在当前的市场环境下，证券行业的竞争状况十分严峻。当今时代，证券公司若想在激烈的市场竞争中立足并长期发展则不得不重视客户管理。重视高价值的客户、加强自身的服务水平、降低服务成本以及合理整合资源是证券公司提高自身核心竞争力的有效手段。对客户进行合理细分，依靠优质的客户资源是企业获取长远效益的基础。
本文以S证券公司作为客户细分的研究对象，分析S证券公司在此方面的现状并对问题进行总结与发现。通过文献分析、行业特点、公司需求从客户价值与客户忠诚度两个方面选取了包括佣金贡献、息差贡献等13个客户细分指标作为客户细分的数据支撑。利用SPSS软件中的因子分析模型对细分指标进行降维处理，在得到5个公共因子之后，采用K-means聚类分析对S证券公司的客户群体进行聚类分析。在得到合理的聚类结果之后，依据不同群体数据的特点，对每个客户群体的特征进行总结与归纳。本文将S证券公司的客户群体分为VIP客户、潜在客户、A类普通客户、B类普通客户、自由客户、睡眠客户6类群体。将不同类群的客户群体进行特点分析后，对证券公司的业务服务进行汇总并将不同标准的客户服务与客户类群相匹配以降低服务成本、提高服务效率。本文最后从组织、制度、技术、资金4个方面对S证券公司客户关系管理的保障措施与建议进行了描述。
综上，本文研究探讨了客户细分的理论并展示了实际应用效果，对于证券行业的客户关系管理优化具有指导性作用，可应用于企业的资源配置与客户维持的战略决策上，具有为证券企业的客户细分战略提供实际参考的意义。

In the current market environment, the competition in the securities industry is already very severe. In today's era, if securities companies want to gain a foothold in the fierce market competition and develop for a long time, they have to pay attention to customer management. Paying attention to high-value customers, strengthening their own service level, reducing service costs and rationally integrating resources are effective means for securities companies to improve their core competitiveness. Reasonable segmentation of customers and relying on high-quality customer resources are the basis for enterprises to obtain long-term benefits.This thesis takes S securities company as the research object of customer segmentation, analyzes the current situation of S company in this aspect, and summarizes and discovers the problems. Through literature analysis, industry characteristics, and company needs, 13 customer segmentation indicators including commission contribution and interest margin contribution are selected from two aspects of customer value and customer loyalty as the data support for customer segmentation. Using the factor analysis model in SPSS software to reduce the dimensionality of the subdivision indicators, after obtaining 5 common factors, K-means cluster analysis is used to cluster the customer group of S securities company. After obtaining reasonable clustering results, according to the characteristics of different groups of data, the characteristics of each customer group are summarized and summarized. This paper divides the customer groups of S securities company into six groups: VIP customers, potential customers, A-class ordinary customers, B-class ordinary customers, free customers, and sleeping customers. After analyzing the characteristics of customer groups of different groups, the business services of securities companies are summarized and customer services of different standards are matched with customer groups to reduce service costs and improve service efficiency. In the end, this paper describes the safeguard measures and suggestions for S securities company's customer relationship management from four aspects: organization, system, technology and capital.To sum up, this study explores the theory of customer segmentation and demonstrates its practical application. It is instructive for the optimization of customer relationship management in the securities industry, and can be applied to the strategic decision-making of enterprise resource allocation and customer maintenance. It has the significance of providing practical reference for the customer segmentation strategy of securities companies.

[1] Alvaro Julio Cuadros,Victoria Eugenia Domínguez. Customer segmentation model based on value generation for marketing strategies formulation[J]. Estudios Gerenciales,2014,30(130):

[2] A. J. Christy, A. Umamakeswari, L. Priyatharsini and A. Neyaa, “RFM Ranking – An Effective Approach to Customer Segmentation”Journal of King Saud [J].University Computer and Information, 2018. https://doi.org/10.1016/j.jksuci. 2018.09.004.

[3] Bambang Eka Cahyana,Umar Nimran,Hamidah Nayati Utami,Mohammad Iqbal.Hybrid cluster analysis of customer segmentation of sea transportation users[J]. Journal of Economics Finance and Administrative Science, 2020,25(50):321-337.

[4] Bottcher M,Soptt M Nauck D,Kruse R.Mining changing customer segments in dynamic markets[J].Expert Systems with Appliactions,2009,(36):155-164.

[5] Chengrong Hu,Huaying Shu,Xinchun Qiao. Customer Segmentation Model Research Based on Organizational Customer Life Cycle in Telecom Operator[P]. Proceedings of the 2014 International Conference on Education Technology and Social Science,2014

[6] Carbajal Santiago García. Customer Segmentation through Path Reconstruction[J]. Sensors,2021,21(6):

[7] Craig Yeack. Five reasons customer relationship management matters to your business [J]. Concrete Products,2020,(01): 10-14.

[8] Dang Lisa,von Arx Widar. How Can Rail Use for Leisure and Tourism Be Promoted? Using Leisure and Mobility Orientations to Segment Swiss Railway Customers[J]. Sustainability,2021,13(11):

[9] Dongyun Nie,Paolo Cappellari,Mark Roantree. A methodology for classification and validation of customer datasets[J]. The Journal of Business & Industrial Marketing,2021,36(5):

[10] Hughes, A. Strategic database marketing: the master plan for starting and managing aprofitable, customer-based marketing program[A]. Irwin Professional, 1994.

[11] Lee ZneJung,Lee ChouYuan,Chang LiYun,Sano Natsuki. Clustering and Classification Based on Distributed Automatic Feature Engineering for Customer Segmentation[J]. Symmetry,2021,13(9):

[12] Monireh Hosseini, Mostafa Shabani. New approach to customer segmentation based on changes in customer value [J]. Journal of Marketing Analytics, 2015,(3): 110-121.

[13] M. Aryuni, E. D. Madyatmadja and E. Miranda, “Customer Segmentation in XYZ Bank using K-Means and K-Medoids Clustering” in International Conference on Information Management and Technology (ICIMTech), Jakarta, 412-416, 2018.https://doi.org/10.1109/ICIMTech.2018.8528086.

[14] Mehrbakhsh Nilashi,Sarminah Samad,Behrouz Minaei-Bidgoli,Fahad Ghabban,Eko Supriyanto‬. Online Reviews Analysis for Customer Segmentation through Dimensionality Reduction and Deep Learning Techniques[J]. Arabian Journal for Science and Engineering,2021,(3): 110-121.

[15] Nicolas Glady, Pareto Baesens, Christophe Croux. A modified Pareto/NBD approach for predicting customer lifetime value[J]. Expert Systems with Applications. 2009,36(2):2062-2071.

[16] Peter Kolarovscki. The New Model of Customer Segmentation in Postal Enterprises[J]. Procedia-Social and Behavioral Sciences, 2016,(9): 121-127.

[17] pratomo edwin agung,Najib Mukhamad,Mulyati Heti. CUSTOMER SEGMENTATION ANALYSIS BASED ON THE CUSTOMER LIFETIME VALUE METHOD[J]. Jurnal Aplikasi Manajemen,2019,17(3):

[18] R. Venkatesan, “Cluster Analysis for Segmentation,” University of Virginia Darden School, vol. 38, no. 91, pp. 92–98, 2007

[19] Ravasan, A. Z., & Mansouri, T. (2015). A Fuzzy ANP Based Weighted RFM Model for Customer Segmentation in Auto Insurance Sector. International Journal of Information Systems in the Service Sector (IJISSS), 7 (2), 71-86. http://doi.org/10.4018/ijisss.2015040105.

[20] Roland T Rust, Valarie A Zeithaml, Katherine N Lemon. Driving Customer Equity: How Customer Lifetime Value is Reshaping Corporate Strategy[J]. Bankers Magazine, 2012,6(2):

[21] Rahim Mussadiq Abdul,Mushafiq Muhammad,Khan Salabat,Arain Zulfiqar Ali. RFM-based repurchase behavior for customer classification and segmentation[J]. Journal of Retailing and Consumer Services,2021,61

[22]Wei Gao,Huiting Jia,Ruzhen Yan. Customer segmentation model based on two-step optimization in big data era[P]. Proceedings of the 4th International Conference on Information Technology and Management Innovation,2015.

[23] Y. Jinping, “Analysis and Improvement Strategy for Profit Contribution of Bank Customer Under Big Data Background” in International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), China, 338-341, 2019.https://doi.org/10.1109/ICITBS.2019.00089.

[24]Youquan He and Qianqian Zhen. Logistics Customer Segmentation Modeling on Attribute Reduction and K-Means Clustering[J]. Journal of Communication and Computer,2013,10(8):

[25] Zimon Dominik,Hrniar Miroslav,arnogurský Karol,Madzík Peter. Comparison of demographic, geographic, psychographic and behavioral approach to customer segmentation[J]. International Journal of Services and Operations Management,2020,1(1):

[26] 宝利嘉顾问组. 细分: 从客户区隔中谋取利润[M]. 北京:中国社会科学出版社, 2003.

[27] 宝音图, 刘真真.基于 Python 的客户分类和挖掘系统[J]. 软件开发与应用, 2018,(15): 72-73.

[28] 陈方芳,傅仰耿.基于聚类算法识别大型百货商场客户价值的RFM模型[J].景德镇学院学报,2021,36(3):68-72.

[29] 陈晓庆.基于客户终身价值理论的商业银行公司客户细分研究[D].河北经贸大学,2012.

[30] 陈巍.基于客户价值测算的M公司客户分类管理研究[D].苏州大学,2018.

[31] 邓典雅, 祁明, 肖林. 金融科技背景下客户价值管理与银行服务创新的耦合研究[J]. 国际金融, 2018(2):11.

[32] 冯娟娟. 基于客户画像和 GBDT 算法的客户价值预测方法.洛阳理工学院学报. 2018(3): 53-56.

[33] 江晓东.证券业客户细分方法及其定制营销策略[J].生产力研究,2007,(21):45-46.

[34]冀慧杰,倪枫,刘姜等.基于灰色关联度和K-Means++的电子商务客户价值分类[J].计算机系统应用,2020,29(9):249-254.

[35] 葛红岩,陈炳亮.基于客户价值分析的客户定位策略研究[J].特区经济,2016,3:157-160.

[36] 刘凤艳.基于聚类分析的证券业客户分层实证研究.赤峰学院学报(自然科学 版),2016(8).

[37] 刘潇,王效俐.基于k-means和邻域粗糙集的航空客户价值分类研究[J].运筹与管理,2021,30(3):104-111.

[38] 陆娜,刘晓文.基于RFM的网店客户价值细分研究[J].电脑知识与技术,2018,14(18):275-284.

[39] 刘英姿,吴昊.客户细分方法研究综述[J].管理工程学报,2006,20(1):53-56.

[40] 林海明.因子分析理论和应用应注意的问题－以R. A. Johnson和D. W. Wichern教授著的多元统计分析等教材为例[J].Journal of Data Analysis,2009,4(4):1-18

[41] 李英,吴圆圆,宁福锦.基于PSO的K-means改进算法在证券客户细分中的应用[J].数据分析与知识发现,2010,(1):88-94.

[42] 蒲晓川,黄俊丽,祁宁,宋长松.基于密度信息熵的K-Means算法在客户细分中的应用[J].吉林大学学报(理学版),2021,59(05):1245-1251.

[43] 仇沭. 数据挖掘在证券业CRM系统客户细分模块的研究与分析[D]. 湖南大学, 2013.

[44] 邱吉福,王园.基于客户生命周期理论的证券业客户细分实证研究[J].上海管理科学,2013,(2):50-56.

[45] 钱维佳,王延清.基于动态聚类的证券业客户细分实证研究[J].计算机应用,2010,30(2):495-498.

[46] 舒宏,李双宏.证券客户价值指标体系及评估模型设计[J].微型电脑应用,2021,37(7):116-119.

[47] 田波,沈江明,桂贝贝等.基于客户细分的客户流失预测研究[J].通讯世界,2020,27(6):183-184.

[48] 吴敏.中信证券客户价值分析及数据模型化研究应用[D].上海交通大学, 2015.

[49] 王婷娜.基于数据挖掘的商业银行个人客户流失挽留研究[D].福州大学,2016.

[50] 王明艳.基于Power BI的RFM客户价值分类模型[J].科技创新与生产力,2021(9):30-33.

[51] 王璐,梅蕾,李春丽.A公司客户细分及客户关系管理[J].内蒙古科技与经济,2020(15):25-28+32.

[52] 熊兰,高炳.基于RFM多层级客户价值模型的客户细分研究[J].商业经济研究,2017(5):55-57.

[53] 许雪晶,林辰玮.基于RFM的电商数据客户价值细分实例研究[J].长春师范大学学报,2021,40(4):60-69.

[54] 徐华杰.K-means聚类在A保险公司客户细分模型中的研究[J].网络安全技术与应用,2021(09):39-41.

[55] 闰磊,刘旭,徐斌.基于客户价值的卷烟零售客户分类研究与应用[J].价值工程,2019,38(25):89-90.

[56] 杨阳.国信证券湖南分公司客户关系管理优化研究[D]. 湖南大学,2019.

[57] 张利利,马艳琴.基于数据挖掘技术的航空客户流失与细分研究及 R 语言程序实现[J].数学的实践与认识,2019,49(06):134-142.

[58] 郑倩.AX公司客户细分方案优化研究[D].上海外国语大学,2021.

[59] 张翔. 客户关系管理——基于营销架构的企业运营问题解决之道[M]. 机械工业出版社, 2007.