A RFMV Model and Customer Segmentation Based on Variety of Products
محورهای موضوعی : Data Mining
Saman Qadaki Moghaddam
1
,
Neda Abdolvand
2
,
Saeedeh Rajaee Harandi
3
1 - Department of Electrical, Computer and IT Engineering, Qazvin Azad University, Qazvin, Iran
2 - Alzahra University
3 - Department of Social Science and Economics, Alzahra University, Tehran, Iran
کلید واژه: Clustering , Data Mining , Customer Relationship Management , Product Variety , RFM Model , ,
چکیده مقاله :
Today, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major changes in the ability of organizations to collect, store and analyze macro-data. Therefore, over thousands of data can be stored for each customer. Hence, customer satisfaction is one of the most important organizational goals. Since all customers do not represent the same profitability to an organization, understanding and identifying the valuable customers has become the most important organizational challenge. Thus, understanding customers’ behavioral variables and categorizing customers based on these characteristics could provide better insight that will help business owners and industries to adopt appropriate marketing strategies such as up-selling and cross-selling. The use of these strategies is based on a fundamental variable, variety of products. Diversity in individual consumption may lead to increased demand for variety of products; therefore, variety of products can be used, along with other behavioral variables, to better understand and categorize customers’ behavior. Given the importance of the variety of products as one of the main parameters of assessing customer behavior, studying this factor in the field of business-to-business (B2B) communication represents a vital new approach. Hence, this study aims to cluster customers based on a developed RFM model, namely RFMV, by adding a variable of variety of products (V). Therefore, CRISP-DM and K-means algorithm was used for clustering. The results of the study indicated that the variable V, variety of products, is effective in calculating customers’ value. Moreover, the results indicated the better customers clustering and valuation by using the RFMV model. As a whole, the results of modeling indicate that the variety of products along with other behavioral variables provide more accurate clustering than RFM model.
Today, increased competition between organizations has led them to seek a better understanding of customer behavior through innovative ways of storing and analyzing their information. Moreover, the emergence of new computing technologies has brought about major changes in the ability of organizations to collect, store and analyze macro-data. Therefore, over thousands of data can be stored for each customer. Hence, customer satisfaction is one of the most important organizational goals. Since all customers do not represent the same profitability to an organization, understanding and identifying the valuable customers has become the most important organizational challenge. Thus, understanding customers’ behavioral variables and categorizing customers based on these characteristics could provide better insight that will help business owners and industries to adopt appropriate marketing strategies such as up-selling and cross-selling. The use of these strategies is based on a fundamental variable, variety of products. Diversity in individual consumption may lead to increased demand for variety of products; therefore, variety of products can be used, along with other behavioral variables, to better understand and categorize customers’ behavior. Given the importance of the variety of products as one of the main parameters of assessing customer behavior, studying this factor in the field of business-to-business (B2B) communication represents a vital new approach. Hence, this study aims to cluster customers based on a developed RFM model, namely RFMV, by adding a variable of variety of products (V). Therefore, CRISP-DM and K-means algorithm was used for clustering. The results of the study indicated that the variable V, variety of products, is effective in calculating customers’ value. Moreover, the results indicated the better customers clustering and valuation by using the RFMV model. As a whole, the results of modeling indicate that the variety of products along with other behavioral variables provide more accurate clustering than RFM model.
M. Khajvand, K. Zolfaghar, S. Ashoori, and S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study”. Procedia Computer Science, Vol. 3, 2011, pp. 57-63.#
P T. Kord Asiabi,, and R. Tavoli, "A Review of Different Data Mining Techniques in Customer Segmentation." Journal of Advances in Computer Research. Vol. 6, No.3, 2015, pp. 51-63.#
V. L. Miguéis, D. Van den Poel, A. S. Camanho, and J. F. e Cunha, “Modeling partial customer churn: On the value of first product-category purchase sequences”. Expert systems with applications, Vol. 39, No.12, 2012, pp.11250-11256.#
D. A. Kandeil, A. A. Saad, and S. M. Youssef, “A Two-Phase Clustering Analysis for B2B Customer Segmentation”. In: Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference on. IEEE, 2014. pp. 221-228.#
C. Liu, “Customer Segmentation and Evaluation Based on RFM, Cross-Selling and Customer Loyalty”. In: 2011 International Conference on Management and Service Science. 2011.#
E. S. Margianti, R. Refianti, A. B. Mutiara, and K. Nuzulina, “Affinity Propagation and Rfm-Model for CRM's Data Analysis". Journal of Theoretical and Applied Information Technology, Vol. 84, No. 2, 2016, p. 272.#
H. Hwang, T. Jung, and E. Suh, “An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry”. Expert systems with applications, Vol. 26, No. 2, 2004, pp. 181-188.#
C. F. Tsai, Y. H. Hu, and Y. H. Lu, “Customer segmentation issues and strategies for an automobile dealership with two clustering techniques”. Expert Systems, Vol. 32, No.1, 2015, pp. 65-76.#
W. Wang, and S. Fan, “Application of Data Mining Technique in Customer Segmentation of Shipping Enterprises”. In 2010 2nd International Workshop on Database Technology and Applications, on. IEEE. 2010, November. pp. 1-4.#
K. N. Lemon, and T. Mark, “Customer lifetime value as the basis of customer segmentation: Issues and challenges”. Journal of Relationship Marketing, Vol. 5, No. 2-3, 2006, pp.55-69.#
B. Noori, “An Analysis of Mobile Banking User Behavior Using Customer Segmentation”. International Journal of Global Business, Vol. 8, No.2, 2015, p. 55.#
Y. L. Chen, M. H. Kuo, S. Y. Wu, and K. Tang, “Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data”. Electronic Commerce Research and Applications, Vol. 8, No. 5, 2009, pp. 241-251.#
C. H. Cheng, and Y. S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory”. Expert systems with applications, Vol. 36, No. 3, 2009, pp.4176-4184.#
J. T. Wei, S. Y. Lin, C. C. Weng, and H. H. Wu, “A case study of applying LRFM model in market segmentation of a children’s dental clinic”.Expert Systems with Applications, Vol. 39, No. 5, 2012, pp. 5529-5533.#
A. Asllani, and D. Halstead, “A Multi-Objective Optimization Approach Using the RFM Model in Direct Marketing”. Academy of Marketing Studies Journal, Vol. 19, No. 3, 2015, p. 49.#
[16] S. M. S. Hosseini, A. Maleki, and M. R. Gholamian, “Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty”. Expert Systems with Applications, Vol. 37, No. 7, 2010, pp. 5259-5264.#
R. Venkatesan, and V. Kumar, “A customer lifetime value framework for customer selection and resource allocation strategy”. Journal of marketing, Vol.68, No. 4, 2004, pp.106-125.#
M. Alvandi, S. Fazli, and F. S. Abdoli, “K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services”. International Research Journal of Applied and Basic Sciences, Vol. 3, No. 11, 2012, pp. 2294-2302.#
W. J. Reinartz, and V. Kumar, “On the profitability of long-life customers in a noncontractual setting: An empirical investigation and implications for marketing”. Journal of marketing, Vol. 64, No. 4, 2000, pp. 17-35.#
R. A. Soeini, and E. Fathalizade, “Customer Segmentation based on Modified RFM Model in the Insurance Industry”, Proceedings of 4th International Conference on Machine Learning and Computing IPCSIT. Singapore: IACSIT Press. 2012, Vol. 25, pp. 104-108.#
A. Ansari, and A. Riasi, “Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms”. International Journal of Business and Management, Vol. 11, No. 7, 2016, p.59.#
H. C. Chang & H. P. Tsai, “Group RFM analysis as a novel framework to discover better customer consumption behavior”. Expert Systems with Applications, Vol. 38, No. 12, 2011, pp. 14499-14513.#
D. T. Larose, Data mining methods & models. John Wiley & Sons, 2006.#
S. Moro, R. Laureano, & P. Cortez, “Using data mining for bank direct marketing: An application of the crisp-dm methodology”. In Proceedings of European Simulation and Modelling Conference-ESM, Eurosis, 2011, pp. 117-121.#
P. N. Tan, Introduction to data mining. Pearson Education India, 2006.#
