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Enabling Customer Reward with a Hybrid Intelligent System (Case Study: J&J Shopping Mall)

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Okon E. Uko, Enyindah P., Enefiok A. Etuk
10.5120/ijca2017914481

Okon E Uko, Enyindah P. and Enefiok A Etuk. Enabling Customer Reward with a Hybrid Intelligent System (Case Study: J&J Shopping Mall). International Journal of Computer Applications 168(8):15-22, June 2017. BibTeX

@article{10.5120/ijca2017914481,
	author = {Okon E. Uko and Enyindah P. and Enefiok A. Etuk},
	title = {Enabling Customer Reward with a Hybrid Intelligent System (Case Study: J&J Shopping Mall)},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {8},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {15-22},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume168/number8/27895-2017914481},
	doi = {10.5120/ijca2017914481},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

A reward program (also known as a loyalty program) is a marketing technique often adopted by some companies to appreciate customers who frequently make purchases with such companies. Normally, this kind of program leads to giving a loyal customer gifts in forms of customer free merchandise, coupons, rewards and advance released products. In most situations, reward programs in these companies are often biased due to the mechanism employed in determining a loyal customer. Hence, this article introduces a method that uses two efficient artificial intelligent techniques namely; fuzzy logic and expert system (fuzzy expert system) to tackle the challenge of biasness in loyal customer selection. These Hybrid system worked efficiently when executed on Jane and Juliet (J&J) Shopping Mall’s data and sales manager (domain expert) ’s rule. It was able to fuzzify the different linguistic variables and also aggregated the firing rules (from the expert rule-base) to generate a crisp loyalty output after applying a fuzzy inference model. Object Oriented and Design (OOAD) Methodology was used in the design of the system and JAVA was used to implement it. Matlab’s Simulink was also used in the simulation of the results.

References

  1. Bia/Kesley (2015). U.S. CRM; Key to business successes. Bia/Kelsey report. U. S.
  2. Cheng C. and Chen Y. (2009) “Classifying the segmentation of customer value via RFM model and RS theory”, Elsevier Science Ltd, 36(3), 4176–4184.
  3. Hosseini S. M. S., Maleki A. and Gholamian M. R. (2010) “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty”, Elsevier Science Ltd, 37(7), 5259–5264.
  4. Ng K. and Liu H. (2000) “Customer Retention via Data Mining”, Kluwer Academic Publishers, 14(6), 569–590.
  5. Nilashi M. and Ibrahim, O. B. (2014). “A Model for Detecting Customer Level Intentions to Purchase in B2C Websites Using TOPSIS and Fuzzy Logic Rule-Based System”. Arabian Journal for Science and Engineering. 1907–1922.
  6. Nugroho I., Manongga, D. and Utomo W. H. (2013) " ID3 Algorithm to Identify Customer Loyalty Factor at Semarang Ceramics Company", International Journal of Computer Applications, 0975 – 8887.
  7. Rygielski, C., Wang J. and Yen D. C. (2002) “Data mining techniques for customer relationship management”. Elsevier Science Ltd., 483–502.
  8. Setiyawati N., Utomo W. H. and Manongga, D. (2015). “Analytical Customer Relationship Management for Garage Services Recommendation Using the Generalized Sequential Pattern Method”. International Journal of Computer Science and Software Engineering (IJCSSE), 95-101.
  9. Silver D., Newnham L., Barker D., Weller S., and McFall J. (2003) “Concurrent Reinforcement Learning from Customer Interactions”. Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA. 1-9.
  10. Tsai C. and Lu Y. (2009) “Customer Churn Prediction by Hybrid Neural Networks”, Elsevier Science Ltd, 36(10), 12547–12553.
  11. Verbeke W., Martens D., Mues C. and Baesens B. (2011) “Building comprehensible customer churn prediction models with advanced rule induction techniques”, Elsevier Science Ltd, 38(3), 2354–2364.
  12. Xie Y., Liu X., Ngai E. W. T. and Ying W. (2009) “Customer churn prediction using improved balanced random forests”, Elsevier Science Ltd, 36(3), 5445–5449.

Keywords

Customer reward, loyalty, customer relationship management, hybrid intelligence, fuzzy expert system, artificial intelligence, systems integration, rule-base, fuzzification, defuzzification, crisp output, rule-firing, loyalty analysis.