Journal of Travel Research
http://jtr.sagepub.com/
Perishability, Yield Management, and Cross-Product Elasticity: A Case Study of Deep Discount Season
Passes in the Colorado Ski Industry
Richard R. Perdue
Journal of Travel Research 2002 41: 15
DOI: 10.1177/0047287502041001003
The online version of this article can be found at:
http://jtr.sagepub.com/content/41/1/15
Published by:
http://www.sagepublications.com
On behalf of:
Travel and Tourism Research Association
Additional services and information for Journal of Travel Research can be found at:
Email Alerts: http://jtr.sagepub.com/cgi/alerts
Subscriptions: http://jtr.sagepub.com/subscriptions
Reprints: http://www.sagepub.com/journalsReprints.nav
Permissions: http://www.sagepub.com/journalsPermissions.nav
Citations: http://jtr.sagepub.com/content/41/1/15.refs.html
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
AUGUST 2002
JOURNAL
OF TRAVEL RESEARCH
Perishability, Yield Management, and
Cross-Product Elasticity: A Case Study
of Deep Discount Season Passes
in the Colorado Ski Industry
RICHARD R. PERDUE
In September 1998, selected ski resorts in Colorado initiated a deeply discounted season pass program, offering a
75% discount from the previous year’s prices to FrontRange skiers (Colorado residents). The purpose of this study
was to assess the effects of this discount program on skier behavior. Specifically, the influence of the discount programs
on skier behavior, cross-product elasticity, and market
alienation was assessed, comparing the 1997-1998 and
1998-1999 seasons. While the results are somewhat confounded by the poor snow conditions of the 1998-1999 season, the discount programs appear to have significantly influenced skier loyalty to the respective resorts. Finally, while
declines in destination skier satisfaction with the levels of lift
line and slope crowding were also identified, the discounted
pass programs do not appear to have directly influenced the
destination skiers’ overall satisfaction.
The evolution of tourism marketing theory and strategy is
predicated on understanding the distinguishing characteristics of the tourism experience (Pine and Gilmore 1999).
Building on the services marketing literature (Shostack
1977; Berry 1980; Fisk, Brown, and Bitner 1993), intangibility and perishability are clearly two of those characteristics.
Intangibility, the inability to effectively sense the service
product (particularly prior to purchase and consumption),
has been described as “the most basic and universally cited
difference between goods and services” (Zeithaml and Bitner
2000, p. 12). Due to service intangibility, in combination
with the related concepts of heterogeneity and simultaneous
production and consumption, service quality has evolved as
the central or core research topic in services marketing (Fisk,
Brown, and Bitner 1993; Rust, Zahorik, and Kenningham
1996; Lovelock 2000; Zeithaml and Bitner 2000). Numerous
researchers have extended this service quality focus to tourism experiences (i.e., Bejou, Edvardsson, and Rakowski
1996; Baker and Fesenmaier 1997; Childress and Crompton
1997; Augustyn and Ho 1998). Perishability, the inability to
inventory unsold services, has received substantially less
attention from services researchers but is equally if not more
important to tourism marketing strategy.
By itself, the perishability of the tourism experience is a
relatively benign characteristic. However, in combination
with the high fixed costs and cyclical demand patterns of
most resorts, perishability is a key marketing strategy issue
(Relihan 1989; Lieberman 1993; Lewis, Chambers, and
Chacko 1995). Specifically, if demand is relatively constant
and predictable, production can be tailored to meet the level
of demand. However, the demand for most tourism destinations, particularly resorts, varies widely on both weekly and
seasonal cycles (Allcock 1989; Williams and Dossa 1998).
Furthermore, most tourism products are characterized by
high fixed or sunk costs for infrastructure such as lodging
units and attraction venues (Moutinho 1989). Consequently,
tourism marketing strategy focuses heavily on attempting to
manipulate demand to fit the available capacity. Yield management is the underlying conceptual basis of these strategies. While pricing and discounting have historically been
the predominant marketing strategies of yield management,
virtually all aspects of marketing strategy are increasingly
being focused on the problem.
In September 1998, selected ski resorts in Colorado initiated a deeply discounted season pass program, offering a
75% discount from the previous year’s prices. The purpose
of the research reported in this article was to assess the
effects of this discount program on skier behavior. Yield
management was used as the conceptual framework to guide
this assessment. The following sections first will present a
conceptual discussion of yield management, identifying the
key variables to be included in the study. Next, a historical
perspective on the season pass program will be presented,
followed by the research methodologies and results. The
conclusions will focus on both an assessment of the season
pass programs and its implications for yield management in
the ski resort industry.
YIELD MANAGEMENT
Initially developed by the airline industry, yield management is commonly defined as “the application of information
systems and pricing strategies to sell the right capacity to the
right customers at the right time” (Kimes and Chase 1998,
Richard R. Perdue is a professor of tourism management in the
Leeds School of Business at the University of Colorado at Boulder.
Journal of Travel Research, Vol. 41, August 2002, 15-22
© 2002 Sage Publications
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
16
AUGUST 2002
p. 156). Implicit in this definition are the key yield management criteria of (a) a time-perishable, relatively fixed capacity; (b) cyclical or fluctuating demand; (c) multiple market
segments that have different purchase processes and elasticities; (d) a combination of low marginal sales costs and high
fixed costs for capacity; and (e) a “price structure” whereby
different market segments pay different prices for essentially
the same service product consumed at essentially the same
time (Kimes 1989; Lewis, Chambers, and Chako 1995). The
common airline example is the situation wherein different
coach passengers, flying on the same flight at the same time,
pay different prices, depending on a variety of purchase
restrictions or “fences” such as time of purchase, length of
stay, and willingness to stay away from home over a Saturday night. Since the 1980s, yield management has been
extended to applications in the lodging, rental car, health
care, and restaurant sectors as well as with nonprofit firms
(Relihan 1989; Chapman and Carmel 1992; Carroll and
Grimes 1995; Metters and Vargas 1999; Waters 2000).
Withiam (1999) reported that switching to a yield management system at the Vail Resort, Inc. properties at Keystone
Resort and the Great Divide Lodge in Breckenridge, Colorado, resulted in more than $1 million in incremental revenue. Specific illustrations of yield management in the ski
resort industry include Best (1997), Devlin (1999), and Lazarus (2000).
The process of yield management is straightforward. A
resort must first identify its primary market segments and
establish a target price or price structure for each segment.
Second, for a particular time period (e.g., night), the resort
forecasts demand by each target price. Third, starting at the
highest price or “rack rate,” the available capacity is allocated to “price buckets,” a target number of units to be sold at
each price. This continues until the available capacity is allocated. For example, on a high-demand night, all of the rooms
may be allocated to the highest price, and none are made
available at any of the discounted prices. On a low-demand
night, a significant portion of the resort’s rooms may be
available at a very deep discount. Fourth, the resort tries to
establish rate restrictions or “fences” to limit access to the
discounted prices to the intended market segments. Finally,
most resorts forecast the no-show and cancellation rates at
each price and develop a corresponding overbooking policy.
Importantly, for a given night, this process is continuously
being updated based on reservation sales. Resorts that cater
to convention visitors may develop the initial yield solution
as much as 3 years in advance and then update it on a monthly
basis until a year in advance, after which it is updated on a
weekly basis. If the number of reservations exceeds forecasts, the availability of discounted rooms might be reduced.
Similarly, the level of discounting may be increased if necessary. A significant advancement in recent years is the use of
the Internet for last-minute discounting of unsold inventory
through e-mail mailing lists and Web sites such as
Travelocity.com, Priceline.com, and Intellitrip.com (Wald
1998; Keates 1998; Stoddart 1999). A substantial body of
research exists concerning each of these steps (Weatherford
and Bodily 1992), particularly on sales forecasting
(Schwartz and Hiemstra 1997; Chang, Garcia-Diaz, and Var
1998), customer perceptions of price fairness (Kimes 1994),
and the effects of yield management on service quality
(Kimes and Chase 1998).
There are two major criticisms of yield management systems. First, and most important, yield management systems
tend to focus a company on short-term profit maximization,
resulting in potential declines in service quality and customer
satisfaction (Desiraju and Shugan 1999; Kraus 2000; Lovelock 2000; Zeithaml and Bitner 2000). Two potential service
quality problems exist. Service quality naturally tends to
deteriorate when operations are continuously run at or near
full capacity (Rust, Zahorik, and Kenningham 1996). As
waiting times and crowding increase, there is a natural
decline in service quality. In addition, employee fatigue frequently adds to the problem. Another major concern is the
possibility of the customers who are paying higher prices
becoming alienated by perceived price unfairness (Kimes
1994; Zeithaml and Bitner 2000). In either situation, loss of
high-value customers or a resort’s service reputation in
exchange for short-term revenue maximization is not a viable long-term strategy (Rust, Zahorik, and Kenningham
1996; Hallowell and Schlesinger 2000).
The second yield management criticism is the common
failure to include ancillary spending in the decision process
(Kimes 1989; Reece and Sobel 2000). The formula for yield
is as follows:
Yield % = (Occupancy Rate %) (Price Efficiency %)
where
Occupancy Rate % =
average number of units sold
,
number of units available
Price Efficiency % =
average daily rate collected
.
rack rate (normal full price)
Occupancy rate, price efficiency, and yield are all expressed
as percentages. Yield is essentially the percentage of the total
potential revenue collected during a specific time frame. If a
resort operates at 100% occupancy and 100% price efficiency, it is achieving its full revenue potential or 100% yield
(Moutinho 1989).
Importantly, occupancy rate and price efficiency tend to
be negatively correlated. In a given demand environment, as
prices increase (i.e., the availability of discounts decrease),
sales generally tend to decrease, resulting in a yield curve
such as that shown in Table 1. Operating at the maximum
yield point (A) is not necessarily the best choice, even if
focusing on short-term profit maximization. As unit variable
costs increase, a more viable alternative may be to operate at
a slightly higher price efficiency and lower occupancy rate
(B), thereby reducing variable costs with relatively little
change in yield. Given that most resort environments tend to
have relatively small variable costs, a more important strategy may be to focus on supplemental product sales revenues
(e.g., restaurant, equipment rental, souvenirs, activity lessons, etc.). If supplemental spending is high, a viable strategy
may be to operate at a lower price efficiency and higher occupancy point (C) and rely on supplemental sales for increased
profits (Lewis, Chambers, and Chacko 1995; Norman and
Mayer 1997; Adams 2000). Cross-product elasticity is
defined as the probability of supplementary purchases, given
the initial purchase. As cross-product elasticity increases,
lead product prices can be lowered to attract greater occupancy and supplemental product sales. Furthermore, target
market selection may be a function of differences in cross-
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
JOURNAL OF TRAVEL RESEARCH
TABLE 1
HYPOTHETICAL YIELD TABLE (IN PERCENTAGES)
Price Efficiency
20
30
40
50
60
70
80
90
100
Occupancy Rate
Yield
100
90
80
70
60
50
40
30
20
20
27
32
35 C
36 A
35 B
32
27
20
product elasticity across different potential markets. Given
that supplemental product profit margins tend to be higher
than for lead products, focusing on cross-product elasticity
can be a very successful strategy.
EVOLUTION OF THE BUDDY PASS
As with many service sectors (Meidan 1989; Braun,
Soskin, and Cernicky 1992), pricing strategy in the ski resort
industry, particularly for lift tickets, tends to be competition
based. In late spring or early summer, one of several major
resort firms assumes a price leadership role by announcing
lift ticket prices for the upcoming season. The other resorts
then tend to follow the price leader by adjusting their prices
accordingly.
For most Colorado ski resorts, particularly those located
on the Interstate 70 corridor west of Denver, two major market segments exist: the destination/nonresident skier who
visits on a ski vacation lasting at least 2 nights and the FrontRange/resident skier who typically visits on a day or weekend trip basis. As would be expected, the destination skier
tends to spend substantially more per day than the FrontRange skier, but the Front-Range skiers, by their sheer numbers, frequently make up the majority of lift tickets sold. The
lift ticket price announced in the late spring/early summer is
generally the “window price” aimed at FIT destination skiers
who stay in lodging not owned by the resort company. A
number of different discount programs exist depending on
the time of year and a variety of eligibility fences. At any
given time, most Colorado ski resorts have a price structure
of 15 to 20 different prices for a lift ticket on a given day.
In September 1998, three resort corporations in Colorado
initiated a deeply discounted season pass program, offering a
75% discount from the previous year’s prices. The initial
entry into the program was Keystone Resort, which offered a
“family” season pass wherein a Colorado resident family
composed of two adults and two children could purchase
four season passes for $800. Prior to 1998, an individual season pass at Keystone was priced at $800. Thus, in an attempt
to position itself as a “family resort,” Keystone essentially
offered a family pass program for the same price as the previous year’s individual season pass. Copper Mountain and
Winter Park resorts both immediately matched the Keystone
program. Winter Park then extended the program to four
“friends or buddies,” not necessarily related to each other.
Again, Copper Mountain and Keystone matched the new
17
price program. To gain competitive differentiation, Keystone
then extended its pass program to include Breckenridge and
Arapahoe Basin ski areas. Vail Resorts, Inc. owns both Keystone and Breckenridge and has a marketing partnership with
Arapahoe Basin. The program grew into a “price war” to
determine which resort could sell the most “buddy passes”
and gain the related publicity. As the program evolved, the
resort companies kept reducing the eligibility fences by
including nonresidents of Colorado, extending the purchase
deadline, and recruiting “buddy groups” composed of people
unknown to each other. Overall, the resorts sold 66,000
“buddy passes.” In prior years, the resorts combined had normally sold less than 10,000 season passes. A small discount
program aimed specifically at families grew into a major
sales promotion with international publicity.
Importantly, as the program evolved and changed, the
resort managers were forced to make immediate decisions on
whether to match the program price and eligibility changes.
As identified through interviews with resort marketing
research managers, there were four basic assumptions underlying these decisions. Because of the reduced lift ticket
prices, skiers owning the buddy pass would (a) increase their
frequency of skiing, (b) increase their loyalty to the associated resort, and (c) increase their cross-product elasticity. In
addition, it was also assumed that destination skiers, who pay
substantially higher overall prices, would not be negatively
affected by the buddy pass program. More specifically, destination skiers’ crowding perceptions and overall satisfaction
would not be negatively affected.
The purpose of this research was to test these four
assumptions. Two surveys were conducted: a panel survey of
Front-Range skiers and on-mountain surveys of destination
skiers. While the different resorts developed unique names
for their passes, the term buddy pass will be used generically
to represent the passes offered by all three resort companies.
FRONT-RANGE SKIER PANEL STUDY
Method
A panel study was used to monitor Front-Range skier
behavior over the 1998-1999 ski season. As opposed to a single end-of-season survey, the panel methodology allowed for
better measurement with fewer recall problems and for monitoring changes in behavior over the course of the season
(Babbie 1992; Kinnear and Taylor 1991). Panel recruitment
and the initial survey were conducted in November 1998,
with subsequent panel survey waves in January, March, and
May 1999. Panel recruitment was by random digit dialing
telephone surveys within the Front-Range area of Colorado.
The panel was screened to include individuals who (a) either
downhill skied or snowboarded, (b) were at least 16 years of
age, (c) did not work in the ski or market research industries,
(d) skied at least 1 day the previous season, and (e) planned to
ski during the upcoming (1998-1999) season. A total of
1,209 panel members were recruited, of whom 300 owned
one or more of the buddy passes. Of these individuals, 885
(73.2%) completed all four survey waves, 257 (85.7%) and
628 (69.1%), respectively, for buddy pass holders and
nonholders.
The initial survey collected demographic information,
1997-1998 skiing behavior, planned 1998-1999 ski behavior,
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
18
AUGUST 2002
and preference for selected ski mountain attributes. The skiing behavior questions asked respondents which areas they
had skied last year and, for each skied area, the number of
days. The planned 1998-1999 ski behavior questions focused
simply on whether the respondents planned to ski more frequently during the upcoming season as compared to the previous season. The preference data were collected by asking
respondents to indicate the importance of a selected set of ski
mountain attributes on a scale ranging from 1 = not at all to
5 = very important. The attributes were selected on the basis
of previous on-mountain consumer satisfaction results.
Finally, the initial survey also collected yes/no data on the
use of various discount programs that were available during
1997-1998 ski season, on ownership of various discount
cards and coupons available for the 1998-1999 season, and
on ownership of the three buddy passes.
The subsequent surveys waves focused on ski participation during the interim periods, again measured by asking,
“Which areas?” and “How many days at visited areas?” During the March survey, respondents were asked to recall their
last ski trip and indicate how much they had spent for lift
tickets, lodging, meals/drinks/entertainment, parking, resort
retail shops, ski lessons, and rental equipment. To convert to
per capita expenditures, respondents were asked, “Including
yourself, how many people were these expenses for?” In
addition, respondents were asked to “recall a similar trip that
you took last year” and indicate whether they spent more,
less, or about the same this year as compared to last year.
Respondents who had not skied during the January to March
period were not asked the expenditure questions.
Results
Tables 2 and 3 present demographic, ski involvement,
and 1997-1998 ski behavior comparisons of buddy pass
owners and nonowners. The buddy passes holders tended to
be younger, male, and more likely to be single. Furthermore,
the pass holders also tended to be more involved and active
skiers. Specifically, the pass holders were more likely to rate
themselves as expert skiers/snowboarders and were more
likely to own their equipment. While the nonholders (being
older) tended to have more years of experience, the pass
holders skied almost twice as many days during the preceding 1997-1998 season and took significantly more overnight
ski trips. On the importance of ski mountain attributes, when
compared to the nonholders, the pass holders rated terrain
and snow conditions as significantly more important. Conversely, the nonholders gave higher ratings to family skiing,
food and drink, friendly staff, and lodging. Lift ticket prices
were slightly but not significantly more important to the
nonholders (4.45) as compared to the pass holders (4.32).
Table 4 presents the differences in 1998-1999 skier
behavior of pass holders versus nonholders. As noted, the
analysis of skier days and overnight trips was weighted to
control for underlying differences in skier involvement
between pass holders and nonholders. Specifically, the analysis was weighted so that there were no differences in 19971998 skier days between the two groups. Even with the
weighted analysis, the buddy pass holders skied more frequently and took more overnight ski trips during the 19981999 season. For both skier days and overnight trips, the
buddy pass average was more than twice the nonholder average. Given that this analysis is weighted for prior skier
TABLE 2
RESPONDENT DEMOGRAPHICS
Respondent
Characteristic
Buddy Pass Nonpass
(n = 300) (n = 909)
Age (%)
16-24
25-34
35-44
45-54
55-64
65 or older
Total
Gender (%)
Female
Male
Total
Annual income (%)
Under $20,000
$20,000-$24,999
$25,000-$29,999
$30,000-$39,999
$40,000-$49,999
$50,000-$74,999
$75,000-$99,000
$100,000-$149,999
$150,000 or more
Total
Marital status (%)
Married
Single
Total
Number of people
in household
Households with
children (%)
Statistic
χ2 = 29.9***
27.2
36.2
21.8
9.4
5.0
0.3
99.9
24.4
24.8
27.8
16.0
3.6
3.3
99.9
30.0
70.0
100.0
43.7
56.3
100.0
10.8
6.0
7.6
12.4
12.7
17.5
17.5
10.8
4.8
100.1
6.4
4.0
6.1
14.2
10.4
20.1
18.8
12.7
7.4
100.1
36.8
63.2
100.0
51.3
48.7
100.0
2.66
21.7
2.97
47.4
χ2 = 17.5***
χ2 = 11.4
χ2 = 18.9***
t = 3.8***
χ2 = 61.8***
***α ≤ .0001.
involvement, much of this difference can be attributed to the
buddy passes. Still, both groups skied less frequently than
they had the previous year. The 1998-1999 season was a relatively poor year for snow. As has been noted by others
(Echelberger and Shafer 1970), the availability and quality of
snow are the predominant determinant of ski area patronage.
It is important to note that while buddy pass skier days
declined by 13% as compared to 61% by the nonpass holders,
this difference cannot be attributed to the buddy passes.
Rather, it is most likely due to the previously noted differences in skier involvement.
Table 4 also presents the effects of the buddy passes on
market share for the three resort companies involved in the
promotion. For resort companies A and B, the buddy pass
holders increased their skier days by about 5 days. By comparison, the nonholder skier days decreased by about 1 day.
For resort C, skier days declined for both pass holders and
nonholders. As noted earlier, the 1998-1999 season was a
relatively poor snow year. Resort C suffered from extremely
poor snow conditions. For all three resort companies, the
buddy pass significantly increased skier loyalty. The resort’s
proportion of skier days by buddy pass holders increased by
an average of 32.4%.
In the March survey wave, respondents were asked “how
much they had spent on their last ski trip” for eight expenditure categories. In addition, they were also asked, “including
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
JOURNAL OF TRAVEL RESEARCH
19
TABLE 3
TABLE 4
RESPONDENT SKI INVOLVEMENT AND
1997-1998 SKI BEHAVIOR
INFLUENCE OF BUDDY PASSES ON SKIER BEHAVIOR
Respondent
Characteristic
Buddy Pass Nonpass
(n = 300) (n = 909)
Ski involvement
Years of skiing
experience
17.2
Self-rated skiing
ability (%)
Expert
31.9
Advanced
27.6
Advanced
intermediate
31.9
Intermediate
5.6
Beginner
2.6
Don’t know
0.4
Total
100.0
Years of snowboarding experience
6.2
Self-rated snowboarding ability (%)
Expert
16.5
Advanced
30.1
Advanced
intermediate
22.3
Intermediate
18.4
Beginner
6.8
Don’t know
5.8
Total
99.9
Equipment
ownership (%)
Own
94.0
Rent
3.3
Both
2.7
Total
100.0
Mountain attribute
importance ratingsa
Family skiing
2.25
Terrain
4.65
Food and drink
2.51
Snow conditions
4.64
Friendly staff
3.44
Lodging
2.75
1997-1998 ski behavior
Days skied/snowboarded last year
16.2
Number of overnight
ski trips
3.0
19.0
Measure of
Skiing Behavior
Statistic
t = 2.1*
χ2 = 42.3***
15.6
23.9
43.3
13.6
2.5
1.1
100.0
8.5
t = 1.1
χ2 = 16.4**
6.1
20.9
30.1
22.4
15.8
4.6
99.9
χ2 = 66.9**
71.6
23.7
4.7
100.0
3.18
4.43
2.89
4.46
3.62
2.95
t = 9.3***
t = 5.3***
t = 4.9***
t = 4.0***
t = 2.3*
t = 2.3*
9.4
t = 6.9***
1.8
t = 4.1***
a. Only those mountain attributes with significant differences
between buddy pass holders and nonholders are reported.
Respondents indicated the importance of a selected set of ski
mountain attributes on a scale ranging from 1 = not at all to 5 =
very important.
*α ≤ .05. **α ≤ .001. ***α ≤ .0001.
yourself, how many people were these expenses for.” The
expenditure category data were converted to expenditures
per person and added to determine total expenditures per person. As shown in Table 5, there were few differences in
expenditures by category. As would be expected, the
nonpass holders spent more per person on tickets. Similarly,
given the earlier data on self-reported skiing ability, the
higher expenditure on lessons by nonpass holders was also
expected. Overall, the nonpass holders spent more per
Skier daysa
Total days for
1998-1999 season
Changes in days
skied from
1997-1998 season
Overnight ski trips for
1998-1999 season
Changes in overnight
trips from 1997-1998
season
Market shareb
Resort A
Days
Change in days from
1997-1998 season
Proportion
Change in proportion
from 1997-1998
season
Resort B
Days
Change in days from
1997-1998 season
Proportion
Change in proportion
from 1997-1998
season
Resort C
Days
Change in days from
1997-1998 season
Proportion
Change in proportion
from 1997-1998
season
Buddy Pass Nonpass
(n = 257) (n = 628) t-Values
14.1
6.3
10.3***
–2.1
–10.0
7.9***
1.6
0.7
5.0***
–1.2
–1.3
11.1
1.8
13.9***
4.8
74.7
–1.3
24.2
7.1***
20.3***
36.9
1.5
10.5***
12.0
0.8
5.9***
5.3
77.4
–0.8
11.7
3.0**
12.3***
31.4
–1.9
4.0***
5.8
0.9
6.0***
–2.4
68.4
–1.0
12.4
0.7
10.2***
29.0
–3.0
3.9***
0.2
a. The analyses of 1998-1999 skier days comparing pass
holders and nonholders were weighted by 1997-1998 skier
days to correct for differences in skier involvement between
the two groups.
b. The analysis of market share is based on actual (unweighted skier days). Resort names have been deleted for
confidentiality purposes.
**α ≤ .001. ***α ≤ .0001.
person, but the difference is not statistically significant.
Cross-product elasticity for the pass holders is estimated as
the mean per person expenditure of $54.05. For every additional day of skiing at the respective resorts, the buddy pass
programs resulted in $54.05 in incremental spending. Based
on previous research on resort spending behavior, it is estimated that 35% to 40% of this spending ($19 to $22)
occurred at the study resorts.
Due to the associated recall error (Babbie 1992), it was
unreasonable to ask respondents to report expenditures by
category for a specific trip the previous year. Consequently,
respondents were asked, “Compared to a similar trip last
year, did you spend more, less, or about the same this year?”
Generally, the respondents were equally spread across the
“more this year,” “more last year,” and “about the same”
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
20
AUGUST 2002
TABLE 5
EXPENDITURES PER PERSON
Expenditure
Measure
Buddy Pass Nonpass
(n = 257) (n = 628) t-Values
Last trip (per person,
in U.S. dollars)
Tickets
9.08
Lodging
16.29
Meals
21.22
Parking
0.72
Retail
4.97
Lessons
0.19
Rental equipment
1.49
Other
0.09
Total
54.05
Expenditures compared to
similar trip last year (%)
Spent more this year
35.1
Spent more last year
37.7
Spent same as last
year
27.2
Total
100.0
19.90
19.64
18.31
0.81
4.43
1.64
1.99
3.36
70.08
3.9***
0.5
0.8
0.3
0.3
2.4*
0.9
0.9
1.2
χ2 = 2.1
34.9
32.1
33.0
100.0
*α ≤ .05. ***α ≤ .0001.
categories (see Table 5). There were no significant differences between buddy pass holders and nonholders. Similarly, it is not possible to determine if buddy pass ownership
resulted in reallocation of the total expenditure amount
across the different categories. It is not possible to determine
if the buddy pass resulted in more expenditures in other categories, such as meals and retail, which are somewhat higher
for the pass holders.
DESTINATION SKIER
SATISFACTION STUDY
Method
Most ski resorts monitor customer satisfaction throughout the ski season, using on-mountain surveys. To determine
the influence of the buddy pass on destination skiers, we conducted a secondary analysis of the data for the 1997-1998
and 1998-1999 seasons from four resorts, two of which
offered and two which did not offer buddy passes. The questionnaire and data collection methods were essentially similar over the four mountains. At randomly assigned times and
lifts, skiers were approached as they entered the lift lines and
asked to participate in the survey. Those who agreed to participate completed the survey during the lift ride. The number
of surveys completed per week was proportionate to the level
of business. Similarly, the number of surveys completed on
each lift was proportionate to the lift’s relative level of use.
Surveys were completed in the afternoons to allow consumers an opportunity to experience the mountain prior to completing the survey. This analysis was limited to nonresidents
of Colorado. Still, the analysis included 6,342 cases, with a
minimum of 1,177 cases in each of the season-by-mountain
type cells.
Factor and Cronbach reliability analyses were used to
develop summary measures of crowding and overall
customer satisfaction. Specifically, principal axis factor
analysis with varimax rotation was used to determine the
unidimensionality of the proposed scales. Cronbach reliability analysis was then used to assess the final set of scale
items. Crowding was measured by asking respondents their
satisfaction (1 = dissatisfied to 5 = satisfied) with three items:
base area line wait, upper mountain line wait, and crowding
on the slopes. As expected, these items loaded heavily on one
factor (eigenvalue = 2.21, KMO = .752, Bartlett χ2 = 3,808).
All factor loadings exceeded .60. The Cronbach reliability
analysis resulted in a standardized alpha = .696. Given the
relatively small number of scale items and the disparity in the
wording of the individual items, this alpha coefficient was
deemed acceptable (DeVellis 1991; Spector 1992). Importantly, since this is a measure of crowding satisfaction,
higher scores mean crowding is decreasing.
The overall satisfaction measure is a summary measure
of four items: satisfaction (1 = dissatisfied to 5 = satisfied)
with the overall mountain experience and agreement (1 = disagree to 5 = agree) with “I will encourage friends to go to
this resort,” “I will say positive things to friends about this
resort,” and “I will consider this resort as my first choice for
future Colorado trips.” Again, the items loaded heavily on
one dimension (eigenvalue = 3.01), with factor loadings
exceeding .50 (KMO = .752, Bartlett’s χ2 = 25,914). The
Cronbach alpha was equal to .759.
Results
As shown in Table 6, satisfaction with the level of crowding on the buddy pass mountains declined significantly
between the 1997-1998 and 1998-1999 seasons. Conversely,
the crowding measure for the nonpass mountains was essentially constant. Furthermore, the ANOVA results reflect a
strong interaction effect of mountain type by season on
crowding. However, these crowding results did not carry
over to overall satisfaction. Both the buddy pass and nonpass
mountains experienced declines in overall skier satisfaction.
As noted earlier, the 1998-1999 season was characterized by
relatively poor snow conditions, especially during the Christmas/New Years period, which is particularly important to
destination skiers. Still, the Pearson correlation between the
crowding and overall satisfaction measures was .365, implying that increases in crowding would result in some loss in
overall satisfaction.
CONCLUSIONS
Prior to discussing the conclusions of this study, it is
important to point out a key limitation. As noted at various
points in the article, much of what happens at a ski resort in a
given year is a function of snow conditions. Both levels of
skier days and skier satisfaction are largely a function of this
very uncontrollable factor. Hence, the effects of the buddy
pass programs on skier behavior and satisfaction are confounded by changes in snow conditions from season to season and from resort to resort. Research involving a larger
number of resort settings with corresponding variance in
snow conditions would be necessary to control for this limitation. Still, the following four conclusions seem warranted.
First, the evolution of the buddy pass programs provides
important insights into resort pricing practices. Previous
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
JOURNAL OF TRAVEL RESEARCH
TABLE 6
DESTINATION SKIER SATISFACTION
Season
Satisfaction Measure
1997-1998 1998-1999 t-Values
Satisfaction with level
of crowdinga
Buddy pass mountains
12.8
Nonpass mountains
13.3
Factorial ANOVA
results
Differences between
mountain type
182.3***
Differences between
seasons
21.0***
Interaction of
mountain type
and seasons
21.0***
Overall satisfaction
Buddy pass mountains
17.7
Nonpass mountains
18.2
Factorial ANOVA
results
Differences between
mountain type
57.6***
Differences between
seasons
22.3***
Interaction of
mountain type
and seasons
2.2
12.3
13.3
5.8***
0.3
17.4
17.8
2.2*
4.7***
a. Higher scores equate to lower levels of crowding.
***α ≤ .0001.
research (Meidan 1989; Braun, Soskin, and Cernicky 1992)
had documented the importance of competitive pricing practices in tourism. The evolution of the buddy passes illustrates
the speed and complexity that often accompanies resort pricing, particularly for promotions and special programs. Resort
managers literally have to make critical pricing decisions
within an hour or two of becoming aware of competitor
actions. There is very little time for research and analysis.
Furthermore, responding to competitor actions can cause
dramatic shifts in marketing strategy and objectives. The
Keystone family pass was initially intended to be a relatively
small program targeted to Colorado families. As it evolved
and changed, the program became dramatically larger and
shifted to targeting young, single, male, frequent skiers. If
Vail Resorts/Keystone evaluates the program relative to the
initial target market and positioning, it was clearly a failure.
However, if evaluated relative to the new target market, the
program clearly reached this market and significantly influenced their skiing behavior.
Second, the buddy pass programs clearly influenced skier
behavior. Due to the relatively poor snow year, overall frequency of skiing declined, despite the buddy pass programs.
The decline in skier days was significantly less for the buddy
pass holders, but they were the more involved skiers anyway.
However, as expected, the buddy passes clearly influenced
resort loyalty. Pass holders became much more loyal to the
respective resorts, averaging a 32% increase in the proportion of days skied at the respective resorts.
Third, cross-product elasticity was relatively high for the
buddy pass holders. The buddy pass holders averaged
21
spending $54.05 per ski day per person, with approximately
$21 being spent at the respective resorts. Thus, to the extent
that the buddy pass programs increased skier loyalty (days
spent at the respective resorts), spending in other categories
increased overall resort revenue. For resort company A (see
Table 4), each buddy pass sold resulted in 4.8 additional skier
days. At $21.00 per day, that equates to $100,800 in incremental revenue for every 1,000 buddy passes sold. This more
than offsets the decline in lift ticket revenue associated with
the buddy pass program.
Lacking longitudinal spending data, it is not possible to
determine if the buddy pass programs resulted in a reallocation of spending across other expenditure categories. However, the buddy pass holders did not spend significantly less
than the nonholders. One conjecture would be that pass holders reallocated the money historically spent on tickets into
other spending categories. If so, the buddy pass may have
actually increased cross-product elasticity.
Fourth, destination skier satisfaction with the level of
crowding decreased significantly at the buddy pass resorts.
To the extent that crowding dissatisfaction carries over to
declines in destination skier repeat visitation and word-ofmouth recommendations, the buddy pass program may significantly influence the resort’s mix of customers and longterm profitability. However, it will take several years for this
impact to manifest itself. While there were no differences in
overall destination skier satisfaction between the buddy pass
and nonpass mountains, a substantial body of research indicates that overall satisfaction in outdoor recreation settings
declines with increasing levels of crowding (Tarrant and
English 1996; Manning et al. 1999). In this study, satisfaction declined between the 1997-1998 and 1998-1999 seasons
for both buddy pass and nonpass resorts, reinforcing the
importance of snow conditions as the dominant factor influencing both ski frequency and satisfaction.
The influence of the buddy pass programs on skier satisfaction is also confounded. Recruiting and retaining a viable
workforce in rural resort communities is frequently cited as a
major challenge for Colorado ski resorts (Wyrick 1995; Gonzalez 1998; Blevins 2000). Many resorts, including all of the
buddy pass resorts, have traditionally used numerous parttime employees to help meet this shortage. One of the primary benefits of these part-time jobs was a free ski pass.
When season pass prices fell from $800 to $200, many of
these part-time employees were no longer interested in the
resort positions. Consequently, again based on interviews
with resort marketing research managers, resort service quality suffered due to an unintended, significant increase in
resort employee shortages as a result of the buddy pass
programs.
In conclusion, yield management provided a robust conceptual framework for determining the impacts of the buddy
pass program. Clearly, the issues of cross-product elasticity
and market alienation are important to this evaluation. Furthermore, the results show the importance of moving beyond
yield management as an isolated pricing process. Rather,
yield management should be viewed as a strategic process,
including not only pricing but also product and promotional
strategies (Kimes and Chase 1998). Product strategies
include the development of new products aimed at developing off-peak business, such as the current emphasis on conference centers and golf courses at Colorado ski resorts. In
addition, anticipating the implications of new programs on
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010
22
AUGUST 2002
existing service quality and both customer and employee satisfaction is critical. Promotional strategies include both the
timing and utilization of different tactics and media but also,
increasingly, the use of the Internet and e-mail for last-minute discounting promotions.
REFERENCES
Adams, Bruce (2000). “Maxing Out.” Hotel and Motel Management, 215
(14): 38-40.
Allcock, John B. (1989). “Seasonality.” In Tourism Marketing and Management Handbook, edited by Stephen F. Witt and Luiz Moutinho. London: Prentice Hall, pp. 387-92.
Augustyn, Marcjanna, and Samuel K. Ho (1998). “Service Quality and
Tourism.” Journal of Travel Research, 37 (1): 71-75.
Babbie, E. R. (1992). The Practice of Social Research. 6th ed. Belmont, CA:
Wadsworth.
Baker, Dwayne A., and Daniel R. Fesenmaier (1997). “Effects of Service
Climate on Managers’ and Employees’ Ratings of Visitors’ Service
Quality Expectations.” Journal of Travel Research, 36 (1): 15-27.
Bejou, David, Bo Edvardsson, and James P. Rakowski (1996). “A Critical
Incident Approach to Examining the Effects of Service Failures on
Customer Relationships: The Case of Swedish and U.S. Airlines.”
Journal of Travel Research, 35 (1): 35-40.
Berry, Leonard L. (1980). “Services Marketing Is Different.” Business, 30
(2): 24-29.
Best, A. (1997). “That’s Entertainment.” Ski Area Management, 36 (3): 6667.
Blevins, Jason (2000). “Resorts Look Down Under for Hires.” Denver Post,
20 August.
Braun, Bradley M., Mark D. Soskin, and Mark Cernicky (1992). “Central
Florida Theme Park Pricing: Following the Mouse.” Annals of Tourism Research, 19 (1): 131-36.
Carroll, Margaret M., and Richard C. Grimes (1995). “Evolutionary Change
in Product Management: Experiences in the Car Rental Industry.” Interfaces, 25 (5): 84-104.
Chang, Sung-Ho, Alberto Garcia-Diaz, and Turgat Var (1998). “Revenue
Maximization for the Hotel Industry through Nested Booking Limit
Techniques.” Tourism Analysis, 3 (1): 53-56.
Chapman, Stephen N., and Jonathan I. Carmel (1992). “Demand/Capacity
Management in Health Care: An Application of Yield Management.”
Health Care Management Review, 17 (4): 45.
Childress, Rebecca D., and John L. Crompton (1997). “A Comparison of Alternative Direct and Discrepancy Approaches to Measuring Quality of
Performance at a Festival.” Journal of Travel Research, 36 (2): 43-57.
Desiraju, Ramarro, and Steven Shugan (1999). “Strategic Service Pricing
and Yield Management.” Journal of Marketing, 63 (1): 44-56.
DeVellis, R. F. (1991). Scale Development: Theory and Applications.
Newbury Park, CA: Sage.
Devlin, I. (1999). “Resorts as Retailers: Expanding Answer or Wrong
Turn.” Ski Area Management, 38 (5): 61-62.
Echelberger, H. E., and Elwood L. Shafer (1970). “Snow + (X) = Use of Ski
Slopes.” Journal of Marketing Research, 7 (3): 388-92.
Fisk, Raymond P., Stephen W. Brown, and Mary Jo Bitner (1993).
“Tracking the Evolution of the Services Marketing Literature.” Journal of Retailing, 69 (1): 61-103.
Gonzalez, Erika (1998). “New Perks Needed to Lure Ski Workers: Resorts
Provide Passes, Benefits and Bonuses.” The Denver Rocky Mountain
News, 18 October.
Hallowell, Roger, and Leonard A. Schlesinger (2000). “The Service Profit
Chain: Intellectual Roots, Current Realities, and Future Prospects.” In
Handbook of Service Marketing and Management, edited by Teresa A.
Swartz and Dawn Iacobucci. Thousand Oaks, CA: Sage, pp. 203-21.
Keates, Nancy (1998). “How Farebusters Play the Airlines.” The Wall Street
Journal, 20 March.
Kimes, Sheryl E. (1989). “Yield Management: A Tool for CapacityConstrained Service Firms.” Journal of Operations Management, 8
(4): 348-63.
 (1994). “Perceived Fairness of Yield Management.” Cornell Hotel
and Restaurant Administration Quarterly, 34 (1): 22-29.
Kimes, Sheryl E., and Richard B. Chase (1998). “The Strategic Levers of
Yield Management.” Journal of Service Research, 1 (2): 156-66.
Kinnear, T. C., and J. R. Taylor (1991). Marketing Research. 4th ed. New
York: McGraw-Hill.
Kraus, Paul J. (2000). “Pricing the Service Offering.” In Handbook of Service Marketing and Management, edited by Teresa A. Swartz and
Dawn Iacobucci. Thousand Oaks, CA: Sage, pp. 191-200.
Lazarus, Eve (2000). “Not Just a Ski Hill.” Marketing Magazine, 105 (40):
15-16.
Lewis, Robert C., Richard E. Chambers, and Harsha E. Chacko (1995).
Marketing Leadership in Hospitality: Foundations and Practices. 2d
ed. New York: Van Nostrand Reinhold.
Lieberman, Warren H. (1993). “Debunking the Myths of Yield Management.” Cornell Hotel and Restaurant Administration Quarterly, 34
(1): 34.
Lovelock, Christopher (2000). Services Marketing. 4th ed. Upper Saddle
River, NJ: Prentice Hall.
Manning, R. E., W. A. Valliere, B. Wang, and C. Jacobi (1999). “Crowding
Norms: Alternative Measurement Approaches.” Leisure Sciences, 21
(2): 97-115.
Meidan, Arthur (1989). “Pricing in Tourism.” In Tourism Marketing and
Management Handbook, edited by Stephen F. Witt and Luiz
Moutinho. London: Prentice Hall, pp. 305-9.
Metters, Richard, and Vicente Vargas (1999). “Yield Management for the
Nonprofit Sector.” Journal of Service Research, 1 (3): 215-26.
Moutinho, Luiz (1989). “Hotel Marketing Budgeting.” In Tourism Marketing and Management Handbook, edited Stephen F. Witt and Luiz
and Moutinho. London: Prentice Hall, pp. 199-204.
Norman, Ellis D., and Karl J. Mayer (1997). “Yield Management in Las Vegas Casino Hotels.” Cornell Hotel and Restaurant Administration
Quarterly, 38 (5): 28-33.
Pine, J., and J. H. Gilmore (1999). The Experience Economy. Boston: Harvard Business School Press.
Reece, William S., and Russell S. Sobel (2000). “Diagrammatic Approach
to Capacity-Constrained Price Discrimination.” Southern Economic
Journal, 66 (4): 1001-8.
Relihan, W. J. (1989). “The Yield Management Approach to Hotel Room
Pricing.” Cornell Hotel and Restaurant Administration Quarterly, 30
(2): 40-45.
Rust, Roland, Anthony J. Zahorik, and Timothy L. Kenningham (1996).
Service Marketing. New York: HarperCollins.
Schwartz, Zvi, and Stephen Hiemstra (1997). “Improving the Accuracy of
Hotel Reservations Forecasting: Curves Similarity Approach.” Journal of Travel Research, 36 (1): 3-14.
Shostack, G. Lynn (1977). “Breaking Free from Product Marketing.” Journal of Marketing, 41 (1): 73-80.
Spector, P. E. (1992). Summated Rating Scale Construction. Newbury Park,
CA: Sage.
Stoddart, Veronica G. (1999). “Savvy Shoppers Faring Well in Search of
Extensive Cruise Discounts.” USA Today, 22 January.
Tarrant, M. A., and D. B. English (1996). “A Crowding-based Model of Social Carrying Capacity: Applications for Whitewater Boating Use.”
Journal of Leisure Research, 28 (3): 155-68.
Wald, Matthew L. (1998). “So, How Much Did You Pay for Your Ticket?”
The New York Times, 12 April.
Waters, C. D. (2000). “Websites Vie to Bring ‘Dynamic’ Pricing to Restaurants.” Nation’s Restaurant News, 34: 67-68.
Weatherford, L. R., and S. E. Bodily (1992). “A Taxonomy and Research
Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing.” Operations Research, 40 (5): 831-44.
Williams, Peter W., and Karim B. Dossa (1998). “Ski Channel Users: A Discrimination Perspective.” Journal of Travel and Tourism Marketing, 7
(2): 1-29.
Withiam, Glenn (1999). “Better Ideas?” Cornell Hotel and Restaurant Administration Quarterly, 40 (4): 12.
Wyrick, Randy (1995). “Ski Bummer: Resort Towns Face Critical Worker
Shortage.” Colorado Business Magazine, 22 (10): 18-22.
Zeithaml, Valerie A., and Mary Jo Bitner (2000). Services Marketing. 2d ed.
New York: McGraw-Hill.
Downloaded from jtr.sagepub.com at Bibliothek d. Wirtschaftsuniversitaet Wien on December 9, 2010