Google Analytics measure the user engagement of a fashion retail electronic shop for women located in Greece through certain user behavior metrics on a website including the number of engaged sessions, average engagement time, bounce rate, conversions etc. This paper tries to identify the relationship among user engagement metrics and define a set of rules that emerges from that analysis. By using descriptive statistics, it analyses metrics such as event count, sessions, purchase revenue, transactions, conversions, and bounce rate, it aims to identify and classify the factors influencing user engagement. Following, it uses machine learning to provide a data classification for selected engagement metrics. Primarily, this paper aims to generate a series of recommendations to help the decision makers and marketers to optimize their marketing strategies. Secondly, it justifies the introduction of artificial intelligence best practices in digital marketing enhancing the decision-making process. The findings suggest that personalized content approach and user journey optimization, can increase user engagement.