09-09-2016, 02:36 PM
Effect of decision-support system group characteristic on Client Satisfaction
1454145394-Adecisionsupportsystemforbusinesst.rtf (Size: 9.74 MB / Downloads: 5)
Abstract
Purpose – The main purpose of this paper is to review the effect of new decision-support-system (DSS) and knowledge-driven marketing to help related-field graduate students and marketing managers.
Design/methodology/approach – Reviews a range of the most important works published between 1966 and 2004 in order to demonstrate both practical and theoretical aspects. The main method of this research is analytical and conceptual and the approach to this subject was to investigate the gap between marketing DSSs and analytical CRM.
Findings – Provides information about a customized marketing DSS in a B2B context, indicates related literature and frameworks and, finally, tests the ideas with a case study.
Practical implications – Outcomes and applications are identified for developing new activities in improving marketing decision making and marketing planning based on customer orientation and customer satisfaction.
Originality/value – Despite such interdependencies, the research in the fields of DSSs and CRM solutions has not adequately considered the integration of such systems. The novel contribution of this paper lies in integrating marketing DSSs and CRM with regard to knowledge-driven marketing in B2B marketing, in both theoretical and practical aspects.
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).
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 e-business 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
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
Introduction to DSS
DSS
A DSS is an interactive computer-based system designed to help in decision making situations by utilizing data and models to solve unstructured problems.
The aim of DSSs is to improve and expedite the processes by which management makes and communicates decisions – in most cases the emphasis in DSSs is on increasing individual and organizational effectiveness. It is very difficult to tell precisely where the interrelatedness of various business functions to one another vertically and horizontally is emphasized (Talvinen, 1995).
A DSS is a coordinated collection of data, system tools, and techniques with supporting software and hardware by which an organization gathers and interprets relevant information from business and the environment and turns it into a basis for making management decisions[1]. The system, usually based on a model and computer software package, describes the implications of specific marketing decisions and/or recommends specific marketing actions, using a set of input information. This information may either reside permanently in the DSS or be input for the particular scenario of interest (or both). The information can consist of primary information (e.g. sales and cost information from company records, or subjective judgments by managers about the likely impact of increased advertising spending) and/or secondary information (e.g. sales of competitors’ products from a syndicated database constructed via store audits).
An important aspect of many DSSs is the facilitation of “what if” analyses, i.e. the sensitivity of optimal marketing strategy to the assumptions in the input information.
DSSs are divided into four main parts in systematic view:
1 Input: low-volume data or massive databases, analytical models.
2 Processing: interactive, simulations, data analysis tools.
3 Output: special reports, decision analyses, responses to queries.
4 Users: professionals, managers.
Organizations are becoming increasingly complex with emphasis on decentralized decision making. This trend necessitates enterprise DSSs for effective decision making. In the process of decision-making, decision makers combine different types of data (e.g. internal data and external data) and knowledge (both tacit knowledge and explicit knowledge) available in various forms in the organization. The decision-making process itself results in improved understanding of the DSSs
Despite such interdependencies, the research in the fields of DSSs and CRM solutions has not adequately considered the integration of such systems. Proper integration of DSSs and CRM will support the required interaction and will present new opportunities for enhancing the quality of support provided by each system. A synergy can be created through the integration of decision support and CRM, as these two technology consist of activities that complement each other. More specifically, the knowledge acquisition, storage and distribution activities in CRM enable the dynamic creation and maintenance of decision models, in this way, enhancing the decision support process. In return, the application and evaluation of various decision models and the documentation of decision instances, supported by a DSS, provide the means for acquiring and storing the tacit and explicit knowledge of different decision makers and facilitate the creation of new knowledge. Such integration is expected to enhance the quality of support provided by the system to decision makers and also to help in building up organizational memory and knowledge bases. Decision makers, through the experience of using such tools and techniques, gain new knowledge pertaining to the specific problem area. Specific DSSs are built using data extracted from various data sources and models extracted from various knowledge sources.
Digital technology and marketing strategy
The emergence of digital technology is creating fundamental changes to the way that business is conducted. These changes are altering the way in which every enterprise acquires wealth and creates shareholder value. The myriad of powerful computing and communications technology allow organizations to streamline their business processes, enhance customer service and offer products and services.
In the process of forming a marketing strategy to respond to the challenges of environmental change, a firm should analyze its active customers to identify opportunities for marketing innovation. Choice of appropriate marketing strategy could lead to superior performance (Chang et al., 2003).