基于改进GMDH的目标客户选择模型研究

近年来,目标客户选择建模成为客户关系管理领域的研究热点。为了解决用于目标客户选择建模的训练样本类别分布高度不平衡的问题,本文首先提出了混合抽样方法。进一步地,将数据分组处理(GMDH)神经元网络引入到客户特征选择中,提出新的特征选择算法Log-GMDH。该算法分别从传递函数的选择和新的外准则的构建两个方面对传统GMDH网络模型进行了改进。最后,将提出的混合抽样、Log-GMDH和Logistic回归分类算法相结合,构建目标客户选择模型LogGMDH-Logistic。在CoIL2000预测竞赛中某汽车保险公司的目标客户选择数据集上进行实证分析,结果表明,LogGMDH-Logistic模型不仅在性能上优于已有的一些目标客户选择模型,而且具有很好的可解释性。     

Variable precision rough set theory and data discretisation: an application to corporate failure prediction

Since the seminal work of Pawlak (International Journal of Information and Computer Science, 11 (1982) 341–356) rough set theory (RST) has evolved into a rule-based decision-making technique. To date, however, relatively little empirical research has been conducted on the efficacy of the rough set approach in the context of business and finance applications. This paper extends previous research by employing a development of RST, namely the variable precision rough sets (VPRS) model, in an experiment to predict between failed and non-failed UK companies. It also utilizes the FUSINTER discretisation method which neglates the influence of an ‘expert’ opinion. The results of the VPRS analysis are compared to those generated by the classical logit and multivariate discriminant analysis, together with more closely related non-parametric decision tree methods. It is concluded that VPRS is a promising addition to existing methods in that it is a practical tool, which generates explicit probabilistic rules from a given information system, with the rules offering the decision maker informative insights into classification problems.     

A Simple Algorithm for the Mathematical Programming Approach to Joint Cost Allocation for Decision Making (Demand-Adjusted-Sales-Value Algorithm)*

This paper investigates the mathematical programming (MP) approach for decision making and cost allocation in the context of joint products. Through analysis of the MP approach to marginal-value-based cost allocations, an algorithm is developed to show that the allocations contain two elements: an element that is specific to individual joint products due to differences in demand elasticities, and an element based on relative adjusted sales values. If demand elasticities are identical for all joint products, then the traditional sales value method will generate the same cost allocation as the MP method. The new algorithm, the demand-adjusted-sales-value algorithm (DASV), is especially helpful for solving the MP model with linear demand functions. The DASV algorithm provides an easy way to generate costs that are useful for optimal decision making.     

客户利润贡献度评价的数据挖掘方法

讨论了客户关系管理中的客户利润贡献度评价这一关键问题. 为了提高客户利润贡献度评 估的可操作性,本研究在评价方法中引入数据挖掘技术,给出了这一方法的步骤和基于判定树的分 类模型. 最后结合实例对该方法的应用进行了说明.     

动静态属性数据相结合的客户分类方法研究

研究了一种客户动态、静态属性数据相结合的客户分类方法。提出了客户时间序列的加权处理方法,并应用客户时间序列的统计特征作为聚类特征向量,采用混合式遗传算法对客户聚类,使每一类客户具有相似的时序特征。在此基础上将聚类结果与客户的静态属性数据相结合,对客户进一步分类。实验结果表明,与传统的基于静态属性数据的客户分类方法相比,本文的方法提高了客户分类的准确性。     

基于客户价值的客户序位评价研究

客户关系管理是目前管理研究的热点,企业对客户价值的序位评价是其基础问题之一,其有效性是实施客户关系管理的基础.基于价值的客户序位评价是根据客户的价值大小来定位客户.本文首先在现有的客户价值评价体系的基础上结合客户生命周期理论设计了一种新的客户价值评价指标体系,从客户的生命周期阶段和客户发展潜力两个方面来评价客户价值.然后根据该指标体系的指标特性,选择神经网络作为评价方法.最后根据客户价值评价结果进行客户序位,并对每个类别的客户特征及其相应的营销策略进行了分析.     

The systemic perspective of service processes: underlying theory,architecture and approach

As competition in the service industry grows, delivering high-quality service to meet customers’ needs and expectations becomes paramount. In order to achieve a thorough understanding of customers, companies are increasingly adopting Customer Relationship Management (CRM) initiatives. However, CRM does not have the process vision necessary to cope with complex and cross-functional processes. This problem is better handled by Business Process Management (BPM) initiatives, which, by their turn, struggle to built-in customer intelligence in their systems. Based upon systems theory, we discuss the roles of CRM and BPM in an integrated approach to deliver customer fulfillment. As the evolution of service-oriented approaches requires us to consider both customer interactions and experiences as key elements of service processes, we present a broader perception of service processes involving these two perspectives. Also, we discuss the architecture of a customer-facing system comprising customer processes that are aimed at better dealing with customer interactions.     

Comparing Decision Rules that Incorporate Customer Diversion in Perishable Asset Revenue Management Situations

Most of the initial work on perishable asset revenue management (PARM) situations assumed no possibility of customer diversion from one product class to another when the former is made unavailable. In this paper, an evaluation is made of the few published decision rules that incorporate the realistic and common behavior of customer diversion (or sell-up). We present a new heuristic approach that incorporates diversion and could be used by airlines and other relevant industries to achieve improvements in expected contribution of 0.25 to 2.5% over the approach currently used. The sensitivity of the difference in expected contribution between these rules is tested relative to changes in the input parameters, and managerial insights are presented.     

Evaluating new product decision processes

The development of a new product is often a sequential-decision process with many available stages at which the product may take any one of a number of alternate courses of action. This paper identifies an efficient algorithm for solving this sequential-decision process. A computer program, involving the algorithm, has been written to solve problems containing up to one thousand stages. Input into the program includes the subjective probabilities of success for each decision branch, the cost associated with each decision stage and level, the rate for discounting all monetary values to the present plus an indicator for each decision stage and level of the desired decision criterion. The algorithm utilizes the dynamic-programming solution approach. The output of the algorithm contains the minimum, or maximum, expected discounted decision values for each stage and level of the network. The algorithm and computer program have been primarily utilized in the pharmaceutical industry. An illustrative, exemplary use of the algorithm in this industry is presented.     

基于平衡计分卡的CRM有效性评价模型研究

客户关系管理(CRM)是二十一世纪的主导战略之一.由于CRM的复杂性,目前对CRM有效性评价的研究还很少.考虑到平衡计分卡(BSC)的优点,本文建立了一个基于BSC的CRM有效性评价模型,该模型包含四个视角:客户知识、客户交互、客户价值、客户感知价值.通过对四个视角的作用分析,构造了四个视角的评价矩阵,并通过一个案例研究验证了该模型的可行性和有效性.     

CRM赢回策略对消费者购买行为影响的实证研究

一直以来,营销学界都把客户关系管理(Customer Rehtionship Management)的重点放在如何获得新顾客和维持现有顾客身上,而流失顾客赢回管理工作的重要性却长久地被忽视.为了证明失而复得顾客的重要价值,作者把顾客在流失前、赢回后两个不同阶段的满意度和购买份额作对比分析,结果发现,赢回后的顾客满意度和购买份额较流失前显著增加;用关系投资或价格促销策略赢回来的顾客满意度和购买份额,较被关系投资或价格促销吸引来的新顾客满意度和购买份额显著提高;流失前,顾客满意度与购买份额之间拟合的函数曲线呈反S型,厂商策略对这一阶段的顾客行为影响不显著;赢回后,顾客满意度与购买份额之间拟合的函数曲线呈S型,赢回策略对这一阶段的顾客行为影响显著;满意度与购买份额拟合的函数曲线参考点发生了位移.     

CRM中客户忠诚对价格敏感性的影响研究

综合考虑了CRM中的客户竞争、客户保持和转移成本因素,建立了基于客户忠诚度的客户关系保持的数学模型。然后以客户保持模型为基础,从理论上分析了不同忠诚度水平的客户的价格敏感性的不同及其原因。最后以客户价格敏感性为基础,得到了可供企业CRM系统借鉴的客户保持的定量策略。     

Coping with Group Behaviors in Uncertain Quality Function Deployment

Quality function deployment (QFD) is a planning and problem‐solving tool gaining wide acceptance for translating customer needs (CNs) into technical attributes (TAs) of a product. It is a crucial step to derive the prioritization of TAs from CNs in QFD. However, it is not so straightforward to prioritize TAs due to two types of uncertainties: human subjective perception and user variability. The main focus of this article is to propose a group decision‐making approach to uncertain QFD with an application to a flexible manufacturing system design. The proposed approach performs computations solely based on the order‐based semantics of linguistic labels to eliminate the burden of quantifying qualitative concepts in QFD. Moreover, it incorporates the importance weights of users and the concept of fuzzy majority into aggregations of individual fuzzy preference relations of different TAs in order to model the group behaviors in QFD. Finally, based on a quantifier‐guided net flow score procedure, the proposed approach derives a priority ranking with a classification of TAs into important and unimportant ones so as to provide a better decision‐support to the decision‐maker. Due to the easiness in articulating preferential information, our approach can reduce the cognitive burden of QFD planning team and give a practical convenience in the process of QFD planning.     

基于遗传算法的装备采购决策优化研究

在装备采购中，由于需求单位地域分布和担负的任务各不相同，对装备的品种、数量、时限要求也就不一样，如何使装备采购科学化、合理化，是一个涉及多变量、多目标的复杂系统问题。在综合考虑装备采购各项因素的基础上，构建多约束条件下的多目标模糊指派模型，提出了基于遗传算法的解决方案，最后通过案例进行仿真实验，验证该算法的可行性和有效性，解决了采用传统优化方法难以解决的装备采购优化决策问题。     

分支蚁群动态扰动算法求解TSP问题

蚁群优化算法是一种求解组合优化难题的强启发式算法,它利用正反馈和并行计算原理,具备很强的搜索能力。近年来,蚁群优化算法广泛应用于TSP问题的研究。本文提出分支蚁群动态扰动(DPBAC)算法,该算法主要从5个方面对基本蚁群算法做出改进:引入分支策略选取出发城市;改进状态转移规则;引入变异策略改进蚂蚁路径;改进信息素更新规则;引入条件动态扰动策略。实验表明,该算法可以有效改善基本蚁群算法搜索时间较长、容易陷入局部极小等缺点。     

客户关系管理基础理论体系框架探讨

CRM基础理论是整个CRM的基石,但对于CRM基础理论体系的确立并没有一个清晰的说法.识别有价值客户和培育有价值客户忠诚是狭义CRM的两项基本任务,从支持完成这两项基本任务的思路出发,本文提出了一个由客户价值识别理论和客户忠诚理论构成的CRM基础理论总体框架及两大理论本身的基本框架,阐述了两大基础理论的关键研究进展.     

动态客户关系管理的内涵及其模型

在分析DCRM内涵的基础上,利用随机博弈及ESDP理论,给出了DCRM建模技术及求解思路。结合实际问题提出了使客户效用及公司利润都最大的DCRM模型的三种形式,将所提模型用于超市非季节性产品的消费者数据库中,验证了所提模型是可行的、有效的,并指出研究对企业CRM实践的指导意义。     

The Design of Feature‐Limited Demonstration Software: Choosing the Right Features to Include

Today, software supports many important tasks in a variety of industries. In the specialized nature of these environments, a common problem faced by software vendors is to correctly signal the true value of a software product to the end users. For example, telecommunications equipment manufacturers design complex software for important functions like provisioning new users in the network. These software products automate various functions that would otherwise need to be done manually. In order to enable potential customers—telecommunications providers—to evaluate and recognize the full value of the product, equipment vendors often provide a free, feature‐limited version of the product to the customer. As the specific features included in the feature‐limited version influence whether the full product is purchased or not, it is essential that the features included in the feature‐limited version be selected judiciously. While the importance of identifying the best set of features has been well recognized, there has been little research to date that systematically addresses this fundamental business decision. This study fills this gap in the literature by providing an objective approach to the design of demonstration software. We illustrate the benefits of our approach through a case study involving the design of a feature‐limited demo for a wireless telecommunications equipment manufacturer.     

Asymptotics for Statistical Treatment Rules

This paper develops asymptotic optimality theory for statistical treatment rules in smooth parametric and semiparametric models. Manski (2000, 2002, 2004) and Dehejia (2005) have argued that the problem of choosing treatments to maximize social welfare is distinct from the point estimation and hypothesis testing problems usually considered in the treatment effects literature, and advocate formal analysis of decision procedures that map empirical data into treatment choices. We develop large‐sample approximations to statistical treatment assignment problems using the limits of experiments framework. We then consider some different loss functions and derive treatment assignment rules that are asymptotically optimal under average and minmax risk criteria.     

LEARNING WITH DETERMINISTIC DECISION RULES

While many problems of uncertainty are commonly analyzed by means of stochastic models, under certain circumstances this may not be an appropriate approach. The latter situation arises when the decision maker knows that the uncertain variables are not generated by a stochastic process, or when he is unwilling, or unable, to compute subjective probabilities. One of the nonstochastic approaches to uncertainty is the expectational approach in which the decision maker forms deterministic expectations about the uncertain aspects of his environment. This paper is concerned with some criteria for selecting among available expectations, or anticipations functions, and the possibility of ordering them according to these criteria. This study focuses especially on the learning criterion. The discussion brings out conceptual problems in connection with the definition of learning, as well as some technical difficulties that one encounters when attempting to compare different anticipations functions from the point of view of the learning criterion. As an illustration of the issues discussed, the paper reports on the results of some simulated decision rules. These show that decision rules in which no learning takes place, and in which some information is ignored, may perform better than more sophisticated rules.