Using data mining techniques to extract profitability strategy

Zahedeh Jafari; Zahra Jiriayi Sharahi; Ali Dehbashi

Zahedeh Jafari Pooyesh Qom higher education institute, Iran
Zahra Jiriayi Sharahi University of Yazd, Yazd, Iran
Ali Dehbashi University of Semnan, Iran

Keywords: Customer relationship management, Clustering, K-Means, Classification, Decision Tree

Abstract

Today, with the development of base systems and the high volume of data stored in them, there is a need for tools to process and use these data. The best tool is to discover rules between data, data mining and data mining techniques. In this article, we are looking for data mining tools to extract the profitability rules. In order to obtain the tangible results of this research, the information of customers of a restaurant in two parts has been collected through the distribution of a questionnaire. The first part is customer personal information, and the second part is the importance of each of the loyalty factors assigned by each customer. After collecting the data, the Excel file was imported and analyzed by Rapid Miner software, and clustering was performed on all customer information and customers were placed in clusters based on similarity of features and their behavior. Clustering is performed using the K-means algorithm. Then, an index is defined for categorization and is categorized on each cluster of clustering, and customers of each cluster are divided into two groups of profitable and non-profitable, and after extracting the rules from the decision tree, the strategies are presented for making more profit in the studied location.

Downloads

Download data is not yet available.

Author Biographies

Zahedeh Jafari, Pooyesh Qom higher education institute, Iran

Ms. Industrial engineering, Pooyesh Qom higher education institute, Iran

Zahra Jiriayi Sharahi, University of Yazd, Yazd, Iran

PhD. Candidate in industrial engineering, University of Yazd, Yazd, Iran

Ali Dehbashi, University of Semnan, Iran

Ms. MBA student, University of Semnan, Iran

References

Aleksandrovna Maximova, O & V. Aleksandrovich Belyaev (2017). Generational Indigenation in a Multi-Ethnic and -Religious Society (Tatarstan, Russia). Opción, Año 33, No. 84 (2017): 38-64.

Bong-Horng, C., Ming-Shian,T. , Cheng-Seen ,H. ,( 2007) ,Toward a hybrid data mining model for customer retention, Knowledge-Based Systems 20, 703–718.

Brunner, J.J & Ganga-Contreras, F. (2017). Vulnerabilidad educacional en América Latina: Una aproximación desde la sociología de la educación con foco en la educación temprana Opción, Año 33, No. 84 (2017): 12-37.

Buckinx, W., & Poel, D. V. D. (2005). Customer base analysis: Partial defection of behaviorally-loyal clients in a non-contractual FMCG retail setting. European Journal of Operational Research, 164, 252–268.

G Soleimani, M Amiri, SM Khatami, MJ Isfahani :Using S Technology, in the Automotive Industry, with the Approach of Its Implementation in Commercial Vehicles . Industrial Engineering & Management Systems.2016; 15(4): pp.290-297.

Keshtkar (2018). Numerical analysis of transcritical carbon dioxide compression cycle: a case study, 7 (1), 1-6.

Keshtkar M. M., (2017). Energy, exergy analysis and optimization by a genetic algorithm of a system based on a solar absorption chiller with a cylindrical PCM and nano-fluid, Int. Journal of Heat and Technology, 35 (2), 416-420.

Keshtkar M. M., Talebizadeh, P., (2018). Investigation of transient conduction–radiation heat transfer in a square cavity using combination of LBM and FVM, Sadhana, 43 (4), 145-155.

Keshtkar M. M.; Dadkhoda Zadeh M. (2018). Thermal Simulation of the Symmetric and Asymmetric Arrangement of Barriers on Heat Transfer Enhancement in a Porous Gas Heat Exchanger, Journal of Thermal Science and Engineering Applications, 10 (1), 120-135.

Kim, J. K., Song, H. S., Kim, T. S., & Kim, H. K. (2005). Detecting the change of customer behavior based on decision tree analysis. Expert Systems with Applications, 22, 193–205.

Leung, R. W. K., Lau, H. C. W., & Kwong, C. K. (2003). On a responsive replenishment system: A fuzzy logic approach. Expert Systems with Applications, 20, 20–32.

Seyed Hosseini ,M. , A. , Maleki, Gholamian, M. , Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty, (2010),Expert Systems with Applications 37, 5259–5264Bahari , F . , Elayidom , S,. (2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behavior, Procedia Computer Science, 46, 725 – 731.