Enhancing Personalized Brand Recommendations through Machine Learning- Driven Analysis of User Behavior and Brand Interaction

In today’s digital world, every business relies on web-scraped data to understand customer preferences and to increase sales via personalized brand recommendations. The market is becoming increasingly competitive in the data-driven world. Businesses start focusing on targeted marketing to save expenses and boost their competitiveness and marketing effectiveness. To analyze this type of data, there is an increasing demand for advanced analytical techniques and customized brand recommendations. This study aims to provide an insightful perspective on the utility of data analytics methods to find user behavior in brand interactions. Furthermore, the research offers a comprehensive overview of the procedure for creating and evaluating platforms to enhance machine learning techniques for business development. To achieve accurate marketing, this paper deploys machine learning algorithms like K-nearest neighbor (KNN), super vector machine (SVM), and Naive Bayes (NB) to analyze customer attributes and characteristics with previous purchase records. The article attempts to investigate how customer purchase intention and the phenomenon of personalization relate to each other. According to the findings, KNN gives 92% accuracy for estimating brand and behavior to enhance the sales force.