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

Year: 2024

Type: article

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.

Source: International Journal For Multidisciplinary Research

DOI: 10.36948/ijfmr.2024.v06i06.38184

Author: Preetham Reddy Kaukuntla -

Language: English

Topic: Customer churn and segmentation

Subfield: Marketing

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4408100906

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