知识管理系统中的CBR技术研究

基于案例的推理技术尝试在计算机上将叙述能力与知识整理进行结合,在为知识管理系统的实现提供了基本的技术保障的同时,也拓宽了推理技术的应用领域。首先讨论了基于案例的推理的一些基本问题,然后借助一个实例探讨了CBR技术在知识管理系统中的应用,并提出了进一步的研究方向。     

分析型CRM的软件体系结构

随着客户关系管理概念的普及推广,各厂家的CRM产品及人们对CRM的讨论层出不穷,由于这些讨论大多集中在CRM的功能、效益及发展前景等方面,因而对CRM本身的软件体系结构却未能形成统一的标准。该文针对CRM系统中重要的支撑部分-分析型CRM,依据层次化体系结构和中间件的思想,提出一种分析型CRM的分层软件体系结构。     

基于顾客满意度的改进设计决策模型

由于科学技术发展越来越快,产品生命周期缩短,生产成本降低空间缩小,技术创新空间降低,各企业对顾客满意度的关注度越来越高,但是随着产品生命周期的缩短,产品更新换代速度的加快,企业对于产品改计的切入点寻找难度增加,改进成本也随之提高,本文从顾客满意度的角度,对卡诺（Kano）模型进行改进,引入优先指数,将待改进指标进行排序,使企业的改进方向更为明确,为改进决策起导向作用。     

数据挖掘在电子商务中的应用

数据挖掘技术作为解决“数据爆炸”时代出现的“信息缺乏”的最有效手段之一,受到了企业界的极大关注。文章阐述了电子商务中数据挖掘技术的框架、数据资源、专业人员、基本方法,分析了数据挖掘在电子商务中的具体应用。     

基于聚类的数据挖掘技术在电子商务CRM中的应用研究

本文通过对电子商务中客户关系管理聚类数据挖掘技术的研究,提出基于CABOSFV算法的客户聚类算法,用于解决客户关系管理中大量高维稀疏数据组成的客户行为数据集聚类分析和信息管理问题。     

A Design Process for a Customer Journey Map: A Case Study on Mobile Services

A customer journey map (CJM) is a widely used tool to represent user experience with a service. Although numerous companies have used this tool to improve existing services or to develop new services, the maps are neither consistent nor mutually compatible because no clear design process for a CJM has been presented. This study aimed to develop a design process and rule sets for a CJM based on a human factors approach. The 10‐step process and the rule sets were built on case studies of 25 categories of mobile services. Large‐scale case studies were conducted with mobile service providers and combined with the result of a user diary method that collected users’ daily activities and the difficulties that the user perceived when performing a task. We suggested various methods for using the CJM to generate new service opportunities. The proposed design process and the way for opportunity discovery can help service designers to develop unified CJMs and to identify innovative service ideas.     

A linear goal programming approach to determining the relative importance weights of customer requirements in quality function deployment

Quality function deployment (QFD) is a planning tool used in new product development and quality management. It aims at achieving maximum customer satisfaction by listening to the voice of customers. To implement QFD, customer requirements (CRs) should be identified and assessed first. The current paper proposes a linear goal programming (LGP) approach to assess the relative importance weights of CRs. The LGP approach enables customers to express their preferences on the relative importance weights of CRs in their preferred or familiar formats, which may differ from one customer to another but have no need to be transformed into the same format, thus avoiding information loss or distortion. A numerical example is tested with the LGP approach to demonstrate its validity, effectiveness and potential applications in QFD practice.     

数据挖掘在客户关系管理的应用分析

介绍了数据挖掘的一般概念和主要用途,详细描述了实施数据挖掘的闭环流程,并通过案例“客户流失模型的建立”进一步分析了数据挖掘在客户关系管理中的应用。     

A stochastic model on the profitability of loyalty programs

Effectiveness of customers’ loyalty programs has been the focal point of some recent studies. While empirical research shows mixed findings, analytical studies on the efficacy of loyalty programs are in their early stages. In this paper, we develop an analytical model on the profitability of loyalty programs in which customers’ valuations along with their satisfaction levels are incorporated as stochastic variables. The model consists of a revenue-maximizing firm selling a product through two periods. A loyalty reward is offered to two-period buyers in the form of an absolute-value discount on the price in the second period. The satisfaction level is represented by the difference between a customer’s original and post-purchase valuation. The formulation yields a stochastic programming problem with a nonlinear non-convex objective function. The problem is solved in terms of the model parameters. The results reveal that depending on the mean and variance of the satisfaction levels, the firm may be better off not to offer a loyalty reward. Specifically, if the mean of satisfaction levels turns out to be positive with a coefficient of variation less than a certain threshold, not adopting the loyalty program is optimal.     

Soft computing applications in customer segmentation: State-of-art review and critique

Segmentation has been taken immense attention and has extensively been used in strategic marketing. Vast majority of the research in this area focuses on the usage or development of different techniques. By means of the internet and database technologies, huge amount of data about markets and customers has now become available to be exploited and this enables researchers and practitioners to make use of sophisticated data analysis techniques apart from the traditional multivariate statistical tools. These sophisticated techniques are a family of either data mining or machine learning research. Recent research shows a tendency towards the usage of them into different business and marketing problems, particularly in segmentation. Soft computing, as a family of data mining techniques, has been recently started to be exploited in the area of segmentation and it stands out as a potential area that may be able to shape the future of segmentation research. In this article, the current applications of soft computing techniques in segmentation problem are reviewed based on certain critical factors including the ones related to the segmentation effectiveness that every segmentation study should take into account. The critical analysis of 42 empirical studies reveals that the usage of soft computing in segmentation problem is still in its early stages and the ability of these studies to generate knowledge may not be sufficient. Given these findings, it can be suggested that there is more to dig for in order to obtain more managerially interpretable and acceptable results in further studies. Also, recommendations are made for other potentials of soft computing in segmentation research.