In the new era of marketing, being at the top results of search engines constitutes one of the most competitive advantages to the organizations’ overall online advertising strategy. In search engines, users type their search terms to cover their informational or purchasing needs and subsequently, search engines rank websites to the relevance of users’ search terms. The higher are the rankings of the websites, the more is the percentage of visitors who explicitly come from search engines. Nevertheless this obvious one marketing advantage, there is no prior research evidence as regards the level of engagement between users and content, after they visit the websites from search engines’ results. That is, users probably visit a website that comes at the top of search engines’ results, however, they do not spend an amount of time, or they do not browse in several webpages inside of it and vice versa. Against this backdrop, the authors proceed into the construction of a methodology composed of the retrieval of web analytics datasets and the development of computational models with the purpose to evaluate users’ engagement and content use within the websites. At the first stage, the authors proceed into the retrieval of web behavioral analytics at certain metrics for 125 sequential days as regards the time users are spending, the number of pageviews they are browsing, the percentage of immediate abandonments, and the percentage of traffic that explicitly comes from search engines. Following a data-driven methodological approach for the development of computational models, the fuzzy cognitive mapping at the descriptive modeling stage is adopted with the purpose to indicate the possible correlations between web analytics metrics. One step further, a corroborative and predictive model is proposed through the agent-based modeling method in order to compute the date ranges that resulted in the highest and the lowest engagements of users as regards the content of seven examined courseware websites. The proposed methodology and the results of this study work as a practical toolbox for decision makers while computing and evaluating through a data-driven way the level of engagement between visitors and the content they receive for online presence optimization on the web.