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How Airlines Compete Fighting it out in a City-Pair Market

How Airlines Compete Fighting it out in a City-Pair Market. William M. Swan Chief Economist Seabury Airline Planning Group Nov 200 Papers: http://www.seaburyapg.com/company/research.html Contact: bill.swan@cyberswans.com. A Stylized Game With Realistic Numbers.

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How Airlines Compete Fighting it out in a City-Pair Market

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  1. How Airlines CompeteFighting it out in a City-Pair Market William M. Swan Chief Economist Seabury Airline Planning Group Nov 200 Papers: http://www.seaburyapg.com/company/research.html Contact: bill.swan@cyberswans.com

  2. A Stylized GameWith Realistic Numbers • The Simplest Case, Airlines A & Z • Case 2: Airline A is Preferred • Peak and Off-peak days • Full Spill model version • Airline A is “Sometimes” Preferred • Time-of-day Games

  3. Model the Fundamentals • Capture all relevant characteristics • Different passengers pay high and low fares • Different passengers like different times of day • Different passengers have less or more time flexibility • Airlines block space to accommodate higher fares • Demand varies from day to day • Demand that exceeds capacity spills • to other flights, if possible • Airlines can be preferred, one over another • Passengers have a hierarchy of decisions • Price; Time; Airline • Bigger airplanes are cheaper per seat than smaller ones

  4. Example Simple but True • Example here as simple as we could devise • Covers all fundamentals • Uses simplest possible distributions • Time of day • Fares paid • Airline choices • Demand variations • Choice Hierarchy • Means and Standard Deviations are realistic • Each is a “cartoon” • Reflects industry experience with detailed models • Based on best practices at • AA; UA; Boeing; MIT • Other airlines that were Boeing customers • University contacts

  5. The Simplest Case: Airlines A & Z • Identical airlines in simplest case • Two passenger types: • Discount @ $100, 144 passengers demand • Full-fare @ $300, 36 passengers demand - Average fare $140 • Each airline has • 100-seat airplane • Cost of $126/seat • Break-even at 90% load, half the market

  6. We Pretend Airline A is Preferred • All 180 passengers prefer airline A • Could be quality of service • Maybe Airline Z paints its planes an ugly color • Airline A demand is all 180 passengers • Keeps all 36 full-fare • Fills to 100% load with 64 more discount • Leaves 80 discount for airline Z • Average A fare $172 • Revenue per Seat $172 • Cost per seat was $126 • Profits: huge

  7. Airline Z is not Preferred • Gets only spilled demand from A • Has 80 discount passengers on 100 seats • Revenue per seat $80 • Cost per seat was $126 • Losses: huge “not a good thing”

  8. Preferred Carrier Does Not Want to Have Higher Fares • Pretend Airline A charges 20% more • Goes back to splitting market evenly with Z • Profits now 20% • Profits when preferred were 36% • 25% extra revenue from having all of full-fares • 11% extra revenue from having high load factor • Airline Z is better off when A raises prices • Returns to previous break-even condition

  9. Major Observations • Average fares look different in matched case: • $172 for A vs. $80 for Z • Preferred Airline gains by matching fares • Premium share of premium traffic • Full loads, even in the off-peak • Even though discount and full-fares match Z

  10. More Observations • “Preferred wins” result drives quality matching between airlines • Result is NOT high quality • Everybody knows everybody tries to match • Therefore quality is standardized, not high • Result is arbitrary quality level • add qualities that people value beyond cost?

  11. Variations in Demand Change Answer • Consider 3 seasons, matched fares case • Off peak at 2/3 of standard demand (120) • Standard demand of 180 total, as before • Peak day at 4/3 of standard demand (240) • Each season 1/3 of year • Same average demand, revenue, etc. • Off-peak A gets 24 full-fare, 76 discount • Z gets only 20 discount • Peak A gets 48 full-fare, 52 discount • Z gets 100 discount, still below break-even • Z is spilling 40 discounts, lost revenues • Overall, A at $172/seat and Z at $67 • Compared to $172 & $80 in simple case • Some revenue in the market is “spilled’ – all from Airline Z

  12. Full Spill Model Case • Spill model captures normal full variations of seasonal demand • Spill is airline industry standard model* • Spill model exercised 3 times: • Full-fare demand against A capacity • For full-fare spill, which is zero • Total demand against A capacity • Spill will be sum of discount and full-fare • Total demand against A + Z capacity • Spill will be sum of A and Z spills • K-cyclic = 0.36; C-factorA=0.7; C-factorAZ=0.7 • Results • A $11/seat below 3-season case • Z $1/seat better than 3-season case • Qualitatively the same conclusions: A wins big; Z looses. *See Swan, 1997

  13. Airline A is “Sometimes” Preferred • 2/3 of customers prefer airline A • 1/3 of customers prefer airline Z • Full spill case • Results: • A has 85% load; $133/seat—15% above avg. • Z has 73% load; $97/seat—15% below avg. • If Z is low-cost by 15%, can break even • This could represent new-entrant case

  14. Cases of Increasing Realism

  15. Time-of-Day Games • What if 2/3 preferred case was because Z was at a different time of day? • 1/3 of people prefer Z’s time of day • 1/3 of people prefer A’s time of day • 1/3 of people can take either, prefer Airline A’s quality (or color) • Ground rules: back to simple case • No peak, off-peak spill • Back to 100% maximum load factor • System overall at breakeven revenues and costs • Simple case for clarity of exposition • Spill issues add complication without insight • Spill will merely soften differences

  16. Simple Time-of-Day Model

  17. Both A & Z in MorningA=36F, 64DZ=0F, 80D RAS=$ 80 RAS=$172

  18. Z “Hides” in EveningA=18.9F, 81.1DZ=17.1F, 62.9D RAS=$138 RAS=$114

  19. A Pursues to MiddayA=22.5F, 77.5DZ=13.5F, 66.5D RAS=$145 RAS=$107

  20. Demand Up 50%, A uses 200 seatsA=33.7F, 166.3DZ=20.3F, 49.7D RAS=$134, CAS=$95 RAS=$111; CAS=$126

  21. Larger Airplanes are Cheaper Per Seat

  22. Demand Up 50%, Z adds MorningA=27F, 73DZ=27F, 143D RAS=$154, CAS=$126 RAS=$112; CAS=$126

  23. Demand Up 50%, A adds MorningA=40.5F, 157.4DZ=13.5F, 58.6D RAS=$139, CAS=$126 RAS=$ 99; CAS=$126

  24. A adds Evening InsteadA=54F, 146DZ=0F, 70D RAS=$154, CAS=$126 RAS=$ 70; CAS=$126

  25. Summary and Conclusions • Airlines have strong incentives to match • A preferred airline does best matching prices • A non-preferred airline does poorly unless it can match preference. • A preferred airline gains substantial revenue • Higher load factor in the off peak • Higher share of full-fare passengers in the peak • Gains are greater than from higher prices • A less-preferred airline has a difficult time covering costs • Preferred airline’s advantage is reduced by • Spill • Partial preference • Time-of-day distribution

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