Author(s)

Leila Sadrnia

M. A. Sport Marketing Management, Department of Physical Education, Islamic Azad University, Science and Research Branch, Tehran, Iran.

The requirement to translate and understand the impact of the data on business has become a key. The paper below provides an overview of the emergence of Predictive Modeling and how it optimizes the campaign strategies of the business. The paper seeks to explore the evolution of the model, which is an integral part of marketing strategy formulation. The topic is discussed with the help of a literature review and a detailed framework to provide the findings with the help of mixed method research design is provided. The implications of the model towards marketing are discussed; in addition, the emerging trends and challenges are covered to present the future of marketing ahead. The paper is organized into several key sections. It begins with an introduction that provides background on predictive modeling and states the research objectives. This is followed by a literature review summarizing previous relevant research on using predictive modeling in marketing campaigns. The literature review identifies gaps in the research this study aims to address. The methodology section outlines the proposed research design, data collection methods, and data analysis techniques. Next, the theoretical framework describes relevant models like neural networks and decision trees. The discussion section covers the evolution and applications of predictive modeling, including case studies. Finally, the conclusion summarizes the essential findings and implications, recommends the following steps, acknowledges limitations, and suggests future research directions related to predictive modeling in marketing. Throughout, citations are provided to attribute sources and ideas correctly.

Predictive Modeling, Marketing Strategies, Data Analytics, Campaign Optimization, Future of Marketing

Sadrnia, L. (2023) The Future of Marketing: How Predictive Modeling Optimizes Campaign Strategies. iBusiness, 15, 249-262. doi: 10.4236/ib.2023.154018.