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The Value of User Interaction with Revenue Management Systems

The Value of User Interaction with Revenue Management Systems. Rick Zeni US Airways AGIFORS Reservations and Yield Management Study Group April, 2002. What is AirMax Day?. AirMax is Sabre’s bid price O&D system used at US Airways US Airways does not use AirMax demand forecasting

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The Value of User Interaction with Revenue Management Systems

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  1. The Value of User Interaction with Revenue Management Systems Rick Zeni US Airways AGIFORS Reservations and Yield Management Study Group April, 2002

  2. What is AirMax Day? • AirMax is Sabre’s bid price O&D system used at US Airways • US Airways does not use AirMax demand forecasting • AirMax Day was a departure date that was completely controlled by AirMax with no user adjustments

  3. AirMax Day Objectives • Quantify the value of analyst interaction with the system • Determine where their efforts make the most positive impact • Provide feedback to the analyst so they can effectively prioritize their workload • Evaluate system performance without the effects of analyst interaction

  4. AirMax Day • Wednesday, August 16, 2000 • Study began 12 weeks prior to departure • System had complete control of all domestic flights • All user adjustments were removed • No user adjustments were made throughout the study period • Various performance statistics were tracked

  5. Analsyst Days • The previous and following Wednesdays • Tuesdays and Thursdays surrounding all three Wednesdays • All three Wednesdays (8/9, 8/16, 8/23) had approximately the same projected load factor at the beginning of the study

  6. AirMax Day and Analyst Days

  7. Revenue on Tuesdays and Thursdays • Determine whether there were any factors influencing demand during these periods that might obscure the results of Airmax Day • Revenue for the Tuesday and Thursday during the week of Airmax Day was lower than in the previous and following week • There is an indication that demand during the week of Airmax Day was lower than the previous and following weeks

  8. Tuesday Revenue

  9. Thursday Revenue

  10. Wednesday Revenue

  11. Wednesday Revenue

  12. Wednesday Revenue

  13. Wednesday Revenue

  14. Wednesday Revenue

  15. Load Factors • Although the demand for AirMax Day was probably lower than for the comparison Wednesdays, the number of bookings for AirMax Day was higher • The reservation build for AirMax Day tended to be greater than that for the comparison Wednesdays • Among the three Wednesdays, AirMax Day had the highest load factor and the lowest total revenue

  16. Reservations Build for Wednesday Departures

  17. Load Factors for Wednesday Departures

  18. Percentage of Flights Sold Out too Soon • Tendency for AirMax Day flights to sell out sooner than flights on the analyst Wednesdays • This is an indication that AirMax was not protecting enough seats for high fare passengers.

  19. Flights Sold Out too Soon

  20. Overbooking Levels • Throughout most of the experiment period, the overbooking levels for AirMax Day were above those for the analyst days • This indicates that the system takes a more aggressive approach to overbooking than the analysts are generally comfortable with • Users have a greater tendency to lower cabin authorizations than raise them • Oversales were highest on AirMax day (none involuntary)

  21. Overbooking Levels

  22. Oversales

  23. Spoilage • The number of spoiled seats for AirMax Day exceeded those for the other Wednesdays • Overbooking levels for AirMax Day could have been set even higher with a low risk of additional oversales

  24. Posted Flights 146 172 107 Total Spoilage

  25. Planned and Posted Load Factors • Low planned and posted load factors on AirMax Day indicate further overbooking opportunities

  26. Planned and Posted Load Factors

  27. Total Revenue

  28. Revenue Improvement from Analysts • Revenue on AirMax Day was lower than the average of the previous and following Wednesdays by 7.6%. • However, revenue on the Tuesdays and Thursdays during AirMax week was lower than the average of the previous Tuesdays and Thursdays by 4.7%. • The portion of the decreased revenue that can be attributed to the lack of user adjustments is 7.6-4.7=2.9%

  29. What Advantage do Analysts have over the System? Analysts: • Hedge against the future • Consider sell-up • Consider spill and recapture • Have an intuitive feel for market behavior • Their demand predictions are not constrained • Consider competitive actions • Consider schedule changes

  30. Recommendations to the Users Forecast Adjustments—Continue the practice of adjusting service level demand forecasts, especially when the adjustment is an increase to demand. Overbooking Levels—Identify flights where the overbooking level may be increased without significantly increasing the risk of oversales. The data indicates that many of these flights exist.

  31. Conclusions • Revenue improves up to 3% from analyst interaction • The revenue improvements on the analyst days may be mostly attributed to increases to the system demand forecast. • Revenue performance could be further enhanced with more aggressive overbooking levels. • Results based on only one day should be used with caution!

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