Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era


  • Pham Thi Tam Lien Chieu Ditstrict, Danang city, Vietnam
  • Duong Minh Son Dong A University, Danang city, Vietnam
  • Trinh Le Tan FPT University, Danang city, Vietnam
  • Hoang Ha University of Economics, The University of Danang, Da Nang, 550000, Vietnam



Almost Vietnamese big businesses often use outsourcing services to do marketing researches such as analysing and evaluating consumer intention and behaviour, customers’ satisfaction, customers’ loyalty, market share, market segmentation and some similar marketing studies. One of the most favourite marketing research business in Vietnam is ACNielsen and Vietnam big businesses usually plan and adjust marketing activities based on ACNielsen’s report. Belong to the limitation of budget, Vietnamese small and medium enterprises (SMEs) often do marketing researches by themselves. Among the marketing researches activities in SMEs, customer segmentation is conducted by tools such as Excel, Facebook analytics or only by simple design thinking approach to help save costs. However, these tools are no longer suitable for the age of data information explosion today. This article uses case analysing of the United Kingdom online retailer through clustering algorithm on R package. The result proves clustering method’s superiority in customer segmentation compared to the traditional method (SPSS, Excel, Facebook analytics, design thinking) which Vietnamese SMEs are using. More important, this article helps Vietnamese SMEs understand and apply clustering algorithm on R in customer segmenting on their given data set efficiently. On that basis, Vietnamese SMEs can plan marketing programs and drive their actions as contextualizing and/or personalizing their message to their customers suitably


Data driven; Customer segmentation; Behavioural segmentation; Clustering; Agglomerative


[1] ANDERBERG, M. R. (1973). 6. Hierarchical clustering methods. Cluster Analysis for Applications, 132-156.

[2] Arunachalam, D., & Kumar, N. (2018). Benefit-based consumer segmentation and performance evaluation of clustering approaches: An evidence of data-driven decision-making. Expert Systems with Applications, 111, 11-34.

[3] Bailey, K. D. (1994). Typologies and taxonomies: An introduction to classification techniques (Issue 102). Sage.

[4] Balasubramanian, S., Gupta, S., Kamakura, W., & Wedel, M. (1998). Modeling large data sets in marketing. Statistica Neerlandica, 52(3), 303-323.

[5] Brunner, T. A., & Siegrist, M. (2011). A consumer-oriented segmentation study in the Swiss wine market. British Food Journal, 113(3), 353-373. DOI:

[6] Chan, C. C. H. (2008). Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34(4), 2754-2762.

[7] Chen, J., & Bell, P. C. (2012). Implementing market segmentation using full-refund and no-refund customer returns policies in a dual-channel supply chain structure. International Journal of Production Economics, 136(1), 56-66.

[8] Chen, M.-Y., Huang, M.-J., & Cheng, Y.-C. (2009). Measuring knowledge management performance using a competitive perspective: An empirical study. Expert Systems with Applications, 36(4), 8449-8459. DOI:

[9] Christy, A.J., Umamakeswari, A., Priyatharsini, L. and Neyaa, A., 2018. RFM ranking-An effective approach to customer segmentation. Journal of King Saud University-Computer and Information Sciences.

[10] Dibb, S. (1998). Market segmentation: Strategies for success. Marketing Intelligence & Planning, 16(7), 394-406. DOI:

[11] Dibb, S., & Simkin, L. (1997). A program for implementing market segmentation. Journal of Business & Industrial Marketing.

[12] Dolnicar, S. (2002). A review of unquestioned standards in using cluster analysis for data-driven market segmentation.

[13] Dunn, G., Everitt, B. S., & Pickles, A. (1993). Modelling Covariances and Latent Variables Using EQS. CRC Press.

[14] Dursun, A., & Caber, M. (2016). Using data mining techniques for profiling profitable hotel customers: An application of RFM analysis. Tourism Management Perspectives, 18, 153-160.

[15] Fahy, J., & Jobber, D. (2015). Foundations of marketing.

[16] Grover, R., & Srinivasan, V. (1987). A Simultaneous Approach to Market Segmentation and Market Structuring. Journal of Marketing Research, 24(2), 139-153. DOI:

[17] Haley, R. I. (1968). Benefit segmentation: A decision-oriented research tool. Journal of Marketing, 32(3), 30-35.

[18] Harrigan, K. R. (1985). An application of clustering for strategic group analysis. Strategic Management Journal, 6(1), 55-73. DOI:

[19] Jenkins, M., & McDonald, M. (1997). Market segmentation: Organizational archetypes and research agendas. European Journal of Marketing, 31(1), 17- 32. DOI:

[20] Jurek-Loughrey, A., & P, D. (Eds.). (2019). Linking and Mining Heterogeneous and Multi-view Data (1st ed. 2019). Springer International Publishing: Imprint: Springer. DOI:

[21] Kamakura, W. A., & Wedel, M. (1997). Statistical Data Fusion for Cross-Tabulation. Journal of Marketing Research, 34(4), 485-498. DOI:

[22] Kao, Y.-T., Wu, H.-H., Chen, H.-K., & Chang, E.- C. (2011). A case study of applying LRFM model and clustering techniques to evaluate customer values. Journal of Statistics and Management Systems, 14(2), 267-276.

[23] Kassambara, A. (2018). Machine learning essentials (Edition 1). STHDA.

[24] Kotler, P. (1997). Marketing management: Analysis, planning, implementation and control.

[25] Li, D.-C., Dai, W.-L., & Tseng, W.-T. (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.

[26] Luhn, H. P. (1958). A Business Intelligence System. IBM Journal of Research and Development, 2(4), 314-319. DOI:

[27] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1(14), 281-297.

[28] McDonald, M. (2010). A brief review of marketing accountability, and a research agenda. Journal of Business & Industrial Marketing, 25(5), 383-394. DOI:

[29] MCDONALD, M., & DUNBAR, I. (1998). Segmentation. How to Do It, I-low to Profit from It. London: MacMillan Press.

[30] Myers, J. H., & Tauber, E. M. (2011). Market structure analysis. Marketing Classics Press.

[31] Palmer, R. A., & Millier, P. (2004). Segmentation: Identification, intuition, and implementation. Industrial Marketing Management, 33(8), 779-785. DOI:

[32] Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning.

[33] Singh Minhas, R., & Jacobs, E. M. (1996). Benefit segmentation by factor analysis: An improved method of targeting customers for financial services. International Journal of Bank Marketing, 14(3), 3-13. DOI:

[34] Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3-8.

[35] Soutar, G. N., & McNeil, M. M. (1991). A Benefit Segmentation of the Financial Planning Market. International Journal of Bank Marketing, 9(2), 25-29. DOI:

[36] Twedt, D. W. (1964). How Important to Marketing Strategy Is the” Heavy User”? Journal of Marketing (Pre-1986), 28(000001), 71.

[37] Wei, J.-T., Lin, S.-Y., Weng, C.-C., & Wu, H.-H. (2012). A case study of applying LRFM model in market segmentation of a children’s dental clinic. Expert Systems with Applications, 39(5), 5529-5533.

[38] Wiedmann, K.-P., Hennigs, N., & Siebels, A. (2009). Value-based segmentation of luxury consumption behavior. Psychology & Marketing, 26(7), 625-651.

[39] Wind, Y. (1978). Issues and Advances in Segmentation Research. Journal of Marketing Research, 15(3), 317- 337. DOI:

[40] Yankelovich, D., & Meer, D. (2006). Rediscovering market segmentation. Harvard Business Review, 84(2), 122.


How to Cite

Tam, P. T., Son, D. M., Tan, T. L., & Ha, H. (2021). Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era. Macro Management & Public Policies, 3(2), 33–43.


Article Type