DYNAMIC BEHAVIOURAL SEGMENTATION IN THE DIGITAL ECONOMY DEVELOPMENT
Keywords:
Digital economy; Behavioural segmentation; Customer analytics; Digital marketing; Personalisation; Consumer behaviourAbstract
Digital platforms record customer behaviour continuously, yet many firms still segment markets with static demographic, geographic and psychographic variables. This study examines dynamic behavioural segmentation, an approach in which customers are grouped and regrouped over time on the basis of real-time behavioural signals. Using an integrative synthesis of secondary evidence from marketing analytics, customer experience research and data mining, the paper builds a conceptual Dynamic Behavioural Segmentation Framework (DBSF) based on a smoothed behavioural state, probabilistic segment assignment and an expected-value decision rule. Evidence is summarised in three tables covering behavioural data and modelling methods, timing and personalisation, and retention and revenue effects. The synthesis indicates that dynamic segmentation improves the timeliness of targeting, supports early retention action and creates a closed learning loop between behaviour and marketing response, while raising issues of privacy, algorithmic transparency and segment stability. The study contributes a formal framework that links segmentation theory with digital analytics and proposes directions for empirical testing..
References
Acquisti, A., Brandimarte, L. and Loewenstein, G. (2015) ‘Privacy and human behavior in the age of information’, Science, 347(6221), pp. 509–514. Available at: https://doi.org/10.1126/science.aaa1465
2. Bleier, A. and Eisenbeiss, M. (2015) ‘Personalized online advertising effectiveness: The interplay of what, when, and where’, Journal of Marketing, 79(1), pp. 1–19. Available at: https://doi.org/10.1509/jm.14.0110
3. Fader, P.S., Hardie, B.G.S. and Lee, K.L. (2005) ‘Counting your customers the easy way: An alternative to the Pareto/NBD model’, Marketing Science, 24(2), pp. 275–284.
4. Gomez-Uribe, C.A. and Hunt, N. (2015) ‘The Netflix recommender system: Algorithms, business value, and innovation’, ACM Transactions on Management Information Systems, 6(4), pp. 1–19. Available at: https://doi.org/10.1145/2843948
5. Grewal, D., Roggeveen, A.L. and Nordfält, J. (2021) ‘The future of retailing’, Journal of Retailing, 97(1), pp. 1–6. Available at: https://doi.org/10.1016/j.jretai.2021.01.001
6. Jain, A.K. (2010) ‘Data clustering: 50 years beyond K-means’, Pattern Recognition Letters, 31(8), pp. 651–666. Available at: https://doi.org/10.1016/j.patrec.2009.09.011
7. Kotler, P. and Keller, K.L. (2016) Marketing Management. 15th edn. Harlow: Pearson.
8. Lambrecht, A. and Tucker, C. (2013) ‘When does retargeting work? Information specificity in online advertising’, Journal of Marketing Research, 50(5), pp. 561–576. Available at: https://doi.org/10.1509/jmr.11.0503
9. Lemon, K.N. and Verhoef, P.C. (2016) ‘Understanding customer experience throughout the customer journey’, Journal of Marketing, 80(6), pp. 69–96. Available at: https://doi.org/10.1509/jm.15.0420
10. McKinsey & Company (2021) The value of getting personalization right—or wrong—is multiplying. Available at: https://www.mckinsey.com
11. Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S. and Floridi, L. (2016) ‘The ethics of algorithms: Mapping the debate’, Big Data & Society, 3(2), pp. 1–21. Available at: https://doi.org/10.1177/2053951716679679
12. Neslin, S.A., Gupta, S., Kamakura, W., Lu, J. and Mason, C.H. (2006) ‘Defection detection: Measuring and understanding the predictive accuracy of customer churn models’, Journal of Marketing Research, 43(2), pp. 204–211.
13. Reichheld, F.F. and Sasser, W.E. (1990) ‘Zero defections: Quality comes to services’, Harvard Business Review, 68(5), pp. 105–111.
14. Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R. and Lichtendahl, K.C. (2018) Data Mining for Business Analytics. Hoboken, NJ: Wiley.
15. Smith, W.R. (1956) ‘Product differentiation and market segmentation as alternative marketing strategies’, Journal of Marketing, 21(1), pp. 3–8.
16. Sutton, R.S. and Barto, A.G. (2018) Reinforcement Learning: An Introduction. 2nd edn. Cambridge, MA: MIT Press.
17. Voigt, P. and von dem Bussche, A. (2017) The EU General Data Protection Regulation (GDPR): A Practical Guide. Cham: Springer.
18. Wedel, M. and Kamakura, W.A. (2000) Market Segmentation: Conceptual and Methodological Foundations. 2nd edn. Boston: Kluwer Academic.
19. Wedel, M. and Kannan, P.K. (2016) ‘Marketing analytics for data-rich environments’, Journal of Marketing, 80(6), pp. 97–121. Available at: https://doi.org/10.1509/jm.15.0413
