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
Domestic US Low Cost Carrier growth.
1Q-06
4Q-04
4Q-03
4Q-02
4Q-01
4Q-00
4Q-99
4Q-98
4Q-97
4Q-96
4Q-95
4Q-94
4Q-93
4Q-92
4Q-91
4Q-90
4Q-89
4Q-88
4Q-87
4Q-86
4Q-85
4Q-84
4Q-83
4Q-82
4Q-81
Low Cost Carrier Mix of ASMs
Industry Structure obviously
changed…
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
Easier to See by Taking Southwest
and America West Out…
Rise of Internet
Wave I
10.0%
8.0%
Wave II
6.0%
4.0%
2.0%
Domestic US Low Cost Carrier growth (No WN,HP).
1Q-06
4Q-04
4Q-03
4Q-02
4Q-01
4Q-00
4Q-99
4Q-98
4Q-97
4Q-96
4Q-95
4Q-94
4Q-93
4Q-92
4Q-91
4Q-90
4Q-89
4Q-88
4Q-87
4Q-86
4Q-85
4Q-84
4Q-83
4Q-82
0.0%
4Q-81
Low Cost Carrier Mix of ASMs
12.0%
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 Trend
September 28, 2005
$140
WTI:
Crack Spread:
Jet Fuel:
$120
$66.35
$58.01
$124.36
$100
Jet Fuel/W TI Crack Spread
W TI Crude
$60
$40
Sep-05
Jul-05
May-05
Mar-05
Jan-05
Nov-04
Sep-04
Jul-04
May-04
Mar-04
Jan-04
Nov-03
Sep-03
Jul-03
May-03
Mar-03
Jan-03
Nov-02
Sep-02
Jul-02
May-02
$0
Mar-02
$20
Jan-02
($ per BBL)
$80
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=23
Or Google: Brunger Impact Airline
Five Findings
1.
2.
3.
4.
5.
Switch was not perceived primarily about lower
fares; about control & transparency/search breadth.
Unexpectedly, the actual search protocols that most
respondents perform are quite simple.
- Effects of trip type, FFP status & demography?
Some formed new levels of “involvement” with the
Search. Some became “search enthusiasts”.
For some, enabled, facilitated, reinforced rich new
set of traditional (and web) social interactions.
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 w ere the same)
February '06
/
June '06
On Average, Internet Agency customers pay 11.5% less
EWR-RDU
EWR-PHX
EWR-LAX
EWR-ORL
IAH-SEA
IAH-ORD
IAH-LGA
CLE-SFO
Traditional TA
CLE-LAS
EWR-RDU
EWR-PHX
EWR-LAX
EWR-ORL
IAH-SEA
IAH-ORD
IAH-LGA
CLE-SFO
CLE-LAS
Internet Agency
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 Studies
of 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 #0607, 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
Fare
ap
gs
ls
pkd
pkh
orig
none
si
go
pl
hi
sh
sz
dist
lcc
leis
abf
pp
opv
opv7
Booked through Travel Agents
21706 Obs.
Mean Std. Dv. Skew
Kurt.
294.65 109.39
1.81
7.49
66.7
53.8
2.07
6.38
2.3
1.5
1.11
0.57
7.6
8.0
9.61
184.24
0.600
0.490
-0.41
-1.83
0.336
0.472
0.69
-1.52
0.829
0.376
-1.75
1.07
0.228
0.419
1.30
-0.31
0.057
0.232
3.82
12.60
0.031
0.174
5.38
26.91
0.030
0.171
5.49
28.18
0.363
0.153
0.92
-0.33
41.0
22.1
0.69
-1.07
2625.0 1809.2
0.39
-1.35
1434.6
667.8
0.46
-1.36
27.7
16.5
-0.36
-1.10
0.408
0.08
-0.63
-0.10
285.1
67.3
0.32
-1.37
19.1
6.5
-0.59
-0.40
274.92 128.96
0.70
1.85
505.24 253.61
0.33
-0.05
std.err.
0.02
0.03
Booked at OLAs
25543 Obs.
Mean Std. Dv. Skew
Kurt.
266.23
83.11
0.80
1.34
54.3
39.4
2.26
9.59
2.4
1.4
1.03
0.58
7.4
8.5
9.84
168.30
0.604
0.489
-0.43
-1.82
0.322
0.467
0.76
-1.42
0.687
0.464
-0.81
-1.35
0.176
0.381
1.70
0.88
0.011
0.105
9.29
84.34
0.003
0.058
17.15
292.07
0.003
0.050
19.90
394.19
0.367
0.157
0.94
-0.25
41.1
22.5
0.71
-1.04
2476.8 1702.2
0.55
-1.02
1510.0
645.5
0.38
-1.36
26.4
17.4
-0.20
-1.29
0.401
0.08
-0.50
0.06
287.0
68.7
0.22
-1.42
19.5
6.4
-0.61
-0.24
241.2
109.26
0.36
0.04
472.5
256.32
0.43
-0.03
std.err.
0.015
0.031
Diff.
Means
(OLA-TA)
-28.42
-12.4
0.0
-0.2
0.004
-0.014
-0.142
-0.051
-0.046
-0.028
-0.028
0.004
0.1
-148.1
75.4
-1.3
-0.007
1.9
0.5
-33.71
-32.77
Regression Coefficients
Intercept
ota
ap
gs
ls
pkd
pkh
orig
none
si
go
pl
hi
sh
sz
dist
lcc
leis
abf
pp
opv
adj.R
2
Model 1
Beta
s.e.
1.039 0.002
-0.108 0.002
0.039
Model 2
Beta
s.e.
1.136 0.003
-0.125 0.002
-0.001 0.000
-0.011 0.001
-0.002 0.000
0.043 0.002
0.017 0.003
0.111
Model 3
Beta
s.e.
1.054 0.004
-0.100 0.002
-0.001 0.000
-0.007 0.001
-0.001 0.000
0.045 0.002
0.018 0.002
0.055 0.003
0.025 0.003
0.106 0.007
0.153 0.009
0.227 0.010
0.138
Model 4
Beta
s.e.
1.116 0.020
-0.096 0.002
-0.001 0.000
-0.012 0.001
-0.001 0.000
0.044 0.002
0.014 0.002
0.056 0.003
0.030 0.003
0.111 0.007
0.157 0.009
0.232 0.010
0.131 0.024
-0.002 0.000
0.000 0.000
0.000 0.000
-0.001 0.000
0.293 0.019
0.000 0.000
-0.003 0.000
Model 5
Beta
s.e.
1.191 0.015
-0.047 0.002
-0.001 0.000
-0.009 0.001
0.000 0.000
-0.004 0.002
-0.011 0.002
0.019 0.002
0.009 0.002
0.051 0.005
0.071 0.007
0.152 0.007
0.245 0.018
-0.003 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.237 0.014
-0.002 0.000
-0.001 0.000
0.002 0.000
0.153
0.537
DV = fare paid as percent of mean; All coefficients significant at .01 level except the red