【作者】 王芳；

【导师】 邱玉辉；

【作者基本信息】 西南师范大学 ， 计算机应用， 2003， 硕士

【摘要】 经济的全球化导致行业的市场竞争日益激烈，信息时代的企业必须利用大量数据中隐含的知识才能抓住时机，提升核心竞争力。 客户是企业至关重要的成功因素和利润来源。将数据挖掘技术应用于客户关系管理，能够为企业提供经营和决策的量化依据，使企业更有效地利用有限的资源，拓展利润上升空间。 客户流失预测和控制是当今所有企业面临的一大难题。大量而频繁的客户流失延长了企业利润回收的周期，给企业造成了巨大的损失。目前国内外对流失控制的研究一般是采取提供个性化服务、进行客户满意度和客户忠诚度分析的方法，这些方法的有效性很难验证，而且不能从根本上解决问题。 本文将多种数据分析技术应用于客户流失研究，针对目前相关研究中存在的问题，给出了客户流失研究中涉及的主要问题的解决方案，包括客户描述、属性约简、流失模型发现、流失原因分析以及流失预测与控制策略等，重点解决其中流失模型的建立问题。 客户流失模型是通过对流失客户的数据进行分析后得出的，包括基本模型和行为模型。对客户的基本数据实施关联规则挖掘，可以发现描述流失客户基本特征的关键属性集合。论文中采取的是一种能自动调节最小支持度的、受限的关联规则挖掘算法CAARM，该算法是在前人研究的基础上，作了一些调整和改善后得到的。客户流失的行为模型采用序列模式发现方法，识别出流失客户的典型行为序列，用作流失趋势的预测。 论文对客户价值分析也作了初步的探讨，认为应将客户流失预测群体中价值较高的子群体作为市场策略的目标群体，并结合消费者心理学的有关知识对客户流失原因进行了简单的分析。 最后给出了部分关键算法的详细描述和分析。更多还原

【Abstract】 With the increasingly keen industry competition caused by the globalization of economy, enterprises in Information Age are compelled to capture opportunities and build up their core competition ability by utilizing knowledge concealed in large amount of data.For most enterprises, customers are the key success factor and the most important source of profit. Customer Relationship Management (CRM) based on data mining techniques provides a quantitative criterion in business management and decision-making. CRM helps enterprises utilize their limited resources more effectively so as to broaden their profit development space.It is a tough problem for all enterprises to predict and control customer churn. They have suffered heavy losses caused by the frequently occurred customer churn that prolongs their cost recover cycle. Research routine of decreasing customer churn is to provide customized service, or analyze customers’ satisfaction and loyalty. The effectiveness of these methods is hard to be verified. Furthermore, they could not solve the problem essentially.In this thesis, data analysis techniques are merged into the research of customer churn. Solutions of existing problems involved are proposed in detail, including customer profiling, attribute reducing, customer churn model building, analysis of churn causation, churn prediction and controlling strategies. Among those we focus on the customer churn model building problem.Customer churn model, consists of basic model and behavioral model, is built based on analysis of the churned customers’ basic data and behavioral history. The setof key attributes that describe churned customers’ basic character are founded by applying association rule mining to their basic profiling data. After adjusting and revising algorithms proposed by precedent researchers, we develop a Constrained Adaptive Association Rule Mining (CAARM) algorithm that can find rule with specific head, and it does not require a given minimum support. Sequence pattern discovery method is used to build behavioral churn model, by which we can distinguish the typical behavioral sequences of the churned customers and predict present customers’ churn tendency.Based on the superficially discussed issue of customer lifetime value, we suggest that customer subgroups with higher lifetime value should be selected as target of specific market strategy.Finally, there are central algorithms described in detail and the analysis results of them as well.更多还原