Personalized Marketing Strategies Through Machine Learning: Enhancing Customer Engagement

Abstract

Regarding the volunteered information, this paper aims to describe the machine learning (ML) and personal marketing communication strategies adopted up to 2019. This paper explores various categories of ML; collaborative filtering, content-based filtering, and hybrid methods with regard to recommendation systems, prediction, and customer profiling. The research assesses the impact of the identified strategies in furthering the customer interaction goals like the click-through rate the conversion rate and the customer lifetime value. We also talk about the issues in the implementation, strategies in ethical issues, and direction for the further study of such a rapidly developing field. From our investigation, it emerges that ML-enhanced recommendations can substantially increase customers’ engagement, and some firms quoted receiving up to 80 percent customer interactions leveraged on ML. Nevertheless, issues like the data privacy, the problem of algorithmic biases and the requirement of real-time response are still huge obstacles for AI’s mainstream perception.