International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.4, No.1, Feb. 2012

A Data Mining-Based Response Model for Target Selection in Direct Marketing

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Eniafe Festus Ayetiran,Adesesan Barnabas Adeyemo

Index Terms

Data warehouse,Data Mining,Direct Marketing,Target Selection,Naïve Bayes


Identifying customers who are more likely to respond to new product offers is an important issue in direct marketing. In direct marketing, data mining has been used extensively to identify potential customers for a new product (target selection). Using historical purchase data, a predictive response model with data mining techniques was developed to predict a probability that a customer in Ebedi Microfinance bank will respond to a promotion or an offer. To achieve this purpose, a predictive response model using customers’ historical purchase data was built with data mining techniques. The data were stored in a data warehouse to serve as management decision support system. The response model was built from customers’ historic purchases and demographic dataset.

Bayesian algorithm precisely Naïve Bayes algorithm was employed in constructing the classifier system. Both filter and wrapper feature selection techniques were employed in determining inputs to the model.

The results obtained shows that Ebedi Microfinance bank can plan effective marketing of their products and services by obtaining a guiding report on the status of their customers which will go a long way in assisting management in saving significant amount of money that could have been spent on wasteful promotional campaigns.

Cite This Paper

Eniafe Festus Ayetiran, Adesesan Barnabas Adeyemo, "A Data Mining-Based Response Model for Target Selection in Direct Marketing", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.1, pp.9-18, 2012. DOI: 10.5815/ijitcs.2012.01.02


[1]Adamson C. and M. Venerable. “Data Warehouse Design Solutions”. J. Wiley & Sons, Inc., 1998

[2]Alejandro Gutiérrez and Adriana Marotta. “An Overview of Data Warehouse Design Approaches and Techniques”, 2000

[3]Almquist, E. and Wyner G. “Boost Your Marketing ROI with Experimental Design”. Harvard Business Review, p.135-141., 2001

[4]Ballard. C. “Data Modelling Techniques for Data Warehousing”. SG24-2238-00. IBM Red Book. ISBN number 0738402451., 1998

[5]Berry, M. J. A. and Linoff, G. S. “Data Mining Technique for Marketing, Sale, and Customer Relationship Management” (2nd edn), Indiana, Indianapolis Publishing Inc., 2004

[6]Changyun Wang. “Bayesian Belief Network Simulation”, Department of Computer Science Florida State University, 2003

[7]Chen, H.Y., Chen, T.C., Min, D., Fischer, G. and Wu, Y.M.‘Prediction of tacrolimus blood levels by using the neural network with genetic algorithm in liver transplantation patients’, Therapeutic Drug Monitoring, 21, 1, 50– 56., 1999

[8]Cheung, K.-W., Kwok, J. K., Law, M. H. and Tsui, K.-C. ‘Mining customer product rating for personalized marketing ’, Decision Support Systems, 35, 231–243., 2003

[9]Chiu, C. “A case-based customer classification approach for direct marketing”, Expert Systems with Applications, 22, 163–168., 2002

[10]Colin McGregor. Oracle Database 2 Day DBA, 10g Release 2 (10.2), 2005

[11]Date, C. J. An introduction to database systems (7th ed.). Reading, Mass.: Addison-Wesley, 2000

[12]Gang Luo. Techniques for Operational Data Warehousing. PhD Thesis (Computer Sciences), University of Wiscosin-Madison, 2004

[13]Han, J., & Kamber, M. Data mining: Concepts and Techniques. San Francisco: Morgan Kaufmann Publishers., 2001

[14]Ho, T.B. (nd). Knowledge Discovery and Data Mining Techniques and Practice Available on URL:, 2006

[15]Inmon. W.H. Building the Data Warehouse. Wiley Computer Publishing, 1996

[16]Scalzo, B. (2003). Oracle DBA guide to data warehousing and star schemas. Upper Saddle River, N.J.: Prentice Hall PTR.

[17]Shapiro, G. P. ‘Knowledge Discovery in Databases: 10 years after’, SIGKDD Explor. Newsl, 1, 2 (Jan. 2000), 59- 61. Available from:, 2000

[18]Shin, H. J. and Cho, S. ‘Response modelling with support vector machines’, Expert Systems with Applications, 30, 4, 746–760., 2006

[19]Ou, C., Liu, C., Huang, J. and Zhong, N. ‘One Data mining for direct marketing’, Springer-Verlag Berlin Heidelberg, pp. 491–498., 2003

[20]Paul Lane, Viv Schupmann and Ingrid Stuart. Oracle Database Data Warehousing Guide 11g Release 1(11.1), 2007

[21]Petrison, L. A., Blattberg, R. C. and Wang, P. ‘Database marketing: Past present, and future’, Journal of Direct Marketing, 11, 4, 109–125, 1997

[22]Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From Data Mining to knowledge Discovery: An Overview, Advances in Knowledge Discovery and Data Mining, (1996). AAAI Press, , pp 1-34.