تکنیک های داده کاوی برای مدیریت ارتباط با مشتری

Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of today’s customer relationship management philosophy.

A new business culture is developing today. Within it, the economics of customer relationships are changing in fundamental ways, and companies are facing the need to implement new solutions and strategies that address these changes. 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 tools and technologies of data warehousing, data mining, and other customer relationship management (CRM) techniques afford new opportunities for businesses to act on the concepts of relationship marketing. 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 high cost of acquiring new customers, it makes better sense to conduct business with current customers. In so doing, the marketing focus shifts away from the breadth of customer base to the depth of each customer’s needs. The performance metric changes from market share to so-called “wallet share”. Businesses do not just deal with customers in order to make transactions; they turn the opportunity to sell products into a service experience and endeavor to establish a long-term relationship with each customer. The advent of the Internet has undoubtedly contributed to the shift of marketing focus. As on-line information becomes more accessible and abundant, consumers become more informed and sophisticated. They are aware of all that is being offered, and they demand the best. To cope with this condition, businesses have to distinguish their products or services in a way that avoids the undesired result of becoming mere commodities. One effective way to distinguish themselves is with systems that can interact precisely and consistently with customers. Collecting customer demographics and behavior data makes precision targeting possible. This kind of targeting also helps when devising an effective promotion plan to meet tough competition or identifying prospective customers when new products appear. Interacting with customers consistently means businesses must store transaction records and responses in an on-line system that is available to knowledgeable staff members who know how to interact with it. The importance of establishing close customer relationships is recognized, and CRM is called for. It may seem that CRM is applicable only for managing relationships between businesses and consumers. A closer examination reveals that it is even more crucial for business customers. In business-to-business (B2B) environments, a tremendous amount of information is exchanged on a regular basis. For example, transactions are more numerous, custom contracts are more diverse, and pricing schemes are more complicated. CRM helps smooth the process when various representatives of seller and buyer companies communicate and collaborate. Customized catalogues, personalized business portals, and targeted product offers can simplify the procurement process and improve efficiencies for both companies. E-mail alerts and new product information tailored to different roles in the buyer company can help increase the effectiveness of the sales pitch. Trust and authority are enhanced if targeted academic reports or industry news are delivered to the relevant individuals. All of these can be considered among the benefits of CRM. Cap Gemini conducted a study to gauge company awareness and preparation of a CRM strategy [1]. Of the firms surveyed, 65% were aware of CRM technology and methods; 28% had CRM projects under study or in the implementation phase; 12% were in the operational phase. In 45% of the companies surveyed, implementation and monitoring of the CRM project had been initiated and controlled by top management. Thus, it is apparent that this is a new and emerging concept that is seen as a key strategic initiative. This article examines the concepts of customer relationship management and one of its components, data mining. It begins with an overview of the concepts of data mining and CRM, followed by a discussion of evolution, characteristics, techniques, and applications of both concepts. Next, it integrates the two concepts and illustrates the relationship, benefits, and approaches to implementation, and the limitations of the technologies. Through two studies, we offer a closer look at two data mining techniques: Chi-square Automatic Interaction Detection (CHAID) and Neural Networks. Based on those case studies, CHAID and neural networks are compared and contrasted on the basis of their strengths and weaknesses. Finally, we draw conclusions based on the discussion.

7. Conclusions In choosing a suitable technology for personalization or CRM, organizations must be aware of the tradeoffs when considering differing data mining software applications. The choice among different options is not as critical as the choice to use data mining technologies in a CRM initiative. Data mining represents the link from the data stored over many years through various interactions with customers in diverse situations, and the knowledge necessary to be successful in relationship marketing concepts. In order to unlock the potential of this information, data mining performs analysis that would be too complicated and time-consuming for statisticians, and arrives at previously unknown nuggets of information that are used to improve customer retention, response rates, attraction, and cross selling. Through the full implementation of a CRM program, which must include data mining, organizations foster improved loyalty, increase the value of their customers, and attract the right customers. As customers and businesses interact more frequently, businesses will have to leverage on CRM and related technologies to capture and analyze massive amounts of customer information. Businesses that use customer data and personal information resources effectively will have an advantage in becoming successful. However, businesses must also bear in mind that they have to use technology responsibly in order to achieve a balance between privacy rights and economic benefits. Different technologies vary in terms of effectiveness and ease of use. It is businesses and managers who determine how to exploit collected data, in other words, more of a policy issue than a technology issue. Several precautions have to be taken by business to assure consumers that their privacy will be respected and personal information will not be disclosed without permission. Businesses also have a duty to execute their privacy policy so as to establish and maintain good customer relationships. For such a sensitive issue as privacy, the burden is on businesses when it comes to building and keeping trust. The nature of trust is so fragile that once violated, it vanishes. Current CRM solutions focus primarily on analyzing consumer information for economic benefits, and very little touches on ensuring privacy. As privacy issues become major concerns for consumers, surely an integrated solution that streamlines and enhances the entire process of managing customer relationships will become even more necessary