基于客户价值的客户分类模型研究（管理科学与工程专业优秀论文）
合集下载
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
(4) The customer loyalty is analyzed from the two aspects of sensibility and behavior in the paper and a guideline system of the customer loyalty is set up. The guideline system can be used to count numerical value of the customer loyalty. Finally, a mode based on the CPN is established to predict the customer loyalty.
华中科技大学博士学位论文
摘要
随着经济全球化以及网络技术的发展，信息交流越来越通畅，企业面临着更加 激烈的市场竞争，对客户的争夺能力成为企业生存和发展的决定性因素。企业成功 获取客户的关键是确定企业的客户，合理的客户分类是企业改善与客户关系的前提 条件，也是非常关键的因素。对客户的合理分类有助于企业有效分配稀少的资源， 加强与客户的联系，获得真正的竞争优势。基于此，本文对客户分类方法及模型进 行了深入的研究，运用管理决策理论及数据挖掘技术和统计技术，对客户分类的变 量及方法进行了分析，分析过程及结果概括为以下几个方面：
(3) For the cross-selling capability of the customer, which denotes the customer potential value, two understanding are provided. Two cross-selling models are then established based on the Counter Propagation Network (CPN). The investigation indicates that both models can forecast the cross-selling activity efficiently. The numerical analysis methods which can denote the cross-selling capability of the customer are put forward through estimating the two cross-selling models. The numerical value forecast of the cross-selling capability can be acquired through analyzing the cross-selling capability based on the customer maturity and the product degree of the latest product bought by the customer. Combined with the result of the customer classification based on RFM, the customer is classified again and the current value and potential value of each kind of customer are also explained.
(5) To sum up the theories of the RFM, the cross-selling and the customer loyalty in the customer classification, a customer classification system can be set up base on the customer values (the RFM, the cross-selling and the customer loyalty) and a model is hence established. The value order of each kind of customer classified according to the model can be used to provide a direction for distributing resource of the enterprise reasonable.
(2) For the analysis of the customer current value using RFM, the customer classification model based on Self Organization Map (SOM) is established. The optimal export frame of the SOM customer classification model is 4×4 through analyzing the SOM model with the export frame of 2×2、3×3、4×4. Each kind of customer is thought
(1) 根据客户价值的内涵，对客户价值的构成进行全面深入的分析，提出基于 客户当前价值、潜在价值及客户忠诚度构建客户价值模式；分析了客户价值与客户 当前价值、客户潜在价值及客户忠诚度之间的关系；提出以 RFM 表示客户当前价 值，以交叉销售能力表示客户潜在价值。
(2) 以 RFM 分析客户的当前价值，并基于自组织神经网络建立客户分类模型。 通过对输出层结构分别为 2×2、3×3、4×4 的自组织神经网络模型进行实例分析，确 定最佳的自组织神经网络分类模型输出层结构为 4×4；通过对基于 RFM 所划分的 16 类客户进行分析，说明了每种类型客户的不同当前价值；利用层次分析法分析 RFM 对客户当前价值的贡献权重，并对每类客户当前价值排序，得到每类客户当前 价值的具体排序情况。
(1) Customer value is used as a criterion for the customer classification in the paper. According to the connotation of the customer value, the constitution of the customer value is first analyzed in detail, including the relations between the customer value and the customer current value, the customer potential and the customer loyalty. The theory of constructing model based on the customer current value, the customer potential and the customer loyalty is then provided. The customer current value is expressed using RFM (Recency, Frequency and Money value), while the customer potential value is expressed using the cross-selling capability of the customer.
(3) 针对表示客户潜在价值的客户交叉销售能力，提出了两种理解方式，并基 于对向传播神经网络分别建立这两种不同理解的交叉销售模型，研究表明两种模型 都能有效预测客户交叉销售活动。通过对这两种交叉销售模型的分析评价，提出了 表征客户交叉销售能力的数值分析方法，并基于客户成熟度及客户最近一个购买产 品的产品等级分析客户交叉销售能力，解决了客户交叉销售能力的数值预测问题。 在此基础上，结合 RFM 及交叉销售能力对客户进行分类，对每种类型客户从当前 价值及潜在价值进行了说明。
II 知识水坝@pologoogle为您整理
华中科技大学博士学位论文
Abstract
With globalization of economy, development of network and easier information exchange within people, market competition of enterprises become more and more severity. The decisive factor for survival and development of an enterprise is the capability of the enterprise for contending for customers, and that the crucial factor of customer acquisition successfully for the enterprise is to know the information clearly of the key customers of the enterprise. Therefore, a reasonable customer classification is a premise condition for improving the relation between the enterprise and its customers. The effective classification of the customer groups redounds to distribute exiguous resource and strengthen the contact between the enterprise and the customer. A competitive advantage is hence obtained actually. Based on the theory, the classification methods and models are investigated in detail in this paper. In the paper, the theory of management decision-marketing and the technologies of data mining and statistical data are used to analyze the variables and models of the customer classification. The main content is summarized as follows:
I
ቤተ መጻሕፍቲ ባይዱ
华中科技大学博士学位论文
(4) 对客户忠诚度从情感和行为两方面进行分析，构建了客户忠诚度指标体系， 通过该指标体系可以计算客户忠诚度的数值，并建立了基于神经网络的客户忠诚度 模型，该模型可以预测客户的忠诚度值。
(5) 综合 RFM、交叉销售能力和客户忠诚度在客户分类中的应用，构建了基于 客户价值（RFM、交叉销售能力和客户忠诚度）的客户分类体系，依此提出综合 RFM、交叉销售能力及忠诚度三方面对客户进行分类的模型，并对所划分的每类客 户的价值进行排序，为企业合理分配资源提供了依据。 关键词：客户价值；客户忠诚度；交叉销售；对向传播神经网络；自组织神经网络
III 知识水坝@pologoogle为您整理
华中科技大学博士学位论文
having different current value by the analysis of the 16 kinds of customer classified on RFM. Furthermore, the contributing right of the customer current value is analyzed using analytic hierarchy process (AHP). The order of the current value of every kind of customer is also acquired through arrangement.
华中科技大学博士学位论文
摘要
随着经济全球化以及网络技术的发展，信息交流越来越通畅，企业面临着更加 激烈的市场竞争，对客户的争夺能力成为企业生存和发展的决定性因素。企业成功 获取客户的关键是确定企业的客户，合理的客户分类是企业改善与客户关系的前提 条件，也是非常关键的因素。对客户的合理分类有助于企业有效分配稀少的资源， 加强与客户的联系，获得真正的竞争优势。基于此，本文对客户分类方法及模型进 行了深入的研究，运用管理决策理论及数据挖掘技术和统计技术，对客户分类的变 量及方法进行了分析，分析过程及结果概括为以下几个方面：
(3) For the cross-selling capability of the customer, which denotes the customer potential value, two understanding are provided. Two cross-selling models are then established based on the Counter Propagation Network (CPN). The investigation indicates that both models can forecast the cross-selling activity efficiently. The numerical analysis methods which can denote the cross-selling capability of the customer are put forward through estimating the two cross-selling models. The numerical value forecast of the cross-selling capability can be acquired through analyzing the cross-selling capability based on the customer maturity and the product degree of the latest product bought by the customer. Combined with the result of the customer classification based on RFM, the customer is classified again and the current value and potential value of each kind of customer are also explained.
(5) To sum up the theories of the RFM, the cross-selling and the customer loyalty in the customer classification, a customer classification system can be set up base on the customer values (the RFM, the cross-selling and the customer loyalty) and a model is hence established. The value order of each kind of customer classified according to the model can be used to provide a direction for distributing resource of the enterprise reasonable.
(2) For the analysis of the customer current value using RFM, the customer classification model based on Self Organization Map (SOM) is established. The optimal export frame of the SOM customer classification model is 4×4 through analyzing the SOM model with the export frame of 2×2、3×3、4×4. Each kind of customer is thought
(1) 根据客户价值的内涵，对客户价值的构成进行全面深入的分析，提出基于 客户当前价值、潜在价值及客户忠诚度构建客户价值模式；分析了客户价值与客户 当前价值、客户潜在价值及客户忠诚度之间的关系；提出以 RFM 表示客户当前价 值，以交叉销售能力表示客户潜在价值。
(2) 以 RFM 分析客户的当前价值，并基于自组织神经网络建立客户分类模型。 通过对输出层结构分别为 2×2、3×3、4×4 的自组织神经网络模型进行实例分析，确 定最佳的自组织神经网络分类模型输出层结构为 4×4；通过对基于 RFM 所划分的 16 类客户进行分析，说明了每种类型客户的不同当前价值；利用层次分析法分析 RFM 对客户当前价值的贡献权重，并对每类客户当前价值排序，得到每类客户当前 价值的具体排序情况。
(1) Customer value is used as a criterion for the customer classification in the paper. According to the connotation of the customer value, the constitution of the customer value is first analyzed in detail, including the relations between the customer value and the customer current value, the customer potential and the customer loyalty. The theory of constructing model based on the customer current value, the customer potential and the customer loyalty is then provided. The customer current value is expressed using RFM (Recency, Frequency and Money value), while the customer potential value is expressed using the cross-selling capability of the customer.
(3) 针对表示客户潜在价值的客户交叉销售能力，提出了两种理解方式，并基 于对向传播神经网络分别建立这两种不同理解的交叉销售模型，研究表明两种模型 都能有效预测客户交叉销售活动。通过对这两种交叉销售模型的分析评价，提出了 表征客户交叉销售能力的数值分析方法，并基于客户成熟度及客户最近一个购买产 品的产品等级分析客户交叉销售能力，解决了客户交叉销售能力的数值预测问题。 在此基础上，结合 RFM 及交叉销售能力对客户进行分类，对每种类型客户从当前 价值及潜在价值进行了说明。
II 知识水坝@pologoogle为您整理
华中科技大学博士学位论文
Abstract
With globalization of economy, development of network and easier information exchange within people, market competition of enterprises become more and more severity. The decisive factor for survival and development of an enterprise is the capability of the enterprise for contending for customers, and that the crucial factor of customer acquisition successfully for the enterprise is to know the information clearly of the key customers of the enterprise. Therefore, a reasonable customer classification is a premise condition for improving the relation between the enterprise and its customers. The effective classification of the customer groups redounds to distribute exiguous resource and strengthen the contact between the enterprise and the customer. A competitive advantage is hence obtained actually. Based on the theory, the classification methods and models are investigated in detail in this paper. In the paper, the theory of management decision-marketing and the technologies of data mining and statistical data are used to analyze the variables and models of the customer classification. The main content is summarized as follows:
I
ቤተ መጻሕፍቲ ባይዱ
华中科技大学博士学位论文
(4) 对客户忠诚度从情感和行为两方面进行分析，构建了客户忠诚度指标体系， 通过该指标体系可以计算客户忠诚度的数值，并建立了基于神经网络的客户忠诚度 模型，该模型可以预测客户的忠诚度值。
(5) 综合 RFM、交叉销售能力和客户忠诚度在客户分类中的应用，构建了基于 客户价值（RFM、交叉销售能力和客户忠诚度）的客户分类体系，依此提出综合 RFM、交叉销售能力及忠诚度三方面对客户进行分类的模型，并对所划分的每类客 户的价值进行排序，为企业合理分配资源提供了依据。 关键词：客户价值；客户忠诚度；交叉销售；对向传播神经网络；自组织神经网络
III 知识水坝@pologoogle为您整理
华中科技大学博士学位论文
having different current value by the analysis of the 16 kinds of customer classified on RFM. Furthermore, the contributing right of the customer current value is analyzed using analytic hierarchy process (AHP). The order of the current value of every kind of customer is also acquired through arrangement.