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.
References
[1]
ACG Analytics Consulting Group (2023). Understanding Visual and Predictive Analytics.
https://www.analyticsconsultinggroup.com/uncategorized/understanding-visual-and-predictive-analytics/
[2]
Chaffey, D. (2017). The ROI of Predictive Analytics for Marketing. Smart Insight.
https://www.smartinsights.com/user-experience/customer-experience-management-cxm/roi-predictive-analytics-marketing/
[3]
Fischler, A. (2018). Mixed Method. From NOVA Southeastern University.
https://education.nova.edu/Resources/uploads/app/35/files/arc_doc/mixed_methods.pdf
[4]
Grand View Research (2018). Market Analysis Report.
https://www.grandviewresearch.com/industry-analysis/predictive-analytics-market#
[5]
IBM (2022). How to Use Predictive Analytics in Advertising.
https://www.ibm.com/watson-advertising/thought-leadership/how-to-use-predictive-analytics-in-advertising
[6]
Johnson, M. (2023). Predictive Modeling in Marketing: The What, Why and the How.
https://getrecast.com/predictive-modeling/
[7]
Kelley, K. (2023). What is Data Analysis? Process, Types, Methods, and Techniques.
https://www.simplilearn.com/data-analysis-methods-process-types-article
[8]
Lawton, G., Carew, J. M., & Burns, E. (2022). Predictive Modeling.
https://www.techtarget.com/searchenterpriseai/definition/predictive-modeling
[9]
Rosset, S., Neumann, E., Eick, U., Vatnik, N., & Idan, I. (2001). Evaluation of Prediction Models for Marketing Campaigns. Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 456-461.
https://doi.org/10.1145/502512.502581
[10]
Selamat, S. A. (2018). Survey on the Emergence of Predictive Analytics.
file:///C:/Users/ASUS/Downloads/2.EmergingTech-PredictiveAnalytics-20150115-FINAL.pdf
[11]
Statista (2023). Predictive Analytics Revenues/Market Size Worldwide, from 2016 to 2022.
https://www.statista.com/statistics/819415/worldwide-predictive-analytics-market-size/