1. 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

2. A Stylized Game With Realistic Numbers 1.The Simplest Case, Airlines A & Z 2.Case 2: Airline A is Preferred 3.Peak and Off-peak days 4.Full Spill model version 5.Airline A is “Sometimes” Preferred 6.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: 1.Discount @ $100, 144 passengers demand 2.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 1.Off peak at 2/3 of standard demand (120) 2.Standard demand of 180 total, as before 3.Peak day at 4/3 of standard demand (240) 4.Each season 1/3 of year 5.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-factor A =0.7; C-factor AZ =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 Airline A$/seat Load Factor Avg. Fare Simple$172100%$172 3-seasons$172100%$172 Spilled$16189%$181 2/3 Pref.$13385%$157 Airline Z$/seat Load Factor Avg. Fare Simple$ 8080%$100 3-seasons$ 6767%$100 Spilled$ 6868%$100 2/3 Pref.$ 9773%$133 Total A & Z$/seatLoad FactorAvg. Fare Simple$12690%$140 3-seasons$11983%$143 Spilled$11579%$146 2/3 Pref.$11579%$146

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 Total Demand MorningMiddayEvening Only17.5% AM15% PM15% any17.5%

17. Both A & Z in Morning A=36F, 64D Z=0F, 80D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$172 RAS=$ 80

18. Z “Hides” in Evening A=18.9F, 81.1D Z=17.1F, 62.9D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$138RAS=$114

19. A Pursues to Midday A=22.5F, 77.5D Z=13.5F, 66.5D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$145RAS=$107

20. Demand Up 50%, A uses 200 seats A=33.7F, 166.3D Z=20.3F, 49.7D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$134, CAS=$95RAS=$111; CAS=$126

21. Larger Airplanes are Cheaper Per Seat

22. Demand Up 50%, Z adds Morning A=27F, 73D Z=27F, 143D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$154, CAS=$126RAS=$112; CAS=$126

23. Demand Up 50%, A adds Morning A=40.5F, 157.4D Z=13.5F, 58.6D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$139, CAS=$126RAS=$ 99; CAS=$126

24. A adds Evening Instead A=54F, 146D Z=0F, 70D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$154, CAS=$126RAS=$ 70; CAS=$126

25. case A pax F A Pax D A Avg Fare A Rev/ Seat B pax F B Pax D B Avg Fare B Rev/ Seat A in morning B in morning 3664$170 080$100$80 A in morning B in evening 1981$137 1763$142$112 A in midday B in evening 2278$144 1466$133$105 A 200 in midday B in evening 35165$135 2151$158$114 A in midday B morn & eve 2872$156 28144$133$114 A morn & mid B in evening 42156$142$1411460$138$102 A morn & eve B in evening 56144$156 072$100$72

26. 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 1.Spill 2.Partial preference 3.Time-of-day distribution