"Prognose." . . "Prognose" . "Regression analysis." . . "Niedrigpreisstrategie" . . "Science économique." . . "Analyse de régression." . . "Operations Research/Decision Theory." . . "Operations Research/Decision Theory" . "Mathematisches Modell." . . "Dynamische Optimierung" . . "Preisbildung." . . "Kaufentscheidung." . . "Kaufentscheidung" . "Preispolitik." . . "Preispolitik" . "Affaires." . . "Economics/Management Science." . . "Economics/Management Science" . "Logit-Modell." . . "Logit-Modell" . "Luftverkehrsgesellschaft" . . "Nachfrage." . . "Nachfrage" . "Pricing Mathematical models." . . "Prix Fixation Modèles mathématiques." . . "Lineares Regressionsmodell." . . "Lineares Regressionsmodell" . "Economics." . . . . "Billigflieger." . . "Billigflieger" . "Bayes-Statistik" . . "Luftfahrtindustrie." . . "Preismodell." . . "Preismodell" . . "Operationalizing dynamic pricing models bayesian demand forecasting and customer choice modeling for low cost carriers"@en . "Operationalizing dynamic pricing models bayesian demand forecasting and customer choice modeling for low cost carriers" . . . . . . . "Dynamic Pricing of services has become the norm for many young service industries - especially in today's volatile markets. 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. He proves that the development of the necessary forecasting models is indeed possible, i.e., through the usage of real-time data of online sales channels."@en . . . . . . "Operationalizing Dynamic Pricing Models Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers"@en . "Operationalizing Dynamic Pricing Models Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers" . . . "Dynamic Pricing of services has become the norm for many young service industries {u2013} especially in today{u2019}s volatile markets. 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. He proves that the development of the necessary forecasting models is indeed possible, i.e., through the usage of real-time data of online sales channels."@en . "Dynamic Pricing of services has become the norm for many young service industries {u2013} especially in today{u2019}s volatile markets. 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. He proves that the development of the necessary forecasting models is indeed possible, i.e., through the usage of real-time data of online sales channels." . . "Electronic books"@en . "Electronic books" . . . . . . . . . "Operationalizing dynamic pricing models : Bayesian demand forecasting and customer choice modeling for low cost carriers" . . . . . . . . . . "Operationalizing Dynamic Pricing Models : Bayesian Demand Forecasting and Customer Choice Modeling for Low Cost Carriers" . . . . . . . . . . . . . . . . . . . "Operationalizing dynamic pricing models Bayesian demand forecasting and customer choice modeling for low cost carriers" . . . "Konsumentenverhalten" . . "Betriebliche Preispolitik" . . "Luft- und Raumfahrtindustrie." . . "Bayes-Entscheidungstheorie." . . "Theorie" . .