Behavioral-based consumer segmentation as a tool for developing effective brand marketing strategies: an economic perspective

Abstract

Purpose. This study aims to analyse how behavioral-based consumer segmentation affects brand performance when considering the combined effect of digital marketing investment, e-commerce development, social media engagement, artificial intelligence adoption, and macroeconomic conditions. The goal of the study was to look for the most important factors determining the effectiveness of brand marketing strategies and to explore the empirical evidence for the impact of behavioral analytics and digital transformation on sustainable brand competitiveness in selected countries with varying economic and technological development.

Methods. The study uses a quantitative econometric method using panel data for Germany, Poland, Estonia and Ukraine for the years 2021-2025. The influence of the various factors listed above was estimated using a Fixed Effects regression model between the BPI and the aforementioned variables. Descriptive statistics, correlation analysis, Hausman test, Wooldridge test, multicollinearity diagnostics using Variance Inflation Factor  were used to validate the model specification. These statistical procedures guaranteed the robustness, consistency and reliability of the relationships estimated.

Results. The empirical results show that behavioral-based consumer segmentation is indeed the most significant explanatory variable in the model, in terms of brand performance. The estimated coefficient further substantiates the importance of consumer segmentation, as it helps to target and market to customers more effectively and accurately, thus increasing the brand's effectiveness. Digital marketing investment, AI marketing adoption, e-commerce penetration, and social media engagement are other factors with statistically significant positive impacts, showing that digital transformation has significant effects on bolstering marketing performance. Macroeconomic instability has a positive influence on brand competitiveness, as confirmed by the positive impact of GDP per capita, and a negative effect is statistically significant in the case of inflation, since it dilutes marketing effectiveness. The comparative analysis shows that there are significant variations in the journey of digital transformation within all the countries studied. Germany remains the leader in brand performance thanks to its well-developed digital framework, while Poland and Estonia are catching up with a growing digitalization and AI adoption. Despite a short-term slowdown due to unfavorable macroeconomic factors, the recovery of Ukraine indicates a growing ability to withstand this challenge and a rapid pace of embracing digital marketing technologies by enterprises. The explanation power of the estimated model is around 84%, which indicates high explanation power and empirical validity of the model.

Conclusions. The study validates the need to use behavioral-based consumer segments as a strategic instrument in the modern brand management. The combination of behavioral analytics and digital marketing technologies and artificial intelligence offers significant opportunities to enhance customer engagement, build market advantage, and boost brand performance. The results of the study are useful for both business managers looking to maximize marketing strategies and policymakers in the promotion of digital transformation and innovation-driven economic development. The expansion of the geographical sample, firm-level data and dynamic panel estimation techniques in future research can further explore the long-term relationships between consumer behavior, digital innovation and brand competitiveness.