Exploring patterns of evolution for successful global brands: A data-mining approach

Abstract

The sustainable development of a global brand needs to consider the balance between the economy, the environment, and society. Brands that want to be ranked among the best global brands over time need to have competitive strengths, but what defines a successful global brand’s profile is underexplored in the extant literature. This study adopts a data-mining approach to analyze the time-series data collected from Interbrand’s Best Global Brands ranking lists. A total of 168 global brands from 19 countries across 24 industries between 2001 and 2017 were examined. Using the affinity propagation clustering algorithm, this study identified certain patterns of brand evolution for different brand clusters, labeled as fast riser, top tier, stable, slow grower, decline, fall, potential, and so on. Finally, the rankings from 2018 to 2020 were also added to check the model’s predictive power. The findings of this study have important marketing implications.