Dynamic Price Prediction for Revenue Management System in Hospitality Sector

Susanna Saitta, Susanna Saitta, Vito D’Amico, Giovanni Farinella

2024

Abstract

Dynamic pricing prediction is widely adopted in many different sectors. In receptive structures, the price of services (e.g. room price) is usually set dynamically by the Revenue Manager (RM) which continuously monitors the Key Performance Indicators (KPIs) recorded over time, together with market conditions and other external factors. The prices of services are dynamically adjusted by the RM to maximize the revenue of the receptive structure. This manual adjustment of prices performed by the RM is costly and time-consuming. In this work we study the problem of automatic dynamic pricing. To this aim, we collect and exploit a dataset related to real receptive structures. The dataset is annotated by revenue management experts and takes into account static, dynamic and engineered features. We benchmark different machine learning models to automatically predict the price that a RM would dynamically set for an entry level room forecasting the price in the next 90 days. The compared approaches have been tested and evaluated on three different hotels and could be easily adapted to other room types. To the best of our knowledge, the problem addressed in this paper is understudied and the results obtained in our study can help further research in the field.

Paper Citation

in Harvard Style

Saitta S., D’Amico V. and Farinella G. (2024). Dynamic Price Prediction for Revenue Management System in Hospitality Sector. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 218-228. DOI: 10.5220/0012707700003756

in Bibtex Style

@conference{data24,
author={Susanna Saitta and Vito D’Amico and Giovanni Farinella},
title={Dynamic Price Prediction for Revenue Management System in Hospitality Sector},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={218-228},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012707700003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Dynamic Price Prediction for Revenue Management System in Hospitality Sector
SN - 978-989-758-707-8
AU - Saitta S.
AU - D’Amico V.
AU - Farinella G.
PY - 2024
SP - 218
EP - 228
DO - 10.5220/0012707700003756
PB - SciTePress