Customer-oriented product development has become increasingly necessary for competitive reasons. This paper describes a framework and a methodology for the design, development, and implementation of knowledge-based decision support systems for customer satisfaction assessment. A generic approach is presented that integrates knowledge-based systems with both a well-known and accepted modeling technique (scoring models), and several decision support techniques (such as the analytic hierarchy process and discriminant analysis). In addition to the fexibility and developmental advantages of knowledge-based systems, additional benefits of this approach include reduced information processing and gathering time, improved communications with senior management, and better management of scarce development resources. To simplify the exposition, we illustrate the framework and methodology within the context of a successful system implementation. The resulting system, known as the Customer Satisfaction Assessment System (CSAS), is designed to provide the decision support necessary to evaluate whether or not full-scale development of a candidate product should proceed. The system assesses and estimates the extent to which a potential new product will meet the expectations of the customer. CSAS incorporates market research findings, as well as strategic evaluation factors and their interrelationships. It can function as a stand-alone system or in conjunction with other evaluation systems (e.g., those providingfinancial, technological, manufacturing, and marketing evaluations) to provide a complete assessment of the product under consideration. Since its implementation, the experts' and other users' expressions of complete satisfaction and commitment to the system has been an indication of its value as an important decision support tool. The paper concludes with a discussion of the lessons learned for future implementations and some important extensions of this research.
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"In the research literature, most of the previous DSS research focused on enterprise-level decision support rather than on consumer support with regard to personalized preferences. Even for those works mentioning consumer or customer-focused EC and DSS applications were eventually focusing on supporting business organizations to make management decisions related to relationship marketing or product development  . Among very few research efforts, O' Keefe and Mceachern (1998) considered the role of customer DSS in web-based marketing and proposed a framework that comprised system functions including agents and event notification, visual catalog and search facilities, samples and evaluation models, pointers to existing customers, payment facilities, and email and newsgroup supports . "
[Show abstract][Hide abstract] ABSTRACT: Due to the rapid advancement of electronic commerce and web technologies in recent years, the concepts and applications of decision support systems have been significantly extended. One quickly emerging research topic is the consumer-oriented decision support system that provides functional supports to consumers for efficiently and effectively making personalized decisions. In this paper we present an integrated framework for developing web-based consumer-oriented intelligent decision support systems to facilitate all phases of consumer decision-making process in business-to-consumer e-services applications. Major application functional modules comprised in the system framework include consumer and personalized management, navigation and search, evaluation and selection, planning and design, community and collaboration management, auction and negotiation, transactions and payments, quality and feedback control, as well as communications and information distributions. System design and implementation methods will be illustrated using an example. Also explored are various potential e-services application domains including e-tourism and e-investment.
[Show abstract][Hide abstract] ABSTRACT: Although knowledge elicitation, the process of extracting knowledge from human experts to be incorporated into a knowledge-based system, has been the subject of some notable studies, less attention has been paid to the methods of analysing the raw data once it has been extracted from the expert. When knowledge elicitation sessions are interview-based, the resultant form of raw data is usually a transcript of the interviewee's utterances. This paper describes an investigation into the preliminary stage of analysing such transcripts. It outlines the development of an approach to eliminate unnecessary detail from interview transcripts, thus enabling attention to be focused upon the remaining, more relevant data via a simple technique based upon cheap and readily available technology. The paper then outlines a rapid-prototyping approach for evaluating this method, the results of which were felt to be very encouraging.
[Show abstract][Hide abstract] ABSTRACT: A review of marketing applications of the analytic hierarchy process (AHP) shows that major concerns involve the accuracy of knowledge elicitation and choice of a suitable knowledge base. Decision support is suggested with a series of decision rules based on different variations of AHP for both structuring and evaluation, presented in the user interface. Models that solved historical information needs can also be adapted for current marketing conditions by incorporating AHP within a marketing knowledge-based decision support system (KB-DSS). Current decision rules might be chosen on the basis of past behaviour that has proved successful, sharing similar marketing conditions and characteristics, captured within a suitable knowledge base. Decision rules are then chosen on the basis of their relevance to the current problem, based on historical AHP case data that are continuously revised. This proposal reduces data collection and processing time.
Full-text · Article · Jul 2001 · European Journal of Marketing