ADVANCED CUSTOMER SEGMENTATION IN E-COMMERCE: CLUSTERING TECHNIQUES AND THEIR IMPACT ON MARKETING STRATEGY OPTIMIZATION
Keywords:
Customer Segmentation, Demographic Segmentation, E-commerce, K-means Clustering, Marketing StrategyAbstract
This study compares traditional demographic segmentation and K-means clustering to optimize customer segmentation in e-commerce. Using the "Customer Personality Analysis" dataset from a UK-based retailer, it evaluates the effectiveness of these methods based on behavioral variables, including product expenditures, promotional engagement, and purchase channels. To test the hypotheses, K-means clusters were compared with demographic clusters. ANOVA assessed spending differences, while MANOVA examined whether K-means clustering provided distinct and actionable insights. Findings confirm that K-means clustering identifies behaviorally distinct customer groups, offering deeper insights and better marketing applications than traditional segmentation. However, practical challenges may limit its adoption. This research underscores the value of data-driven clustering techniques for precise and effective customer segmentation, improving business strategies. Future research should explore additional behavioral variables and validate these findings in real-world marketing applications.
References
Bauder, S. (2023, May). 10.8% of Americans Own Gold, while 11.6% own silver, according to a new survey. Gold IRA Guide. https://goldiraguide.org/10-8-of-americans-own-gold-while-11-6-own-silver-according-to-a-new-survey/#comments
Beane, T. P., & Ennis, D. M. (1987). Market Segmentation: A Review. European Journal of Marketing, 21(5), 20–42. https://doi.org/10.1108/EUM0000000004695
Bruwer, J., Jiranek, V., Halstead, L., & Saliba, A. (2014). Lower alcohol wines in the UK market: Some baseline consumer behaviour metrics. British Food Journal, 116(7), 1143–1161. https://doi.org/10.1108/BFJ-03-2013-0077
Cao, J. (2023). E-Commerce Big Data Mining and Analytics. Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3588-8
Chikkaswamygowda, A. (2023). New RFMT Model for Segmentation [Dasaset; CSV]. https://www.kaggle.com/code/vionaclarissa/new-rfmt-model-for-segmentation-1st-in-kaggle/input
Dimitriadis, N., Dimitriadis, N. J., & Ney, J. (2018). Advanced marketing management: Principles, skills and tools (1 Edition). Kogan Page.
Govzman, S., Looby, S., Wang, X., Butler, F., Gibney, E. R., & Timon, C. M. (2021). A systematic review of the determinants of seafood consumption. British Journal of Nutrition, 126(1), 66–80. https://doi.org/10.1017/S0007114520003773
Haley, R. I. (1968). Benefit Segmentation: A Decision-oriented Research Tool. Journal of Marketing, 32(3), 30–35. https://doi.org/10.1177/002224296803200306
Hunt, S. D., & Arnett, D. B. (2004). Market Segmentation Strategy, Competitive Advantage, and Public Policy: Grounding Segmentation Strategy in Resource-Advantage Theory. Australasian Marketing Journal, 12(1), 7–25. https://doi.org/10.1016/S1441-3582(04)70083-X
Johnson, S. C. (1967). Hierarchical Clustering Schemes. Psychometrika, 32(3), 241–254. https://doi.org/10.1007/BF02289588
Kim, K. Y., & Lee, B. G. (2015). Marketing insights for mobile advertising and consumer segmentation in the cloud era: A Q–R hybrid methodology and practices. Technological Forecasting and Social Change, 91, 78–92. https://doi.org/10.1016/j.techfore.2014.01.011
Kotler, P., Armstrong, G., & Opresnik, M. O. (2018). Principles of marketing (17th Global Ed.). Harlow: Pearson.
Kusumah, E. P. (2018). Customer Loyalty Model: Customer Satisfactionas Intervining Variable. EcoForum, 7(2). https://journalsfeaa.usv.ro/ojs/index.php/eco/article/view/2121/2064
Lugmayr, A., Stockleben, B., Scheib, C., & Mailaparampil, M. A. (2017). Cognitive big data: Survey and review on big data research and its implications. What is really “new” in big data? Journal of Knowledge Management, 21(1), 197–212. https://doi.org/10.1108/JKM-07-2016-0307
Moore, A. (2001). K-means and Hierarchical Clustering. CMU School of Computer Science. https://www.cs.cmu.edu/~cga/ai-course/kmeans.pdf
Public Health England. (2015). Sugar reduction: the evidence for action. Annexe 2: A Mixed Method Review of Behavior Changes Resulting From Experimental Studies That Examine the Effect of Fiscal Measure Targeted at High Sugar Food and Non-Alcoholic Drink.
Scheelbeek, P. F. D., Moss, C., Kastner, T., Alae-Carew, C., Jarmul, S., Green, R., Taylor, A., Haines, A., & Dangour, A. D. (2020). United Kingdom’s fruit and vegetable supply is increasingly dependent on imports from climate-vulnerable producing countries. Nature Food, 1(11), 705–712. https://doi.org/10.1038/s43016-020-00179-4
Rajput, L., & Singh, S. N. (2023). Customer Segmentation of E-commerce data using K-means Clustering Algorithm. 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 658–664. https://doi.org/10.1109/Confluence56041.2023.10048834
Schoenwald, M. (2001). Psychographic segmentation: Used or abused. Brandweek, 42(4), 34-38.
Sharda, R., Delen, D., & Turban, E. (2021). Analytics, data science, & artificial intelligence: Systems for decision support (Eleventh edition, global edition). Pearson.
Smith, W. R. (1956). Product Differentiation and Market Segmentation as Alternative Marketing Strategies. Journal of Marketing, 21(1), 3–8. https://doi.org/10.1177/002224295602100102
Stewart, C., Piernas, C., Cook, B., & Jebb, S. A. (2021). Trends in UK meat consumption: Analysis of data from years 1–11 (2008–09 to 2018–19) of the National Diet and Nutrition Survey rolling programme. The Lancet Planetary Health, 5(10), e699–e708. https://doi.org/10.1016/S2542-5196(21)00228-X
Stillman, D. & Stillman, J. (2017). Gen Z Work: How the Next Generation Is Transforming the Workplace (1st ed). HarperCollins Publishers.
Vipin, J., Bindoo, M., & Satyendra, A. (2021). An Overview of Electronic Commerce (e-Commerce). Journal of Contemporary Issues in Business and Government, 27(3). https://doi.org/10.47750/cibg.2021.27.03.090
Wang, C., & Wang, Z. (2006). The impact of Internet on service quality in the banking sector [Master Thesis, Lulea University of Technology]. https://www.diva-portal.org/smash/get/diva2:1029746/FULLTEXT01.pdf
Wang, R. J.-H., Malthouse, E. C., & Krishnamurthi, L. (2015). On the Go: How Mobile Shopping Affects Customer Purchase Behavior. Journal of Retailing, 91(2), 217–234. https://doi.org/10.1016/j.jretai.2015.01.002
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231–1247. https://doi.org/10.1016/j.ijinfomgt.2016.07.009
Yse, D. L. (2019, May). Guide to K-Means Clustering Algorithm. KDNuggets. https://www.kdnuggets.com/2019/05/guide-k-means-clustering-algorithm.html