Operationalizing dynamic pricing models : Bayesian demand forecasting and customer choice modeling for low cost carriers
Author(s)
Bibliographic Information
Operationalizing dynamic pricing models : Bayesian demand forecasting and customer choice modeling for low cost carriers
（Gabler research）
Gabler, 2011
Available at 2 libraries
Note
Includes bibliographical references (p. [331]-351)
Description and Table of Contents
Description
Steffen Christ shows how theoretic optimization models can be operationalized by employing self-learning strategies to construct relevant input variables, such as latent demand and customer price sensitivity.
Table of Contents
- Dynamic Pricing
- Forecasting Latent Demand
- Self-Learning Linear Models
- Estimating Price Sensitivity
- Discrete Customer Choice Analysis
by "Nielsen BookData"