Isolating the Internet Price Effect.

Bill Brunger,SVP, Network,

Continental Airlines (ret.) and

Doctoral Candidate,Case Western

Reserve University

Motivation:So What Has Happened?(Some level of causality seems obvious)

Percent of Continental Airlines Domestic Tickets sold through Internet

1998 1999 2000 2001 2002 2003 2004 2005

Continental Airlines' Average Yield in 2004 Cents

1998 1999 2000 2001 2002 2003 2004 2005

Industry Structure obviously changed…

Domestic US Low Cost Carrier growth.

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Easier to See by Taking Southwestand America West Out…

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Wave I

Wave II

Rise of Internet

Most customers believe that Airline Pricing Behavior Changed

• But I’m not sure…• We still match and go on sale and run off-peak

sales and amuse ourselves with our alphabet soup of fares and restrictions…

• And DCA3 and PFS et al. limited “Internet-only” and channel-specific activity…

• There have been relatively few innovations: Priceline/Hotwire, weekly specials, clubs,…

The Costs of Distribution Definitely Changed…

Continental Airlines' Average Distribution Expense as a Percent of Fare Paid

1998 1999 2000 2001 2002 2003 2004

But other cost changes overwhelmed it…

Crude Oil and Jet Fuel Price TrendWTI: $66.35Crack Spread: $58.01Jet Fuel: $124.36

September 28, 2005

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W TI Crude

Distribution Became More Concentrated!!!!

We Had Expected

Fragmentation

And, most importantly, Customers Changed

Expectations

& Behaviors

Preliminary Qualitative Study• Method

– 15 open-ended interviews; all referrals; mixed demography and geography, and

– All were “Experienced travelers”• All had purchased in the pre-Internet time• Limitation: homogeneity of age; all between about 30 and 60.• Advantage: Perspective; Most previous studies have been on

students (who never used a TA) or clients of a particular firm

• Data – Analyzed using Glaser and Strauss– Initial set of codes from literature (11): search duration,

dynamics, range, timing, fare levels, fit, loyalty, and adjectives and descriptors of control, trust, choice and cooperation; evidence of co-production

– Final set (50) cluster into 6 categories

weatherhead.case.edu/edm/archive/details.cfm?id=10288&topic=23Or Google: Brunger Impact Airline

Five Findings1. Switch was not perceived primarily about lower

fares; about control & transparency/search breadth.

2. Unexpectedly, the actual search protocols that most respondents perform are quite simple.

- Effects of trip type, FFP status & demography? 3. Some formed new levels of “involvement” with the

Search. Some became “search enthusiasts”. 4. For some, enabled, facilitated, reinforced rich new

set of traditional (and web) social interactions.5.  Change with respect to timing, specifically the

decision about when to purchase the ticket.

& They Believe that They Find Lower Fares

Can We See Evidence of the Change?

Yield by Channel

Online Agencies Travel Agents

But this is primarily a market segmentation effect…

Fare Paid for "clearly LEISURE" Itineraries (Net of all fees; fares and inventory were the same)

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February '06 / June '06

Internet Agency Traditional TA

On Average, Internet Agency customers pay 11.5% less

What am I going to look at next?

Customers who use Internet/OnlineTravel Agencies (OTAs) to purchase leisure trips pay significantly less (11.5% in our sample) for similar itineraries in the same markets than those who purchase through traditional travel agencies even though the fares and inventory offered by the airlines are identical. The purpose of this study is to examine this Internet Price Effect (IPE).

Other than Transparency Effects, what could account for 11.5%

differential in the IPE?• Trip characteristics

• Customer differences

• Market structure

• The “Value” of the seat

• Then the question is, controlling for these attributes, does IPE persist?

What do I

expect to find???

Using My Regression Equation:

FP= ß0 + ß1*DC + ß2*TC+ ß3*CD + ß4*MS + ß5*OpV + ε

Previous Regression-based Studiesof Airlines and Distribution

• Borenstein, S., and Rose, N. 1994. Competition and Price Dispersion in the U.S. Airline Industry. Journal of Political Economy, 102 (4): 653-682.

• Clemons, E., Hann, I., and Hitt, L. 2002. Price dispersion and differentiation in online travel: An empirical investigation. Management Science, 48 (4), April: 534-549.

• Granados, N., Gupta, A., Kauffman, R. 2006. Internet-enabled Market transparency: Impact of price elasticity of demand in the air travel industry. Working paper, Carlson School of Management, University of Minnesota, May 8, 2006.

• Lane, L. 2003. Price Discrimination in the U.S. Domestic Airline Industry: The Effect of the Internet. Unpublished Third Year Research Project, EDM Program, Weatherhead School of Management, Case Western Reserve University.

• Sengupta, A., and Wiggins, S. 2006. Airline Pricing, Price Dispersion and Ticket Characteristics On and Off the Internet. Working paper #06-07, NET Institute, Texas A&M University, November, 2006.

• Stavins, J. 2001. Price Discrimination in the Airline Market: The Effect of Market Concentration. Review of Economics and Statistics, 83, February: 200-202.

Some Very Early Findings…

• Continental’s Top-25 Markets

• June,2006, every nonstop simple roundtrip

• Only “clearly leisure”• OTA and Traditional Agencies (No CO.com)

• Group size < 9; Coach cabin only

• CO “shipped” the same Fares and Inventory to all channels!

Preliminary Run: Statistics by Channel Diff.

MeansMean Std. Dv. Skew Kurt. Mean Std. Dv. Skew Kurt. (OLA-TA)

Fare 294.65 109.39 1.81 7.49 266.23 83.11 0.80 1.34 -28.42ap 66.7 53.8 2.07 6.38 54.3 39.4 2.26 9.59 -12.4gs 2.3 1.5 1.11 0.57 2.4 1.4 1.03 0.58 0.0ls 7.6 8.0 9.61 184.24 7.4 8.5 9.84 168.30 -0.2pkd 0.600 0.490 -0.41 -1.83 0.604 0.489 -0.43 -1.82 0.004pkh 0.336 0.472 0.69 -1.52 0.322 0.467 0.76 -1.42 -0.014orig 0.829 0.376 -1.75 1.07 0.687 0.464 -0.81 -1.35 -0.142none 0.228 0.419 1.30 -0.31 0.176 0.381 1.70 0.88 -0.051si 0.057 0.232 3.82 12.60 0.011 0.105 9.29 84.34 -0.046go 0.031 0.174 5.38 26.91 0.003 0.058 17.15 292.07 -0.028pl 0.030 0.171 5.49 28.18 0.003 0.050 19.90 394.19 -0.028hi 0.363 0.153 0.92 -0.33 0.367 0.157 0.94 -0.25 0.004sh 41.0 22.1 0.69 -1.07 41.1 22.5 0.71 -1.04 0.1sz 2625.0 1809.2 0.39 -1.35 2476.8 1702.2 0.55 -1.02 -148.1dist 1434.6 667.8 0.46 -1.36 1510.0 645.5 0.38 -1.36 75.4lcc 27.7 16.5 -0.36 -1.10 26.4 17.4 -0.20 -1.29 -1.3leis 0.408 0.08 -0.63 -0.10 0.401 0.08 -0.50 0.06 -0.007abf 285.1 67.3 0.32 -1.37 287.0 68.7 0.22 -1.42 1.9pp 19.1 6.5 -0.59 -0.40 19.5 6.4 -0.61 -0.24 0.5opv 274.92 128.96 0.70 1.85 241.2 109.26 0.36 0.04 -33.71opv7 505.24 253.61 0.33 -0.05 472.5 256.32 0.43 -0.03 -32.77

std.err. 0.02 0.03 std.err. 0.015 0.031

Booked through Travel Agents Booked at OLAs21706 Obs. 25543 Obs.

Regression Coefficients

DV = fare paid as percent of mean; All coefficients significant at .01 level except the red

Beta s.e. Beta s.e. Beta s.e. Beta s.e. Beta s.e.Intercept 1.039 0.002 1.136 0.003 1.054 0.004 1.116 0.020 1.191 0.015

ota -0.108 0.002 -0.125 0.002 -0.100 0.002 -0.096 0.002 -0.047 0.002ap -0.001 0.000 -0.001 0.000 -0.001 0.000 -0.001 0.000gs -0.011 0.001 -0.007 0.001 -0.012 0.001 -0.009 0.001ls -0.002 0.000 -0.001 0.000 -0.001 0.000 0.000 0.000

pkd 0.043 0.002 0.045 0.002 0.044 0.002 -0.004 0.002pkh 0.017 0.003 0.018 0.002 0.014 0.002 -0.011 0.002orig 0.055 0.003 0.056 0.003 0.019 0.002none 0.025 0.003 0.030 0.003 0.009 0.002

si 0.106 0.007 0.111 0.007 0.051 0.005go 0.153 0.009 0.157 0.009 0.071 0.007pl 0.227 0.010 0.232 0.010 0.152 0.007hi 0.131 0.024 0.245 0.018sh -0.002 0.000 -0.003 0.000sz 0.000 0.000 0.000 0.000

dist 0.000 0.000 0.000 0.000lcc -0.001 0.000 0.000 0.000leis 0.293 0.019 0.237 0.014abf 0.000 0.000 -0.002 0.000pp -0.003 0.000 -0.001 0.000

opv 0.002 0.000

adj.R2 0.039 0.111 0.138 0.153 0.537

Model 5Model 1 Model 2 Model 3 Model 4