TY - JOUR
T1 - Empirical Evidence of Revenue Management in the Cruise Line Industry
AU - Ayvaz-Cavdaroglu, Nur
AU - Gauri, Dinesh K.
AU - Webster, Scott
N1 - Publisher Copyright:
© The Author(s) 2017.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Revenue management (RM) has received considerable attention from both academic and business professionals. It encompasses several techniques regarding capacity allocation, pricing, and resource management of fixed, time-sensitive capacity. RM can be roughly divided into two categories defined by the control mechanism that increases revenue: capacity allocation or price optimization. Our work falls in the latter category. In our model, we allow for partial substitutability among products (e.g., a customer making a purchase decision may consider multiple alternatives—different departure dates, different destinations, different cabin types). We also include marketing expense in addition to prices as a lever for increasing revenue. These features are relevant to dynamic pricing in practice. The method is illustrated with booking data from a cruise company, yielding optimal advertising and prices for 300 products. The application of the model results in an increase in revenue in the range of 8%–20%.
AB - Revenue management (RM) has received considerable attention from both academic and business professionals. It encompasses several techniques regarding capacity allocation, pricing, and resource management of fixed, time-sensitive capacity. RM can be roughly divided into two categories defined by the control mechanism that increases revenue: capacity allocation or price optimization. Our work falls in the latter category. In our model, we allow for partial substitutability among products (e.g., a customer making a purchase decision may consider multiple alternatives—different departure dates, different destinations, different cabin types). We also include marketing expense in addition to prices as a lever for increasing revenue. These features are relevant to dynamic pricing in practice. The method is illustrated with booking data from a cruise company, yielding optimal advertising and prices for 300 products. The application of the model results in an increase in revenue in the range of 8%–20%.
KW - cruise industry
KW - empirical application
KW - multinomial choice model
KW - revenue management
UR - http://www.scopus.com/inward/record.url?scp=85054977352&partnerID=8YFLogxK
U2 - 10.1177/0047287517737178
DO - 10.1177/0047287517737178
M3 - Article
AN - SCOPUS:85054977352
SN - 0047-2875
VL - 58
SP - 104
EP - 120
JO - Journal of Travel Research
JF - Journal of Travel Research
IS - 1
ER -