This paper explores the evolution of the efficiency of a hotel chain and its implications in terms of competitiveness. A gravity model and a data envelopment analysis (DEA) are implemented in a dynamic framework. The former generates the tourism demand towards each hotel of the chain whereas DEA Window analysis is run to capture efficiency changes over time. A DEA based Malmquist productivity index is used to measure the productivity change and to decompose any change into the catching-up and the frontier-shift effect. We find that policies implemented according to DEA Window analysis increase the efficiency scores for the hotel chain and its competitiveness.