Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Enhancing Direct Marketing using Data Mining: A Case of YAA Asantewaa Rural Bank Ltd. in Ghana

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 153 - Number 7
Year of Publication: 2016
Authors:
Anokye Acheampong Amponsah, Kwaku Agyepong Pabbi
10.5120/ijca2016912092

Anokye Acheampong Amponsah and Kwaku Agyepong Pabbi. Enhancing Direct Marketing using Data Mining: A Case of YAA Asantewaa Rural Bank Ltd. in Ghana. International Journal of Computer Applications 153(7):6-12, November 2016. BibTeX

@article{10.5120/ijca2016912092,
	author = {Anokye Acheampong Amponsah and Kwaku Agyepong Pabbi},
	title = {Enhancing Direct Marketing using Data Mining: A Case of YAA Asantewaa Rural Bank Ltd. in Ghana},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2016},
	volume = {153},
	number = {7},
	month = {Nov},
	year = {2016},
	issn = {0975-8887},
	pages = {6-12},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume153/number7/26413-2016912092},
	doi = {10.5120/ijca2016912092},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

With fields like the banking industry, data are being generated massively on a regular basis. So Managers and Administrators are finding ways to turn these data into very valuable information. Marketing risk is one of the most serious problems of every bank. Fortunately, all marketing promotions are highly dependent on the data about customers stored in electronic format. Data mining, machine learning and artificial intelligence promise the use of models to go through and analyse huge data that are very difficult for human brains. This study is conducted to demonstrate with practical methods, experiments and dataset that data mining can be used to assist in direct marketing. The classifiers used are J48 decision tree and Naïve Bayes. The University of Waikato open source data mining software (Weka) is used to perform all the experiments. Confusion matrix is used to calculate the accuracy, sensitivity and specificity which are used to evaluate the performance of the classifiers. Receiver Operating Characteristic curves are also used to pictorially display the performance of the model. The results of the experiments show DT performs better than NB. It also denotes with high precision that the models can be used in detecting prospects for marketing campaigns. The decision tree and the Naive Bayes classifiers produced an accuracy of 92.5% of 91.6% respectively.

References

  1. Du, X. (2006). Data Mining Analysis andModeling for Marketing Based on Attributes of Customer Relationship
  2. Chopra, B., Bhambri, V., Krishan, B.(2011), Implementation of Data Mining Techniques for Strategic CRM Issues, Int. J. Comp. Tech. Appl., Vol 2 (4), 879-883 Available online at www.ijcta.com (Accessed ‎Wednesday, ‎August ‎26, ‎2015, ‏‎10:29:24 PM)
  3. Radhakrishnan B., Shineraj G., Anver Muhammed K.M. (2013), Application of Data Mining In Marketing, International Journal of Computer Science and Network, Volume 2, Issue 5, October 2013, 41 – 46, available online at www.ijcsn.org
  4. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C. and Wirth, R. (2000), CRISP-DM 1.0 - Step-by-step data mining guide, CRISP-DM Consortium.
  5. Patil, N., Lathi, R., & Chitre, V. (2012). Comparison of C5. 0 & CART classification algorithms using pruning technique. International Journal of Engineering Research and Technology (Vol. 1, No. 4 (June-2012)). ESRSA Publications.
  6. Sun, H., Huai, J., Liu, Y., & Buyya, R. (2008). RCT: A distributed tree for supporting efficient range and multi-attribute queries in grid computing. Future Generation Computer Systems, 24(7), 631-643.
  7. http://www.saedsayad.com/decision_tree.htm (accessed Friday, October 09, 2015 09:41:36 AM)
  8. Linoff, G. S., & Berry, M. J. (2004). Data mining techniques: for marketing, sales, and customer relationship management. John Wiley & Sons.
  9. Fuchs, C. A., and Peres, A. (1996). Quantum-state disturbance versus information gain: Uncertainty relations for quantum information. Physical Review A, 53(4), 2038.
  10. Quinlan, J.R.(1986). Induction of Decision Trees, Machine Learning, vol. 1, pp. 81-106, 1986.
  11. Kohavi, R. & Provost, F. (1998). Glossary of Terms. Machine Learning, 30(2-3), 271-274.
  12. Nisbet, R., Miner, G., & Elder IV, J. (2009). Handbook of statistical analysis and data mining applications. Academic Press.
  13. Elsalamony, H. A. (2014). Bank Direct Marketing Analysis of Data Mining Techniques. Network, 5, 0. International Journal of Computer Applications (0975 – 8887) Volume 85 – No 7, January 2014
  14. Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: the kappa statistic. Fam Med, 37(5), 360-363.

Keywords

Direct Marketing, Data Mining, Decision Tree, J48, banking sector