Transformations  in
Business & Economics

DEMYSTIFYING RETAIL GROWTH IN THE EUROPEAN UNION: A STUDY OF MARKET DYNAMICS USING CLUSTER ANALYSIS¹
Lucian Belascu, Alexandra Horobet, Alexandra Smedoiu Popoviciu, Razvan Zaharia, Rodica Milena Zaharia

ABSTRACT. Our paper explores non-specialised food and beverages predominant retail trade in EU between 2011 and 2019, aiming at identifying major determinants of growth and profitability at firm level. We have applied the k-means statistical algorithm to find homogeneous groups of retailers that are as dissimilar as possible from each other, and the machine-learning based random forests classification to reveal the most important drivers behind the performance of retailers included in different clusters. Despite a diverse picture of EU retailers' operational and financial attributes the main constituents of performance are the long-term debt level, efficiency and operational profitability margins. They evidence the importance of size and scale in the industry, accompanied by companies' ability to transform inventories into cash faster, and of stable, although low, profitability margins for long-term growth. Our results are relevant for retailers' business strategies in the post-pandemic world, considering extrinsic or intrinsic factors that drive industry and individual performance.

KEYWORDS:  European Union, retail industry, performance analysis, cluster analysis, random forests.

JEL classification:  L11, L25, L81, G32.

¹ Acknowledgement:  All authors have equally contributed to the research design, implementation, and manuscript writing. This study was possible thanks to a project supported by Lucian Blaga University of Sibiu & Hasso Plattner Foundation research grants LBUS-IRG-2020-06.