The impact of Artificial Іntelligence technologies on the effectiveness of marketing strategies of small and medium-sized businesses: an econometric analysis

Анотація

Purpose: This study is aimed at providing the empirically-based evaluation of the influence of Artificial intelligence (AI) technologies on the efficiency of marketing strategies in small and medium-sized enterprises, considering the differences between the digital development, institutional conditions, and organizational capabilities of national economies. The study answers the question of the necessity of quantitative data describing the role of AI in the performance of marketing at different levels of technological preparedness and economic revolution.

Methods: The panel econometric analysis is based on the data of SMEs in Poland, Estonia, and Ukraine during the period of 2021-2024. The multidimensional index of AI adoption is created, with key elements of machine learning applications, marketing automation, personalization systems, and data analytics tools. The effectiveness of marketing is gauged using a composite indicator consistingof return on marketing investment, customer conversion rates, customer acquisition efficiency, as well as long-term value indicators. The empirical approach comprises fixed-effects panel regression, nonlinear modeling to identify threshold effects, interaction terms to test moderating influences and a dynamic panel model that is estimated using the generalized method of moments to control endogeneity and persistence.

Results: The findings suggest the statistically significant and economically significant positive correlation between the use of AI and the marketing performance. Simultaneously, the analysis indicates a nonlinear tendency that consists of decreasing marginal returns on increasing the intensity of AI past an optimal mark. The results indicate that the effectiveness of the AI is significantly improved in terms of increased levels of digital maturity and human capital, which supports the role of complementary assets in technology-based performance. Comparative analysis across countries reveals that Estonia has the highest efficiency gains because of its well-developed digital ecosystem, Poland has the most stable and consistent improvement, and Ukraine has the strongest potential of improvement in marginal terms despite structural limitations. The dynamic specification proves the persistence of marketing performance and the cumulative impact of AI technologies over time.

Conclusions: The research supports the argument of artificial intelligence as a driving force of enhancing marketing performance in SMEs and the need to be balanced in its application and compatibility with organizational capabilities. The findings are useful in advancing the econometric study of digital transformation and offer some practical implications on business strategy and public policy in improving the capacity to innovate and be competitive.