Data mining techniques for customer relationship management
TL;DR: While differing approaches abound in the realm of data mining, the use of some type of datamining is necessary to accomplish the goals of today’s customer relationship management philosophy.
About: This article is published in Technology in Society.The article was published on 2002-11-01. It has received 494 citations till now. The article focuses on the topics: Enterprise relationship management & Customer intelligence.
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TL;DR: It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer, and the direction of any future developments in DMT methodologies and applications is discussed.
Abstract: In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques and their applications and development, through a survey of literature and the classification of articles, from 2000 to 2011. Keyword indices and article abstracts were used to identify 216 articles concerning DMT applications, from 159 academic journals (retrieved from five online databases), this paper surveys and classifies DMT, with respect to the following three areas: knowledge types, analysis types, and architecture types, together with their applications in different research and practical domains. A discussion deals with the direction of any future developments in DMT methodologies and applications: (1) DMT is finding increasing applications in expertise orientation and the development of applications for DMT is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as psychology, cognitive science and human behavior might implement DMT, as an alternative to the methodologies already on offer. (3) The ability to continually change and acquire new understanding is a driving force for the application of DMT and this will allow many new future applications.
563 citations
TL;DR: In this article, a review of data mining applications in manufacturing engineering is presented, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement.
Abstract: The paper reviews applications of data mining in manufacturing engineering, in particular production processes, operations, fault detection, maintenance, decision support, and product quality improvement. Customer relationship management, information integration aspects, and standardization are also briefly discussed. This review is focused on demonstrating the relevancy of data mining to manufacturing industry, rather than discussing the data mining domain in general. The volume of general data mining literature makes it difficult to gain a precise view of a target area such as manufacturing engineering, which has its own particular needs and requirements for mining applications. This review reveals progressive applications in addition to existing gaps and less considered areas such as manufacturing planning and shop floor control.
499 citations
TL;DR: In this article, the authors provide a comprehensive bibliography and propose a method of classifying academic literature on customer relationship management (CRM), and provide a method for classifying that literature.
Abstract: Purpose – To review the academic literature on customer relationship management (CRM), provide a comprehensive bibliography and propose a method of classifying that literature.Design/methodology/approach – A range of online databases were searched to provide a comprehensive listing of journal articles on CRM. Six hundred articles were identified and reviewed for their direct relevance to CRM. Two hundred and five articles were subsequently selected. Each of these articles was further reviewed and classified. The review and classification process was independently verified. All papers were allocated to the main and sub‐categories based on the major focus of each paper.Findings – Papers and research on CRM falls into five broad categories (CRM – General, Marketing, Sales, Service and Support, and IT and IS) and a further 34 sub‐categories. The most popular areas covered by the papers lay in the sub‐category of CRM management, planning and strategy; and CRM general, concept, and study followed by papers in s...
411 citations
Cites background from "Data mining techniques for customer..."
...Data mining plays a fundamental role in the overall CRM process and is a critical component in the CRM system ( Rygielski et al. , 2002a...
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TL;DR: Compared the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.
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TL;DR: This paper reviews some of the most popular technologies that have been identified in the literature for the development of a customer churn management platform, and the advantages and disadvantages of the identified technologies are discussed.
284 citations
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DOI•
01 Jan 2000
TL;DR: In this article, the authors discuss the issues and opportunities surrounding the value of marketing data intelligence and how it can be used to widen, lengthen, and deepen the customer relationship.
Abstract: The purpose of this white paper is to discuss the issues and opportunities surrounding the value of Marketing Data Intelligence. It reviews how Marketing Data Intelligence delivers marketers with the levels of access and knowledge of the customers required to drive successful Customer Relationship Management strategies that widen, lengthen and deepen the customer relationship.
20 citations
"Data mining techniques for customer..." refers background in this paper
...The process must be measured and tracked to ensure that the results fed to campaign management software produce information that the models created by data mining software find useful and accurate [11]....
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...The outcome of this process is marketing data intelligence, which is defined as “Combining data driven marketing and technology to increase the knowledge and understanding of customers, products and transactional data to improve strategic decision making and tactical marketing activity, delivering the CRM challenge” [11]....
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