Adaptive Hotel Rate Prediction Using External Data: A Competitor-Driven Approach
Files
Date
2025-01-07
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
1817
Ending Page
Alternative Title
Abstract
Traditional dynamic pricing in the hotel industry has predominantly relied on detailed occupancy data, which often disadvantages smaller independent hotels due to their limited access to such comprehensive datasets. This study presents CAMP (Competitor-based Accommodation Market Pricing), an innovative model designed to overcome these limitations by forgoing occupancy data. Instead, CAMP leverages competitor pricing and a range of external factors, including regional search trends, weather conditions, and economic indicators. CAMP accurately identifies competitors through hierarchical clustering and advanced regression techniques and optimizes pricing strategies. By providing a robust and flexible revenue management tool, CAMP enables independent hotels to adjust their pricing strategies dynamically, maximize revenue, and improve social welfare by balancing profitability and consumer satisfaction. This research fills a critical gap in revenue management literature and introduces a novel, data-driven approach to dynamic pricing that anticipates market demands without relying on occupancy data.
Description
Keywords
Technology and AI in Emerging Markets, competitor analysis, dynamic pricing, hotel industry, machine learning, revenue management
Citation
Extent
10
Format
Geographic Location
Time Period
Related To
Proceedings of the 58th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.