1 / 18

Revenue Benefits of Sell-up Models

Revenue Benefits of Sell-up Models. Thomas Gorin, MIT AGIFORS RM Study Group Meeting New York, March 21-25, 2000. Outline. EMSRb Sell-up Models in a Small Network Revenue Gains Effect on Loads Hopperstad-B/W vs. Belobaba/Weatherford Comparisons with BW Revenues and Average Load Factors

nedra
Download Presentation

Revenue Benefits of Sell-up Models

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Revenue Benefits of Sell-up Models Thomas Gorin, MIT AGIFORS RM Study Group Meeting New York, March 21-25, 2000

  2. Outline • EMSRb Sell-up Models in a Small Network • Revenue Gains • Effect on Loads • Hopperstad-B/W vs. Belobaba/Weatherford • Comparisons with BW • Revenues and Average Load Factors • Sell-up in a Bigger Network • Revenue Gains T. Gorin, Sell-up Results

  3. Input Sell-up Rates to BW Model • In the Belobaba/Weatherford heuristic, estimates of sell-up rates are required as input: • We tested various input sell-up rate combinations and determined through literature review and experimentation, that “differential” input sell-up rates were the “best”. • The sell-up rates we refer to as “differential” are shown on the next slide. • These are not necessarily the “actual” sell-up rates in the PODS simulation. T. Gorin, Sell-up Results

  4. “Differential” Input Sell-up Rates T. Gorin, Sell-up Results

  5. Sell-up Models in Small PODS Network • The Network • 6 cities, 2 hubs • 2 competing airlines • 24 flight legs (12 per airline), 54 markets • 4 Fare Classes • Base Case • Both competitors use EMSRb Fare Class Yield Management (FCYM) • Neither airline accounts for sell-up T. Gorin, Sell-up Results

  6. T. Gorin, Sell-up Results

  7. T. Gorin, Sell-up Results

  8. T. Gorin, Sell-up Results

  9. Sell-up in a Small Network: Results • Revenue Gains • In the small network, accounting for sell-up leads to revenue gains for all the methods tested (compared to the base case EMSRb vs. EMSRb) • The incremental revenue gains from the B/W sell-up model are about 1% for EMSRb, and 0.64% to 1.25% for the different O-D methods tested. • These incremental gains are smaller when both airlines account for sell-up • Loads • Average Load Factors decrease as we account for sell-up (compared to the base case) • As expected, passenger loads in high fare classes increase T. Gorin, Sell-up Results

  10. HBW vs. BW Sell-Up Heuristics • Revenues • The following chart shows that, generally, the revenue gains with HBW are 0.1%-0.2% higher than with BW • But, in a few cases that we tested, the revenues were slightly lower with HBW • Loads • The Average Load Factors are lower yet when the airline uses HBW, as compared to the BW sell-up heuristic T. Gorin, Sell-up Results

  11. T. Gorin, Sell-up Results

  12. T. Gorin, Sell-up Results

  13. HBW vs. BW Sell-up Models: Summary • Revenues • In most of the cases tested, HBW leads to slightly higher revenue gains than the original BW heuristic • These revenue gains range between 0.1% and 0.2% • As Craig pointed out earlier, HBW is most beneficial when demand is high relative to capacity • Load Factors • HBW generally leads to slightly lower Average Load Factors than BW T. Gorin, Sell-up Results

  14. Use of Sell-up Models in a Larger PODS Network • The Network • 40 spoke cities, “20 on each side” • 2 hubs (one per airline, with interhub flights) • Unidirectional flow of traffic (West to East) • 3 banks per day per airline, 252 flight legs, 482 O-D markets T. Gorin, Sell-up Results

  15. Sell-up in Larger Network: Results • Revenues • In all cases tested, accounting for sell-up led to increased revenues over the base case (EMSRb vs. EMSRb) • Network Effects • The revenue gains over the “no sell-up” cases are similar to those observed in the smaller PODS network • Using the B/W heuristic, incremental gains of 1.35% for EMSRb FCYM, and about 1% for O-D methods T. Gorin, Sell-up Results

  16. T. Gorin, Sell-up Results

  17. Summary • Benefits of Accounting for Sell-up • Revenue gains from accounting for sell-up in EMSRb-based RM systems (FCYM, GVN, DAVN and HBP) • In our largest and most realistic network to date, accounting for sell-up added about 1% to revenue gains • Unclear to what extent these revenue gains come from: • Properly estimating passenger sell-up behavior • “Forcing” sell-up by increasing unconstrained demand forecasts and closing down low-fare availability • Limited alternative airline and path options in PODS T. Gorin, Sell-up Results

  18. Next Steps • How do we estimate sell-up rates in the real world? • One approach is to use the following approximation: • The sell-up rates generated by this formula are too high and lead to overprotection. • There is a sample bias. The higher the demand, the earlier the lower fare classes close down, and therefore, the higher the estimate of the sell-up rates. T. Gorin, Sell-up Results

More Related