Utilizing Behavioral Analytics and Predictive Modeling to Identify and Optimize Engagement with High-Value users

Author(s)	Preetham Reddy Kaukuntla
Country	United States
Abstract	Engaging high-value users is one of the most important strategies businesses use to maximize customer lifetime value (CLV) and overall profitability. This paper will address and suggest a framework that incorporates behavioral analytics and predictive modeling into identifying the highest value users, understanding patterns of behavior, and optimizing the engagement strategy. In this context, the integration of ML techniques and user segmentation enables organizations to make informed data-driven decisions that enhance user retention, satisfaction, and revenue. An example case study shows the power of this methodology: rather high retention rates and engagement metrics are achieved.
Keywords	Behavioral analytics, predictive modeling, high-value users, user segmentation, engagement optimization, machine learning, customer lifetime value
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-11-06
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.38184
Short DOI	https://doi.org/g86xs4

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International Journal For Multidisciplinary Research