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The aim of the paper is to investigate the impact of SEO on the business performance of a private university in Sarajevo. Thus, the main research question provides the finding on how does the implementation of SEO influence the performance of the business. Moreover, the tested hypothesis presents whether SEO positively influences the business performance of International Burch University (IBU). The research strategy is to analyze primary data derived from a case study, which is generated following a conversation with the Head of the IBU Marketing and PR team. The data sample is derived from Google Analytics (focusing on the number of visits and sessions, average engagement time, keywords and SERP positioning). Seobility tools are employed in data analysis. Business performance is calculated through the IBU CRM system, focusing on student enrolment. Findings indicate that increasing a site's rankings on search engine results pages (SERPs) led to a variety of positive outcomes for companies including an increase in the number of visitors to the site, an increase in the average amount of time users spent on the site, increased user engagement, and an increase in student enrollment, which resulted in IBU increased annual sales revenue. It will benefit many different groups, including the government, which will benefit in both microeconomic and macroeconomic senses, digital marketing enthusiasts and SEO experts, and the academic world, which will benefit as a framework for future studies and research in the field of SEO recognition and implementation in business queries.

This paper aims to evaluate the effects of demographic targeting, platform selection, and engagement metrics on the effectiveness of social media advertising campaigns. In this paper, it is also aimed to provide a comprehensive understanding of how these factors influence conversion outcomes. We employed a hybrid analytics method combining ANOVA, correlation, and regression analysis to examine patterns of user engagement and ad performance. A dataset of 200,235 ad campaign records conducted in the United States was analyzed to assess variations across gender, age groups, and platforms. The main findings of the study demonstrate that (i) women consistently exhibited higher conversion rates compared to men, highlighting the potential of gender-specific content strategies; (ii) users aged 35–44 achieved the highest engagement, suggesting that middle-aged audiences may be more responsive to targeted campaigns; and finally, (iii) among platforms, Instagram led in conversion performance, while Facebook showed comparable engagement rates, and both significantly outperformed other platforms in terms of overall impact. Click-through rate was found to be moderately correlated with conversions, whereas impressions showed weak returns, indicating diminishing value at higher exposure levels. These results underscore the importance of optimizing both message and delivery channel to specific audience segments.This study contributes to digital marketing literature by integrating demographic variables, engagement indicators, and platform performance into a unified model. The insights derived offer practical implications for businesses and advertisers aiming to enhance return on investment and optimize campaign strategies in a fast-paced, digitally driven advertising landscape.

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.

This study investigates the strategic effectiveness of social media advertising by analyzing the relationship between platform selection, audience demographics, and campaign performance metrics. Using a dataset encompassing Instagram, Facebook, Pinterest, and Twitter, the research evaluates key indicators such as engagement, conversion rates, and return on investment (ROI). By employing statistical methods like ANOVA and regression analysis in SPSS, the study aims to identify optimal platform-audience combinations and assess how different campaign goals influence outcomes. The findings will provide valuable insights for advertisers and businesses seeking to enhance their social media strategies and maximize advertising effectiveness.

This paper aims to evaluate the effects of demographic targeting, platform selection, and engagement metrics on the effectiveness of social media advertising campaigns. In this paper, it is also aimed to provide a comprehensive understanding of how these factors influence conversion outcomes. We employed a hybrid analytics method combining ANOVA, correlation, and regression analysis to examine patterns of user engagement and ad performance. A dataset of 200,235 ad campaign records conducted in the United States was analyzed to assess variations across gender, age groups, and platforms. The main findings of the study demonstrate that (i) women consistently exhibited higher conversion rates compared to men, highlighting the potential of gender-specific content strategies; (ii) users aged 35–44 achieved the highest engagement, suggesting that middle-aged audiences may be more responsive to targeted campaigns; and finally, (iii) among platforms, Instagram led in conversion performance, while Facebook showed comparable engagement rates, and both significantly outperformed other platforms in terms of overall impact. Click-through rate was found to be moderately correlated with conversions, whereas impressions showed weak returns, indicating diminishing value at higher exposure levels. These results underscore the importance of optimizing both message and delivery channel to specific audience segments.This study contributes to digital marketing literature by integrating demographic variables, engagement indicators, and platform performance into a unified model. The insights derived offer practical implications for businesses and advertisers aiming to enhance return on investment and optimize campaign strategies in a fast-paced, digitally driven advertising landscape.

This study investigates the strategic effectiveness of social media advertising by analyzing the relationship between platform selection, audience demographics, and campaign performance metrics. Using a dataset encompassing Instagram, Facebook, Pinterest, and Twitter, the research evaluates key indicators such as engagement, conversion rates, and return on investment (ROI). By employing statistical methods like ANOVA and regression analysis in SPSS, the study aims to identify optimal platform-audience combinations and assess how different campaign goals influence outcomes. The findings will provide valuable insights for advertisers and businesses seeking to enhance their social media strategies and maximize advertising effectiveness.

This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining the best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions.

The aim of the paper is to investigate the impact of SEO on the business performance of a private university in Sarajevo. Thus, the main research question provides the finding on how does the implementation of SEO influence the performance of the business. Moreover, the tested hypothesis presents whether SEO positively influences the business performance of International Burch University (IBU). The research strategy is to analyze primary data derived from a case study, which is generated following a conversation with the Head of the IBU Marketing and PR team. The data sample is derived from Google Analytics (focusing on the number of visits and sessions, average engagement time, keywords and SERP positioning). Seobility tools are employed in data analysis. Business performance is calculated through the IBU CRM system, focusing on student enrolment. Findings indicate that increasing a site's rankings on search engine results pages (SERPs) led to a variety of positive outcomes for companies including an increase in the number of visitors to the site, an increase in the average amount of time users spent on the site, increased user engagement, and an increase in student enrollment, which resulted in IBU increased annual sales revenue. It will benefit many different groups, including the government, which will benefit in both microeconomic and macroeconomic senses, digital marketing enthusiasts and SEO experts, and the academic world, which will benefit as a framework for future studies and research in the field of SEO recognition and implementation in business queries.