Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Customer Value Analysis Based on WRFM Model With the Combined Data Mining Method (Case Study of Hygienic and Cosmetic Products)

Document Type : Original Article

Authors
Omid Bashardoust
Ezattollah Asgharizadeh
MohammadAli Afsharkazemi
10.35050/JIPM010.2022.035
Abstract
The accumulated volume of customer information due to the growth and development of information technology and the creation of databases has led companies that want to provide better services to their customers to benefit from new tools for customer relationship. One of these tools and methods is data mining techniques that can play an important and key role in customer relationship management. The purpose of this study is to analyze customer value with a combined data mining approach based on the WRFM model.  So 64858 samples from customer database in the period 2019-2020 have been selected by available purposive sampling method. The weight of WRFM attributes has been determined by surveying 3 experts of the company using a hierarchical analysis process. Based on the initial variables of the research and the variables obtained from the attributes of the WRFM model, the purchase value of customers has been analyzed. SPSS Modeller and SPSS software were used to analyze the data.The results show that the K-Means clustering method has a better performance in customer segmentation than the TwoStep clustering and the Cohonen neural network methods. Finally, based on the criteria of purity percentage, repetition, error rate and Normalized Mutual Information (NMI (index, six clusters with NMI (0.631) were selected from different K-Means clustering.
This study introduces the WRFM model for customer value analysis.The weight of the attributes of this model is based on a survey of experts and using a hierarchical analysis process based on the degree of incompatibility (0.052) obtained from the hierarchical analysis method (0.15), (0.29) and (0.56), respectively, have been determined that these values ​​indicate the greater importance of the monetary value index than the other two indices; Finally, these six clusters were divided into 4 general categories using naming market segments methods in research (Chang and Tsai 2004; Babaian and Sarfarazi 2019): key and special customers, golden potential customers, missing uncertain customers and new uncertain customers. According to the research model, the company should focus more on its specific and key customers, ie customers who are in the first, third and fifth clusters, ie loyal customers who have higher than average values in the two attributes of monetary value and frequency and recently they have had purchases with a high value of Rials that the company should consider effective marketing strategies for this group of customers due to its limited resources in order to lead to more profitability for the company while maintaining customer relationship management.
Keywords
Clustering
Cohonen Neural Net
Customer Relationship Management
Customer Value Analysis
Data Mining
WRFM Model
ایزدی، بهرام، بهرام رنجبریان، سعیده کتابی، و فریا نصیری مفخم. 1395. یک رویکرد جامع برای بخش‌بندی بازار و طبقه‌بندی مشتریان با استفاده از روش‌های داده‌کاوی و برنامه‌ریزی خطی. مدیریت تولید و عملیات 7 (1) :1-22.
خواجوند، سمانه، محمدتقی تقوی‌فرد، و اسماعیل نجفی. 1391. بخش‌بندی مشتریان بانک صادرات ایران با استفاده از داده‌کاوی. مطالعات مدیریت بهبود و تحول 22 (67): 179-200.
رنگریز، حسن، و زهرا بایرامی شهریور. 1398. تأثیر مدیریت ارتباط با مشتری الکترونیکی بر وفاداری مشتریان با استفاده از تکنیک‏های داده‌کاوی. مطالعات مدیریت کسب‌و‌کار هوشمند 7 (27): 175-205.
رئیسی وانانی، سینا، ایمان رئیسی وانانی، و محمدتقی تقوی‌فرد. 1399. مدلی برای بخش‌بندی یادگیرندگان و بهبود عملکرد آموزشی با استفاده از الگوریتم‌های داده‌کاوی. مطالعات مدیریت کسب‌و‌کار هوشمند 9 (33): 5-38.
شهرابی، جمال. 1394. داده‌کاوی. تهران: سروش گیتا.
صالحی صدقیانی، جمشید، و مریم اخوان. 1385. مدیریت ارتباط با مشتری. حسابدار 21 (176): 23-36.
صمدی راد، برات‌اله. 1380. اصول مشتری‌گرایی و بازاریابی (با نگاهی به نقش ارتباطات انسانی در مشتری‌گرایی). مجله هنر هشتم، 21 و 22: 94-98.
صنایعی، علی. 1381. بازاریابی و تجارت الکترونیک. اصفهان: جهاد دانشگاهی.
عاشوری، مریم، مونا شریف‌خانی، و محمدجعفر تارخ. 1393. توسعة مدل فرایندی مدیریت دانش مشتری با استفاده از سیستم‌های مدیریت ارتباط با مشتری. رشد فناوری 4 (40): 62-68.
علیزاده، سمیه، و سمیرا ملک‌محمدی. 1393. داده‌کاوی و کشف دانش گام‌به‌گام با نرم‌افزار Clementine. تهران: دانشگاه صنعتی خواجه نصیرالدین طوسی.
کفاش‌پور، آذر، احمد توکلی، و علی علیزاده زوارم. 1391. بخش‌بندی مشتریان بر اساس ارزش دوره عمر آن‌ها با استفاده از داده‌کاوی بر مبنای مدل (RFM). پژوهش‌های مدیریت عمومی 5 (15): 63-84.
کیگان، وارن جی. 2004. مدیریت بازاریابی جهانی. ترجمه و تلخیص عبدالحمید ابراهیمی. 1383. تهران: دفتر پژوهش‌های فرهنگی.
ویسی، هادی. 1396. جزوه درسی روش‌های آماری در پردازش زبان طبیعی (خوشه‌بندی). دانشکده فناوری و علوم مدرن، دانشگاه تهران.
 
Abbasimehr, H., and M. Shabani. 2020. A new methodology for customer behavior analysis using time series clustering: A case study on a bank’s customers. Kybernetes 50 (2): 221-242. 
Alvandi, M., S. Fazli, and F. S. Abdoli. 2012. K-Means clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences 3 (11): 2294-2302.
Armstrong, G., Ph. Kotler, L. Harris, and Nigel Piercy. 2014. Principles of marketing. 6th edition.?: Pearson.
Aryuni, M., E. D. Madyatmadja, and E. Miranda. 2018. Customer segmentation in XYZ bank using K-means and K-medoids clustering. In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 412-416). IEEE Jakarta, Indonesia.
Azadnia, A. H., M. Z. Mat Saman, K. Yew Wong, and A. R. Hemdi. 2011. Integration model of Fuzzy C-means clustering algorithm and TOPSIS Method for Customer Lifetime Value Assessment. In Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on (pp. 16-20), IEEE Singapore.
Babaiyan, V. and S. A. Sarfarazi. 2019. Analyzing Customers of South Khorasan Telecommunication Company with Expansion of RFM to LRFM Model. Journal of AI and Data Mining 7 (2): 331-340.
Bashiri Mosavi, A., and A. Afsar. 2018. Customer value analysis in banks using data mining and fuzzy analytic hierarchy processes. International Journal of Information Technology & Decision Making 17 (3): 819–840.
Bhojani, Shital H., and Nirav Bhatt. 2016. Data Mining Techniques and Trends – a Review. Global Journal for Research Analysis (GJRA), 5 (5): 252-254.
Bin, Deng, Shao Peiji, and Zhao Dan. 2008. 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, 1: 244-247. Taipei, Taiwan.
Blattberg, R. C., B. D. Kim, and Scott A. Neslin. 2008. RFM Analysis in Database Marketing. NewYork, NY: Springer.
Chang, H. H., and S. F. Tsay. 2004. Integrating of SOM and K-means in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management 11 (4): 161-203.
Chiu, Ch.Y., Ch. Yi- Feng, I. T. Kuo, and H. Ch. Ku. 2009. An intelligent market segmentation system using k-means and particle swarm optimization.Expert Systems with Applications 36: 4558-4565.
Han, J., M. Kamber, and J. Pei. 2011. Data Mining: Concepts and techniques. (3rd Ed.). Morgan Kaufmann publications Printed in the United States of America: Elsevier.
Hanafizadeh, P., and M. Mirzazadeh. 2011. Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method – ADSL market of a telecommunication company. Expert Systems with Applications 38: 198-205.
Hosseini, S. M., A. Maleki, and M. R. Gholamian. 2010. Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications 37: 5259–5264.
Hu, W., and J. Zhang. 2008 (December 10-11). Study of segmentation for auto services companies based on RFM model.Proceedings from the 5th International Conference on Innovation and Management. Maastricht, the Netherlands.
Hughes, A. M. 1994. Strategic database marketing: the master plan for starting and managing a profitable, customer-based marketing program. Chicago, IL: Probus Publishing.
Hwang, S., and Y. Lee. 2021. Identifying customer priority for new products in target marketing: Using RFM model and TextRank. Innovative Marketing 17 (2): 125-136.
Indika, H. A, R. M. Kapila Tharanga Rathnayaka, & S. K. Illangarathne. 2015. Mining Profitability of Telecommunication Customers Using K-Means Clustering. Journal of Data Analysis and Information Processing 3 (3): 63-71.
Jintana, J., and T. Mori. 2019. Customer clustering for a new method of marketing strategy support within the courier business. Academia Book Chapter 31 (2): 1-19.
Kabasakal, İ. 2020. Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study E-Retailing. Bilişim Teknolojileri Dergisi 13 (1): 47-56. 
Khajvand, M. and M. J. Tarokh. 2011. Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science 3: 1327–1332.
Khajvand, M., K. Zolfaghar, S. Ashoori, and S. Alizadeh. 2011. Estimating Customer Life Time Value based on RFM analysis of customer purchase behavior: case study. Procedia Computer Science 3: 57-63.
Li, D. Ch., L. D. Wen, and W. T. Tseng. 2011. A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Applications 38 (6): 7186-7191.
Mahdiraji, H. A., E. Kazimieras Zavadskas, A. A. Kazeminia, and A. A. Abbasi Kamardi. 2019. Marketing strategies evaluation based on big data analysis: aClusteing-MCDM approach. Economic Research-Ekonomska Istraživanja 32 (1): 2882-2898.
Margianti, Eko. Sri, Rina Refianti, A. Benny Mutiara, and Nuzulina. 2016. Affinity Propagation and RFM-Model for CRM’S DATA Analysis. Journal of Theoretical and Applied Information Technology. 84 (2): 272-282.
Rokach, L. 2010. Data mining ad Knowledge discovery handbook: A survey of clustering algorithms. US: Springer.
Sheikh, A., T. Ghanbarpour, and D. Gholamiangonabadi. 2019. A Preliminary Study of Fintech Industry: A Two-Stage Clustering Analysis for Customer Segmentation in the B2B Setting. Journal of Business-to-Business Marketing 26 (2): 197-207.
Stone, B. 1995. Successful direct marketing methods. Lincolnwood, IL: NTC Business Books.
Wu, H. H., E. C. Chang, & C. F. Lo. 2009. Applying RFM model and K-means method in customer value analysis of an outfitter. In Global Perspective for Competitive Enterprise, Economy and Ecology - Proceedings of the 16th ISPE International Conference on Concurrent Engineering|Global Perspect. Compet. Enterp., Econ. Ecol. - Proc. ISPE Int. Conf. Concurrent Eng. (pp. 665-672). Springer-Verlag London Limited 2009.
Wei, J. T., L. Shih-Yen. W. Chih-Chien, and W. Hsin-Hung. 2012. A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Application 39 (5): 5529-553
Zaheri, F., H. Farughi, H. Soltanpanah, S. Alaniazar, and F. Naseri. 2012. Using multiple criteria decision making models for ranking customers of bank network based on loyalty properties in weighted RFM model. Management Science Letters 2 (2): 697-704.

  • Receive Date 10 December 2022
  • Revise Date 27 January 2023