Predictive Modeling for Data-Driven Marketing Decision-Making
Abstract
Predictive modeling has emerged as a transformative tool in marketing, enabling businesses to anticipate consumer behavior, optimize marketing campaigns, and enhance customer engagement. This review explores the fundamental principles of predictive modeling, its applications in marketing decision-making, key techniques and algorithms, challenges, and future directions. By leveraging machine learning, artificial intelligence (AI), and big data analytics, businesses can improve their strategic marketing efforts, achieve higher ROI, and foster long-term customer relationships.
References
1. McKinsey & Company. The power of predictive analytics in marketing [Internet]. 2023 [cited 2025 Mar 10].
Available from: https://www.mckinsey.com
2. Kotler P, Keller KL. Marketing Management. 16th ed. Pearson; 2022.
3. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. 2nd ed. Springer; 2020.
Available from: https://www.mckinsey.com
2. Kotler P, Keller KL. Marketing Management. 16th ed. Pearson; 2022.
3. Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. 2nd ed. Springer; 2020.
Published
2025-10-09
How to Cite
SHARMA, Vaidehi.
Predictive Modeling for Data-Driven Marketing Decision-Making.
Journal of Advanced Research in Digital Marketing Strategies and Consumer Behavior Analytics, [S.l.], v. 1, n. 1, p. 30-37, oct. 2025.
Available at: <https://thejournalshouse.com/index.php/JoARDMSCBA/article/view/1711>. Date accessed: 16 nov. 2025.