An increasing amount of attention is paid by the media, and political and business leaders to national competitiveness indices. As globalisation increases and the difficulties of the financial crisis linger on, leaders look towards global benchmarks such as the World Economic Forum‟s Global Competitiveness Index to make policy and resource allocation decisions. Despite this emphasis on national competitiveness, how this translates to economic growth prospects is not well understood, and a universally accepted economic growth model continues to elude macroeconomists. The research seeks to understand whether a more detailed assessment of the Global Competitiveness Index‟s twelve competitiveness pillars can improve its explanatory power for economic growth, by investigating patterns of competitiveness performance from both static and dynamic perspectives. Data was collated over the period 2007-2013 for 118 countries. A hierarchical cluster analysis was performed to segment countries according to homogeneous competitiveness patterns, followed by stepwise multiple regression modelling on the total sample and the resulting clusters in order to assess impacts on adjusted R-squared values. Regressions were performed on stock and flow values for twelve country competitiveness variables. The results show that the cluster analysis coupled with the specified multiple regression models significantly improved the explanatory power of the selected competitiveness variables on economic growth, except for the least competitive countries, where further research is needed to uncover their true drivers of competitiveness.