The relationship between firm size and the financial performance of advertising and marketing companies remains understudied in the academic literature, including in the regional context. Using a panel data methodology, this study analyzes the impact of three proxies for firm size (total assets, number of employees, and sales) on the financial performance (return on assets and profit margin) of the 500 most profitable advertising and marketing companies from 16 Western European countries over the period 2019–2023. Weighted least squares regression analysis revealed statistically significant negative effects of all three proxies for firm size on financial performance, with the strongest negative effects on total assets on return on assets and sales on profit margin, which is similar to return on sales. Empirical data confirm the inverse relationship between total assets and their profitability; this indicates the advantages of resource-optimized business models with high management flexibility and effective use of intellectual capital compared to material-intensive structures. The inverse relationship between the number of employees and financial performance is due to higher operating personnel costs and the difficulty of effectively managing human resources as the number of employees increases. Increased sales negatively affect profit margins, demonstrating a decrease in the efficiency of converting revenue into profits as operations expand. These findings are important for developing effective financial management strategies and making investment decisions in the industry under study. The research contributes to SDGs 8, 9, and 12 by demonstrating how resource-optimized structures with higher management flexibility and effective use of intellectual capital can outperform material-intensive structures in the advertising and marketing industry.

Background: To improve operational and financial policies regarding the allocation of existing and obtaining new resources, strategic decision-making, managers use indicators of financial performance. Purpose: The aim of this study is to analyze the impact of financial performance on the profitability of advertising agencies in Slovakia. Study design/methodology/approach: A sample of 88 Slovak advertising agencies was analyzed by means of regression modeling the data based on financial statements of the financial year 2020. The hypothesis that the indicators of financial performance of advertising agencies in Slovakia have an impact on their profitability is partly confirmed by the results of this research. The first proposed model was adjusted by excluding the independent variable Current Ratio, which allowed us to build the second model to explain 95.21% of the Return on Assets deviation due to the variation of the selected independent variables. Findings/conclusions: By selecting Return on Assets as a dependent variable that characterizes the financial performance of advertising agencies, research has shown that Total Assets Turnover and Firm Size have significant positive influence on it, but Debt to Equity Ratio has a negative influence. This empirically testifies the expediency of financing the activities of advertising agencies from debt resources, scaling the scope of their activities and increasing sales using innovative approaches for getting more customers. Limitations/future research: The study limitations relate to completeness of information and availability of open access to the necessary data in the published financial statements of Slovak advertising agencies. The results of this research could be applicable and beneficial for providers of capital for advertising agencies. It could be also used as a tool to determine key factors of profitability and to adjust companies' financial and operational policies.

The issue of key determinants affecting the financial performance of advertising and marketing companies in Central and Eastern Europe remains understudied, despite the industry’s rapid growth and regional specifics. This study investigates financial performance determinants of advertising and marketing companies in four CEE countries (the Czech Republic, Poland, the Slovak Republic, and Ukraine) during 2021–2023, employing the least absolute deviations method. The study examines three financial performance measures (Return on Assets, Return on Equity, and Operating Profit Margin) using three independent variables (Current Ratio, Debt to Equity, and Total Asset Turnover) and control variables such as Company Size, Leverage, and Company Type. The results show that Total Asset Turnover consistently has a significant positive impact on ROA and ROE across all studied countries. The study also identified significant regional variations in liquidity and capital structure impacts, particularly in the Polish market, and uncovered distinct patterns in how financial leverage affects various performance metrics across the studied countries. Specifically, while leverage shows a predominantly negative relationship with ROE in most countries, it positively influences OPM for Polish, Slovak, and Ukrainian companies, suggesting that the role of financial leverage in company performance is highly context-dependent. The novelty of the study lies in a comprehensive investigation of specific determinants of financial performance in the CEE advertising and marketing sector, revealing the crucial role of efficient asset and equity management in the region.

The relevance of conducting research in the direction of using Big Data to improve marketing activities has been grounded. A bibliometric analysis of publications on the subject of «Big Data and marketing» in the scientometric database Scopus has been carried out. The main aspects of using Big Data in marketing analytics have been grounded. The main advantages of using Big Data in marketing analytics have been analyzed. The prospects and opportunities for the development of marketing analytics based on the use of Big Data have been identified and characterized (improvement of interaction with customers; improved positioning of brands; optimization of the marketing budget; improvement of the effectiveness of predictive analytics). The main problems of using Big Data for the development of marketing analytics have been described (objective limitation of the use of Big Data; the time gap between the occurrence and receipt of processed data for decision-making; storage and processing of streaming data; the need to acquire new competencies by employees of the marketing department; the need to improve the structure of the marketing department).

The study aims to examine the impact of risk factors of Ukrainian advertising and marketing companies on their financial performance (ROA and ROE). The study was conducted using ordinary least squares regression analysis based on an examination of the activities of 435 companies in 2022. The total number of risk factors (Risk Total) and the ten most common risk factors (10 fixed risk factors) associated with the activities of Ukrainian advertising and marketing companies, calculated using the YouControl database, were selected as independent variables. 10 fixed risk factors were interpreted as dummy variables, which allowed incorporating qualitative information about risk factors of Ukrainian advertising and marketing companies into regression analysis models. Control variables (company age, company size, financial leverage, population at the place of company registration, and total solvency ratio) were added to enhance the determination level of the models. Of these, the statistically significant ones were Company size, which increases ROA and ROE; Financial leverage, which increases ROE; and Company age, which decreases ROE. Of the 11 independent variables that characterized companies’ risk factors, only three were confirmed to significantly impact financial performance indicators (risk factor “Location in the housing stock” reduces ROA and ROE; risk factor “Frequent institutional changes” increases ROA; risk factor “Found match by full name with a politically exposed person” reduces ROE).

The study examines the correlation between marketing-related intangible assets and net income for the last reporting periodof 100 U.S. stock market leaders. Given that 102 companies were included in this ranking, but not all of them reported thenecessary information for the calculations, the sample for regression analysis was 44 companies. Based on information onmarketing-related intangible assets available in the companies’ reports, we concluded that the hypothesis that marketingrelated intangible assets determine net income is rejected because this factor is considered insignificant (Model 1). Of the fouranalyzed models, Model 4 is optimal for further use, which confirms the significant impact of marketing costs (expenses) onthe company’s net income. Our study makes two important contributions. First, the regression analysis based on the reportingdata of companies refutes the hypothesis that marketing-related intangible assets affect the company’s net income. Second,the proposed model (Model 4) confirms the significant impact of marketing expenses (costs) on net income, which leads toappropriate recommendations for further improvement of this model.