STRATEGIC MARKETING TRANSFORMATION THROUGH BIG DATA UTILIZATION: IN-DEPTH ANALYSIS OF TRANSLATING GIGANTIC INFORMATION INTO DECISIONS INFLUENCING TACTICAL AND BUSINESS STRATEGIES
Keywords:
Big Data, strategic marketing, transformation, tactical decisions, business strategies, decision-making, data-driven insights.Abstract
In today's data-driven landscape, the convergence of strategic marketing and Big Data catalyzes a transformative journey for organizations. This research, comprising a comprehensive analysis, explores the profound impact of Big Data on reshaping marketing strategies. Delving into the intricate dynamics of decision-making processes, the study elucidates how vast data sets influence tactical and business strategies. With an emphasis on translating gigantic information into actionable insights, the research unveils the symbiotic relationship between data-driven decision-making and organizational agility. The abstract underscores the pivotal role of Big Data in enhancing customer engagement, informing targeted promotions, and contributing to broader business objectives. A critical evaluation of existing literature identifies strengths and weaknesses in current studies, emphasizing the need for in-depth exploration into industry-specific contexts and ethical considerations. In considering future directions, the study anticipates a continued evolution toward more sophisticated analytics tools, including artificial intelligence and machine learning. The implications for businesses involve staying abreast of emerging trends, investing in advanced analytics capabilities, and addressing ethical considerations. This research contributes to a comprehensive understanding of leveraging Big Data for strategic marketing transformation, offering insights into the dynamic intersection of data-driven decision-making and organizational success.
References
Bickley, S. J., Macintyre, A., & Torgler, B. (2021). Artificial intelligence and big data in sustainable entrepreneurship. Journal of Economic Surveys.
Brynjolfsson, E., & McAfee, A. (2014). The second Machine Age: Work, progress, and Prosperity in a time of brilliant technologies. WW Norton & Company.
Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review.
Chaffey, D., & Smith, P. R. (2022). Digital marketing excellence: planning, optimizing, and integrating online marketing. Taylor & Francis.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to significant impact. MIS Quarterly, 1165-1188.
Cukier, K., & Mayer-Schoenberger, V. (2013). The rise of big data is changing how we think about the world. Foreign Aff., 92, 28.
Davenport, T. (2014). Big data at work: dispelling the myths, uncovering the opportunities. Harvard Business Review Press.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: the new science of Winning. Language, 15(217p), 24cm.
Fink, A. (2019). Conducting research literature reviews: From the internet to paper. Sage publications.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International journal of information management, 35(2), 137-144.
Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Kaisler, S., Armour, F., Espinosa, J. A., & Money, W. (2013, January). Big data: Issues and challenges moving forward. In 2013, the 46th Hawaii International Conference on System Sciences (pp. 995-1004). IEEE.
Kim, A. J., & Ko, E. (2012). Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brands. Journal of Business Research, 65(10), 1480-1486.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004), 1-26.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics, and the path from insights to value. MIT Sloan management review.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68.
Nayar, S., & Stanley, M. (2023). Qualitative Research Methodologies for Occupational Science and Occupational Therapy.
Papaioannou, D., Sutton, A., & Booth, A. (2016). Systematic approaches to a successful literature review. Systematic approaches to a successful literature review, 1-336.
Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. " O'Reilly Media, Inc.".
Ridley, D. (2012). The literature review: A step-by-step guide for students.
Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data ' can significantly impact: Findings from a systematic review and a longitudinal case study. International journal of production economics, 165, 234-246.
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS quarterly, xiii-xxiii.
Wixom, B., Ariyachandra, T., Douglas, D., Goul, M., Gupta, B., Iyer, L., ... & Turetken, O. (2014). The current state of business intelligence in academia: The arrival of big data. Communications of the Association for Information Systems, 34(1), 1.
Wu, X., Zhu, X., Wu, G. Q., & Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26(1), 97-107.
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