Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
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.
Recommended Citation
Kim, Jungwoo; Kwon, Soonjae; and Park, Sung-Hyuk, "Adaptive Hotel Rate Prediction Using External Data: A Competitor-Driven Approach" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/da/emerging_markets/4
Adaptive Hotel Rate Prediction Using External Data: A Competitor-Driven Approach
Hilton Waikoloa Village, Hawaii
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.
https://aisel.aisnet.org/hicss-58/da/emerging_markets/4