A Comprehensive Review of Behavioral Customer Segmentation For A Better Understanding

International Journal of Computer Science and Engineering

© 2021 by SSRG - IJCSE Journal

Volume 8 Issue 1

Year of Publication : 2021

Authors : Mahesh T R, Dr.B Mohan Kumar Naik

10.14445/23488387/IJCSE-V8I1P101

How to Cite?

Mahesh T R, Dr.B Mohan Kumar Naik, "A Comprehensive Review of Behavioral Customer Segmentation For A Better Understanding," SSRG International Journal of Computer Science and Engineering , vol. 8,  no. 1, pp. 1-4, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I1P101

Abstract:

Segmentation of customers has always been relevant. But now that orchestrating journeys that embody the overall experience, rather than their latest encounter inside a siloed touchpoint, is integral to business success today, an absolute must is a successful segmentation. But only 33 percent of businesses using customer segmentation claim that they discover that it is significantly impactful, as per a recent Forrester survey. The key reason businesses struggle, according to the study, is that the conventional approaches are still being used to customer segmentation, even without exploiting the scope of data of the customer and more sophisticated analytical techniques that are present today. Particularly, they do not use a modern technique for behavioral Segmentation. The traditional Segmentation approaches focused primarily on who the customers are, and considered segments were purely based upon demographic factors such as age or gender and business characteristics such as company size or sector. Only knowing who the customers are is no longer adequate. In this article, to achieve business goals, multiple approaches to behavioral Segmentation that can be used to understand better the client's and their priorities are discussed.

Keywords:

behavior, Segmentation, segments, customers, marketers

References:

[1] Juni Nurma Sari , Ridi Ferdiana, Lukito Nugroho, Paulus Insap Santosa Review on Customer Segmentation Technique on Ecommerce, Journal of Computational and Theoretical Nanoscience ·DOI: 10.1166/asl.2016.7985, (2016).
[2] Dodwell A. Effective Marketing email strategy _ Segmentation RFM.http://www.sailthru.com/marketing-blog/written-effectiveemail- marketing-strategies-segmentation-rfm/,(2015).
[3] Ma H. A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Trees.; (2015), 813-818.
[4] Chen J. Retail Customer Segmentation. (2020).
[5] Magento. An Introduction to Customer Segmentation. info2.magento.com/.../ An_Introduction_to_Customer_Segmentation, (2014).
[6] Cherna Y, Tzenga G. Measuring Consumer Loyalty of B2C e-Retailing Service by Fuzzy Integral: a FANP-Based Synthetic Model. In: International Conference on Fuzzy Theory and Its Applications iFUZZY.;: (2012), 48-56.
[7] Baer D. CSI: Customer Segmentation Intelligence for Increasing Profits. SASGlobForum.:1-13,(2012). http://support.sas.com/resources/papers/proceedings12/103- 2012.pdf.
[8] Schneider G. Electronic Commerce, 9th Edition.,643. doi:10.1002/1521-3773(20010316), (2011).
[9] Colica R. Customer Segmentation And Clustering Using SAS Enterprise Minner Part I The Basics.: (2011), 1-14,
[10] Lieberman M. Target , golden egg , consumer to achieve maximum ROI.: ( 2009), 50-51.
[11] Al-Qaed F, Sutcliffe A. Adaptive Decision Support System (ADSS) for B2C E-Commerce. 2006 ICEC Eighth Int Conf Electron Commer Proc NEW E-COMMERCE Innov ConquCurr BARRIERS, Obs LIMITATIONS TO Conduct Success Bus INTERNET.: (2006), 492-503
[12] Mobasher B, Cooley R, Srivastava J. Automatic Personalization Based on Web Usage Mining. Commun ACM.;43(8) (2000).
[13] Cherna Y, Tzenga G. Measuring Consumer Loyalty of B2C e-Retailing Service by Fuzzy Integral: a FANP-Based Synthetic Model. In: International Conference on Fuzzy Theory and Its Applications iFUZZY.; , (2012) 48-56,
[14] Magento. An Introduction to Customer Segmentation. (2014). info2.magento.com/.../ An_Introduction_to_Customer_Segmentation.
[15] Baer D. CSI: Customer Segmentation Intelligence for Increasing Profits. SAS Glob Forum.: (2012) 1-13, http://support.sas.com/resources/papers/proceedings12/103-2012.pdf.
[16] Colica R. Customer Segmentation And Clustering Using SAS Enterprise Minner Part I The Basics.: (2011) 1-14.
[17] Schneider G. Electronic Commerce, 9th Edition.; 2013:643. doi:10.1002/1521-3773(20010316)40:6<9823::AIDANIE9823> 3.3.CO;2-C.
[18] Venkatesan R. Cluster Analysis For Segmentation. (2007).
[19] Ezenkwu CP, Ozuomba S. Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services.;4(10) (2015), 40-44.
[20] Lieberman M. Target “ golden egg ” consumer to achieve maximum ROI.: (2009) 50-51
[21] Dodwell A. Effective Marketing email strategy _Segmentation RFM.. http://www.sailthru.com/marketing-blog/written-effectiveemail-marketing-strategies-segmentation-rfm/,(2015).
[22] Birant D. Data Mining Using RFM Analysis. Knowledge- Oriented Appl Data Min. 2011;(iii):91-108. doi:10.5772/13683.
[23] Hua S, Xiu S, Leung SCH. Expert Systems with Applications Segmentation of telecom customers based on customer value by decision tree model. Expert Syst Appl.;39(4):3964-3973. doi:10.1016/j.eswa.2011.09.034, (2012).
[24] Ma H. A Study on Customer Segmentation for E-Commerce Using the Generalized Association Rules and Decision Trees: (2015) 813-818,
[25] Baer D, Ph D. Product Affinity Segmentation Using The Doughnut Clustering Approach. Cust Intell SAS Glob Forum.