scholarly journals Dynamic Pricing, Advance Sales and Aggregate Demand Learning in Airlines

2012 ◽  
Vol 60 (4) ◽  
pp. 697-724 ◽  
Author(s):  
Diego Escobari
Keyword(s):  
Dynamic Pricing ◽  
Aggregate Demand ◽  
Demand Learning ◽  
Advance Sales

Demand learning and dynamic pricing for multi-version products

2010 ◽  
Vol 11 (3) ◽  
pp. 303-318 ◽  
Author(s):  
Guillermo Gallego ◽  
Masoud Talebian
Keyword(s):  
Dynamic Pricing ◽  
Demand Learning

Temporal Distance and Price Responsiveness: Empirical Investigation of the Cruise Industry

2020 ◽  
Vol 66 (11) ◽  
pp. 5362-5388
Author(s):  
Mingyu Joo ◽  
Dinesh K. Gauri ◽  
Kenneth C. Wilbur
Keyword(s):  
Aggregate Demand ◽  
Temporal Distance ◽  
High Price ◽  
High Demand ◽  
Data Set ◽  
Cruise Industry ◽  
Late Season ◽  
Advance Sales ◽  
Demand Effect ◽  
Price Responsiveness

Temporal distance refers to the time between purchase and consumption in advanced-sales industries. We explore how the response of aggregate demand to price changes with temporal distance in a large, proprietary data set of Florida cruise prices, bookings, and product attributes. We offer the first evidence that cruise demand becomes more sensitive to price during the advance sales period, unlike extant findings in other settings. The results also show that demand is greatest late in the advance sales period, providing the first finding that a late-season high-demand period coincides with a late-season increase in aggregate price sensitivity. The high-demand effect more than offsets the high-price-responsiveness pattern, leading the firm to increase prices throughout the advance sales period. Although the data do not disentangle multiple competing explanations for the main findings, they are large enough to appear in simple data visualizations and robust enough to replicate across many model specifications, parameterizations, and partitions of the data. This paper was accepted by Matthew Shum, marketing.

scholarly journals Technical Note—Dynamic Pricing and Demand Learning with Limited Price Experimentation

2017 ◽  
Vol 65 (6) ◽  
pp. 1722-1731 ◽  
Author(s):  
Wang Chi Cheung ◽  
David Simchi-Levi ◽  
He Wang
Keyword(s):  
Dynamic Pricing ◽  
Technical Note ◽  
Demand Learning
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