Web日志挖掘与电子商务

在电子商务中,根据客户的访问数据挖掘出有价值的信息,进而划分客户群体和发现潜在的客户,从而提升业绩,对电子商务网站有重要的意义。Web日志挖掘是数据挖掘在Web页面上的应用,文章对Web日志挖掘进行了介绍,给出了Web日志挖掘的步骤和方法,对Web日子挖掘工具进行了分析,并介绍了其在电子商务网站中的应用。     

电子商务中基于Web挖掘的客户行为探讨

在电子商务发展的过程中,客户行为分析的问题越来越突出。如何保持现有客户、发掘潜在客户;如何对客户成功实施交叉销售或关联销售;如何防范客户的欺诈行为等成为电子商务成败的关键问题。而Web数据挖掘作为一种最新的Web信息分析技术,成为企业更好运行电子商务的解决方法。     

Web挖掘及其在电子商务中的应用

电子商务网站向用户提供及时有效的信息,是提高网站声誉和效益的基础。Web挖掘可以通过分析用户的使用偏好,帮助用户高效地检索到所关心的信息。本文深入讨论了Web结构挖掘、Web内容挖掘和Web使用挖掘等技术,并提出了商务网站基于Web挖掘技术为用户提供个性化推荐的一种框架。     

电子商务中客户行为特征的挖掘方法

本文讨论了Web数据挖掘的一个重要分支—Web用法挖掘在电子商务客户行为特征挖掘中的应用。介绍了客户行为特征挖掘的主要方法,并详细描述了一个基于粗糙集的电子商务客户行为特征挖掘模型。     

Web结构挖掘在电子商务网站结构优化中的应用

本文概述了Web结构挖掘技术并描述了其常见算法。针对Web结构挖掘算法,提出了提高网站结构合理性的对策。使用户通过优化网站结构来获取更多有效客户,扩大网站影响力,从而提升网站价值。     

基于Web使用挖掘的企业电子商务平台研究

随着Internet的普及,电子商务已经成为现代企业进行商务活动的必然趋势。充分利用数据挖掘技术,可进行电子商务海量信息采集,辅助商家理解用户行为,从而改进站点结构,调整销售策略,提供个性化服务。首先介绍Web使用挖掘,然后分析了应用于电子商务中的Web挖掘技术。最后从优化网站设计、改善企业客户关系管理和改进企业网络营销三个方面阐述了Web使用挖掘的应用。     

Web挖掘技术在电子商务中的应用研究

Web挖掘是数据挖掘应用于互联网的具体表现形式,也是从海量的电子商务交易信息中提取知识以掌握网络客户消费习惯的重要技术手段。本文旨在分析Web挖掘常用的技术及其在电子商务中的挖掘流程,并对其在电子商务中的应用领域进行探讨,以协助商家更好的运用网络开展电子商务活动。     

Web挖掘在电子商务中的应用探讨

顾客对电子商务网站的访问行为产生了大量数据,这些数据中包含了很多对市场分析及预测有用的潜在信息,可以运用Web数据挖掘技术,从用户的浏览行为数据中获取用户的访问模式,从而开展个性化的信息服务和有针对性的电子商务活动和构建智能化电子商务站点。本文系统分析了Web挖掘的过程和关键技术,探讨了Web挖掘在电子商务中的应用模式。     

数字化校园中Web数据挖掘的应用研究

数字化校园中各部门网站每天都会产生大量的数据,运用数据挖掘技术可以从这些数据中发现对师生行为分析及预测非常有益的信息。本文探讨了：数字化校园中Web数据挖掘的过程,重点研究了Web数据挖掘方法,从而有效提高学校的办公效率。     

Web文本挖掘技术探析

Web数据挖掘是目前信息技术中的研究热点。对Web挖掘的分类和Web文本挖掘的主要过程如文本预处理、特征表示、特征提取等进行了讨论,最后对Web文本挖掘的应用领域作了展望。     

Web site customer orientations, Web site quality, and purchase intentions: The role of Web site personality

This research extends the concept of salesperson customer orientation to Internet marketing by conceptualizing and studying the effects of Web site customer orientation on perceived Web site quality and consumer behavior. This research also adapts the concept of brand/store personality to Internet marketing by investigating the effects of Web site personality on perceived Web site quality and consumer purchase intention. The model is tested on apparel Web sites using data from multiple sources. Implications of managing Web site personality and Web site customer orientation are discussed.     

Understanding Personal Web Usage in Organizations

Personal web usage, that is, non-work-related use of the Internet for personal purposes during work hours, is a pervasive behavior observed in the daily work environment. U.S. companies have implemented several countermeasures to cope with personal Web usage, but those measures have not successfully mitigated this behavior. Considering the significance of personal Web usage, we need to understand why personal Web usage is not alleviated in current organizations. The goal of this study is to present an empirical investigation of why employees continue personal Web usage based on an extended theory of planned behavior. We are particularly interested in how people's perceptions of moral dimensions contribute to personal Web usage, recognizing that our work environment becomes more Web-embedded. We conducted a field survey of 426 U.S. business professionals and analyzed the data by using partial least squares. As a result, we demonstrate that an individual's attitude, subjective norm, denial of responsibility, self-efficacy, personal computer availability, seclusion of office, and workload are significant factors affecting personal Web usage. Interestingly, current organizational preventive efforts (e.g., Web-based activity monitoring and filtering systems and policies) and moral obligation are not as significant as we originally expected. Key implications for theory and practice are discussed.     

基于数据挖掘技术的客户关系管理

通过利用数据挖掘技术建立成功的客户关系管理系统。分析基于数据挖掘技术的客户行为和重点客户的发现 ,基于数据挖掘技术的客户关系管理模式 ,建立客户关系管理系统将极大地提升企业的竞争优势 ,提高企业的竞争水平     

数据挖掘及其在企业管理中的应用

企业在管理过程中产生了大量的数据,这些数据的背后隐藏着与企业密切相关的极其重要的知识。聚类、关联规则、序列模式、统计分析、特征规则等数据挖掘方法能从这些海量数据中发现有用的知识,使数据真正成为企业的财富,为企业的决策和发展服务。目前数据挖掘已被广泛应用于银行、电信等行业,用来对客户数据进行正确的分析,挖掘消费模式,预测客户未来的行为,针对客户的需求提供个性化的服务。     

基于数据挖掘技术的企业客户关系管理(CRM)

由于竞争的全球化、需求的拉动和管理理念的更新,使客户关系管理(CRM)得到产生和发展。面对企业海量的数据,如何从其中发现有价值的知识和规律是企业急需解决的难题。数据挖掘技术为此提供了工具和途径。在了解CRM的概念和框架、数据挖掘的各种技术后,还必须了解数据挖掘在CRM中的应用流程和应用的业务领域。     

Choice and the Internet: From Clickstream to Research Stream

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.     

基于数据挖掘技术的客户关系管理应用研究

本文先分析了客户关系管理定义和功能、接着分析了数据挖掘的流程,最后浅析了数据挖掘技术在企业客户关系管理中的具体应用。     

Measuring the Value of Corporate Web Sites

ABSTRACT

As marketing executives respond to increased pressure to measure the impact of their activities, corporate Web sites can be a useful tool in measuring marketing performance. This study presents the results of a series of interviews with Web site managers aimed at describing the most common methods of determining the value contribution of corporate Web sites. However, despite the availability of large amounts of site usage data, Web site performance measurement tends to be performed ad hoc by most organizations, and there is little assessment of the relationship between the performance of a company's external Web site and overall corporate results or increase in shareholder value.     

Web retailing adoption: exploring the nature of internet users Web retailing behaviour

The interaction of consumers and marketers within the Web environment, particularly for retailing/purchasing is a growing area of importance. This paper focuses on examining Internet users adoption of the Web for retail usage. It uses the Technology Acceptance Model Davis (Int. J. Man-Mach. Studies 38 (1993) 475) as a theoretical foundation to explore adoption of this technology for retail usage. The study also adds what are argued to be key consumer characteristics such as Opinion Leadership, Impulsiveness, Web Shopping Compatibility, Internet Self-Efficacy, Perceived Web Security, Satisfaction with web sites, and Shopping Orientation to understand the adoption of Web retailing by Internet users’. A Web based survey was developed and administered, yielding 392 responses. The findings indicate that TAM is a valid theoretical framework to understand users adoption of the Web for retail purposes. Also Internet users’ perceived usefulness and perceived ease of use were effected differentially by Opinion Leadership, Web Shopping Compatibility, Internet Self-Efficacy, Perceived Web Security, Impulsiveness, Satisfaction with web sites, and Shopping Orientation.     

All That Glitters is Not Gold: Digging Beneath the Surface of Data Mining

This article develops a more comprehensive understanding of data mining by examining the application of this technology in the marketplace. In addition to exploring the technological issues that arise from the use of these applications, we address some of the social concerns that are too often ignored.As more firms shift more of their business activities to the Web, increasingly more information about consumers and potential customers is being captured in Web server logs. Sophisticated analytic and data mining software tools enable firms to use the data contained in these logs to develop and implement a complex relationship management strategy. Although this new trend in marketing strategy is based on the old idea of relating to customers as individuals, customer relationship management actually rests on segmenting consumers into groups based on profiles developed through a firm's data mining activities. Individuals whose profiles suggest that they are likely to provide a high lifetime value to the firm are served content that will vary from that which is served to consumers with less attractive profiles.Social costs may be imposed on society when objectively rational business decisions involving data mining and consumer profiles are made. The ensuing discussion examines the ways in which data mining and the use of consumer profiles may exclude classes of consumers from full participation in the marketplace, and may limit their access to information essential to their full participation as citizens in the public sphere. We suggest more ethically sensitive alternatives to the unfettered use of data mining.