Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study in E-Retailing

Year 2020, , 47 - 56, 31.01.2020

İnanç Kabasakal

https://doi.org/10.17671/gazibtd.570866

Cited By: 8

Abstract

Marketing studies have often drawn attention to the importance of customers for businesses that aim to endure in a harsh competitive environment. Customer Relationship Management (CRM) has been a prominent approach in marketing management that aims to improve relationships with customers. A practical implication of the CRM approach is the analysis of customer data to extract value for businesses, as well as customers. In this context, customer segmentation has been a useful task that helps to group customers with similar attributes and designate better-tailored marketing strategies for customer groups. Among a variety of approaches for customer segmentation, Recency Frequency Monetary (RFM) Model stands out as an easy-to-adopt and effective technique. Based on three dimensions regarding the sales data, the RFM Model depends on scoring customers with different approaches. In this study, a prototype software is introduced that helps to apply the RFM technique with two scoring approaches. Moreover, the sales data obtained from an e-retailer has been analyzed for clustering using the prototype software, and clusters discovered with RFM variants were compared using cluster evaluation metrics. Finally, the segments were presented along with relevant offers for marketing strategies.

Keywords

Customer Segmentation, RFM Model, Analytical CRM

Güncellik Sıklık Parasallık Modeline Dayalı Müşteri Bölümlendirme: E-Perakende Sektöründe Bir Uygulama

Abstract

Pazarlama alanındaki çalışmalarda, yoğun rekabet ile başa çıkmaya çalışan işletmeler açısından müşterilerin önemine sıklıkla dikkat çekildiği görülmektedir. Pazarlama yönetimi bağlamında öne çıkan bir yaklaşım olan Müşteri İlişkileri Yönetimi (MİY), işletmeler ile müşterileri arasında kurulan ilişkilerin geliştirilmesini amaçlamaktadır. Müşteri verisinin işletme ve müşterileri için değer yaratmak üzere analiz edilmesi, MİY’in uygulamada gereksinimlerinden birisi olarak ifade edilebilir. Bu bağlamda müşteri bölümlendirme, benzer niteliklere sahip müşteri gruplarının ortaya çıkarılarak grup odaklı pazarlama stratejilerinin uyarlanması için yararlı bir işlev görmektedir. Müşteri bölümlendirme için ortaya konulmuş çeşitli yaklaşımlar arasında RFM Modeli, etkin ve kolay uyarlanabilir olmasıyla öne çıkmaktadır. Müşterilerin satış verisine ilişkin 3 farklı boyut üzerinden sıralanmasına dayanan yöntem, sıralamada kullanılan puanlama biçimine göre çeşitli yaklaşımlara konu olmaktadır. Bu çalışmada, RFM yöntemini iki farklı puanlama yaklaşımı ile yürütmek üzere geliştirilmiş bir prototip yazılım tanıtılmaktadır. Bir e-perakende işletmesinden alınan satış verisi sözü edilen yazılım ile incelenmiş, RFM modeline ilişkin her iki puanlama yöntemi ile bölümlendirme yapılmış, bulgular veri madenciliği bağlamında değerlendirme ölçütleri ile karşılaştırılmıştır. Son olarak ortaya çıkarılan müşteri bölümleri sunulmuş ve seçilen gruplara yönelik öneriler sıralanmıştır.

Keywords

Müşteri Bölümlendirme, RFM Modeli, Analitik CRM

References

• S. Gupta, D. R. Lehmann, “Customers as assets”. Journal of Interactive Marketing, 17(1), 9–24, 2003.
• P. E. Pfeifer, “The optimal ratio of acquisition and retention costs”, Journal of Targeting, Measurement and Analysis for Marketing, 13(2), 179–188, 2005.
• F. Provost, T. Fawcett, “Data science and its relationship to big data and data-driven decision making”, Big Data, 1(1), 51–59, 2013.
• G. Phillips-Wren, A. Hoskisson, “An analytical journey towards big data”, Journal of Decision Systems, 24(1), 87-102, 2015.
• P. Kotler, G. Armstrong, Principles of Marketing, 14th Edition; Pearson Education, USA, 2012.
• E. Chablo, “The importance of marketing data intelligence in delivering successful CRM”, Customer Relationship Management, Editor: SCN Education B.V., Vieweg+ Teubner Verlag, Wiesbaden, 57–70, 2000.
• W. T. Venturini, Ó. G. Benito, “CRM software success: a proposed performance measurement scale”, Journal of Knowledge Management, 19(4), 856-875, 2015.
• M. A. H. Farquad, V. Ravi, S. B. Raju, “Churn prediction using comprehensible support vector machine: An analytical CRM application”, Applied Soft Computing, 19, 31-40, 2014.
• C. H. Cheng, Y. S. Chen, “Classifying the segmentation of customer value via RFM model and RS theory”, Expert Systems with Applications, 36(3), 4176–4184, 2009.
• T. F. Bahari, M. S. Elayidom, “An efficient CRM-data mining framework for the prediction of customer behavior”, Procedia Computer Science, 46, 725-731, 2015.
• C. H. Liu, “A Conceptual Framework of Analytical CRM in Big Data Age”, International Journal of Advanced Computer Science and Applications, 6(6), 194-152, 2015.
• R. Agrawal, R. Srikant, “Fast algorithms for mining association rules”, In Proceedings of the 20th International Conference of Very Large Data Bases, Santiago, Chile, 487–499, September 12-15, 1994.
• Internet: K. Elliott, R. Scionti, M. Page, The confluence of data mining and market research for smarter CRM, https://www. researchgate.net/publication/228851712, 20.04.2019.
• C. F. Lin, “Segmenting customer brand preference: demographic or psychographic”, Journal of Product & Brand Management, 11(4), 249–268, 2002.
• B. Cooil, L. Aksoy, T. L. Keiningham, “Approaches to customer segmentation”, Journal of Relationship Marketing, 6(3-4), 9–39, 2008.
• K. Storbacka, “Segmentation based on customer profitability - retrospective analysis of retail bank customer bases”, Journal of Marketing Management, 13(5), 479–492, 1997.
• M. Xu, J. Walton, “Gaining customer knowledge through analytical CRM”, Industrial Management & Data Systems, 105(7), 955–971, 2005.
• S. A. C. Madeira, Comparison of target selection methods in direct marketing, Master’s Thesis, Universidade Técnica de Lisboa, Instituto Superior Técnico, 2002.
• S. Peker, A. Kocyigit, P. E. Eren, “LRFMP model for customer segmentation in the grocery retail industry: A case study”, Marketing Intelligence & Planning, 35(4), 544–559, 2017.
• J. T. Wei, S. Y. Lin, H. H. Wu, “A review of the application of RFM model”, African Journal of Business Management, 4(19), 4199–4206, 2010.
• V. Aggelis, D. Christodoulakis, “Customer Clustering Using RFM Analysis”, In Proceedings of the 9th World Scientific and Engineering Academy and Society International Conference on Computers, Athens, Greece, 2–7, July 14-16, 2005.
• J. T. Wei, S. Y. Lin, Y. Z. Yang, H. H. Wu, “The application of data mining and RFM model in market segmentation of a veterinary hospital”, Journal of Statistics and Management Systems, 22(6), 1049-1065, 2019.
• P. A. Sarvari, A. Ustundag, H. Takci, “Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis”, Kybernetes, 45(7), 1129-1157, 2016.
• V. Kumar, “CLV: the databased approach”, Journal of Relationship Marketing, 5(2-3), 7–35, 2006.
• D. Altan, Güncellik/Sıklık/Parasallık (RFM) Analizi ile Hedef Kitle Seçimi: Hava Yolu Sektöründe Bir Uygulama, Master’s Thesis, Hacettepe University, Graduate School of Social Sciences, 2019.
• M. Khajvand, K. Zolfaghar, S. Ashoori, S. Alizadeh, “Estimating customer lifetime value based on RFM analysis of customer purchase behavior: Case study”, Procedia Computer Science, 3, 57–63, 2011.
• B. Sohrabi, A. Khanlari, “Customer lifetime value (CLV) measurement based on RFM Model”, Iranian Accounting & Auditing Review, 14(47), 7–20, 2007.
• R. Srivastava, “Identification of Customer Clusters using RFM Model: A Case of Diverse Purchaser Classification”, International Journal of Business Analytics and Intelligence, 4(2), 45–50, 2016.
• J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2006.
• Z. Ceylan, S. Gürsev, S. Bulkan, “İki Aşamalı Kümeleme Analizi ile Bireysel Emeklilik Sektöründe Müşteri Profilinin Değerlendirilmesi”, Bilişim Teknolojileri Dergisi, 10(4), 475–485, 2017.
• O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J.M. Pérez, I. Perona, “An extensive comparative study of cluster validity indices”, Pattern Recognition, 46(1), 243-256, 2013.
• O. Maimon, L. Rokach, The Data Mining and Knowledge Discovery Handbook, Springer, USA, 2005.
• Leonard Kaufman, Peter J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, USA, 1990.
• Z. Halim, J.H. Khattak, “Density-based clustering of big probabilistic graphs”, Evolving Systems, 10(3), 333-350, 2019.
• Internet: SQLite – What is SQLite?, https://www.sqlite.org, 25.04.2019.
• Internet: SQLite – Most widely deployed and used database engine”, https://sqlite.org/mostdeployed.html, 25.04.2019.
• Y. Gökşen, H. Aşan, “Veri büyüklüklerinin veritabanı yönetim sistemlerinde meydana getirdiği değişim: NOSQL”, Bilişim Teknolojileri Dergisi, 8(3), 125–131, 2015.
• M. Fuchs, W. Höpken, M. Lexhagen, “Big data analytics for knowledge generation in tourism destinations – A case from Sweden”, Journal of Destination Marketing & Management, 3(4), 198–209, 2014.
• Internet: S. Sayad, An introduction to data science: data preparation, https://www.saedsayad.com/data_preparation.htm, 20.04.2019.
• J. R. Miglautsch, “Thoughts on RFM scoring”, Journal of Database Marketing & Customer Strategy Management, 8(1), 67–72. 2000.
• K. K. Tsiptsis, A. Chorianopoulos, Data mining techniques in CRM: inside customer segmentation, John Wiley & Sons, 2009.
• P. Anitha, M.M. Patil, “RFM model for Customer Purchase Behavior using K-Means Algorithm”, Journal of King Saud University-Computer and Information Sciences, In Press, 2019.
• D. Birant, “Data mining using RFM analysis”, Knowledge-oriented applications in data mining, Editor: Funatsu, K., IntechOpen, 91-108, 2011, Available: https://www.intechopen.com/books/knowledge-oriented-applications-in-data-mining/data-mining-using-rfm-analysis.
• Internet: Putler Analytics – RFM analysis for successful customer segmentation, https://www.putler.com/rfm-analysis, 26.04.2019.
• S. A. Sutresno, A. Iriani, E. Sediyono, “Metode K-Means Clustering dengan Atribut RFM untuk Mempertahankan Pelanggan”, Jurnal Teknik Informatika dan Sistem Informasi, 4(3), 443–440, 2018.
• T. Jackson, “CRM: From art to science”, Journal of Database Marketing & Customer Strategy Management, 13(1), 76–92, 2005.
• P. Harrigan, E. Ramsey, P. Ibbotson, “Critical factors underpinning the e-CRM activities of SMEs”, Journal of Marketing Management, 27(5-6), 503–529, 2011.