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Reseach Article

Academic Analytics in Customer Relationship Management Perspective using Data Mining

Published on February 2013 by Reshma Desai
International Conference on Recent Trends in Information Technology and Computer Science 2012
Foundation of Computer Science USA
ICRTITCS2012 - Number 1
February 2013
Authors: Reshma Desai
4a22ad53-6ab9-4858-8655-20556c8e4865

Reshma Desai . Academic Analytics in Customer Relationship Management Perspective using Data Mining. International Conference on Recent Trends in Information Technology and Computer Science 2012. ICRTITCS2012, 1 (February 2013), 28-32.

@article{
author = { Reshma Desai },
title = { Academic Analytics in Customer Relationship Management Perspective using Data Mining },
journal = { International Conference on Recent Trends in Information Technology and Computer Science 2012 },
issue_date = { February 2013 },
volume = { ICRTITCS2012 },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 28-32 },
numpages = 5,
url = { /proceedings/icrtitcs2012/number1/10249-1319/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Information Technology and Computer Science 2012
%A Reshma Desai
%T Academic Analytics in Customer Relationship Management Perspective using Data Mining
%J International Conference on Recent Trends in Information Technology and Computer Science 2012
%@ 0975-8887
%V ICRTITCS2012
%N 1
%P 28-32
%D 2013
%I International Journal of Computer Applications
Abstract

Customer relationship management (CRM) comprises a set of processes and enabling systems supporting a business strategy to build long term, pro?table relationships with speci?c customers. Customer data and information technology (IT) tools form the foundation upon which any successful CRM strategy is built. In addition, the rapid growth of the Internet and its associated technologies has greatly increased the opportunities for marketing and has transformed the way relationships between companies and their customers are managed. Many organizations have collected and stored a wealth of data about their current customers, potential customers, suppliers and business partners. Data mining tools could help these organizations to discover the hidden knowledge in the enormous amount of data. The emerging fields of academic analytics and educational data mining are rapidly producing new possibilities for gathering, analyzing, and presenting student data. Faculty might soon be able to use these new data sources as guides for analyzing student dropout rate, student retention, course redesign and as evidence for implementing new assessments and lines of communication between instructors and students. This paper uses the college admission data with data mining techniques to objectively and methodically comment on the retention percentage in the college.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Student Retention Classification Categorization