting for Inventory Control Springer, 2014 25. Nikolaus Kimla pipeline management success with CRM 26. Patterson, L. (2007). Marketing and sales alignment for improved effectiveness. Journal of digital asset management, 3(4), 185-189 27. Saffo, P. (2007). Six rules for effective forecasting. Harvard business review, 85(7/8), 122. 28. Sales and market forecasting for entrepreneurs [electronic resource] Berry, Timothy. 2010 New York, N.Y. : Business Expert Press 2010 29. Shapiro, Kenneth A, 9 WAYS TO FILL YOUR PROSPECT PIPELINE Life Insurance Selling; Mar 2010; 85, 3; ProQuest pg. 54 30. Smartt, C., Ferreira, S., Rosenberger, J., & Corley, H. W. (2014). A Framework for Optimizing the Use of Systems Engineering on Proposals. Procedia Computer Science, 28, 120-129. 31. Söhnchen, F., & Albers, S. (2010). Pipeline management for the acquisition of industrial projects. Industrial Marketing Management, 39(8), 1356-1364. 32. Yensen, Kermit, 4 Sales-Funnel Misconceptions, Target Marketing; Mar 2004; 27, 3; ABI/INFORM Collection. pg. 56 33. Zhao, X., Xie, J., & Leung, J. (2002). The impact of forecasting model selection on the value of information sharing in a supply chain. European Journal of Operational Research, 142(2), 321-344.

相較於B2C的需求預測多在討論單一消費者的採購屬性與特定機型銷售數量等,B2B的交易預測則更關注企業於每一個商機提案(Leads)的可能成功率。準確的B2B交易預測對企業資源分配最佳化、降低庫存,改善金流等有相當的助益,對資本密集與交貨期高度敏感的產業來說更是不可或缺的工具。 當交易預測顯示對某一商機提案有高度的贏案率,企業即可開始展開計畫生產排程,以便縮短交期。反之,當贏案率預測機率低時,企業可以針對其銷售策略進行調整,來提高其贏案機率,甚或在適當的時機及時放棄該提案來減少損失。


On big data analysis trend, how to use transaction data analysis to support problem solving in every corporate has drawn high attention of scholars and cooperate. Predictive analysis is one of often mentioned and discussed technology. It has been adopt in sales, operation, marketing, and risk analysis. Most of sales prediction analysis focus more on B2C, but studies of B2B transaction analysis are less discussed.

Instead of discussion end users purchasing attribute on B2C predictive analysis, B2B transaction prediction care more on the win rate of each leads in pipeline. By more accurate transaction result prediction, cooperate would be able to better allocate limited resources, reduce stock inventory, improvement cash flow.

This study is to use logistic regression to predict transaction result of sales pipeline final progress by using extracted data from sales pipeline management module of CRM system. The factors being booked in the sales pipeline system, such as customer, estimate revenue, payment term, warranty, lead time, sales channel have shown obvious effect to the independent factor. The predictive win rate by our calculation is much higher than actual situation, thus we could conclude that our study is in good direction and the predictive result could be a helpful tool to sales managers.
Rights: 不同意授權瀏覽/列印電子全文服務
Appears in Collections:高階經理人碩士在職專班

Files in This Item:
File SizeFormat Existing users please Login
nchu-107-5105027006-1.pdf1.4 MBAdobe PDFThis file is only available in the university internal network    Request a copy
Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.