QUALITY OF SERVICE INDEX Advanced Presented by: David Dague ICF SH&E Principal 2012 Air Service Data and Planning Seminar January 22-24, 2012 0
Workshop Agenda Introduction QSI/CSI Overview QSI Uses Historical Development of QSI Methodologies Factors Affecting Current QSI Application of QSI Methodologies Discussion 1
Introduction 2
Air Service Development is a Key Economic Driver for Communities of All Sizes Estimated Annual Economic Impact as Filed in the 2007 U.S. China Route Allocation Proceeding Dallas/Ft. Worth Beijing Detroit Shanghai PEK $180 Million DFW PVG $160 Million DTW Washington Beijing PEK $275 Million WAS 3
U.S. Airports Don t Compete with Each Other for International Aircraft They Compete Against Airports All Over the World From London Heathrow, the B787 Reaches Most of the World Only Southern Australia Falls Outside Its 8,500 mi. Range Shanghai Manila Tokyo Honolulu Vancouver Los Angeles LHR Mumbai Kuala Lumpur Cairns Perth Sydney Santiago Rio de Janeiro Johannesburg 4
Future U.S. Passenger Growth is Expected to be Concentrated in International Markets FAA Aerospace Forecast Passengers: 2010-2030 Boeing Current Market Outlook RPKs: 2009-2029 Average Annual Growth Average Annual Growth 6% 5% 5.1% 5.1% 6% 5% 4.8% 5.3% 4.6% 4% 4.0% 3.4% 4% 3% 2.5% 3% 2.8% 2% 2% 1% 1% 0% Domestic Atlantic Canada Latin America Pacific 0% Within North America Atlantic Latin America Asia/Oceania 5
Capacity Levels in 2012 Are Still 14% Lower than the Peak in 2005 Weekly Seats (Millions) from U.S. Airports January 2005 January 2012 21 20 19 2005 18 17 2012 16 15 Source: OAG Schedules. 6
With the Decline in Capacity, Load Factors Have Reached Historic Highs Average Domestic Load Factors at U.S. Airports CY 2000 YE 2Q 2011 85% 80% 75% 70% 65% 68% 66% 67% 69% 71% 73% 75% 76% 75% 76% 76% 78% 60% 55% 50% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 Source: USDOT T100 Databank, via Database Products. 7
After Reaching Bottom in the First Half of 2009, the Cuts in Capacity Have Allowed Carriers to Increase Average Fares Average Fare, US Domestic Markets Q1 2008 Q2 2011 $180 $170 $160 $150 $140 $130 $120 1Q08 2Q08 3Q08 4Q08 1Q09 2Q09 3Q09 4Q09 1Q10 2Q10 3Q10 4Q10 1Q11 2Q11 Source: O&D Survey 8
Nationally, the Smaller Airports Have Seen the Steepest Declines in Scheduled Airline Capacity Weekly Seats Departures by Airports August 2006 August 2011 16,000,000 14,000,000 12,000,000 10,000,000 8,000,000-3% August 2006 August 2011 6,000,000 4,000,000 2,000,000-18% -9% 0 Large Hubs Medium Hubs Small/Non Hubs Source: OAG Schedules. 9
ICF SH&E Surveyed Airline Planning Executives to Understand What Has the Most Impact in Developing New Service 6 US Legacy Carriers 4 US LCCs 3 European Carriers 2 Middle East/Gulf Carriers 4 Latin American Carriers 10
As Important as Developing Thoughts on Strategic Fit and Providing New Market Information, Traffic Forecasts are Still Critical Data Valued in Presentations by Category of Carrier US Legacy US Low Cost Europe Based Middle Eastern Based Latin American Based Total Traffic Forecasts 6 (100%) 4 (100%) 2 (100%) 2 (100%) 4 (100%) 19 (100%) Airport Service Demographics Strategic Fit into Expansion Plans Primary Research (Surveys) P and L Proformas 6 (100%) 4 (100%) 2 (100%) 1 (50%) 0 (0%) 13 (68%) 4 (66%) 3 (75%) 2 (100%) 1 (50%) 3 (75%) 13 (68%) 6 (100%) 3 (75%) 2 (100%) 1 (100%) 0 (0%) 12 (63%) 1 (16%) 3 (75%) 1 (50%) 1 (50%) 1 (25%) 7 (37%) 11
Airline Planners Are Looking for Interesting and Insightful Analysis Unique Information offering a new, distinctive viewpoint and analysis is more impactful than showing information an airline already knows Accurate Data airlines do not believe inflated revenues or growth, illustrating realistic data estimates makes a more compelling case Comparative Analysis 95% of airlines surveyed compare consultants analysis to their own however they like having an analysis from a third party In House Analysis all airlines perform their own analysis and have access to the same industry data as an airport 12
Airlines Choose New Markets Based on Quantifiable Facts Existing Passenger Demand, Strong Local Component Fare Environment Access to Corporate Travelers Right Fit With Existing Network Optimal Equipment Availability Balanced Originating Demand Long-Term Sustainable Return 13
To Make Your Airport s New Service Proposition Stand Out Amongst Your Competitors Nationwide or Worldwide, Credible QSI Results Are Key To Set Your Airport Apart, You Should Only Go After Service that the Market Can Support With a Straight Face The Quantity and Quality airline service has long been measured by Quality of Service Index ( QSI ) methodologies QSI Results Will Quantify Market Share, Predicted Passenger Traffic and Ultimately Demonstrate Route Profitability for the Carrier Since Each Airline Uses Its Own Model to Predict Passenger Behavior and Traffic, Having Your Own Opinion Allows You to Engage in Discussion with the Carrier 14
QSI Overview 15
QSI Methodology is Straightforward Until it s Time to Adjust for the Real World 1. Determine factors that passengers consider when choosing flights 2. Incorporate these factors in quantitative weighting system 3. Calibrate the coefficients based on hard empirical data 4. Apply coefficients to predict how traffic will divide between competing airlines and services 16
QSI Uses 17
QSI is Used by Airline Route Planners to Answer a Variety of Questions All Targeted to Predicting Passenger Behavior How many passengers will my flight carry in this market at these times? How much will the market be stimulated with this new service? What percentage of the overall market will I capture? Against whom am I competing? From whom will this flight steal market share? Do I want to take that carrier on? Am I taking passengers off my own flights elsewhere? Is that worth it? What is the makeup of the passengers onboard? Local Connecting 18
QSI Results Help You Decide on Which Markets Offer the Most Potential and Aid Airline Route Planners in Making Decisions How much money will this flight make? Revenue by passenger type Profitability Does it contribute positively to my network? How does this flight s performance compare to the other flights that are possible with this aircraft? 19
Historical Development of QSI Methodologies 20
The CAB QSI methodology was initially developed to determine expected changes in traffic due to changes in service Originally developed to determine expected gain or loss in traffic due to transfer of routes from Trunk to Local Service airlines % Increase in Traffic 30 25 QSI Traffic Stimulation Formula Later refined to evaluate airline service proposals in route cases involving new or additional competitive services Original QSI methodology used weighting factors for aircraft type and number of stops and were applied only to direct flights CAB Staff conducted many analyses of traffic stimulation associated with increase in QSI 20 15 10 5 0 0 10 20 30 40 50 60 70 80 90 100 % Increase in QSI Traffic Stimulation formula Traffic Chg = QSI Chg / (.3741 + (.5561 x QSI Chg) /.075 21
The CAB QSI methodology has been refined and adapted to the current air transport environment, but many of the basis element are the same Primary reliance on quantifying competitive value of airline services based on published schedules (OAG and Innovata data) Significant refinements to measure relative value of different types of connections On-line, code share and interline service Trip times and travel circuity More difficult customization of QSI models Differences in fares offered Turnaway / yield management algorithms Adaption to international markets Capture of airline and airport preference weights QSI or similar market share models are used by nearly all airlines in the route system planning process Most are similar in logic and structure, but incorporate different weighting factors 22
Factors Affecting Current QSI 23
Currently, Factors Affecting QSI are the Result of a Changing Industry and Competitive Environment Emergence of Airline Networks and Connecting Opportunities In many markets, connecting routings carry a significant share of the market This required developing a weighting system for connecting flights In some competitive situations, aircraft size is not an accurate predictor of market share Point-to-point carrier competes against a network carrier Network carriers seat capacity may be diluted with connecting traffic from outside O&D market Often this phenomenon is handled by adjusting the aircraft size weights or assigning a premium to the point-to-point carrier 24
Currently, Factors Affecting QSI are the Result of a Changing Industry and Competitive Environment Multiple Airport Markets Multi-airport markets have become more prevalent as congestion and delays have constrained service growth at primary airports serving major cities NYC, LA, SFO Bay Area, Washington/Baltimore, Chicago, etc. Traffic forecasts need to reflect airport choice on a market-by-market basis Ground access, comparative fare levels and service availability all influence the distribution of passengers between airports Credible forecasts should reflect empirical data how has traffic distribution changed with service additions in existing markets? 25
Currently, Factors Affecting QSI Are the Result of a Changing Industry and Competitive Environment Array of Fares Offered In the deregulated environment, carriers often compete based on fares QSI formula doesn t generally reflect fare differences Fare differentials can be captured by assigning a QSI premium to low-cost airlines This approach can be used to account for partial or full matching of reduced fares Predicted changes in O&D market fare levels are also necessary to estimate traffic stimulation resulting from new low-cost carrier entry 26
Application of QSI Methodologies 27
Developing and applying QSI methodology to estimate expected market shares is normally a two step process 1 2 Baseline QSI Estimates Service Frequency Aircraft type /seat capacity No. of Stops Connection penalty (on-line or interline) Elapsed time factor Routing circuity Calibration Process Share Premium or Gap Analysis Airline preference factor (e.g., hub dominance, loyalty programs, low or high fares) Airport preference factor (near-in versus more remote airport) Time of day factor Pre-emption of seats in local market due to connecting passenger traffic Passenger turn-away due to high load factors Problem of many poor connections picking up too much QSI share points 28
Calibration process is based on share premium and share gap analysis for comparable markets where empirical data is available Measure actual market shares by carrier for selected time periods against baseline QSI service shares Determine magnitude and pattern of variances Make logical assumptions of the reasons for premium and/or gap variances and make adjustments to QSI weighting factors Keep adjustments as simple as possible Ability to calibrate depends on data that is available Ideal situation is to have both industry and carrier detailed data 29
Examples of typical adjustments to baseline QSI results Problem: Many poor connections pick up too much weight in total market QSI Adjustment: Tighten rules for including flights (e.g., circuity, elapsed times, minimum share for inclusion, roundtrip requirement, etc.) Problem: LCC carrier traffic share consistently above its QSI share Check fare differential versus legacy carriers add carrier preference factor or share adjustment outside of QSI model Check amount of connecting passengers for legacy carriers adjust legacy carriers aircraft capacity value downward Problem: Hub carrier s share variance in hub markets are consistently high or low Apply an airline premium or penalty factor to carrier s baseline QSI Adjustment factors are often made outside the model for transparency 30
QSI methodology and expected traffic stimulation The QSI methodology measures changes in the quantity and quality of service it does not directly estimate traffic changes Separate analysis can be performed that quantify and/or account for the stimulation in traffic due to changes in service Traffic changes from traffic data bases Service changes using the QSI methodology for measurement Despite its age, The CAB QSI traffic stimulation formula often provides a good estimate of expected changes in passengers associated with the provision of new services 1st nonstop service in a market is often not well predicted by QSI, and analysis based on a comparable market approach is recommended 31
Discussion 32
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