2021 |
Drivas, Ioannis C; Kouis, Dimitris; Kyriaki-Manessi, Daphne; Giannakopoulos, Georgios Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology Journal Article In: Information, vol. 12, no. 7, pp. 18, 2021. Abstract | Links | BibTeX | Tags: archives, content management systems, libraries, museums, search engine optimization, web analytics @article{Drivas2021b,While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services. |
Drivas, Ioannis C; Kyriaki-Manessi, Daphne; Giannakopoulos, Georgios A Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content Book Chapter In: pp. 13, Springer, 2021, ISBN: 978-3-030-57065-1. Abstract | Links | BibTeX | Tags: agent-based models, analytics, behavioral analytics, search engine optimization, web analytics @inbook{Drivas2021,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. |
2020 |
Drivas, Ioannis C; Kyriaki-Manessi, Daphne; Georgios, Giannakopoulos; Sakas, Damianos P Big Data Analytics for Search Engine Optimization Journal Article In: Big Data and Cognitive Computing, vol. 4, no. 2, pp. 23, 2020. Abstract | Links | BibTeX | Tags: Big data, cultural institutions, search engine optimization, web analytics @article{Drivas2020c,In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web. |
All publications
2021 |
Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology Journal Article In: Information, vol. 12, no. 7, pp. 18, 2021. |
Search Engines’ Visits and Users’ Behavior in Websites: Optimization of Users Engagement with the Content Book Chapter In: pp. 13, Springer, 2021, ISBN: 978-3-030-57065-1. |
2020 |
Big Data Analytics for Search Engine Optimization Journal Article In: Big Data and Cognitive Computing, vol. 4, no. 2, pp. 23, 2020. |