The Impact of Events on Key Performance Indicators in the Hotel Industry: the Case of Lisbon

Abstract

The aim of the is to understand how Events relate to and impact on hotels’ Key Performance Indicators (KPIs) in the city of Lisbon. It also analyses the direct impact of the Web Summit in those hotels’ KPIs. It studies events and hotels’ daily data between 2013 to 2019. The period is divided in two parts: before,and after the Web Summit. The Events in both periods are analysed in terms of evolution and days of the week patterns. Simultaneously, the hotels’ KPIs are also analysed in both periods. The search was for variances that could support the fact that Web Summit was beneficial to the city of Lisbon. Two datasets were used making it possible to have a daily view of the major hotels’ KPIs combined with the events happening in Lisbon. Hotels’ KPIs are influenced by the strategies used by companies when seeking to improve revenues and profits. Revenue Management’s objective is to develop policies that result in profitable dynamic pricing strategies. Studying prices throughout 7 years was necessary to investigate if events are demand generators that influence and trigger dynamic pricing policies improving hotels’ results. Measures of central tendency were the basis of this analysis and the support for the results found. Results show that despite the expected growing results in occupancy levels in the city of Lisbon, after the first Web Summit, that growth becomes more accentuated. This evidence is even clearer regarding the Average Daily Rates (ADR) and Revenue per Available Room (RevPAR). Results also show that both ADR and RevPAR have distinctly increased after 2016. Their coefficients of variation are wider resulting from pricing dynamics which overall increase performance. Limitations come from the samples, that can display some bias regarding the KPIs and the limitation of events data. Regardless this is a starting point for further investigation concerning the relationships between demand generators and performance indicators, and comparisons with similar events’ impacts in different cities and destinations, and the long run impacts they might have. Major contributions are both to academics and practitioners. Academics can use this model of research and apply it to similar research or adapt it to studies using continuous data that need an assessment in its variation. Practitioners can use this model to assess their internal practices regarding pricing dynamics, comparisons to competition, or adjusting the model to fit their own needs.