Abstract
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.
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References
Agarwal, S., Nishar, D., Rubin, A.E., & Google LLC. (2013). Providing digital content based on expected user behavior. U.S. Patent Application 13/616,699.
Aksakalli, V. (2012). Optimizing direct response in internet display advertising. Electronic Commerce Research and Applications, 11(3), 229–240.
An, L. (2012). Modeling human decisions in coupled human and natural systems: Review of agent-based models. Ecological Modelling, 229, 25–36.
Aslam, B., & Karjaluoto, H. (2017). Digital advertising around paid spaces, E-advertising industry’s revenue engine: A review and research agenda. Telematics and Informatics, 34(8), 1650–1662.
Barbati, M., Bruno, G., & Genovese, A. (2012). Applications of agent-based models for optimization problems: A literature review. Expert Systems with Applications, 39(5), 6020–6028.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(suppl 3), 7280–7287.
Bruch, E., & Atwell, J. (2015). Agent-based models in empirical social research. Sociological Methods and Research, 44(2), 186–221.
Collado, S. H. (2009). Towards a data-driven approach for agent-based modelling: Simulating Spanish postmodernisation. Madrid: Universidad Complutense de Madrid.
Dimitrios, N. K., Alexandros, N. K., Sakas, D. P., & Vlachos, D. S. (2017c). Comparing scrum and XP agile methodologies using dynamic simulation modeling. In Strategic innovative marketing. Springer proceedings in business and economics (pp. 391–397). Cham: Springer.
Dimitrios, N. K., Plessias, A. A., & Giakomidou, D. S. (2017a). Modeling of market segmentation in social networks and media. In Strategic innovative marketing. Springer proceedings in business and economics (pp. 523–529). Cham: Springer.
Dimitrios, N. K., Plikas, J. H., Sakas, D. P., & Vlachos, D. S. (2017b). Modeling the promotion process of academic conferences through social media. In Strategic innovative marketing. Springer proceedings in business and economics (pp. 499–505). Cham: Springer.
Dimitrios, N. K., Sakas, D. P., & Vlachosv, D. S. (2014a). Modeling the scientific dimension of academic conferences. Procedia-Social and Behavioral Sciences, 147, 576–585.
Dimitrios, N. K., Sakas, D. P., & Vlachos, D. S. (2014b). Modeling publications in academic conferences. Procedia-Social and Behavioral Sciences, 147, 467–477.
Dimitrios, N. K., Sakas, D. P., & Vlachos, D. S. (2014c). Simulation of generation of new ideas for new product development and IT services (pp. 60–68). AIP Conference Proceedings: AIP Publishing.
Dimitrios, N. K., Sakas, D. P., & Vlachos, D. S. (2015). Modeling of market segmentation for new IT product development. AIP Conference Proceedings, 1644, 51–59.
Dimitrios, N. K., Sakas, D. P., & Vlachos, D. S. (2013). The contribution of dynamic simulation model of depiction of knowledge, in the leading process of high technology companies. Key Engineering Materials, 543, 406–409.
Fontaine, C. M., & Rounsevell, M. D. (2009). An agent-based approach to model future residential pressure on a regional landscape. Landscape Ecology, 24(9), 1237.
Fulgoni, G. M., & Mörn, M. P. (2009). Whither the click? How online advertising works. Journal of Advertising Research, 49(2), 134–142.
GarcĂa, M. D. M. R., GarcĂa-Nieto, J., & Aldana-Montes, J. F. (2016). An ontology-based data integration approach for web analytics in e-commerce. Expert Systems with Applications, 63, 20–34.
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. (2018). Data analytics competency for improving firm decision making performance. The Journal of Strategic Information Systems, 27(1), 101–113.
Gimblett, H. R. (2002). Integrating geographic information systems and agent-based technologies for modeling and simulating social and ecological phenomena (pp. 1–20). New York: Oxford University Press.
Google Ads Help. (2019a). Measuring brand awareness. Retrieved December 20, 2019, from https://support.google.com/google-ads/answer/1722064
Google Ads Help. (2019b). Increase brand awareness. Retrieved Decemeber 20, 2019, from https://support.google.com/google-ads/answer/1722100?hl=en
Google Ads Help. (2019c). Things you should know about Ads Quality. Retrieved December 20, 2019, from https://support.google.com/google-ads/answer/156066?hl=en
Grignard, A., Taillandier, P., Gaudou, B., Vo, D. A., Huynh, N. Q., & Drogoul, A. (2013). GAMA 1.6: Advancing the art of complex agent-based modeling and simulation. In International conference on principles and practice of multi-agent systems (pp. 117–131). Berlin: Springer.
Grigoryev, I. (2015). AnyLogic 7 in three days. A quick course in simulation modeling, 2. Scotts Valley, CA: CreateSpace Independent Publishing.
Järvinen, J., & Karjaluoto, H. (2015). The use of Web analytics for digital marketing performance measurement. Industrial Marketing Management, 50, 117–127.
Kavak, H., Padilla, J. J., Lynch, C. J., & Diallo, S. Y. (2018). Big data, agents, and machine learning: towards a data-driven agent-based modeling approach. In In Proceedings of the Annual Simulation Symposium (p. 12). San Diego, CA: Society for Computer Simulation International.
Kireyev, P., Pauwels, K., & Gupta, S. (2016). Do display ads influence search? Attribution and dynamics in online advertising. International Journal of Research in Marketing, 33(3), 475–490.
Lamping, J., & Pearson, M. (2011). Deriving and using document and site quality signals from search query streams. US Patent 7,962,462.
Mavridis, T., & Symeonidis, A. L. (2015). Identifying valid search engine ranking factors in a Web 2.0 and Web 3.0 context for building efficient SEO mechanisms. Engineering Applications of Artificial Intelligence, 41, 75–91.
Pauwels, K., Ambler, T., Clark, B., LaPointe, P., Reibstein, D., Skiera, B., et al. (2009). Dashboards as a service: Why, what, how, and what research is needed? Journal of Service Research, 12(2), 175–189.
Richardson, M., Dominowska, E., & Ragno, R. (2007). Predicting clicks: estimating the click-through rate for new ads. In In Proceedings of the 16th international conference on World Wide Web (pp. 521–530). New York: ACM.
Sakas, D. P., Nasiopoulos, D. K., & Reklitis, P. (2019a). Modeling and simulation of the strategic use of marketing in search engines for the business success of high technology companies. In Strategic innovative marketing. Springer proceedings in business and economics (pp. 217–226). Cham: Springer.
Sakas, D. P., Nasiopoulos, D. K., & Reklitis, P. (2019b). Modeling and simulation of the strategic use of marketing in search engines for the business success of high technology companies. In Strategic innovative marketing. Springer proceedings in business and economics (pp. 227–236). Cham: Springer.
Sakas, D. P., Nasiopoulos, D. K., & Vlachos, D. S. (2014). Modeling the development of the online conference’s services. Library Review, 65(3), 160–184.
Wordstream. (2019). Average click-through rate: Average CTR calculator. Retrieved December 20, 2019, from https://www.wordstream.com/average-ctr
Xu, L., Duan, J. A., & Whinston, A. (2014). Path to purchase: A mutually exciting point process model for online advertising and conversion. Management Science, 60(6), 1392–1412.
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Drivas, I.C., Sakas, D.P., Giannakopoulos, G.A. (2021). Display Advertising and Brand Awareness in Search Engines: Predicting the Engagement of Branded Search Traffic Visitors. In: Sakas, D.P., Nasiopoulos, D.K., Taratuhina, Y. (eds) Business Intelligence and Modelling. IC-BIM 2019. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-57065-1_1
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