Applying Data Mining Techniques to WIFLY in Customer Relationship Management

2010 ◽  
Vol 9 (3) ◽  
pp. 488-493 ◽  
Author(s):  
Yi-Hsin Wang ◽  
Ding-An Chiang ◽  
Sheng-Wei Lai ◽  
Cheng-Jung Lin
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Mining Techniques

scholarly journals Data Mining For Customer Relationship Management

2011 ◽  
Vol 1 (6) ◽  
Author(s):  
Savitha S. Kadiyala ◽  
Alok Srivastava
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Integrated Model ◽  
Customer Relationship ◽  
New Model ◽  
Data Mining Techniques ◽  
Existing Data

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.

E-Commerce and Data Mining

2008 ◽  
pp. 2888-2899
Author(s):  
Parviz Partow-Navid ◽  
Ludwig Slusky
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
Web Mining ◽  
Data Gathering ◽  
Relationship Management ◽  
Customer Relationship ◽  
Data Mining Techniques ◽  
Research Areas ◽  
Challenging Research ◽  
Usage Patterns

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.

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

2013 ◽  
Vol 846-847 ◽  
pp. 1048-1051
Author(s):  
Xiao Qian Zhang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Customer Relationship ◽  
Customer Management ◽  
Large Database ◽  
Data Mining Techniques ◽  
Fierce Competition ◽  
Support Decision Making

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.

Applications for Data Mining Techniques in Customer Relationship Management

2011 ◽  
pp. 188-192
Author(s):  
Natalie Clewley ◽  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Customer Relationship Management ◽  
New Technologies ◽  
Relationship Management ◽  
Customer Relationship ◽  
Future Research ◽  
Supporting Evidence ◽  
Business Decisions ◽  
Data Mining Techniques ◽  
Hidden Knowledge

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.

Sign in / Sign up

Export Citation Format

Share Document