Overview of MDSS and related literature

This section briefly reviews the marketing DSS literature and also examines related. IT support for marketing planning can aid in the use of marketing tools, facilitate group planning, and support moves towards continuous planning based on a live marketing model of the business. But, amongst other factors, achieving these benefits depends on the style of support provided by the system. And a review of relevant DSS literature was seen (Wilson and McDonald, 2001).

Marketing was the first functional area to embrace the concept of a management information system (MIS) and tailor it to the needs of managers. Kotler (1994) coined the term "marketing nerve center" and explained how a firm could create a separate area for its computer resources dedicated to supporting marketing activity. This notion was immediately grasped by a number of marketing academicians who developed conceptual models of marketing information systems (later given the acronym MKIS) to illustrate system components and uses (Lia et al., 2001). Mitchell and Wilson

Table I Another comparison of B2C and B2B

B2C commerce

B2B commerce

Customer acquisition methods Entry barriers for competitors Relationship types

Selling Market size

Mass communication: advertising, affiliate programs

Low: audience size, logistics capability, experience quality

Browsing of catalogs

Placement of orders

Payment execution

Status tracking

Smaller buyers

Consumer markets are measured in the "millions"

Personal selling: direct salesforce, trade shows

High: domain expertise, buyer/supplier relationships

MRO procurement

Direct procurement

Payment execution

Status tracking

Catalog information management Order fulfillment

Collaborative forecast management Promotions management Returns management Design collaboration Work-in-process tracking Collaborative planning management Larger buyers

B2B firms have customer bases over "thousands"

Source: Kaplan (2000) and Olsen (2000)

Behrooz Noori and Mohammad Hossein Salimi

(1998) reviewed some current guidance on when and how to segment B2B markets. Since the early 1980s, the concept of relationship management in marketing area has gained its importance. Acquiring and retaining the most profitable customers are serious concerns of a company to perform more targeted marketing campaigns. For effective CRM, it is important to gather information on customer value. Few have considered customer lifetime value (CLV).

From the perspective of niche marketing, all customers are not equal (they have different lifetime value or purchase behaviors), even if they purchase identical products or services; market segmentation is therefore necessary. Firms are increasingly recognizing the importance of the lifetime value of customers (Berger and Nasr, 1998). Several studies have considered the use of CLV. Generally, recency, frequency, and monetary (RFM) methods have been used to measure it (Kahan, 1998; Miglautsch, 2000). The concept has been applied to cluster customers for niche marketing (Ha and Park, 1998). Prediction models have focused mainly on expected future cash flow derived from customers' past profit contribution. Hwang et al. (2004) suggest an CLV model considering past profit contribution, potential benefit, and defection probability of a customer.

The concept of segmentation is central to marketing. A search on this keyword in article titles only resulted in more than 30 articles in the Journal of Marketing, more than 50 in the Journal of Marketing Research. In the early marketing applications, the process of dividing a population of customers by means of clustering techniques into homogeneous groups was often done without the use of a dependent/target variable. However, marketers realized that segmentation should not be an end in itself, but rather a means to an end. As most companies want to maximize profits (or some others quantity, e.g. sales), marketers quickly realized that a segmentation should ensure that "better" customers are separated from other customers. This largely explains the popularity of clustering techniques using a dependent variable such as chi-square automated interaction detection (CHAID) (Jonker et al., 2004). Also the application of segmentation and predictive modeling is an important topic in the database marketing (DBM) (Verhoef et al., 2002). subsystems in marketing information systems (MKIS) support new product evaluation, forecasting demand or sales, product deletion, pricing strategy, analyzing sales profit, promotion strategy, computing operating budgets, selecting advertising media, assigning sales representatives to territories, approving customer credit, location of facilities (e.g. warehouses or stores), routing of salesperson or deliveries, computing economic order quantities (EOQ) and computing reorder points (Lia et al., 2001).

Talvinen (1995) clarified the applicability of marketing information systems (MKIS) to other marketing and management related IS, such as MIS and DSS. In terms of classification, customer-centric analytic applications belong to the business intelligence (BI) or decision support domain (we use these terms synonymously). They're not software that you use to do business. Because, they're software that you use to analyze business. Further, within BI or DSS, there are many types of analytic applications - customer-centric or CRM analytics, business operations analytics, financial analytics, and supply chain analytics, just to name a few. Because our interest, really our corporate focus, is on the customer, we consider the domain to be customer-centric intelligence, and

we're most interested in customer-centric analytic applications, also commonly called CRM analytics or analytical CRM customer-centric analytic applications are tools that help make you more customer-focused (Seybold, 2002).Montgomery and Urban (1970) and Crissy and Mossman (1977) viewed the MKIS as a DSS, whereas King and Cleland (1974) recognized its value in planning marketing strategy. Eom (1999) investigates the changing intellectual structure of the general DSS field by means of an empirical assessment of the DSS literature over two successive time periods, 1971-1990 and 1991-1995. Other related and important studies are van Bruggen et al. (1996, 1998), Little (1979), Zinkhan et al. (1987), Higby and Farah (1991), Duan and Burrell (1995), Benbasat and Peter (1996), Hoch and Schkade (1996), Claire (1997), Wierenga and van Bruggen (1997), Bucklin et al. (1998), Jiang et al. (1998), Beynon et al. (2001), Kohli et al. (2001), Berg and Rietz (2003), Beroggi (2003) and Tsaia and Chen (2004). Bose and Sugumaran (2003) proposed an integrated framework for CRM through the application of knowledge management technology. Davies (2001) discussed comprehensively application of AHP within a marketing knowledge-based DSS (KB-DSS). Perhaps even more remarkably, despite quite widespread computer literacy and the adoption of ERP, SCM and CRM systems, the utility of information systems is still an issue (Hulbert, 2003). The above referenced studies deal with the use of DSSs and related modules for real-life marketing decision making in companies.

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