MICRO-SEGMENTATION AND CUSTOMER PERSONALIZATION IN THE AGE OF AI AND BIG DATA
Downloads
Abstract
This conceptual study examines the transformation of marketing strategies through micro-segmentation and customer personalization in the era of Artificial Intelligence and Big Data. The rapid development of AI and Big Data analytics has fundamentally transformed contemporary marketing practices, yet the integration of these technologies into comprehensive frameworks remains inadequately understood. Drawing upon Dynamic Capabilities Theory, Relationship Marketing Theory, AI-Driven Marketing Theory, and Big Data Analytics Capability Theory, this study synthesizes existing literature to develop a coherent conceptual framework using a systematic literature review approach. The results indicate that AI-powered micro-segmentation significantly enhances targeting accuracy, customer engagement, customer satisfaction, and marketing performance. Artificial Intelligence Capability and Big Data Analytics Capability positively influence Micro-Segmentation effectiveness, enabling organizations to identify customer patterns more accurately and create highly detailed customer segments. Micro-Segmentation positively influences Customer Personalization, allowing organizations to develop highly relevant and customized marketing strategies. Customer Personalization positively influences Marketing Performance and mediates the relationship between Micro-Segmentation and Marketing Performance, indicating that the effectiveness of micro-segmentation is realized primarily through personalized customer experiences. However, concerns regarding privacy, algorithmic bias, data governance, and ethical marketing practices remain critical challenges. The study contributes to the marketing literature by integrating AI-driven personalization and micro-segmentation into a comprehensive conceptual framework that can guide future empirical research and managerial decision-making.
Keywords:
Micro-Segmentation Artificial Intelligence Big Data Customer Personalization Marketing PerformanceReferences
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Arora, N., Dreze, X., Ghose, A., Hess, J. D., Iyengar, R., Jing, B., Joshi, Y., Kumar, V., Lurie, N., Neslin, S., Sajeesh, S., Su, M., Syam, N., Thomas, J., & Zhang, Z. J. (2008). Putting one-to-one marketing to work: Personalization, customization, and choice. Marketing Letters, 19(3–4), 305–321. https://doi.org/10.1007/s11002-008-9056-z
Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390–409. https://doi.org/10.1016/j.jretai.2015.04.001
Campbell, C., Sands, S., Ferraro, C., Tsao, H. Y., & Mavrommatis, A. (2022). From data to action: AI and customer engagement. Journal of Business Research, 141, 104–117.
Chatterjee, S., Rana, N., Tamilmani, K., & Sharma, A. (2021). Machine learning adoption in marketing. Technological Forecasting and Social Change, 168, 120780. https://doi.org/10.1016/j.techfore.2021.120780
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
De Luca, L. M., Herhausen, D., Troilo, G., & Rossi, A. (2021). How and when do big data investments pay off? The role of marketing affordances and service innovation. Journal of the Academy of Marketing Science, 49(4), 790–810. https://doi.org/10.1007/s11747-020-00739-x
Dianti, A. R., Mualifah, T., & Dirgantara, I. M. B. (2024). Artificial intelligence for marketing: Systematic literature review. Research Horizon, 4(6), 1–15.
Dibb, S., & Simkin, L. (2016). Market segmentation success. European Journal of Marketing, 50(3–4), 664–688.
Dolnicar, S., Grün, B., & Leisch, F. (2018). Market segmentation analysis. Springer.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial intelligence in marketing. International Journal of Information Management, 57, 102267.
Gao, B., Wang, Y., Xie, H., & Hu, Y. (2023). Artificial intelligence in advertising: Advancements, challenges, and ethical considerations in targeting, personalization, content creation, and ad optimization. SAGE Open, 13(4), 1–18.
Gupta, S., & George, J. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064.
Herhausen, D., Bernritter, S. F., Ngai, E. W. T., Kumar, A., & Delen, D. (2024). Machine learning in marketing: Recent progress and future research directions. Journal of Business Research, 170, 114254.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
Kietzmann, J., Paschen, J., & Treen, E. (2018). Artificial intelligence in advertising. Journal of Advertising Research, 58(3), 263–267.
Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. Wiley.
Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service. Journal of the Academy of Marketing Science, 47(1), 138–160.
Labib, E. (2024). Artificial intelligence in marketing: Exploring current and future trends. Cogent Business & Management, 11(1), 2348728.
Ma, L., Sun, B., & Kekre, S. (2020). Predictive analytics in marketing. Management Science, 66(3), 1237–1255.
Malthouse, E. C., & Copulsky, J. (2023). AI and customer experience transformation. Journal of Interactive Marketing, 63, 1–15.
Mikalef, P., Krogstie, J., Pappas, I., & Pavlou, P. (2020). Big data analytics capabilities and innovation. Information & Management, 57(2), 103169.
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. https://doi.org/10.2307/1252308
Moriuchi, E. (2021). Examination of AI marketing communications and privacy concerns. Journal of Marketing Management, 37(13–14), 1408–1433.
Paschen, J., Pitt, L., & Kietzmann, J. (2020). Artificial intelligence and marketing. Business Horizons, 63(2), 147–155.
Rini, A. S., Wandrial, S., Lutfi, & Jaya, I. (2024). Data-driven marketing: Harnessing artificial intelligence to personalize customer experience and enhance engagement. Journal of Social Science, 1(6), 282–295.
Rust, R. T., & Huang, M. H. (2021). The service revolution and AI personalization. Journal of Service Research, 24(1), 3–18.
Salminen, J., Mustak, M., Sufyan, M., & Jansen, B. J. (2023). How can algorithms help in segmenting users and customers? A systematic review and research agenda for algorithmic customer segmentation. Journal of Marketing Analytics, 11, 677–692.
Saura, J. R. (2024). Algorithms in digital marketing: Does smart personalization promote a privacy paradox? Global Business Review, 1–18.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance. International Journal of Human-Computer Studies, 146, 102551.
Shin, D., & Yoo, J. E. (2022). Algorithmic transparency and the right to explanation. AI & Society, 37(4), 1589–1601.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7
Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating value with big data analytics. Routledge.
Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance. Journal of Business Research, 70, 356–365.
Wedel, M., & Kamakura, W. A. (2012). Market segmentation: Conceptual and methodological foundations. Springer.
Wieringa, M. (2020). What to account for when accounting for algorithms. FAT Conference Proceedings, 1–18.
Zhang, Z. (2024). Market segmentation and personalized marketing strategy optimization driven by big data analysis. Advances in Transdisciplinary Engineering, 1–10.
Ziakis, C., & Vlachopoulou, M. (2023). Artificial intelligence in digital marketing: Insights from a comprehensive review. Information, 14(12), 664.
License
Copyright (c) 2026 Ummu Salmah Tanjung, Shofy Mazaya Siregar, Beby Karina Fawzeea Sembiring
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
