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Pricing Simulation Natascha Jung, Senior Operation Research Specialist

Presentation, AGIFORS 2000 24th March 2000 in New York. Pricing Simulation Natascha Jung, Senior Operation Research Specialist. Proven solutions for open skies. Agenda. Supported Pricing Processes. Pricing Simulation Modeling. Summary. Pricing Simulation in Reactive Pricing.

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Pricing Simulation Natascha Jung, Senior Operation Research Specialist

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  1. Presentation, AGIFORS 200024th March 2000 in New York Pricing Simulation Natascha Jung, Senior Operation Research Specialist Proven solutions for open skies

  2. Agenda Supported Pricing Processes Pricing Simulation Modeling Summary

  3. Pricing Simulation in Reactive Pricing Pricing Simulation ManualMatching AutoMatching Trigger Decision Action AutomatedDecision Decision Support Statistics Competitor Fare Action No No Don’t know ! Yes Yes Automated Distribution

  4. Pricing Simulation in Proactive Pricing Pricing Simulation Evaluate Scenario Trigger Possible Actions Simulation/ Action Evaluation Open Capacity Special Event Pricing What-If Modeling Automated Distribution Yes New Destination ...

  5. Agenda Supported Pricing Processes Pricing Simulation Modeling Summary

  6. What should a Pricing Simulation Model do? • Simulating the impact of • Amount Changes • Condition and Restriction changes • New or Canceled Fares • on • Market Share • Passenger Demand • Revenue • by considering • Cannibalization • Competitor Reaction • Market Stimulation • Revenue Management effects

  7. How should a Pricing Simulation Model work ? Market Share Passenger Demand Revenue Market Stimulation Revenue Management Simulation Competitor Reaction Unconstrained Processing Constrained Processing Constrained Processing Unconstrained DemandModel RevenueCalculation Price Elasticity Model

  8. Price Elasticity Model Price Elasticity Model

  9. What should a Price Elasticity Model do? Price Elasticity Model Depiction of Passenger Behavior: Passenger books on a specialTicketing Day and chooses among the offered fares, which are valid on his Travel day - The day, on which passenger wants to fly • Customer makes decision along several attributes of the fare

  10. How could passenger behavior be depicted? Price Elasticity Model • Qualitative Choice Model • Multinomial Logit Model • Choice Set: Applicable fares per Ticketing/Travel - combination • Day Application • Advance Purchase • Booking Class open (Constrained Processing) • Attribute Set: • Compartment • Carrier • Amount • Minimum/Maximum Stay

  11. The difficulties of the Price Elasticity Model ...... Price Elasticity Model • Independence of Irrelevant Alternatives (IIA - Property) • Customer Heterogeneity • Calibration data for estimation ofthe parameters

  12. How could IIA - Property be avoided ? Price Elasticity Model • Selection of the choice set dependent on the Ticketing - and Travel day • Clustering of the choice set • Compartment • Carrier • Amount • Minimum/Maximum Stay

  13. Customer Heterogeneity -Which passengers might behave homogenous? Price Elasticity Model Spilled Passengers (Constrained Processing) Spilled Passengers (Constrained Processing) • Business passengers • Leisure passengers • Stimulated passengers

  14. How could the Price Elasticity Model be estimated? Price Elasticity Model • For each passenger type • Passenger Preference Parameter • Compartment • Carrier Preference/Schedule Quality • Amount • Minimum Stay • Maximum Stay • Calibration Input Data • Merge of MIDT- and ATPCO Data

  15. Revenue ManagementSimulation Revenue Management Simulation

  16. Why Revenue Management Simulation? Passenger Preference Airline Interest

  17. What should a Revenue Management Simulation do ? RevenueManagementSimulation • Optimization of revenue by determining the size of booking classes • Capacities • expected Demand Depiction the yield management impact on the passenger behavior

  18. How should a Revenue Management Simulation work? RevenueManagementSimulation • Algorithm for optimizing revenue • Back Loop to Price Elasticity Modelfor depicting influence of the yield management on passengerbehavior

  19. The difficulties of Revenue Management Simulation .... RevenueManagementSimulation • The need of simulating revenue management effects for all carriers • Optimization Algorithm • Re - Calculation of Protection Level • Estimation of expected Demand and Capacities

  20. How could the difficulties be solved? RevenueManagementSimulation • Optimization Algorithm • Usage of a common Algorithm • nested EMSRb • Re- Calculation of Protection Level • Re-Calculation in view to the results of the PEM in the Back-Loop • Estimation of expected Demand/Capacities • Estimation with • MIDT Data • Actual Flown Data • OAG

  21. Agenda Supported Pricing Processes Pricing Simulation Modeling Summary

  22. Summary • Pricing Simulation should take into account all essential Pricing Decision Rules • Competitor Reaction • Cannibalization • Market Stimulation • Revenue Management Effects The model should be designed along data sources available in practice

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