Big data analytics for marketing in database-driven companies
DOI:
https://doi.org/10.14419/52nxcm69Keywords:
Big Data Analytics (BDA, Marketing Efficacy, Predictive Targeting, Data Processing Methods, Algorithmic BiasAbstract
Database-driven businesses are using Big Data Analytics (BDA) to increase marketing efficacy as a result of the boom in data generation brought about by the spread of digital technologies. This study examines the use of BDA by well-known companies, including Amazon, Netflix, Coca-Cola, Nike, and H&M, to accomplish strategic marketing goals like real-time engagement, predictive targeting, and customization. The study examines several data processing methods, from machine learning and neural networks to sentiment analysis and recommendation systems, using a comparative case-based methodology. It also emphasizes the different business implications that are realized across sectors. In addition to being in line with earlier theoretical frameworks, the research connects them to useful, real-world applications. Results show that BDA dramatically increases ROI, conversion rates, and customer retention; yet, it also comes with drawbacks including algorithmic bias, data privacy, and dynamic market adaptation. Finally, by offering a thorough examination of BDA in marketing and highlighting the strategic significance of integrated, moral, and technologically sophisticated data practices for long-term competitive advantage, this study adds to the body of knowledge.
References
- A. Gandomi and M. Haider, "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, vol. 35, no. 2, pp. 137-144, 2015. https://doi.org/10.1016/j.ijinfomgt.2014.10.007.
- H. Chen, R. H. Chiang, and V. C. Storey, "Business Intelligence and Analytics: From Big Data to Big Impact," MIS Quarterly, vol. 36, no. 4, pp. 1165-1188, 2012. https://doi.org/10.2307/41703503.
- S. Erevelles, N. Fukawa, and L. Swayne, "Big Data consumer analytics and the transformation of marketing," Journal of Business Research, vol. 69, no. 2, pp. 897-904, 2016. https://doi.org/10.1016/j.jbusres.2015.07.001.
- M. Wedel and P. K. Kannan, "Marketing analytics for data-rich environments," Journal of Marketing, vol. 80, no. 6, pp. 97-121, 2016. https://doi.org/10.1509/jm.15.0413.
- S. Fan, R. Y. Lau, and J. L. Zhao, "Demystifying big data analytics for business intelligence through the lens of marketing mix," Big Data Re-search, vol. 2, no. 1, pp. 28-32, 2015. https://doi.org/10.1016/j.bdr.2015.02.006.
- A. McAfee and E. Brynjolfsson, "Big data: the management revolution," Harvard Business Review, vol. 90, no. 10, pp. 60-68, 2012.
- F. Kache and S. Seuring, "Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain manage https://doi.org/10.1108/IJOPM-02-2015-0078.ment," International Journal of Operations & Production Management, vol. 37, no. 1, pp. 10-36, 2017.
- U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, vol. 70, pp. 263-286, 2017. https://doi.org/10.1016/j.jbusres.2016.08.001.
- T. H. Davenport and J. G. Harris, Competing on Analytics: Updated, with a New Introduction: The New Science of Winning, Harvard Business Press, 2017.
- M. Ghasemaghaei and G. Calic, "Can big data improve firm decision quality? The role of data quality and data diagnosticity," Decision Support Systems, vol. 120, pp. 38-49, 2019. https://doi.org/10.1016/j.dss.2019.03.008.
- H. Lee, J. Kim, and M. Park, "Analyzing consumer trends using big data: Applications of the Apriori algorithm," Journal of Marketing Analytics, vol. 9, no. 2, pp. 123–134, 2021.
- C. Chen, L. Zhang, and S. Wang, "Social media analytics for targeted marketing," Social Media Research Journal, vol. 18, no. 3, pp. 45–62, 2020.
- M. De Luca, R. Hernandez, and T. Chang, "Predictive analytics in marketing: Real-time affordances and ROI," Journal of Business Research, vol. 128, pp. 202–214, 2021.
- K. Koh, R. Lin, and Y. Huang, "Retail optimization with big data: A case study in demand forecasting," Retail Management Journal, vol. 14, no. 1, pp. 56–69, 2023.
- S. Gupta and R. Kumar, "Big data applications in customer retention strategies," Customer Relationship Management Quarterly, vol. 8, no. 3, pp. 45–58, 2020.
- M. Al Adwana, F. El Hadi, and R. Said, "A framework for campaign effectiveness: Data-driven approaches," International Journal of Digital Mar-keting, vol. 22, no. 4, pp. 101–112, 2023.
- P. Wilfred, "Social media influence on consumer purchasing in Tanzania," African Marketing Studies, vol. 11, no. 2, pp. 78–90, 2023.
- J. Saura, D. Palos-Sanchez, and J. Penín, "Web analytics for dynamic marketing strategies," Marketing Innovations Journal, vol. 15, no. 5, pp. 221–236, 2021.
- E. Tanaka and J. Yamada, "The role of big data in programmatic advertising," Digital Advertising Insights, vol. 14, no. 1, pp. 67–80, 2021.
- A. Verdenhofs and T. Tambovceva, "Customer segmentation using predictive modeling," European Journal of Marketing Research, vol. 12, no. 1, pp. 67–79, 2019.
- R. Arora and S. Thota, "AI in targeted advertising: Personalization and engagement," Digital Marketing Horizons, vol. 25, no. 3, pp. 134–147, 2024.
- F. Lopez, G. Rivera, and T. Allen, "AI-driven insights for market segmentation," Journal of Advanced Marketing Research, vol. 18, no. 1, pp. 25–39, 2023.
- S. Iyer, L. Brown, and K. Green, "Geolocation and marketing ROI," Retail Technology Journal, vol. 17, no. 4, pp. 201–214, 2021.
- R. Patel, "Personalization techniques in big data marketing," Data-Driven Insights Journal, vol. 13, no. 2, pp. 78–92, 2021.
- D. Sakas, M. Vlachos, and A. Nikolaou, "Omnichannel integration with big data," Customer Experience Journal, vol. 9, no. 3, pp. 89–102, 2022.
- M. Hossain, T. Khan, and A. Rahman, "Data integration for seamless customer experience," Information Systems and Marketing Quarterly, vol. 6, no. 2, pp. 33–47, 2017.
- B. O'Connor, S. Moore, and K. Smith, "Big data and consumer journey mapping," Marketing Strategy Review, vol. 7, no. 2, pp. 34–44, 2020.
- N. Theodorakopoulos, M. Savvides, and E. Georgiou, "Ethics and big data in marketing," Ethical Marketing Review, vol. 19, no. 1, pp. 12–25, 2024.
- F. Ahmed and T. Nguyen, "Balancing personalization and privacy in marketing," Data Ethics Journal, vol. 7, no. 4, pp. 99–115, 2021.
- K. Martin, L. Peterson, and J. Collins, "Privacy challenges in data-driven marketing," Marketing Ethics Review, vol. 10, no. 2, pp. 45–61, 2020.
- A. Chatterjee, "Big data and the evolution of influencer marketing," Influencer Marketing Quarterly, vol. 5, no. 3, pp. 10–25, 2023.
- J. Roberts, L. Newton, and P. Walker, "Augmented reality and big data integration in marketing," Emerging Technologies Journal, vol. 9, no. 5, pp. 300–315, 2021.
- J. Saura, D. Palos-Sanchez, and T. López, "Emerging trends in big data and AI for marketing," Journal of Emerging Marketing Technologies, vol. 20, no. 6, pp. 345–364, 2023.
- M. Singh, "Challenges in implementing big data for SMEs," Small Business Tech Review, vol. 11, no. 2, pp. 88–102, 2022.
- T. Smith, J. Kelly, and R. Doe, "The evolution of big data marketing through EIII approaches," Enterprise Information Integration Insights, vol. 8, no. 4, pp. 200–215, 2023.
- U. Sivarajah et al., "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, vol. 70, pp. 263-286, 2017. https://doi.org/10.1016/j.jbusres.2016.08.001.
- V. Kumar, W. Reinartz, and R. P. Leone, "Leveraging Big Data for Competitive Advantage," Journal of Marketing, vol. 77, no. 6, pp. 1-10, 2013.
- P. Mikalef, I. O. Pappas, J. Krogstie, and M. Giannakos, "Big data analytics capabilities: a systematic literature review and research agenda," In-formation Systems and e-Business Management, vol. 18, no. 3, pp. 547-578, 2019. https://doi.org/10.1007/s10257-017-0362-y.
- A. M. Kaplan and M. Haenlein, "Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artifi-cial intelligence," Business Horizons, vol. 62, no. 1, pp. 15-25, 2019. https://doi.org/10.1016/j.bushor.2018.08.004.
- H. Chen, Y. Mao, and Y. Liu, "Big data: A survey," Mobile Networks and Applications, vol. 19, no. 2, pp. 171-209, 2014. https://doi.org/10.1007/s11036-013-0489-0.
- S. Fosso Wamba, S. Akter, A. Edwards, G. Chopin, and D. Gnanzou, "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, vol. 165, pp. 234-246, 2015. https://doi.org/10.1016/j.ijpe.2014.12.031.
- D. Delen and H. Demirkan, "Data, information and analytics as services," Decision Support Systems, vol. 55, no. 1, pp. 359-363, 2013. https://doi.org/10.1016/j.dss.2012.05.044.
- M. Lycett, "'Datafication': making sense of (big) data in a complex world," European Journal of Information Systems, vol. 22, no. 4, pp. 381-386, 2013. https://doi.org/10.1057/ejis.2013.10.
- D. Kiron, P. K. Prentice, and R. B. Ferguson, "Raising the bar with analytics," MIT Sloan Management Review, vol. 55, no. 2, pp. 29-33, 2014.
- J. Zeng and K. W. Glaister, "Value creation from big data: Looking inside the black box," Strategic Organization, vol. 16, no. 2, pp. 105-140, 2018. https://doi.org/10.1177/1476127017697510.
- G. George, M. R. Haas, and A. Pentland, "Big data and management," Academy of Management Journal, vol. 57, no. 2, pp. 321-326, 2014. https://doi.org/10.5465/amj.2014.4002.
- P. Kotler and K. L. Keller, Marketing Management, 15th ed., Pearson, 2016.
- M. Stone and N. Woodcock, "Interactive, direct and digital marketing," Journal of Research in Interactive Marketing, vol. 8, no. 1, pp. 4-17, 2014. https://doi.org/10.1108/JRIM-07-2013-0046.
- A. Payne and P. Frow, Strategic Customer Management: Integrating Relationship Marketing and CRM, Cambridge University Press, 2013. https://doi.org/10.1017/CBO9781139057417.
- W. H. Inmon and D. Linstedt, Data Architecture: A Primer for the Data Scientist, Morgan Kaufmann, 2014. https://doi.org/10.1016/B978-0-12-802044-9.00044-1.
- D. Chaffey and F. Ellis-Chadwick, Digital Marketing: Strategy, Implementation, and Practice, 7th ed., Pearson, 2019.
- U. Sivarajah et al., "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, vol. 70, pp. 263-286, 2017. https://doi.org/10.1016/j.jbusres.2016.08.001.
- https://www.geeksforgeeks.org/database-marketing-meaning-benefits-challenges-and-types/
- F. Provost and T. Fawcett, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly Media, 2013.
- M. I. Jordan and T. M. Mitchell, "Machine learning: Trends, perspectives, and prospects," Science, vol. 349, no. 6245, pp. 255-260, 2015. https://doi.org/10.1126/science.aaa8415.
- V. Dhar and S. Mazumdar, "Big data in marketing: The role of analytics," Journal of Consumer Marketing, vol. 31, no. 3, pp. 136-144, 2014.
- X. Liu et al., "Real-time analytics in big data environments," IEEE Access, vol. 5, pp. 11611-11623, 2017.
- S. Akter and S. F. Wamba, "Big data analytics in e-commerce: A systematic review and agenda for future research," Electronic Markets, vol. 26, no. 2, pp. 173-194, 2016. https://doi.org/10.1007/s12525-016-0219-0.
- I. A. T. Hashem et al., "The rise of ‘big data’ on cloud computing: Review and open research issues," Information Systems, vol. 47, pp. 98-115, 2015. https://doi.org/10.1016/j.is.2014.07.006.
- T. White, Hadoop: The Definitive Guide, 3rd ed., O'Reilly Media, 2012.
- E. Brynjolfsson and A. McAfee, Machine, Platform, Crowd: Harnessing Our Digital Future, W. W. Norton & Company, 2017.
- J. Bughin et al., "Artificial Intelligence: The Next Digital Frontier?" McKinsey Global Institute, 2018.
- Statista, "Data-Driven E-commerce Strategies in Amazon," Statista Reports, 2023.
- Amazon, "How Amazon Leverages Big Data to Enhance Customer Experience," Amazon Corporate Website, 2023. [Online]. Available: https://www.amazon.com
- AWS, "Big Data Use Cases," [Online]. Available: https://aws.amazon.com/big-data/use-cases/
- Netflix, "How Netflix’s Recommendation System Works," Netflix Official Website, 2021. [Online]. Available: https://help.netflix.com
- Netflix Tech Blog, "The Science Behind Netflix’s Content Recommendations," Netflix Engineering Team, 2022. [Online]. Available: https://netflixtechblog.com
- McKinsey & Company, "Real-Time Personalization in Streaming Platforms: Lessons from Netflix," 2022. [Online]. Available: https://www.mckinsey.com
- R. Johnson, "Big Data-Driven Strategies for Customer Retention in Media Services," Forbes, 2021. [Online]. Available: https://www.forbes.com
- Netflix, "Balancing Personalization and Content Discovery," Netflix Corporate Blog, 2023. [Online]. Available: https://media.netflix.com
- Coca-Cola, "Leveraging Big Data to Stay Relevant," Coca-Cola Official Website, 2021. [Online]. Available: https://www.coca-cola.com
- Marketing Dive, "How Coca-Cola Uses Real-Time Feedback to Shape Marketing Campaigns," 2022. [Online]. Available: https://www.marketingdive.com
- Statista, "Big Data and Sales Analytics in the Beverage Industry," Statista Reports, 2023.
- Forbes, "Coca-Cola’s Marketing Strategy for Major Events: A Data-Driven Approach," 2022. [Online]. Available: https://www.forbes.com
- Coca-Cola Newsroom, "The Share a Coke Campaign: A Data-Driven Success Story," 2021. [Online]. Available: https://www.coca-colacompany.com/newsroom
- McKinsey & Company, "Real-Time Marketing in the Beverage Industry: Lessons from Coca-Cola," 2022. [Online]. Available: https://www.mckinsey.com
- Nike, "How Nike Run Club Drives Personalized Fitness Experiences," Nike Official Website, 2021. [Online]. Available: https://www.nike.com
- Statista, "Consumer Behavior Analysis in Sportswear Industry," Statista Reports, 2023.
- Marketing Dive, "How Nike Leverages Social Media for Targeted Campaigns," 2022. [Online]. Available: https://www.marketingdive.com
- Forbes, "The Role of Predictive Analytics in Modern Marketing Strategies," 2022. [Online]. Available: https://www.forbes.com
- Nike Newsroom, "Nike's Women’s Fitness Campaign: A Case Study in Personalization," 2021. [Online]. Available: https://www.nike.com/newsroom
- Nike, "Sustainability Report: How Data Shapes Our Green Initiatives," 2023. [Online]. Available: https://www.nike.com
- H&M Group, "How H&M Uses Data to Understand Customer Preferences," H&M Official Website, 2021. [Online]. Available: https://www.hm.com
- Statista, "Customer Loyalty Programs in the Fashion Retail Industry," Statista Reports, 2023.
- Marketing Dive, "H&M’s Use of Social Media for Trend Spotting and Marketing," 2022. [Online]. Available: https://www.marketingdive.com
- Forbes, "Big Data in Retail: How H&M Stays Ahead," 2022. [Online]. Available: https://www.forbes.com
- H&M Newsroom, "Adapting to Change: H&M's Response to Shifting Consumer Demands During the Pandemic," 2021. [Online]. Available: https://www.hmgroup.com/newsroom
- H&M Group, "Sustainability Report: Data-Driven Efforts in Reducing Overproduction," 2023. [Online]. Available: https://www.hmgroup.com.
Downloads
How to Cite
Received date: April 19, 2025
Accepted date: May 1, 2025
Published date: May 11, 2025