The Application of Data Mining in Customer Management of Magazine Editing System

2013 ◽  
Vol 846-847 ◽  
pp. 1048-1051
Author(s):  
Xiao Qian Zhang

China's commercial magazine faces of increasingly fierce competition in the customer, so it must improve its management and marketing method to enhance competitiveness. It is the key point to strengthening customer relationship management. The study in this paper uses data mining techniques to enhance the management of the customer to explore new customers, maintain overall customers and accelerate the development of the magazine. Through the establishment of large database and data mining, we find useful data and the relevance to support decision-making and better improve the competitiveness of the magazine.

2010 ◽  
Vol 9 (3) ◽  
pp. 488-493 ◽  
Author(s):  
Yi-Hsin Wang ◽  
Ding-An Chiang ◽  
Sheng-Wei Lai ◽  
Cheng-Jung Lin

Author(s):  
Savitha S. Kadiyala ◽  
Alok Srivastava

Data mining has various applications for customer relationship management. In this article, we introduce a framework for identifying appropriate data mining techniques for various CRM activities. This article attempts to integrate the data mining and CRM models and to propose a new model of Data mining for CRM. The new model specifies which types of data mining processes are suitable for which stages/processes of CRM. In order to develop an integrated model it is important to understand the existing Data mining and CRM models. Hence the article discusses some of the existing data mining and CRM models and finally proposes an integrated model of data mining for CRM.


2008 ◽  
pp. 2888-2899
Author(s):  
Parviz Partow-Navid ◽  
Ludwig Slusky

Web mining is the application of data mining techniques to discover the usage patterns of Web data, in order to better serve the needs of Web site visitors. Web mining consists of three phases: data gathering, analysis and reporting. This chapter describes each of these phases in detail along with a discussion of electronic customer relationship management (eCRM). Several challenging research areas that need to be investigated for further enhancement of this field are also presented.


Author(s):  
Calin Gurau

Electronic commerce requires the redefinition of the firm’s relationships with partners, suppliers, and customers. The goal of effective customer relationship management (CRM) practice is to increase the firm’s customer equity, which is defined by the quality, quantity, and duration of customer relationships (Fjermestad & Romano, 2003). The explosive development of the online market and the rapid evolution of customer management applications have determined the companies to implement electronic customer relationship management (eCRM) systems, which are using advanced technology to enhance customer relationship management practices. The successful implementation of an eCRM system requires a specific combination of IT applications that support the classic domains of the CRM concept: marketing, sales, and service (Kennedy, 2006). Electronic marketing aims for acquiring new customers and moving existing customers to further purchases. Electronic sales try to simplify the buying process and to provide superior customer support. Electronic service has the task to provide electronic information and services for arising questions and problems or to convey customers to the right contact person in the organization. The eCRM system comprises a number of business processes, interlinked in a logical succession: • Market segmentation: The collection of historical data, complemented with information provided by third parties (such as marketing research agencies), is segmented on the basis of customer life-time value (CLV) criteria, using data mining applications. • Capturing the customer: The potential customer is attracted to the Web site of the firm through targeted promotional messages, diffused through various communication channels. • Customer information retrieval: The information retrieval process can be either implicit or explicit. When implicit, the information retrieval process registers the Web behaviour of customers, using specialized software applications, such as “cookies.” On the other hand, explicit information can be gathered through direct input of demographic data by the customer (using online registration forms or questionnaires). Often, these two categories of information are connected at database level. • Customer profile definition: The customer information collected is analyzed in relation with the target market segments identified through data mining, and a particular customer profile is defined. The profile can be enriched with additional data (e.g., external information from marketing information providers). This combination creates a holistic view of the customer, his needs, wants, interests and behaviour (Pan & Lee, 2003). • Personalization of firm-customer interaction: the customer profile is used to identify the best customer management campaign (CMC), which is applied to personalize the company-customer online interaction. • Resource management: The company-customer transaction require complex resource management operations, which are partially managed automatically, through specialized IT-applications, such as Enterprise Resource Planning (ERP) or Supply Chain Management (SCM), and partly through the direct involvement and coordination of operational managers.


2013 ◽  
Vol 321-324 ◽  
pp. 3026-3029
Author(s):  
Qi Zheng

Today, businesses face the challenges of using the past to predict the future and using past experiences to communicate effectively with the customer. The purpose of this study is to find ways to study text data in order to discover more latent knowledge. There was not a good information system in place, and the structured data was sparse and overly dispersed. Data mining did not yield any significant discoveries, so the data analysis was indeed cursory. Therefore, the studys recommendations still focus on the execution process of complete customer relationship management and on establishing a more complete system loop in order to reinforce interactions with customers.


Author(s):  
Natalie Clewley ◽  
Sherry Y. Chen ◽  
Xiaohui Liu

With the explosion in the amount of data produced in commercial environments, organizations are faced with the challenge of how to collect, analyze, and manage such large volumes of data. As a consequence, they have to rely upon new technologies to efficiently and automatically manage this process. Data mining is an example of one such technology, which can help to discover hidden knowledge from an organization’s databases with a view to making better business decisions (Changchien & Lu, 2001). Data mining, or knowledge discovery from databases (KDD), is the search for valuable information within large volumes of data (Hand, Mannila & Smyth, 2001), which can then be used to predict, model or identify interrelationships within the data (Urtubia, Perez-Correa, Soto & Pszczolkowski, 2007). By utilizing data mining techniques, organizations can gain the ability to predict future trends in both the markets and customer behaviors. By providing detailed analyses of current markets and customers, data mining gives organizations the opportunity to better meet the needs of its customers. With such significance in mind, this chapter aims to investigate how data mining techniques can be applied in customer relationship management (CRM). This chapter is organized as follows. Firstly, an overview of the main functionalities data mining technologies can provide is given. The following section presents application examples where data mining is commonly applied within the domain, with supporting evidence as to how each enhances CRM processes. Finally, current issues and future research trends are discussed before the main conclusions are presented.


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