Main Article Content
Abstract
Purpose: The purpose of this study is to explore the transformative impact of data science on marketing strategies and consumer insights.
Research Design and Methodology: Employing a qualitative research design, the study utilizes in-depth case studies and interviews with industry experts to uncover how data-driven approaches enhance traditional marketing practices.
Findings and Discussion: The findings reveal that data science significantly improves customer segmentation, dynamic pricing, customer relationship management (CRM), and personalized marketing. By leveraging behavioral data, companies like Amazon achieve more granular and dynamic segmentation, leading to higher engagement and conversion rates. Dynamic pricing, as implemented by companies like Uber, optimizes revenue and enhances customer satisfaction through real-time adjustments based on demand and competition. The study also highlights the importance of predictive analytics in CRM, allowing businesses to identify at-risk customers and implement targeted retention strategies. Furthermore, ethical considerations such as data privacy and algorithmic bias are critical for maintaining consumer trust.
Implications: The research underscores the need for integrating data science with traditional marketing frameworks and adapting models to diverse cultural contexts. The implications for practice include adopting data-driven segmentation, dynamic pricing, and CRM strategies while ensuring ethical data practices. This study contributes to the broader discourse on data-driven marketing, offering valuable insights for both academic research and practical applications, ultimately advocating for responsible and effective use of data science in marketing.
Keywords
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References
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- Sangarsu, P. (2023). Enhancing customer engagement and return on investment through data analytics. Marketing Intelligence & Planning, 41(2), 178-193. https://doi.org/10.1108/MIP-09-2022-0386
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- Zhou, L. (2024). Big data technologies: The key to personalized marketing and market competition. Journal of Digital Marketing, 15(1), 33-47. https://doi.org/10.1016/j.jdigmar.2023.12.001
References
Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509-514. https://doi.org/10.1126/science.aaa1465
Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org. https://fairmlbook.org/Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning. fairmlbook.org. https://fairmlbook.org/
Baroi, P. (2021). Utilizing social media data to understand consumer perception and behavior. Journal of Consumer Research, 48(4), 567-589. https://doi.org/10.1093/jcr/ucab012
Bose, R., & Chen, Y. (2009). Exploring the role of data mining in marketing. Journal of Business Research, 62(8), 861-866. https://doi.org/10.1016/j.jbusres.2008.10.003
Chen, L., Mislove, A., & Wilson, C. (2016). An empirical analysis of algorithmic pricing on Amazon marketplace. In Proceedings of the 25th International Conference on World Wide Web (pp. 1339-1349). https://doi.org/10.1145/2872427.2883089
Davenport, T. H., Guha, A., & Grewal, D. (2020). How artificial intelligence will transform business and marketing. Journal of Business Research, 116, 8-14. https://doi.org/10.1016/j.jbusres.2020.05.002
Godes, D., & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4), 545-560. https://doi.org/10.1287/mksc.1040.0071
Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance. Marketing Science, 25(6), 718-739. https://doi.org/10.1287/mksc.1060.0221
Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage.
Kabiraj, S. (2023). Business analytics and digital marketing: Synergistic effects on marketing performance. Journal of Business & Industrial Marketing, 38(3), 321-338. https://doi.org/10.1108/JBIM-06-2022-0268
Kumar, V., & Reinartz, W. (2018). Customer relationship management: Concept, strategy, and tools. Springer. https://doi.org/10.1007/978-3-662-55381-7
Kumar, V., Choi, J. B., & Greene, M. (2013). Synergistic effects of advertising and sales promotions in a social network environment. Journal of Marketing Research, 50(3), 310-328. https://doi.org/10.1509/jmr.11.0423
Kuş, M. (2021). The contribution of big data and neuromarketing in analyzing consumer trends. Journal of Marketing Research, 58(6), 1245-1262. https://doi.org/10.1177/0022243721102456
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69-96. https://doi.org/10.1509/jm.15.0420
Martin, K. E., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0490-3
McKinsey & Company. (2017). Advanced analytics: Opportunities and challenges. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world
Milne, G. R., Rohm, A. J., & Bahl, S. (2004). Consumers’ protection of online privacy and identity. Journal of Consumer Affairs, 38(2), 217-232. https://doi.org/10.1111/j.1745-6606.2004.tb00865.x
Putra, I. A. P. (2023). The integration of Big Data analytics into marketing management strategies: Revolutionizing consumer understanding and value creation. Journal of Business Analytics, 12(1), 22-35. https://doi.org/10.1080/2573234X.2023.00015
Reuille-Dupont, M. (2023). Ethical implications of consumer manipulation and privacy in data-driven marketing. Journal of Marketing Ethics, 18(3), 301-315. https://doi.org/10.1177/0276146722110024
Sangarsu, P. (2023). Enhancing customer engagement and return on investment through data analytics. Marketing Intelligence & Planning, 41(2), 178-193. https://doi.org/10.1108/MIP-09-2022-0386
Shavitt, S., Lalwani, A. K., Zhang, J., & Torelli, C. J. (2006). The role of cultural orientation in consumer behavior. Journal of Consumer Psychology, 16(2), 92-108. https://doi.org/10.1207/s15327663jcp1602_3
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. Yale University Press.
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Wu, C. (2023). Leveraging data analytics and consumer insights for targeted marketing campaigns and personalized customer experiences. International Journal of Market Research, 65(2), 145-162. https://doi.org/10.1177/1470785321104229
Zhou, L. (2024). Big data technologies: The key to personalized marketing and market competition. Journal of Digital Marketing, 15(1), 33-47. https://doi.org/10.1016/j.jdigmar.2023.12.001