Market Segmentation and Advertising Strategy in the Digital Age: A Qualitative Analysis of the Impact of Imperfect Targeting
DOI:
https://doi.org/10.70142/ijbmel.v1i3.298Keywords:
Artificial Intelligence (AI), Imperfect Targeting, Market Segmentation, Digital Advertising Strategy, Privacy RegulationsAbstract
This study examines the impact of imperfect targeting on market segmentation and digital advertising strategies through a qualitative literature review. In the rapidly evolving digital era, consumer targeting accuracy has become a major challenge, especially with increasing data privacy regulations such as the GDPR. The study reveals that inaccurate targeting can reduce advertising effectiveness and intensify competition among companies to capture high-value consumers. Additionally, technological and regulatory barriers often hinder the achievement of optimal results in data-driven marketing strategies. However, technological innovations such as artificial intelligence (AI) and machine learning have the potential to improve targeting accuracy, thereby enhancing the outcomes of advertising campaigns. This research also highlights the need for balancing targeting efficiency with compliance to privacy regulations, as well as adopting more adaptive, value-based marketing approaches to foster long-term consumer relationships. These findings offer crucial insights for companies in devising advertising strategies within the dynamic and complex digital landscape.
References
Anderson, S., Fine, A., & Larson, N. (2022). Price discrimination in the information age: Prices, poaching, and privacy with personalized targeted discounts . Review of Economic Studies, 90, 2085–2115.
Armstrong, M., & Vickers, J. (2019). Discriminating against captive customers . American Economic Review: Insights, 1, 257–272.
Armstrong, M., & Vickers, J. (2022). Patterns of competitive interaction . Econometrica, 90, 153–191.
Athey, S. C., & Gans, J. S. (2010). The impact of targeting technology on advertising markets and media competition . American Economic Review, 100, 608–613.
Baye, M.R., Morgan, J., & Scholten, P. (2006). Information, search, and price dispersion . In T. Hendershott (Ed.), Handbook of economics and information systems (Vol. 1, pp. 323–375). Elsevier.
Beauchamp, T. L., & Childress, J. F. (2013). Principles of biomedical ethics (7th ed.). Oxford University Press.
Belleflamme, P., Lam, W.M.W., & Vergote, W. (2020). Competitive imperfect price discrimination and market power . Marketing Science, 39(5), 996–1015.
Bergemann, D., & Bonatti, A. (2011). Targeting in advertising markets: Implications for offline versus online media . RAND Journal of Economics, 42, 417–443.
Bounie, D., Dubus, A., & Waelbroeck, P. (2021). Selling strategic information in digital competitive markets . RAND Journal of Economics, 52(2), 283–313.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology, 3(2), 77–101.
Chen, Y., Narasimhan, C., & Zhang, Z. J. (2001). Individual marketing with imperfect targetability . Marketing Science, 20(1), 23–41.
Chioveanu, I. (2023). Consumer tracking, price discrimination, and nested consideration . Working Paper, University of Nottingham.
D'Annunzio, A., & Russo, A. (2020). Ad networks and consumer tracking . Management Science, 66, 5040–5058.
Denzin, N. K. (2017). The research act: A theoretical introduction to sociological methods (3rd ed.). Aldine Transaction.
Esteves, R.B., & Resende, J. (2016). Competitive targeted advertising with price discrimination . Marketing Science, 35(4), 576–587.
Fink, A. (2019). Conducting research literature reviews: From the internet to paper (5th ed.). Sage Publications.
Galeotti, A., & Moraga-González, J. L. (2008). Segmentation, advertising and prices . International Journal of Industrial Organization, 26(5), 353–372.
Johnson, J. P., & Myatt, D. P. (2006). On the simple economics of advertising, marketing, and product design . American Economic Review, 96(3), 756–784.
Johnson, T., Lee, M., & Wang, Z. (2024). Ethics in AI-driven advertising: Balancing privacy and personalization . Journal of Business Ethics.
Karle, H., & Reisinger, M. (2024). Imperfect targeting and advertising strategies . Management Science, 0(0). https://doi.org/10.1287/mnsc.2023.03632
Kotler, P., & Keller, K. L. (2016). Marketing management (15th ed.). Pearson.
Machi, L. A., & McEvoy, B. T. (2016). The literature review: Six steps to success (3rd ed.). Corwin Press.
Narasimhan, C. (1988). Competitive promotional strategies . Journal of Business, 61(4), 427–449.
Peukert, C., Bechtold, S., & Kretschmer, T. (2022). Regulatory spillovers and data governance: Evidence from the GDPR . Marketing Science, 41(4), 746–768.
Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide . Blackwell Publishing.
Ravitch, S. M., & Carl, N. M. (2021). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass.
Ronayne, D., & Taylor, G. (2021). Competing sales channels with captive consumers . Economic Journal, 132(1), 741–766.
Saldaña, J. (2016). The coding manual for qualitative researchers (3rd ed.). Sage Publications.
Sharma, R. (2023). 10 ways AI technology is changing the future of digital marketing . Emeritus. Retrieved from https://emeritus.org/in/learn/artificial-intelligence-machine-learning-ai-in-digital-marketing/
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review . MIS Quarterly, 26(2), xiii–xxiii.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage Publications.
