Customers Clustring Analysis Based on WRFM Model Using Non-Supervisory Data Mining Approach (Case study of hygienic and cosmetic products)

Document Type : Original Article

Authors

1 PhD Student in Management, Department of Management, Faculty of Management and Accounting, Roudehen Branch, Islamic Azad University, Roudehen, Iran

2 Associate Professor, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran

3 Associate Professor, Department of Management, School of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

In today's competitive world where companies are faced with a huge amount of customer information due to the growth and development of information technology and the creation of various databases, the use of customer relationship management tools that can accurately and timely identify and monitor customer needs and expectations Becomes more necessary;one of the techniques that can play a key and fundamental role in this period along with this important category is data mining of customer databases. The purpose of this study is to analyze customers clustering based on the WRFM model using non-supervisory data mining methods;the researchers seek to discover the existing rules and patterns to provide more effective strategies for each group of customers, especially key customers, in order to have a better profitability and performance for the organization.Using available purposive sampling method, 64858 samples have been selected from the database of customers who have used hygienic and cosmetic products in the period of 2018-2019.The weight of WRFM attributes has been determined by surveying 3 sales experts (senior managers) of the company. Clementine and SPSS soft wares were used for data analysis. According to the research model, 4 customer categories: Specific and key, Potential golden, lost Uncertainty, New Uncertain were identified and named, and different strategies have been presented for each of these customer categories Also the result show that K-mean clustering with six clusters and purity of 0.744% had better performance than other clustering methods.

Keywords

References

[1]    Ashouri, M., Sharifkhani, M., & Tarokh, M. J., Developing Customer Knowledge Management Process Model Using CRM Systems.‏ Journal of Roshd-E-Fanavari, vol. 10, 2014, pp.62-68.
[2]    Kafashpoor, A., Tavakoli, A. & Alizade Zavarem, A., Customer Segmentation According to Customer Life Time Value Using Data Mining Based on RFM Model, journal of Public Management Researches, vol. 5, 2012, pp.63-84. 
[3]    Rangriz, H., and Bayrami Shahrivar, Z., The Impact of E-CRM on Customer Loyalty Using Data Mining Techniques.IT Management Studies, vol. 7, 2019, pp.175-205. 
[4] Babaiyan, V. & Sarfarazi, S., Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model. Journal of AI and Data Mining, Vol.7, 2019, pp. 331-340. 
[5]     Shokohyar, S., Rezaeian, A. & Boroufar, A., Identifying the customer behavior model in life insurance Sector using data mining. Management Research in Iran, Vol.20, 2016, pp. 65-94.
[6]    RaeisiVanani, S., RaeisiVanani, I. & Taghavifard, M., A Model for Learners Segmentation and Educational Performance ImprovementUsing Data Mining Algorithms.IT Management Studies, Vol. 9, 2020, pp. 5-38. 
[7]    SamadiRad, B., Principles of customer orientation and marketing (looking at the role of human communication in customer orientation), Eighth Art Mag., Vol. 21& 22, 2001, pp. 94-98.
[8    Salehi Sadaghiyani, J. & Akhavan, M., Customer Relationships Management, Accountants, Vol.21, 2006, pp. 23-36.
[9]    Keegan, Warren J., Global Marketing Management.Translated and averaged by Abdolhamid Ebrahimi, (2ed.) Iran Cultural Studies Center, Tehran, 2001, pp.193-199.
[10] Hosseini, S.M.S., Maleki, A. &Gholamian, M.R.,Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, Vol.37, 2010, pp. 5259–5264.
[11] Blattberg, R.C., Kim, B.D. & Neslin, S.A., RFM Analysis in Database Marketing.International Series in Quantitative Marketing, 18, Springer, New York, 2008.
[12] Khajvand, M., Zolfaghari, K., Ashoori, S., & Alizadeh, S. Estimating Customer Life Time Value based on RFM analysis of customer purchase behavior: case study. Procedia Computer Science, Vol. 3, 2011, pp. 57-63.
[13] Stone, B., Successful direct marketing methods, Lincolnwood, IL: NTC Business Books, 1995, pp. 37–57.
[14] Bin, D., Peiji, S., & Dan, Z., Data mining for needy students identify based on improved RFM model: a case study of university. Proceedings of the 2008 International Conference on Information Management, Innovation Management, and Industrial Engineering, vol. 1, 2008,pp. 244-247.
[15] Sohrabi, B., RaeesiVanani, I. & ZarehMirkabad, F., "Designing a Recommender System for Optimizing and Managing Bank Facilities through the Utilization of Clustering and Classification Algorithms". Modern Research in Decision Making, Vol.1, 2016, pp. 53-76.
[16] Shahrabi, J., Data Mining, Soroush Gita, Tehran, 2015.
[17]  Alizadeh, S. & Malekmohammadi, S., Data Mining and knowledge discovery step by step by Clementine Software, Khajeh NasiruddinTusi University of Technology Press.Tehran, 2014. 
[18] Han, J., Kamber, M., & Pei, J., Data Mining: Concepts and techniques (3rd Ed.), Elsevier, 2011.
[19] Bhojani, S. & Bhatt, N., Data Mining Techniques and Trends – A Review. Global Journal for Research Analysis (GJRA), Vol. 5, 2016, pp. 252-254.
[20] Hwang, S. & Lee, Y., Identifying customer priority for new products in target marketing: Using RFM model and Text Rank, Innovative Marketing, Vol.17, 2021, pp. 125-136.
[21] Bashardoust, O., Asgharizadeh, E. & AfsharKazemi, M., Presenting a customer classification Pattern with a combined data mining approach (case study: Hygienic and Cosmetic products Industry), vol. 13, 2021, pp.85-111.
[22] Abbasimehr, H. and Shabani, M. "A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers", Kybernetes, Vol. 50, 2021, pp. 221-242.
[23] Kabasakal, İnanç. Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study in E-Retailing.Vol.13, 2020, pp.47-56. 
[24] Salehi, M. & Salari, M. "Comparing data mining and fuzzy logic techniques to identify behavior of customers", Modern Research in Decision Making, Vol. 2, 2017, pp.173-192.
[25] Bashirimousavi, S.A., Afsar, A. and MahjoubiFard, A., Bank customers value analysis using data mining techniques and fuzzy hierarchical analysis, Management Researches in Iran, Vol.19, 2015, pp. 23-43.
[26] Veysi, H., Booklet of Statistical Methods in Natural Language Processing (Clustering).Faculty of Modern Sciences and Technologies, University of Tehran. 2017.  
[27] Chang, H. H., &Tsay, S. F., Integrating of SOM and K-meansin data mining clustering: An empirical study of CRM and profitability evaluationJournal of Information Management,Vol.11,2004,pp. 161-203.
[28] Mahdiraji, H.A., Zavadskas, E.K., Kazeminia, A., Abbasi Kamardi, A.  Marketing strategies evaluation based on big data analysis: a CLUSTERING-MCDM approach, Economic Research-Ekonomska Istraživanja, Vol. 32, 2019, pp. 2882-2898. 

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