مدیریت دانش و داده کاوی برای بازاریابی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|22024||2001||11 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 31, Issue 1, May 2001, Pages 127–137
Due to the proliferation of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of customer data in large databases. However, much of the useful marketing insights into customer characteristics and their purchase patterns are largely hidden and untapped. Current emphasis on customer relationship management makes the marketing function an ideal application area to greatly benefit from the use of data mining tools for decision support. A systematic methodology that uses data mining and knowledge management techniques is proposed to manage the marketing knowledge and support marketing decisions. This methodology can be the basis for enhancing customer relationship management.
In recent years, the advent of information technology has transformed the way marketing is done and how companies manage information about their customers. The availability of large volume of data on customers, made possible by new information technology tools, has created opportunities as well as challenges for businesses to leverage the data and gain competitive advantage. Wal-Mart, the largest retailer in the U.S., for example, has a customer database that contains around 43 tera-bytes of data, which is larger than the database used by the Internal Revenue Services for collecting income taxes . The Internet and the World Wide Web have made the process of collecting data easier, adding to the volume of data available to businesses. On the one hand, many organizations have realized that the knowledge in these huge databases are key to supporting the various organizational decisions. Particularly, the knowledge about customers from these databases is critical for the marketing function. But, much of this useful knowledge is hidden and untapped. On the other hand, the intense competition and increased choices available for customers have created new pressures on marketing decision-makers and there has emerged a need to manage customers in a long-term relationship. This new phenomenon, called customer relationship management, requires that the organizations tailor their products and services and interact with their customers based on actual customer preferences, rather than some assumed general characteristics  and . As organizations move towards customer relationship management, the marketing function, as the front-line to interact with customers, is the most impacted due to these changes. There is an increasing realization that effective customer relationship management can be done only based on a true understanding of the needs and preferences of the customers. Under these conditions, data mining tools can help uncover the hidden knowledge and understand customer better, while a systematic knowledge management effort can channel the knowledge into effective marketing strategies. This makes the study of the knowledge extraction and management particularly valuable for marketing. Developments in database processing , ,  and , data warehousing  and , machine learning ,  and  and knowledge management ,  and  have contributed greatly to our understanding of the data mining process. More recent research on data mining and knowledge discovery ,  and  has further enhanced our understanding of the application of data mining and the knowledge discovery process. But, most research has focused on the theoretical and computational process of pattern discovery and a narrow set of applications such as fraud detection or risk prediction. Given the important role played by marketing decisions in the current customer-centric environment, there is a need for a simple and integrated framework for a systematic management of customer knowledge. But, there is a surprising lack of a simple and overall framework to link the extraction of customer knowledge with the management and application of the knowledge, particularly in the context of marketing decisions. While data mining studies have focused on the techniques, customer relationship studies have focused on the interface to the customer and the strategies to manage customer interactions. True customer relationship management is possible only by integrating the knowledge discovery process with the management and use of the knowledge for marketing strategies. This will help marketers address customer needs based on what the marketers know about their customers, rather than a mass generalization of the characteristics of customers. We address this issue in this paper by presenting an integrated framework for knowledge discovery and management, in the context of marketing decisions. Our paper is further organized as follows. First, we present a taxonomy of data mining tasks and discuss knowledge management as an iterative process (Section 2). We then survey different types of potentially useful marketing and customer knowledge discovered by data mining (Section 3). Marketing decisions based on discovered customer knowledge leads to knowledge-based marketing (Section 4). We close our discussion by identifying the emerging issues to be addressed in the process of managing the discovered marketing knowledge (Section 5).
نتیجه گیری انگلیسی
Though data mining techniques are used in several areas such as fraud detection, bankruptcy prediction, medical diagnosis, and scientific discoveries, their use for marketing decision support highlights unique and interesting issues such as customer relationship management, real-time interactive marketing, customer profiling and cross-organizational management of knowledge. In the current customer-centric business environment, it is our firm belief that there is a need for deeper understanding of use of data mining and knowledge management for marketing decision support. Towards that end, in this paper, we have shown how data mining can be integrated into a marketing knowledge management framework. With the availability of large volume of data, made possible by modern information technology, a major problem is to filter, sort, process, analyze and manage this data in order to extract the information relevant to the user. The growth in the size and number of existing databases far exceeds human abilities to analyze such data using traditional tools and thus creates both a need and an opportunity for data mining tools. With the shift from mass marketing to one-to-one relationship marketing, one area that could greatly benefit from data mining is the marketing function itself. A systematic application of data mining techniques will enhance the knowledge management process and arm the marketers with better knowledge of their customers leading to better service to customers. To us, it is also clear that the Web technology will have a major impact on the practice of data mining and knowledge management, and that should present interesting challenges for future information systems research