Advanced Algorithmic Approaches to Data-Driven Optimization for Improving Strategic Decision-Making in Marketing Analytics
Keywords:
Data-driven optimization, Algorithmic approaches, Strategic decision-making, Marketing analytics, Machine learning, Artificial intelligence, Marketing strategies, Customer segmentationAbstract
In recent years, the application of advanced algorithmic approaches to data-driven optimization has garnered significant attention in the field of marketing analytics. This paper explores how these approaches enhance strategic decision-making processes by leveraging large-scale data. By integrating machine learning models, artificial intelligence (AI), and optimization algorithms, marketers can derive actionable insights from complex data sets to improve customer segmentation, personalization, and overall marketing strategies. We review key developments in the field, analyze existing methodologies, and propose a comprehensive framework for implementing data-driven optimization in marketing. Additionally, the paper discusses challenges and opportunities in adopting these techniques, emphasizing their potential to revolutionize marketing analytics. The results of this research suggest that adopting advanced algorithmic approaches can lead to substantial improvements in both the efficiency and effectiveness of marketing decision-making processes.
References
Rust, R. T., & Verhoef, P. C. (2017). Optimizing the marketing ecosystem. Journal of Marketing Research, 54(4), 419-438.
Gupta, S., & Zeithaml, V. (2018). Predictive analytics for customer segmentation. Marketing Science, 37(3), 501-514.
Fader, P., Hardie, B., & Lee, K. (2019). Customer-base analysis using predictive models. Journal of Marketing, 83(5), 94-110.
Ascarza, E., Neslin, S. A., & Netzer, O. (2020). Reinforcement learning for optimizing customer retention strategies. Management Science, 66(7), 3058-3077.
Lee, J., & Bradlow, E. (2019). Optimization models for marketing budget allocation. Marketing Letters, 30(1), 7-16.
Jansen, B., Zhang, M., & Sobel, K. (2018). Real-time optimization in digital advertising. Journal of Interactive Marketing, 43, 12-25.
Blanchard, S., & Aloisi, J. (2020). Artificial intelligence and machine learning in digital marketing: Transforming customer experiences. Journal of Business Research, 121, 632-643.
Verbeke, W., Dejaeger, K., & Martens, D. (2018). Predictive analytics in marketing: Applications in customer profiling and segmentation. Expert Systems with Applications, 112, 100-115.
Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing optimization strategies for improved ROI. Marketing Review Quarterly, 71(2), 321-338.
Wilson, H. J., Daugherty, P. R., & Morini-Bianzino, N. (2017). The future of AI in marketing. Harvard Business Review, 95(4), 76-84.https://iscsitr.com/index.php/ISCSITR IJSRAIML8
Davenport, T. H., & Bean, R. (2020). How companies are using AI to create deeper customer connections. MIT Sloan Management Review, 61(2), 35-43.
Berman, R., & Katona, Z. (2019). The role of artificial intelligence in marketing and its ethical implications. Journal of Marketing Behavior, 5(3), 225-240
