Listing Segmentation - A Research on Airbnb's Listing in Amsterdam

Abstract

This thesis explores the transformative impact of Airbnb, a pioneer in the digital platform econ- omy, on the travel and lodging industry. This analysis focuses on Airbnb's activities in Amster- dam, investigating the company's innovative approach to connecting hosts and guests, which has disrupted traditional hospitality sectors and redefined tourism practices. The research utilizes data-driven insights, an essential element in modern decision-making pro- cesses, to comprehend Airbnb's distinct value offer. It examines how Airbnb, which operates in 191 countries, utilizes statistics obtained from diverse sources such as customer feedback and booking trends to improve its services and optimize its operations. The primary objectives of the research are threefold. Firstly, it aims to identify key factors influ- encing customer satisfaction, using customer ratings as an indicator. This analysis will provide insights into renters’ priorities during their Airbnb experiences. Secondly, the study seeks to de- velop diverse strategies for different listing segments in Amsterdam, aiming to optimize listing performance, thereby enhancing profitability and customer satisfaction. Lastly, the research aims to facilitate informed decision-making for Airbnb and its renters, potentially bridging the technical gap and generating sustainable value for both parties. The research aims to be relevant to the Airbnb dataset particular to Amsterdam, using selected data science approaches customized for this dataset. The results will be consistent with the study goals, providing a replicable approach for use in other cities and metropolitan regions.