The Study on Cross-Industry Standard Process for Data Mining in E-Marketing

Article Preview

Abstract:

According to the standard data mining process, CRISP-DM, one can directly collect data that are essential and useful for the mining results. It also offers practical help to those KD researchers both from industry and academia. In this paper, a KD procedure and method of E-marketing is discussed thoroughly, and a simple operating and easy understanding process model is presented to marketing people with little data mining background. In addition, the traditional market research methods are highly subjective; it is difficult to support the objective marketing decisions. While the KDDM process model can be effective in helping market analyst find the distribution and propensity of customers, thus to predict customer needs, determine the marketing strategy and ultimately to develop effective marketing plans.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2298-2303

Citation:

Online since:

July 2011

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2011 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] S. Krishnamurthy, Introducing e-markplan: A practical methodology to plan e-marketing activities, Business Horizons, vol. 49, pp.51-60, (2006).

DOI: 10.1016/j.bushor.2005.05.008

Google Scholar

[2] I.S.Y. Kwan, J. Fong and H.K. Wong, An e-customer behavior model with online analytical mining for internet marketing planning, Decision Support Systems, vol. 41, p.189–204, (2005).

DOI: 10.1016/j.dss.2004.11.012

Google Scholar

[3] L.A. Kurgan and P. Musilek, A survey of Knowledge Discovery and Data Mining process models, The Knowledge Engineering Review, vol. 21, pp.1-24, (2006).

DOI: 10.1017/s0269888906000737

Google Scholar

[4] W. Gersten, R. Wirth and D. Arndt, Predictive modeling in automotive direct marketing: tools, experiences and open issues, In Proceeding of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p.398–406, (2000).

DOI: 10.1145/347090.347174

Google Scholar

[5] Z. Bošnjak, O. Grljević and S. Bošnjak, CRISP-DM as a Framework for Discovering Knowledge in Small and Medium Sized Enterprises' Data, 5th International Symposium on Applied Computational Intelligence and Informatics, pp.509-514, (2009).

DOI: 10.1109/saci.2009.5136302

Google Scholar

[6] Z. Mo, S. Zhao, L. Li and A. Liu, A Predictive Model of Churn in Telecommunications Based on Data Mining, 2007 IEEE International Conference on Control and Automation, pp.809-813, (2007).

DOI: 10.1109/icca.2007.4376469

Google Scholar

[7] C. Shearer, The CRISP-DM model: the new blueprint for data mining, Journal of Data Warehousing, vol. 5, p.13–19, (2000).

Google Scholar

[8] Data sources: http: /research. cnnic. cn.

Google Scholar

[9] T.W. Liao and E. Triantaphyllou, Recent Advances in Data Mining of Enterprise Data: Algorithms and Applications, Series on Computers and Operations Research, vol. 6, (2008).

DOI: 10.1142/6689

Google Scholar