Display Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitors

Display advertising constitutes one of the most efficient digital marketing strategies for the development of organizations’ brand awareness. Proper targeting of display ads campaigns potentially leads to the improvement of web users’ consideration and engagement about products and services that organizations offer through their websites. As prior studies indicate, this kind of consideration and engagement, which resulted through display ads, leads web users to type the name of the brand in search engines. The submitted search terms that contain the brand name of the organizations are called branded keywords, and the traffic that comes from them as branded search traffic. In this paper, the authors propose a computational data-driven methodology for the estimation and prediction of display advertising effectiveness in terms of optimizing brand popularity in search engines. One step further, preliminary research efforts of the authors indicate that branded search traffic visitors show higher interaction with the content of the websites regarding the time they spend and the number of pageviews they are browsing. In this respect, if display advertising campaigns increase the number of branded keywords and hence, the volume of branded search traffic, then this raises opportunities to optimize users’ engagement inside websites. Against this research gap, the authors proceed into a data-driven methodological process that is expanded in three major stages. In the first stage, the web mining process of extracting several web behavioral analytics metrics takes place for 125 continuous days at 7 courseware websites. At the second stage, analysis and interpretation of possible intercorrelations between the web analytics metrics take place with the purpose to integrate a computational model that relies on web behavioral data harvesting and their statistical analysis. Subsequently, in the third stage, the authors develop a data-driven computational model based on the agent-based modeling approach for estimating and predicting the optimal interaction rates of branded search traffic visitors of the examined websites. The results of the study constitute a practical toolbox for digital marketing practitioners in order to understand their display advertising effectiveness in terms of brand popularity and branded search traffic improvement for their websites.