Personalized Marketing Strategy in Digital Business Using Data Mining Approach
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Abstract
The integration of personalized marketing strategies and data mining techniques in the realm of digital business has garnered significant attention in recent years. This study employs a mixed-methods approach to explore the dynamics between personalized marketing and data mining, specifically investigating customer perceptions and behavior in the Lhokseumawe and Cirebon regions. Through in-depth interviews, 80 respondents' views on personalized marketing were analyzed, highlighting both positive sentiments regarding tailored campaigns and concerns over data privacy. Furthermore, quantitative analysis was conducted using data from platforms such as WhatsApp, Instagram, TikTok, and Shopee Ecommerce. This revealed distinct customer segments, yielded improved product recommendations, and uncovered interesting purchasing patterns. The results emphasize the importance of striking a balance between personalization benefits and privacy protection. By harnessing the insights provided by data mining, businesses can enhance customer engagement and satisfaction, ultimately navigating the dynamic digital landscape more effectively. This study contributes practical implications and strategic insights for businesses seeking to optimize their digital marketing strategies.
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References
DiMicco, J., Millen, D.R., Geyer, W., Dugan, C., Brownholtz, B. and Muller, M., 2008, November. Motivations for social networking at work. In Proceedings of the 2008 ACM conference on Computer supported cooperative work (pp. 711-720).
Mascarenhas, O. A., Kesavan, R., & Bernacchi, M. (2006). Lasting customer loyalty: a total customer experience approach. Journal of consumer marketing, 23(7), 397-405.
Sawlani, D.K. and SE, M., 2021. Digital marketing: brand images. Scopindo Media Pustaka.
Han, J., Kamber, M., & Pei, J. 2011. Data mining: concepts and techniques. Elsevier.
Fayyad, U., Piatetsky-Shapiro, G. and Smyth, P., 1996. From data mining to knowledge discovery in databases. AI magazine, 17(3), pp.37-37.
Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact. MIS quarterly, pp.1165-1188.
Jain, A.K., Murty, M.N. and Flynn, P.J., 1999. Data clustering: a review. ACM computing surveys (CSUR), 31(3), pp.264-323.
Ištvanić, M., Crnjac Milić, D. and Krpić, Z., 2017. Digital marketing in the business environment. International journal of electrical and computer engineering systems, 8(2.), pp.67-75.
Nair, K. and Gupta, R., 2021. Application of AI technology in modern digital marketing environment. World Journal of Entrepreneurship, Management and Sustainable Development, 17(3), pp.318-328.
Özoğlu, B. and Topal, A., 2020. Digital marketing strategies and business trends in emerging industries. Digital business strategies in blockchain ecosystems: Transformational design and future of global business, pp.375-400.
Dawn, S.K., 2014. Personalised Marketing: concepts and framework. Productivity, 54(4), p.370.
Yan, K.Q., Wang, S.C., Wang, S.S. and Lin, Y.P., 2011, April. Application of data mining for enterprise digital marketing strategy making. In 2011 Third International Conference on Communications and Mobile Computing (pp. 509-512). IEEE.
Katsikeas, C., Leonidou, L. and Zeriti, A., 2020. Revisiting international marketing strategy in a digital era: Opportunities, challenges, and research directions. International Marketing Review, 37(3), pp.405-424.
Gupta, N., 2020. Digital marketing: Trends, opportunities, and challenges. Asian Journal of Management, 11(4), pp.434-440.