Introduction

Intense competition is forcing companies to develop innovative marketing activities to capture customer needs and improve customer satisfaction and retention. Businesses can benefit significantly from analyzing customer data to determine their preferences and thus improve marketing decision support (Liu and Shih, 2005; Liang and Lai, 2002).

More and more managers are faced with a rapidly changing and highly competitive marketing environment. Marketing managers are forced to become more competitive through better decision making. A decision can be considered as the output of a productive activity whose inputs include intellectual efforts of an individual or a group of individuals, computing hardware and software, data, etc.

The advances in computer technology and the computer-based techniques for handling information allow the development of decision-support systems (DSSs), than can play a crucial role in the progress of a firm (Alexouda, 2005).

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Journal of Business & Industrial Marketing 20/4/5 (2005) 226-236

e Emerald Group Publishing Limited [ISSN 0885-8624] [DOI 10.1108/08858620510603909]

There is an obvious need for tools, which can improve marketing decision making. Many efforts have been made to develop suitable software tools, that can act as consultants for marketing managers. There are many opportunities for applications of information systems in the marketing area. The modern information technology and information systems can assist a company to manage the increasing information flow and improve its quality. There is a growing interest in the use of marketing-decision-support systems (MDSSs) designed to be used in complicated marketing decision making problems (Talvinen, 1995). An MDSS is defined as "a coordinated collection of data, models, analytic tools and computing power by which an organization gathers information from the environment and turns it into a basis for action" (Little, 1979).

The concepts of mass production and mass marketing, first created during the Industrial revolution, are being supplanted by new ideas in which customer relationships are the central business issue. Firms today are concerned with increasing customer value through analysis of the customer lifecycle. The old model of "design-build-sell" (a product-oriented view) is being replaced by "sell-build-redesign" (a customer-oriented view). The traditional process of mass marketing is being challenged by the new approach of one-to-one marketing.

In the traditional process, the marketing goal is to reach more customers and expand the customer base. But given the

The authors would like to thank the Ministry of Science, Research and Technology of Iran for its financial support and authors gratefully acknowledge Iran Insurance Co. (largest Iranian insurance company) and RWTUV Iran Co. for providing suitable infrastructures for this project.

Journal of Business & Industrial Marketing 20/4/5 (2005) 226-236

e Emerald Group Publishing Limited [ISSN 0885-8624] [DOI 10.1108/08858620510603909]

Behrooz Noori and Mohammad Hossein Salimi high cost of acquiring new customers, it makes better sense to conduct business with current customers.

In business-to-business (B2B) environments, a tremendous amount of information is exchanged on a regular basis. B2B is one of the most broadly used marketing terms in the information technology (IT) world. In its simplest definition a B2B process is any business process between two companies that uses digital technology. The term can represent functions that provide information, or facilitate transactions, or execute transactions or completely integrate shared business processes into separate, existing enterprise resource planning (ERP) systems. B2B markets have been considered an attractive ebusiness venue for the realization of cost reduction and exchange creation utilities (Hunter et al., 2004).

As any perusal of the appropriate journals indicates, the use of quantitative methodologies in business-to-customer (B2C) marketing has been widespread for decades, while B2B marketing has not embraced these techniques to the same extent (Nairn et al., 2004). An increase in the B2B market is potentially of much greater significance than one in the B2C market (Berthon et al., 2003).

The explosion in internet-based B2B is driven by economics - the internet offers the potential for reduced prices for goods and reduced transaction costs, but this is not simply derived from the internet as a communications infrastructure (Kuechler et al., 2001). Furthermore, with the advances in computers, databases, communications and the internet technologies, modern organizations nowadays collect massive amounts of data on about everything like, payment records, financial transactions, loan applications and others. Analyzing data on this scale and converting it into knowledge to help decision making, presents exciting new challenges.

Customer-relationship management (CRM) has become one of the leading business strategies in the new millennium. It is difficult to find out a totally approved definition of CRM. We, however, can describe it as "managerial efforts to manage business interactions with customers by combining business processes and technologies that seek to understand a company's customers", i.e. structuring and managing the relationships with customers. CRM covers all the processes related to customer acquisition, customer cultivation, and customer retention (Hwang et al., 2004). Data mining is a new generation of computerized technologies for discovering knowledge hidden in large amounts of data. Support of domain expertise to make better decisions and new IT techniques to promote B2B marketing are essential (Changchien and Lu, 2001). Data mining techniques are useful for extracting marketing knowledge and further supporting marketing decisions (Bose and Mahapatra, 2001; Shaw et al., 2001).

In this paper, we focus on a very specific DSS on behalf of market managers who want to develop and implement efficient B2B marketing programs by fully utilizing a customer database. This is important because, due to the growing interest in marketing, many firms devote considerable resources to identifying households that may be open to targeted marketing messages. This becomes more critical through the easy availability of data warehouses combining demographic, psychographic and behavioral information (Kim and Street, 2004). In this paper we will focus on DSSs for the B2B market that are driven by data mining modeling and analysis. The buying patterns of individual customers and groups can be identified via analyzing

customer data (Wells et al., 1999), but also allows a company to develop one-to-one marketing strategies that provide individual marketing decisions for each customer (Peppers and Rogers, 1997).

The ultimate goal of DSSs is to provide managers with information that is useful for understanding various managerial aspects of a problem and to choose a best solution among many alternatives.

The paper is organized as follows. Section 2 deals with the presentation of DSS and CRM. Literature review of MDSS studies are provided in section 3. In section 4 the proposed MDSS is presented and in section 5 related case study and implications is discussed. Finally, in section 6, the conclusions of the paper are summarized.

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