基于启发式算法的活期储蓄客户CLV研究
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摘要
非契约环境下,客户未来购买行为逐渐成为营销界研究的热点,学术界通常用随机模型对客户的未来购买行为进行预测,但这种模型在实践界的使用相对较难。如何探索一种简单实用的方法是本研究的主要任务。本文以某商业银行活期储蓄客户的历史交易数据为研究样本,构建启发式算法和随机模型,对客户未来购买行为进行预测,从实证角度证明了启发式算法的确比随机模型预测方法更简单、高效,有效的减少了使用随机模型所付出的机会成本,帮助管理者快速的调整营销策略。本文的主要贡献如下：
1.启发式算法在商业银行活期储蓄领域的应用。将启发式算法首次应用在商业银行活期储蓄领域,对客户进行流失识别、未来交易次数的预测及未来交易金额的预测,其预测结果通过与随机模型BG/NBD和Gamma-Gamma模型实现的预测结果的比较,验证启发式算法在商业银行活期储蓄领域的适用,并再一次说明对客户未来购买行为的预测,启发式算法不逊于随机模型。
2.结合商业银行活期储蓄客户的具体数据特征,改进启发式算法对客户未来交易次数E[x]模型的预测。根据商业银行活期储蓄客户的特点,提出符合活期储蓄客户业务背景的预测模型,使改进后的模型更适用于测算我国商业银行活期储蓄客户未来交易次数,进一步提高全部客户总计交易次数的预测准确率。
3.跳出常规对客户进行分类的研究思路,直接在客户群中根据计算出的客户终身价值选取20%的优质客户,继而从营销资源合理配置的角度为管理者提供决策依据。
Nowadays customers'future purchase behavior has become a hot research in the environment of non-contractual in marketing industry. Academia use stochastic model such as Pareto/NBD, BG/NBD, etc to estimate client's future purchase behavior. However, due to the complexity of this model, this kind of method is not applied in industry. It is not the scholars' intention that the theory and practice mismatching. This paper uses heuristic algorithm identify customer churn on the deposit customers in commercial bank for the first time. In comparison with stochastic model, it's verified that the heuristic algorithm have same effective on the prediction of customers'future purchase behavior, and more simple. This method reduced the opportunity cost effectively and helps managers adjust marketing strategies quickly. It is really a convenient method for managers actual operating. The main contribution of this paperare are as follows:
1. The application of heuristic algorithm in the field of current deposit customers in commercial bank. It's the first time applying the heuristic algorithm in the field of commercial bank's current deposit. This research consist of the following work:customer churn recognition; forecast the future number of transactions and forecast the amount of future transactions. In comparison with stochastic model, such as BG/NBD and Gamma-Gamma, verified the heuristic algorithm for the prediction of customers'future purchase behavior no less favorable than the complex stochastic models.
2. According to the specific data characteristic of saving deposit customer in commercial bank, this paper employ the heuristic algorithm to improve the prediction model of future customer transactions. The research put forward a prediction model that in accordance with the business background of current deposit customers in commercial banks. The improved model is more suitable for measurement of customers'future transactions, and further improves the overall prediction precision of customers. Based on this model we can get a higher forecasting precision about the total number of future purchase.
3. According to the calculated customer lifetime value, we selected 20% of the high value customers from the customer base directly, which out of the conventional research ideas about the classification of customers. Then we can provide the basis for managers to make good decision from the perspective of rational allocation of marketing resources.
1.启发式算法在商业银行活期储蓄领域的应用。将启发式算法首次应用在商业银行活期储蓄领域,对客户进行流失识别、未来交易次数的预测及未来交易金额的预测,其预测结果通过与随机模型BG/NBD和Gamma-Gamma模型实现的预测结果的比较,验证启发式算法在商业银行活期储蓄领域的适用,并再一次说明对客户未来购买行为的预测,启发式算法不逊于随机模型。
2.结合商业银行活期储蓄客户的具体数据特征,改进启发式算法对客户未来交易次数E[x]模型的预测。根据商业银行活期储蓄客户的特点,提出符合活期储蓄客户业务背景的预测模型,使改进后的模型更适用于测算我国商业银行活期储蓄客户未来交易次数,进一步提高全部客户总计交易次数的预测准确率。
3.跳出常规对客户进行分类的研究思路,直接在客户群中根据计算出的客户终身价值选取20%的优质客户,继而从营销资源合理配置的角度为管理者提供决策依据。
Nowadays customers'future purchase behavior has become a hot research in the environment of non-contractual in marketing industry. Academia use stochastic model such as Pareto/NBD, BG/NBD, etc to estimate client's future purchase behavior. However, due to the complexity of this model, this kind of method is not applied in industry. It is not the scholars' intention that the theory and practice mismatching. This paper uses heuristic algorithm identify customer churn on the deposit customers in commercial bank for the first time. In comparison with stochastic model, it's verified that the heuristic algorithm have same effective on the prediction of customers'future purchase behavior, and more simple. This method reduced the opportunity cost effectively and helps managers adjust marketing strategies quickly. It is really a convenient method for managers actual operating. The main contribution of this paperare are as follows:
1. The application of heuristic algorithm in the field of current deposit customers in commercial bank. It's the first time applying the heuristic algorithm in the field of commercial bank's current deposit. This research consist of the following work:customer churn recognition; forecast the future number of transactions and forecast the amount of future transactions. In comparison with stochastic model, such as BG/NBD and Gamma-Gamma, verified the heuristic algorithm for the prediction of customers'future purchase behavior no less favorable than the complex stochastic models.
2. According to the specific data characteristic of saving deposit customer in commercial bank, this paper employ the heuristic algorithm to improve the prediction model of future customer transactions. The research put forward a prediction model that in accordance with the business background of current deposit customers in commercial banks. The improved model is more suitable for measurement of customers'future transactions, and further improves the overall prediction precision of customers. Based on this model we can get a higher forecasting precision about the total number of future purchase.
3. According to the calculated customer lifetime value, we selected 20% of the high value customers from the customer base directly, which out of the conventional research ideas about the classification of customers. Then we can provide the basis for managers to make good decision from the perspective of rational allocation of marketing resources.
引文
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[51]匡奕球.基于生命周期的客户终身价值分析[J].四川工业学院学报,2003：83-90.
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[3]Fader Perter S., Hardie Bruce G.S.,Lee Ka Lok. "Counting Your Customers" the Easy Way: An Alternative to the Pareto/NBD Model[J]. Marketing Science,2005b,24(2):275-284.
[4]齐佳音,李怀祖,舒华英,秦良娟.SMC模型在IT分销业的实证研究[J].中国管理科学,2003,11(6)：71-76.
[5]马少辉,刘金兰.Pareto/NBD模型实证与应用研究[J].管理科学,2006,19(5)：45-49.
[6]王高,李纯青,赵平,童璐琼.随机模型在零售顾客未来经济价值估计中的应用[J].中国管理科学,2007,15(2)：104-110.
[7]Li Chunqing,Wang Suqiang. The application of rando m models in forecasting commercial bank's saving deposits customer purchase behavior[C]. The Eighth Wuhan International conference on E-Business,2009,Vol Ⅱ:1316-1325.
[8]Li Chunqing,Fan Linlin. The Application of BG/NBD Model in Customer Base Analysis—Based on Current Deposit Customer of Commercial Bank[C]. The Ninth Wuhan International Conference on E-Business.2010.
[9]Verhoef,Peter C.,Penny N. Spring,Janny C. Hoekstra,and Peter S.H. Leeflang,"The Commercial Use of Segmentation and Predictive Modeling Techniques for Database Marketing in the Netherlands," Decision Support Systems,2002,34 (4):471-81.
[10]Donkers,Bas,Peter C. Verhoef,and Martijn de Jong,Modeling CLV:A Test of Competing Models in the Insurance Industry,Quantitative Marketing and Economics,2007,5 (2):163-90.
[11]Sharad Borle,Siddharth S. Singh,Customer Lifetime Value Measurement,Management Science,2008,Vol.54(1):100-112.
[12]Markus Wubben,Florian Wangenheim,InstantCustomer Base Analysis Managerial Heuristics Often "Get It Right". Journal of Marketing,2008,Vol.72:82-93.
[13]熊晓.顾客资产价值评估及最大化策略[D].天津大学,2008.
[14].Creyer,Elizabeth H. James R. Bettman,and John Wayne Payne, "The Impact of Accuracy and Effort Feedback and Goals on Adaptive Decision Behavior," Journal of Behavioral Decision Making,1990,3 (1):1-16.
[15].Payne,John Wayne,James R. Bettman,and Eric J. Johnson,The Adaptive Decision Maker. 1993,Cambridge,UK:Cambridge University Press.
[16]Valarie A. Zeithaml,Leonard L Berry,and A. Parasuraman.Communication and Control Processes in the Delivery of Service Quality,Journal of Marketing,April 1988.
[17]Robert B.Woodruff,Customer Value:The Next Source for Competitive Advantage, Academy of Marketing Science. Journal;Spring 1997;25.2:ABI/INFORM Complete pg. 139.
[18]Heskett J.L.,Jones T.O.,Loveman G.W.,Sasser W.E., Schlesinger L. A..Putting the service-profit chain to work,Harvard Business Review,1994,March-April:164-174.
[19]Heskett J.L.,W. Earl Sasser,Leonard A. Schlesinger. The Service Profit Chain[M].New York:Free Press,1997.
[20]Philip Kotler. Marketing Management:Analysis,Planning,Implementation and Control(Ninth Edition) (M).Prent,ee—Hall,Ine.,1997.
[21]Frederick F Reichheld. The loyalty Effect:The Hidden Force Behind Growth, Profits,and Lasting Value[M]. Harvard Business School Press,1996.
[22]王海洲.客户资源价值与管理[J].http://www.crmchina.com.cn/daturm/210000054. html,2001-10-10.
[23]Wolfgang Ulaga. Customer's Value in Business Markets:An Agenda for Inquiry [J]. Industrial Marketing Management,2001,(30):315-319.
[24]Barbara Jackson. Build Customer Relations That Last[J]. Harvard Business Review,1985.
[25]Dwyer,Robert F. Customer Lifetime Valuation to Support Marketing Decision Making[J]. Journal of Direct Marketing,1989,8(2):8-15.
[26]Dwyer,Robert F. Customer Lifetime Valuation to Support Marketing Decision Making[J]. Journal of Direct Marketing,1997,11(4):6-13.
[27]Blattberg,Robert C., John Deighton,Manage Marketing by the Customer Equity Test[J]. Harvard Business Review,1996,74(July-August):136-144.
[28]Courtheoux R. Customer Retention:How Mush to Invest. Research and the Customer Lifecycle,New York,1995.
[29]Berger Paul D.,N.I. Nasr. Customer Lifetime Value Marketing Models and Applications [J]. Journal of Interactive Marketing,1998,12(1):17-30.
[30]Gupta,S.,Lehmann,D. R.,Customers as assets,Journal of Interactive Marketing,2003, 17(l):9-24.
[31]Reinartz Werner J.,Kumar V. On the Profitability of Long-Life Customers in a Noncontractual Setting:An Empirical Investigation and Implications for Marketing[J]. Journal of Marketing,2000,64(October):17-35.
[32]Rust R., Zahorik A. Customer Satisfaction,Customer Retention and Market Share[J]. Journal of Retailing,1993,69(2):193-215.
[33]Berger,P.D.,Nasr-Bechwati,N.,The Allocation of Promotion Budget to Maximize Customer Equity. OMEGA:The International Journal of Management Science,2001,Vol.29:49-61.
[34]Robert E. Wayland,Paul M. Cole Customer Connections:New Strategies for Growth(Hardcover)[M]. Harvard Business School Press Books,1996.1.
[35]Reichheld,Frederick F.,Thmos Teal,The Loyelty Effect,BostonL Massachusetts,Harvard Business School Press,1996
[36]D Jain,SS Singh,Customer lifetime value research in marketing:A review and Future directions,Journal of Interactive Marketing,2002.
[37]Gabriel R. Bitran and Susana V. Mondschein,Periodic Pricing of Seasonal Products in Retailing,Management Science,1997,Vol.43(1):64-79.
[38]J.D. Kalbfleisch and R.L. Prentice.,The Statistical analysis of failure time data,Wiley, 1980.
[39]Daniel A. Levinthal and Mark Fichman,Dynamics of Inter organizational Attachments: Auditor-Client Relationships,Administrative Science Quarterly,1998,Vol.33(3):345-369.
[40]Aswath Damodaran,Corporate Finance:Theory and Practice,John Wiley&Sons Inc;2nd edition, January 2001.
[41]Kamakura Wagner,Sridhar Ramaswami,and Rajendra Srivastava,Applying Latent Trait Analysis in the Evaluation of Prospects for Cross-Selling of Financial Services,International Journal of Research in Marketing,1991,Vol.8:329-349.
[42]Dennis Campbell,Frances Frei,The Persistence of Customer Profitability:Empirical Evidence and Implications From a Financial Services Firm,Journal of Service Research,2004, Vol.7(2):107-123.
[43]Michael Haenlein,Andreas M. Kaplan and Anemone J. Beeser,A Model to Determine Customer Lifetime Value in a Retail Banking Context,European Management Journal, 2007,Vol.25(3):221-234.
[44]Sharad Borle,Siddharth S. Singh,Customer Lifetime Value Measurement,Management Science,2008,Vol.54 (1):100-112.
[45]Angus Jekinson. Valuing your customers:from quality information to quality relationship through database marketing[J]. Biddles Ltd.,1995:326-338.
[46]Wayland Robert E.,Cole Paul M. Customer Connections:New Strategies for Growth(Hardcover)[M]. Harvard Business School Press Books,1997.1.
[47]Hyunseok Hwang,Taesoo Jung,EuihoSuh. An LTV model and Customer Segmentation Based on Customer Value:A Case Value:A Case Study on the Wireless Telecommunication Industry [J].Expert Systems with Applications,2004(26):181-188.
[48]陈明亮.客户保持与生命周期研究[D].西安交通大学博士学位论文,2001.
[49]汪涛,徐岚.顾客资产与竞争优势[J].中国软科学.2002(1)：52-56.
[50]王素芬,汤兵勇.客户终身价值模型的研究[J].黑龙江大学学报.2002：24-28.
[51]匡奕球.基于生命周期的客户终身价值分析[J].四川工业学院学报,2003：83-90.
[52]张华伦,王磊,高涛等.商业银行客户价值指标体系的建立与评估[J].决策与统计,2006,12：57-58.
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