Abstract
|
Article Information:
Using Data Mining Techniques to Increase Efficiency of Customer Relationship Management Process
Mohammad Behrouzian Nejad, Ebrahim Behrouzian Nejad and Ali Karami
Corresponding Author: Mohammad Behrouzian Nejad
Submitted: February 22, 2012
Accepted: July 02, 2012
Published: December 01, 2012 |
Abstract:
|
Recently the Customer Relationship Management (CRM) has been achieved an increasing
popularity in business management. CRM includes all the steps which an organization employs to create
and establish beneficial relationships with the customers. Using technologies such as data warehousing and
data mining CRM can be introduced as a new area where companies can gain the competitive advantage.
Via CRM system a company can improve its processes to deliver better service at a lower cost. By use of
data mining techniques, companies can extract hidden information of the customers from large databases.
So, organizations can determine the value of customers and predict their future behavior and requirements.
Data mining tools can answer business questions which were time-consuming to track in the past. We
believe that it is possible to improve CRM efficiency, to have an effective and rapid response to customer
needs, by integrating CRM and data mining techniques. In this study we investigate major concepts of
CRM and data mining. Also we introduce our idea to employ data mining techniques in CRM. This study
show that using data mining techniques in CRM will improve CRM's efficiency and provide a better
prediction ability to companies, organizations and industries to achieve more Profitability.
Key words: Attract customers , customer retention, data mining, CRM, , ,
|
Abstract
|
PDF
|
HTML |
|
Cite this Reference:
Mohammad Behrouzian Nejad, Ebrahim Behrouzian Nejad and Ali Karami, . Using Data Mining Techniques to Increase Efficiency of Customer Relationship Management Process. Research Journal of Applied Sciences, Engineering and Technology, (23): 5010-5015.
|
|
|
|
![](http://www.maxwellsci.com/images/RJASET-maxw.jpg) |
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
![Submit Manuscript](../images/sub.jpg) |
Information |
|
|
|
Sales & Services |
|
|
|