Abstract

Along with increasing competitiveness in the service industry to increase the number of customers and gain competitive advantage by creating customer satisfaction, using data mining concepts has attracted the attention of the researchers and the industries as a new tool for this purpose. In this regard, fast food industry, as an industry having an increasing growth during recent years, is considered as a very attractive market for the customers. The current study aims to utilize data mining algorithms to categorize the customers in fast food industry and propose marketing strategies tailored to each group of customers identified. The statistical population of this research includes the records submitted in the integrated system of Perperook chain restaurants, which is over 3000 records. Furthermore, the data mining algorithms, specifically decision tree and Quest algorithm, have been used in this study to categorize customers according to the orders submitted in the system. The results of this study indicate that the customers of Perperrok fast food can be categorized into three main groups: healthy, voluminous, and free-living customers. At the end of the research, detailed results and strategies associated with any of the main groups of customers are presented along with practical suggestions.

Keywords: customer relationship management, data mining, Apriori algorithm, decision tree

JEL Classification: M30, M31, M39.

Cite as: Kashani, F. & Shahmirzaloo Z. (2017). Developing marketing strategies using customer relationship management and data mining (case study: Perperook chain restaurants). Marketing and Management of Innovations, 3, 135-148. https://doi.org/10.21272/mmi.2017.3-13

This work is licensed under a Creative Commons Attribution 4.0 International License

References

1. Berson, A., & Smith, S.J. (2002). Building data mining applications for CRM. McGraw-Hill.
2. Cheng, C.H., & Chen, Y.S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36, 4176-4184.
3. Hosseinzadeh-Shahri, M., Karami, M., & Merabani, M. (2015). Customer classification in chain restaurants based on food style (Case study: Boof fast food restaurants in Tehran City). Tehran University publication.
4. Ngai, E.W., Xiu, L., & Chau, D.C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36, 2592-2602.
5. Kim, M.J., Jung, H.S., & Yoon, H.H. (2007). A study on the relationships between food-related lifestyle of undergraduates and the restaurant selection attribute. Journal of the Korean Society of Food Culture, 22(2), 210-217.
6. Ghazanfari, M., Alizadeh, S., & Teimourpour, B. (2008). Data mining and knowledge discovery. Elm-o-Sanat University Press, Tehran, 1st Ed.
7. Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd edition. Morgan Kaufmann Publishers. San Francisco, CA, USA.
8. Funatsu, K., & Hasegawa, K. (2011). New fundamental technologies in data mining. First published January, Printed in India.
9. Pang-Ning, T., Steinbach, M., & Kumar, V. (2006). Introduction to data mining. In Library of congress.
10. Liang, Y.H. (2010). Integration of data mining technologies to analyze customer value for the automotive maintenance industry. Expert systems with Applications, 37, 7489-7496.
11. Liao, S.H., Chen, C.M., & Wu, C.H. (2008). Mining customer knowledge for product line and brand extension in retailing. Expert Systems with Applications, 34, 1763-1776.
12. Kracklauer, A.H., Mills, D.Q., & Seifert, D. (2004). Collaborative customer relationship management: taking CRM to the next level. Springer Science & Business Media.
13. Chang, H. C., & Tsai, H.P. (2011). Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Systems with Applications, 38, 14499-14513.
14. Fan, B., & Zhang, P. (2009). Spatially enabled customer segmentation using a data classification method with uncertain predicates. Decision Support Systems, 47, 343-353.
15. Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study. Procedia Computer Science, 3, 57-63.
16. Shahrabi, J., & Soofi-Neyestani, R. (2008). Analyzing customers’ shopping cart using association rules in Shahrvand chain stores. 2nd Iranian Conference on Data Mining, Amir Kabir University of Technology pubication, Tehran.
17. Liao, S.H., Chu, P.H., & Hsiao, P.Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, 39, 11303-11311.
18. Hsieh, N.C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27, 623-633.
19. Kim, S.Y., Jung, T.S., Suh, E.H., & Hwang, H.S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert systems with applications, 31, 101-107.
20. Wei, J.T., Lee, M.C., Chen, H.K., & Wu, H.H. (2013). Customer relationship management in the hairdressing industry: an application of data mining techniques’, Expert Systems with Applications, 40, 7513-7518.
21. Bahari, T.F., & Elayidom, M.S. (2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour. Procedia Computer Science, 46, 725-731.
22. Jang, Y.J., Kim, W.G., & Bonn, M.A. (2011). Generation Y consumers’ selection attributes and behavioral intentions concerning green restaurants. International Journal of Hospitality Management, 30, 803-811.