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Olympic Ticketing

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Olympic Ticketing

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									                                    Abstract

The aim of this paper is to demonstrate a method of calculating the optimum price
for Olympic tickets. Therefore the objectives of the IOC, OCOG and the host city as
well as potential conflicts need to be considered.
Pricing of Olympic tickets represents as one veteran promoter put it "more art than
science". Today science has become more important than the promoter's desire to
get some kind of a "best price". There appears to be overwhelming evidence that,
practically, scientific methods, techniques, and investigations can contribute to
optimise the revenues generated by ticket sales. The knowledge required to estimate
a price/sales function seems to exist in techniques such as multidimensional scaling
and conjoint measurement. These techniques represent state-of-the-art knowledge in
marketing and are employed in major corporations world-wide to optimise revenues
from all kind of products and service.
Marketing science techniques, consumer behaviour models, and rational choice
theory are used to put the factors which influence the decision of OCOGs in order
when they think about the pricing of Olympic tickets. A research methodology to
collect the most important data for calculating the optimal prices will also be
introduced.
Finally, the array of actions an OCOG has in its power to determine ticket prices is
described and explained.
                                                                                     1


                       Olympic Ticket Pricing
Dr. Holger Preuss
Johannes Gutenberg-Universität Mainz
Germany

1. Introduction
Pricing of Olympic tickets represents as one veteran promoter put it "more art than
science" (Catherwood and van Kirk 1992: 88). Today science has become more
important than the promoter's desire to get some kind of a "best price". Although the
Olympics, the National Football League or the Major League Baseball are extremely
attractive to various media, revenues from ticket sales for the associated events still
come only in third place, way behind revenues from marketing and sales of TV
rights (cf. Howard and Crompton 1995: 139). The somewhat disappointing ranking
of the ticket-based revenues is, at least in part, due to the unexploited pricing
potentials embedded in the current pricing of Olympic tickets.
There are, however, three aspects of Olympic Games that make the calculation of
ticket prices somewhat of a challenge. First, when determining the ticket prices for
the Olympic Games, an organising committee (OCOG) finds itself in a monopolistic
position including the empowerment to "sell" a number of new products of extreme
high quality at the same time. The OCOG cannot simply "copy" the ticket prices of
major league sports events, because many top level sport events compete with each
other. Second, the Games always take place in different macroeconomic and socio-
demographic settings. As a result, an OCOG cannot simply carry-over the ticket
prices from past Games. Third, the Games usually take place in a country only once,
thus preventing a domestic OCOG from basing ticket prices on some past Games-
result.
Historically, the sales of tickets have contributed to the financing of the Olympic
Games since Athens 1896 (cf. Philemon 1896: 119). Not surprisingly, ticketing has
declined in significance, since revenues from television and sponsoring grew at an
astonishing rate. The evolution of ticket sales as a percentage of overall OCOG
revenues and the decrease of the importance of ticket sales since the seventies is
depicted in Figure 1.

Figure 1
Ticket sale revenues as percentage of overall OCOG revenues
2

            As percentage of overall OCOG revenues
      80
                                              72
                                                          66
                                                     61
      60




      40                                                       37
                 32                33
            26                                                                           25
                                                                                              23
                                                                            20
      20

                                                                    5   6            5
                                                                                 3
        0
                                                           4



                                                           6
                                                 tre '72




                                                           0
                                              At na 8
                                                          6




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           ol 08




                                            el in 8
                                                 ur '52
                 2




                                             Sy nta 2
                                 8




                                             rc ou 4
                                                        '6



                                                        '7




                                                        '0
                                               el l '8
                                                        '5




                                                dn '9
                                         M els '4
                               '2




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Sources: calculations by author; British Olympic Council (1908: 394); Organizing
Committee Stockholm (1912:43); Nederlands Olympisch Comité (1930: Bijlage I.),
Organising Committee London (1948: 29); Organizing Committee Helsinki (1952:
197); Organizing Committee Melbourne (1956: 155), Organizing Committee Tokyo
(1964: 62); Organisationskomitee München (1974: 33); Organizing Committee
Montreal (1976: 59); Organizing Committee Los Angeles (1984: 307); Organizing
Committee Seoul (1988: 221); Organizing Committee Barcelona (1992: 85); Or-
ganizing Committee Atlanta (1998: 222); Organizing Committee Sydney (1999: 71)

The aim of this paper is to demonstrate a method of calculating the optimum price
for Olympic tickets. Therefore the objectives of the IOC, OCOG and the host city as
well as potential conflicts need to be considered. Marketing science techniques,
consumer behaviour models, and rational choice theory are used to put the factors
which influence the decision of OCOGs in order. A research methodology to collect
the most important data for calculating the optimal prices will also be introduced.
Finally, the array of actions an OCOG has in its power to determine ticket prices is
described and explained by using econometric models.

2. System of Goals
It is important to know the objectives of the IOC, OCOG and host city before the
optimal ticket pricing can be found.

Table 1
                                                                                     3

Multidimensional goals and expectations regarding Olympic ticketing

                                Goals and expectations
 IOC  A) Free entrance for Olympic family
      B) Good atmosphere by full venues
      C) Complying with TOP-sponsor and TV-station contracts
      D) International quota of ticket sales
      E) Maximising ticket sales revenues
OCOG F) Complying with IOC rules
      G) Complying with sponsor contracts
Host H) Full venues/satisfied citizens due to fair ticket distribution and price
 city I) Many tourists attracted by international ticket sales

Free entrance for the Olympic family (A) is fixed in the Olympic Charter: "The [...]
accreditation card gives . . . access to the sites and events" (IOC 1999, Rule 66).
Comparing past Charters, the number of free seats the OCOG must provide has in-
creased continuously (cf. King 1991: 194-195).
Full venues and appropriate ticket prices for the citizens of the host country (B and
H) will contribute to a good atmosphere in the host city. The atmosphere in a
stadium is transferred by television, thereby influencing the image that the
international community receives of the Olympic Games. Munich'72 applied the
"principle of social pricing" (Organisationskomittee München 1974: 305).
Barcelona'92 declared that this was also its objective: "the prices were not as high as
the market would have stood" (Organizing Committee Barcelona 1992: 395). In
order to gain back trust in their work, the OCOG of Sydney had to give additional
"cheap" tickets to the population (dpa, Oct 29, 1999). However, the IOC wants:
"The prices of admission shall be kept as low as possible in order to ensure wide
attendance" (HCC, Jun 17, 1997: App. K). This issue was also suggested by the
IOC-reform-commission (dpa, Oct 29, 1999).
The packages sold to sponsors (C and G) contain the right to use Olympic emblems
as well as a ticket quota. Apart from the number of free tickets supplied, sponsors
usually buy additional tickets, which proves to be a problem. In Calgary '88, ap-
proximately 10% of all tickets went to sponsors (King 1991: 188-198), in Barcelona
'92 12.6% (Organizing Committee Barcelona 1992: 395), in Atlanta '96 three times
the number of Barcelona (N.N. Apr 18, 1996: 17) and in Sydney 9%. Coca-Cola
alone received 80,000 tickets whereas only about 45,000 tickets were provided for
the whole of Germany (cf. N.N. May 10, 1996: 25). Additionally, the number of
media representatives exceeds the number of free entrance to be provided according
to the Charter. The IOC must meet its obligations towards the stations paying for
their rights, resulting in the necessity for free ticket quotas.
The world-wide sales of tickets (D and I) gives the Olympic Games and the host city
an international atmosphere and enhances tourism in the host city and country.
4

An OCOG exists only for a short time. It has the main objective to run no deficit
(E). C.H. Battle (Managing director of Atlanta‟s OCOG) commented that "it
requires a lot of sensibility to achieve the highest possible profit without annoying
the spectators" (interview Jul 18, 1997). However, the OCOG tries to maximise
ticket sales revenues for example by using a rigid price elasticity concerning
opening ceremony tickets (Preuss 1998: 204). The idea of the Sydney ticket
management to sell some tickets up to three times of the face value in order to avoid
a deficit ended in a scandal. 840,000 tickets where not given to the general public
but held back as "Premium-Tickets" in order to sell them at a higher price to clubs,
agencies, and sponsors (dpa Dec 9, 1999). However, maximisation of profit is an
objective which can be seen by the packages sold to international sellers, forcing
them to buy also non-attractive sports.
The OCOG has to comply with IOC rules (F) written in the host city contract
(HCC). Although the ticket sales are the business of the OCOG, usually five percent
of the revenues must be given to the IOC.
Three systems with different sets of goals necessarily create conflicts. Table 2 shows
both intraorganisational (grey) and interorganisational conflicts.

Table 2
Matrix of conflicts due to the complex system of goals
                          IOC                            OCOG                 Host city
              A       B        C        D       E         F       G          H         I
       A      -     conflict                  conflict
  IOC B                -     conflict         conflict          conflict
       C                        -                                          conflict
       D                                -                                  conflict
       E                                         -                         conflict
 OCOG F                                                   -
       G                                                           -       conflict
  Host H                                                                      -     conflict
  city I                                                                               -

The intraorganisational conflict of the IOC is on the one hand the objective to fill up
the sport venues and on the other to provide free seats for the Olympic family,
sponsors and media representatives (A-B/B-C). Many of the facilities were only
partly filled because tickets given to the Olympic family and sponsors were not
used. For example, in Montreal „76 only 50% of the seats allocated to the IOC were
occupied, compared to 94% of the seats sold to spectators (cf. ERA 1981: 5). In
Seoul „88 people waited outside half filled venues and could not obtain tickets
(Maennig 1992: 50). In Barcelona German Olympic spectators criticised empty seats
                                                                                       5

which were reserved for sponsors and not released for sale (Messing and Müller
1995: 34-35).
The intraorganisational conflict for the host city is to get as many tickets as possible
for the citizens and attract many tourists to visit the city by having a high
international ticket quota (H-I). However, respecting the amount of tickets that have
to be sold internationally, citizens are more important for political reasons than
tourists. While intraorganizational conflicts can easily be solved by ranking the
goals, it is difficult to clear up interorganisational conflicts.
Concerning the pricing of Olympic tickets, the field of interest is the conflicts
involving an OCOG: First, the maximum profit for the OCOG is not necessarily
limited by the capacity as defined by the sports facility utilisation (B-E). The
monopolist OCOG might reach the maximum profit by having high ticket prices and
partly filled facilities. The same conflict exists between the OCOG and the host city
(E-H). Maximising revenues and filling the venue can only be harmonised if the
maximum revenues meet the maximum capacity of the sports venue.
Second, under the assumption that the seats given to the Olympic family, sponsors,
or media representatives could be sold to paying spectators, the OCOG would face
opportunity costs to the amount of the lost revenues (A-E). However, the HCC for
the Games in Athens 2004 states: "The OCOG shall provide to the IOC, at face
value cost . . ., such tickets as may required by the IOC, for itself and for its guests"
(HCC Jun 17, 1997: Appendix K).
Third, many tickets allocated to sponsors and the Olympic family are not used.
Additionally, the customers of the sponsors do not use their tickets and the seats in
the venues remain empty (B-G). This is a conflict of interests: On the one hand, IOC
and OCOG try to maximise the revenues from Olympic marketing by submitting
interesting offers to sponsors. On the other hand, they know about the significance
of the "good atmosphere" which is most likely achieved with full venues. To solve
that problem, the OCOGs sell more than 100% of seats for specific events and
reduce free tickets for events with a high demand. Since Barcelona Olympic family
members had to show a ticket apart from their accreditation card, when entering
highly demanded events. Furthermore, T. Bach (IOC-member) said they were
working on a system which should allow the general public to use the seats in case
sponsors do not occupy their seats (N.N. Apr 18, 1996: 17). The HCC for Athens
2004 already states that the OCOG must have "a proposed method of filling empty
seats on the dates of the events" (Jun 17, 1997: 16). Probably, sponsors will have to
pick up their tickets by a defined point in time. All tickets not picked up will then be
released for sale (Fischer Apr 29, 1998: 42).
The conflict between the IOC/OCOG and the host city is that the amount of tickets
given to sponsors, media representatives, and international agents has grown to a
degree that local citizens can't purchase enough tickets (G-H/C-H). From the
OCOG‟s financial point of view, the sales of as many tickets as possible to sponsors
is appreciated. This caused e.g. the above mentioned ticket scandal of Sydney.
6

3. Pricing of Olympic Tickets
The OCOG has to calculate the ticket prices, while the IOC approves them.
Considering the multidimensional objectives and various conflicts, rational choice
theory can help to bring the decisions of an OCOG in order. Friedmann and Hechter
(1988) created a diagram, combining the basics of all rational choice models:

Figure 2
The various paths to social outcomes in rational choice explanations
                                      Actors

                                   Information

Hierarchy of Preferences        Opportunity Costs         Institutional Constraints

                             Aggregation Mechanism

                                 Social Outcome

Source: Friedmann and Hechter (1988: 202)
The actors are the decision makers of the OCOG. "They act with the express
purpose of attaining ends that are consistent with their hierarchy of preferences"
(Friedmann and Hechter 1988: 202). Before deciding purely based on the OCOGs
preferences, it has to consider the preferences and goals of its environment.
However, constraints derive from a scarcity of resources (opportunity costs) as well
as from institutions. These constraints are set by the preferences of the IOC and the
host city. Therefore, within rational choice models, ticket price variations of
different Olympics can be explained logically due to variations in preferences,
opportunity costs or institutional constraints. Table 3 identifies such influencing
elements when trying to find the best pricing for Olympic tickets.
The ranking of the OCOG‟s preferences is easy, because the main objective is to
maximise revenues. However, through the aggregation mechanism the outcome is
also influenced by opportunity costs and institutional constraints. Opportunity costs
will be different for each strategy the OCOG is using when trying to reach
maximum profit. Here games theory can help to find the best solution. Finally,
institutional constraints imposed by the environment influence the decision forcing
the OCOG not just to follow its preferences. Concerning Sydney, ticket managers
were greatly influenced by the organisation – "prices are set as a function of many
factors such as political pressures, public perception, IOC biases, current economic
situation." (J. Bosiljevac, program manager, ticketing, letter, Feb 6, 2000).
                                                                                                          7


Table 3
Elements influencing the decision to price Olympic tickets
    Hierarchy of Preferences                   Opportunity Costs              Institutional Constraints

1. Maximise ticket sales                1. Bad atmosphere due to          1. Moral connection between
                                           unfilled venues                   OCOG and host city as
                                        2. Dissatisfied IOC                  well as same cultural back-
                                        3. Dissatisfied host citizens        ground of decision makers
                                        4. Dissatisfied sponsors          2. Prosperity level of
                                                                             population
                                                                          3. Complying with IOC
                                                                             - IOC-members free
                                                                             - TV-stations free
                                                                             - International sales
                                                                          4. Given capacity of venues

In contrast to the considerable growth potential inherent in marketing and TV-rights,
the potential revenues from ticket sales are limited. The number of tickets to be sold
depends on the venue capacities. Figure 3 shows the utilisation rate of venues of the
past Olympic Games.
Figure 3
Utilisation rate of tickets from Munich 1972 to Sydney 2000
              Utilization rate
        100
                                                             93
                                          90                                                 89

                                                     80                  80
         80                        **                                               77
                                  74
                   66

         60



         40



         20



          0
                 1972            1976    1980       1984    1988        1992*     1996*     2000

*      Free tickets could not be considered in the utilisation rate since they were included in
       accreditation.
**     ERA (1981: 4) stated a utilisation rate of 65%.
8

Sources: Calculations by author, data see Figure 1 and letter from Elphinston
(SOCOG, general manager Nov 7, 2000)

At each Olympics, most of the tickets have been sold. Therefore, an increase of this
revenue is only possible by finding the optimal price and best distribution system.

3.1 Pricing at past Olympic Games
Both the range of factors influencing the ticket price and the unpredictability of
many factors caused problems for all OCOGs to calculate the optimal ticket price.
The official Montreal „76 report stated: "But in 1973 it was extremely difficult, if
not impossible, to justify this figure [net revenues]: even the number of seats in the
stadium was not known" (Organising Committee Montreal 1976: 80). In Los
Angeles „84 the "LAOOC researched comparable events and prices" (Catherwood
and van Kirk 1992: 88). In Atlanta 1996 they only used the popularity of sports and
the pricing scheme of former Games as an orientation (C.H. Battle: interview Jul 18,
1997). Even in Sydney the OCOG just noticed a miscalculation of the number of
seats (N.N. Feb 4, 2000: 40). The complexity in pricing is best explained by this
quote from J. Bosiljevac (SOCOG): "Many factors were used to arrive at the pricing
– local sport costs, popularity, demand for specific sports – irrespective of price,
previous Olympic prices, budget pressures. All final sessions were able to be greatly
increased because demand always outstripes supply” (letter of Feb 6, 2000).
Preuss (1998: 204) calculated the average ticket prices of past Games. It can be seen,
that the high prices for the Olympics in the USA (1984, 1996) strongly differs from
those in other Western countries (1972, 1976, 1992, 2000) and the low prices in
Asia (1988). The average prices for the tickets give a clear insight into the different
willingness to pay by buyers in the various economic regions of the world. The
average price, however, does not reflect a clear trend. Therefore past Games do not
give advice on how to price tickets of the next Games. The basic factor when
determining prices is possibly the willingness to pay as the result of different levels
of prosperity.
Preuss also looked at the ticket prices for opening ceremonies (1998: 204). In
contrast to the average ticket price, the prices for the opening ceremonies have risen
steadily. The OCOGs have realised the rigid price elasticity referring to this unique
event. This trend can also be applied to other events in frequent demand, such as
gymnastics, swimming, track and field (cf. Kim 1990: 252). The growing gap
between the lowest and the highest price of opening ceremonies is striking. This
indicates a stronger price differentiation. However, political constraints help to
explain this. By offering some relatively cheap tickets, an OCOG can justify high
prices because it offers tickets to almost all residents of the city. In Sydney only
10,000 tickets of the opening ceremony tickets cost US$ 55, while 91,000 tickets
were sold at much higher prices up to US$ 727 (Voigt Jul 7, 1999: 25).
                                                                                        9

Irrespective of the price level of tickets for events in great demand, such as opening
ceremonies, OCOGs tend to be oriented towards profit. Another way to increase
revenues is to sell luxury boxes in the stadium such as the stadium in Sydney which
has 125 boxes (Howard and Crompton 1995: 148-150; Voigt Jul 7, 1999: 25).

3.2 Factors influencing the price of tickets
The continued addition of new Olympic events and sessions increases the average
number of tickets available. The spectator capacity (C) depends on the number of
sessions (E) and the size of the facilities (S). The seating capacity corresponds to the
maximum number of tickets if free seats (F) are subtracted from the entire seating
capacity of the facilities.
                  E
         C =  (Sn - Fn)                                                        (1)
                 n=1
To calculate the possible revenue (R) from ticket sales in advance the utilisation rate
(o) and the average ticket price (P) must be integrated into equation (1):
         R = C x on x P n                                                       (2)
Increasing one of the four factors (E, S, o, P) by keeping the others constant leads to
an increase in revenue. Preuss (2000: 155) examined these factors in detail. Here the
utilisation rate (o) and determination of the optimal price (P) is most important and
will be explained in more detail in the next sections.

3.3 Collecting data
In the following, some guidance to OCOGs in their search for help when
determining ticket prices is offered. Clearly, using prices of other major
competitions or of the past is not sufficiently due to the unique nature of Olympic
Games. Also, classic price theory models can hardly be used since they call for the
knowledge of price/sales and cost functions, which cannot be fulfilled in practice.
However, the marketing discipline has developed techniques and models permitting
to estimate the willingness to pay of spectators at the various Olympic events. These
techniques include, among others, conjoint analysis and multidimensional scaling.
The author has used these techniques in preliminary efforts to gain an understanding
of spectators‟ preferences for Olympic events and their willingness to pay for single
sessions. In the following, the aforementioned techniques will be described and
discussed. First, the Olympic events have to be “broken down” to socalled attributes
and attribute levels.

Table 4
Dimensions to consider when estimating the optimal price
  Dimension                         Attributes                  Flexibility   Uncertainty
               Closeness to final                                  fixes         No
10

     Time       Time of day                                          variable    no
                Day in the week (weekend)                            variable    no
   Location     Distance of facility to Olympic centre                fixes      no
                Distribution of seats in the facility                variable    no
                Attractiveness of sport for national audience         fixes      yes
    Quality     Attractiveness of sport for international audience    fixes      yes
                Price elasticity                                      fixes      yes
                Prosperity of population                              fixes      yes
 Social aspects Price idea                                            fixes      yes
                Willingness of population to pay for leisure         variable    yes
                Willingness to watch sport with a group               fixes      yes
                Consideration for special target groups              variable    no
  Framework IOC or International Federation (IF) contracts            fixes      no

Attributes may be "fixes" in the sense that the levels of the attributes cannot be
determined by the OCOG. It simply has to accept the level of the attribute when cal-
culating the price. However, a problem is the uncertainty an OCOG has about some
attributes and their levels.
Due to knowledge of the competition schedules, there is no uncertainty about the
dimension 'time'. The OCOG has great flexibility, however restricted by contracts
with TV-stations and IFs, claiming a specific timetable for competition. Data can be
used from past Games (cf. Lee 1989: 95-117) and the time when most people are
interested in watching sports as well as the closeness to the final have to be
considered in order to differentiate prices. The dimension 'location' is fixed and
known. However, data about the facilities and their capacity have to be evaluated.
'Quality' is the dimension an OCOG has to do research on. The attractiveness of
sports for national and international audiences has to be figured out. It is also critical
to know the price elasticity for different sports. Maximum revenues can only be
reached by considering the 'social' dimension. While the prosperity level and price
idea is given, the willingness to pay for events can be influenced, e.g. by
commercials. It is important to consider, that spectators are influenced by others
when deciding to buy tickets (cf. Preuß 2000). The consideration for special target
groups depends on the opportunity costs an OCOG has to bear when ignoring them.
Finally, the constraints put up by framework conditions have to be evaluated.
Learning from past Games is limited to Games' schedules (dimension 'time') and
optimal seat distribution (dimension 'location'). Knowledge about the attractiveness
of sport for international audience is reduced to the number of tickets given to
international agents of past Games. A falsification occurs because, since Montreal
„76, international agents have to buy packages by linking "non-attractive" with
"attractive" tickets. Concerning the overall interest in specific sports OCOGs could
also look at venue utilisation rates. Here different seat capacities and free tickets in
order to fill the venues (KIM 1990: 257) create falsifications.
A potential method to collect data for the next Games is to employ questionnaires. In
Sydney the "Domestic Visitors Study" was administered via Roy Morgan's face to
                                                                                    11

face Consumer Opinion Trends omnibus survey. It aimed to collect information on
the level of interest in attending an Olympic event within different target groups
(Roy Morgan 1996: 1). However, the data collected had been at a level to justify a
ranking of sports by attractiveness at most. Generally, using questionnaires to collect
data offers only limited insights into spectators‟ willingness to pay for Olympic
sessions.
A profit-maximising price policy requires a derivation using a consumer perspective.
A sufficient market price for tickets depends on the preferences assumed by the
spectators. Each OCOG has the same problematic situation in finding the optimal
prices, because many new products (events) are offered at the same time in the same
city. Here a solution in six steps is suggested. The assumptions are: The Olympic
Games are more than just top level sports events. The attractiveness of the host
country, the direct Olympic atmosphere, the psychic value to be part of the Olympic
Games etc. determine the decision to visit the Games. Therefore the decision to buy
a ticket is independent from that of visiting the Games. This reduction is realistic
according to Preuß (2000).
1. Multidimensional Scaling (MDS): Through MDS one displays all Olympic events
as a geometrical display with two or three dimensions. The positions of the events
are determined through judged relations reflecting individual perceptions of
similarity. This implies that, MDS reflects perceptions of the different events
through the spectators "eyes". Sport as a product consumed by spectators has
dimensions such as prestige, interest in seeing famous athletes, identification with
respective sport, or the prospect that the own nation wins a medal. The positions of
all events in the prospected geometrical "picture" regarding the relative location to
each other is called a multidimensional map. The map shows, which events are close
to each other in the mind of the spectators. This implies that certain events are in
competition with each other and, therefore, should have similar ticket prices.
To illustrate, ten events had to be judged by German sports students from Mainz.
This homogeneous group of young sports-interested academics (n=40) created the
following aggregated configuration:
12


Figure 4
Positioning model of 10 Olympic events (simulated dimensions)


                                                                  Closing
           personal closeness




                                                                  Ceremony
                                                                      Opening
                                                                      Ceremony
                                     Sailing
                                Wrestling                  Gymnastics
                                                                    Athletics
                                 Judo

                                                                 Swimming
                                                                Basketball
                                                   Volleyball


                                                            attractivity
                            n=40, Stress-formula 1 = 0.14
2. Building of clusters: The result of a multidimensional task (MDS) is a map which
can help to build so-called clusters. Knowing such clusters can have important
implications, for example, when reducing the number of attributions or, as in this
case, limiting the number of the levels that an attribute may have. The resulting,
more parsimonious attribute/attribute-level configuration can subsequently be used
as a basis for conjoint measurement (which will be explained in step 3). In
comparison to a simple ranking of the sports-events MDS has two main advantages:
First, it can be determined which events are located close to each other in spectators‟
cognitive structures (as represented in a multidimensional perceptive space) and
therefore justify a similar ticket price. Second, the relative distance between the
clusters indicates the degree to which the differentiated pricing may be feasible.
3. Conjoint Analysis (CA): The method of conjoint analysis identifies the
contribution of the attributes and their levels toward the total utility of a product or
service. Such products and services can practically anything marketed including
airplane tickets, bank accounts, hotel accommodation or Olympic tickets. When
marketing such products and services, it is important to know what the contribution
of different components or attribute levels toward the total utility of e.g. an Olympic
event is. Knowing these contributions (called part worth in CA) can allow an OCOG
to find optimal prices for their events, such as a price that maximises revenues or a
price that maximises attendance, etc.
A theoretical premise of CA is that the total utility is the sum of the benefits of all
components or attribute levels. To arrive at such insights, data using socalled trade-
off judgements of two events by potential spectators will be needed.
                                                                                          13

Generally, one might argue that the opportunity to watch an event at the Olympic
Games is a service. This seems to be the case since services and Olympic events
share the important property of being non-storable. Also, an interesting hypothesis
has been put forward in the sense that it is only of limited interest to the spectators to
know which person will end up winning the event compared to the spectators‟
enjoyment to simply watch the event. CA allows this hypothesis to be empirically
tested. If in fact spectators‟ "presence" at an event turns out to be much more
important than the final winner of an event, modifying the organisation of Olympic
events as they affect spectators‟ purchase decisions may be in order. Second, only a
few key attributes may determine the decision to buy. These attributes and their key-
levels need to be identified exactly. Third, there is reason to believe that the costs of
staging the Olympic Games are not the criterion determining the ticket prices, but
rather spectators‟ willingness to pay. Fourth, there is no competing event during the
Olympics giving rise to the hope that much pricing and revenue potential remains
untapped (see also Woratschek 2000). Finally, an important challenge when
designing a CA needs to be mentioned as well. CA typically asks for individual
decision making by a spectator. Since many visitors to the Games watch the events
in groups, the decision process can be influenced by other members. This fact needs
to be incorporated in the design of a CA for Olympic ticket pricing.
A unique peculiarity of CA is that the spectator can make realistic decisions due to
the fact that he/she have to judge different events as a whole. The whole service
consisting of attributes illustrated in Table 5.

Table 5
Attributes and levels
                       Attributes                                     Level
Price (considering prosperity level, price idea)                  10 – 300 US$
Closeness to final                                          final / semi-final / round
Time of day                                              morning / afternoon / evening
Day in the week (weekend)                                    working day / weekend
Distribution of seats in the facility                          closed / middle / far
Attractiveness of the event (considering distance of    28 sports, opening ceremony and
facility to Olympic centre)                                     closing ceremony
Visiting the session of an event with a group              with group / without group

In practise, CA may often faces a rather large number of attributes. The OCOG has a
detailed understanding of its products but, usually there are many issues of interest.
As a result, only those attributes most important for the spectators‟ decision to
consume or not consume the session of an event should be included in the CA. Thus
the OCOG faces the problem of reducing the number of questions by limiting the
attributes and their levels. A first reduction of attributes is to take out the distribution
of seats in the facility. This differentiation of prices can be done in step 4, a so-
called hierarchic CA. A second reduction can be done by using the adaptive conjoint
14

analysis (ACA), a PC-based software for conjoint (trade-off) analysis. It is an
alternative to so-called full-profile conjoint when having a large number of
attributes. The term "adaptive" refers to the fact that the computer-administered
interview is customised for each respondent. Data are analysed as the interview
progresses, and questions are chosen on the basis of the likelihood to reveal most
about the respondent's values in the shortest time.
Conducting an ACA for only one cluster (step 2) could further reduce the levels of
the attribute "attractiveness of the event" to 3 or 4 events. Therefore the levels of the
attribute "price" could be limited to a specific price range.
Additionally, ACA lets the OCOG simulate respondent preferences for the events. It
can be used to explore "what if" scenarios such as changes in prices, event
formulation, or marketing activities. Spectator utilities can be used to estimate
strengths of preferences or buying likelihoods for each event. This provides data
about the price elasticity.
In practice first the OCOG has to identify all attributes and levels. Second the data
have to be evaluated from different target groups (see also steps 4 and 5). Therefore,
different events have to be judged. These data are the basis to estimate the part-
worth utility. The values of the part utility have to be interpreted individually by the
respective OCOGs especially by looking at the part worth.
4. CA for every event: A detailed price-analysis should be conducted for every event.
Additionally, event-specific attributes can be incorporated into the design. In this
step data of the price/sales function could be collected and evaluated. However, a
CA can only produce a limited range of the price-sales function. Certain theoretical
steps might not be visible and therefore be ignored which can lead to erroneous
decisions of the OCOG.
5. International survey: Steps 1-4 have to be repeated in countries with a high
demand for tickets.
6. Price policy models: The parts of the price/sales function have to be put into the
price policy model which will be explained in the next section.

3.4 Theory of pricing Olympic tickets
Preuss (2000) discussed the opportunities of raising prices when having different
capacities of venues. The marketable number of tickets depends on the price due to
the fact that the OCOG is a monopolist that attracts the demand: the higher the price
the lower the number of marketable individual tickets and vice-versa. The OCOG's
demand curve (price/sales function), which is, at the same time, its price curve, falls
when the number of entrance tickets rises. An OCOG achieves the maximum profit
when the difference between turnover revenues and event costs is largest.
The ticket sales turnover function which depends on the price/sales function differs
for each sport event based on the levels of attributes shown in Table 5 as well as on
each venue's capacity. Price considerations must therefore be oriented towards
events capacities.
                                                                                                             15

The following models have four assumptions: The prices for the tickets do not
differ. The average of the costs and turnover of all events taking place in one facility
are used. The variable costs increase linearly. The approval of prices by the IOC will
be assumed to be done after the following calculation.

Figure 5
Turnover and cost function of tickets for an assumed price/sales function and
variable capacity

   Price / Turnov er / Prof it / Costs
                                                               T3
                                                               x
   P3                                                    T2
                                                         x



 P2                                         T1
                                             x
                                                                                                  tion
                                                                                             func
                      F'
                                                                                       C ost
                                                     B




                                                                                           Tur
   P1




                                                                                             nov
                                                 A



                                                                                                 er
                                                                                                 fun
   p
   1

                                                                                                    ctio
   p
   2
   p
   3
        E                                                                Price
                                                                              /dem                      n
                                                                                     and
                                                                                         func
                                                                                             tion
            Free tickets
        O             F                       C1          C2        C3                                   G
                           Capacity                                                                   Quantity
                            Temporary extension
                                     Capacity of extension


The monopolist OCOG is nearly free to determine the ticket price. Therefore the
turnover function has the shape of a parabola. The point (F) where it intersects with
the abscissa defines the number of free tickets. The more free tickets that are
distributed, the further the parabola is moved to the right. The cost function remains
fixed. It implies that the turnover to be achieved when the stadium is filled (C1)
moves from T1 in the direction of F as the number of free tickets increases. From the
point when the curve intersects with the cost function, a deficit for the OCOG is the
result.
The turnover function is formed using the price/sales function. However, here only a
part of this function is important. Therefore the certain points of the price/sales
function calculated by the CA can be sufficient. The width of the parabola is
determined by the interest of the population in Olympic events. The height depends
16

on the price the population is willing to pay for tickets. The lower prosperity or
interest in the Games are, the flatter and narrower the turnover function will be.
The costs of an OCOG for a facility are represented by the cost function. The
intersection with the ordinate defines the fixed costs (OE). To simplify, the increase
in the variable costs to the capacity limit (C1) is represented linearly. The fixed step
costs (A) represent the costs for constructing a temporary stand. (B) represents the
costs for an extension. If the fixed step costs intersect the turnover function the
quantity for the minimisation of a deficit would have to be determined instead of
calculating that for maximisation of profit in the following. After the respective
fixed cost step, the further increase in the costs is the same (variable costs).
The maximum profit is gained where the distance between cost and turnover
functions is largest. If the cost function is above the turnover function the deficit is
lowest at the point where the distance between cost and turnover functions is
smallest. Figure 5 represents three cases: For a given demand curve and the assumed
stadium size, the maximum profit is at the capacity limit (P1). The price to just fill
the stadium would be p1. Even if the stadium was expanded by temporary stands the
maximum profit would remain at the size limit (P 2) but the lower ticket price p2
would have to be charged. With an assumed extension, the maximum profit (P3)
would be below the capacity limit. The maximum profit would always be at the
capacity limit as long as the turnover is to the left of the point T 3. The maximum
profit, on the other hand, would lie in T 3 if the capacity utilisation meant a turnover
to the right of T3.
Figure 5 reflects that for all three cases assumed, the turnovers (T 1, T2, T3) are higher
than the costs. In other words, a profit was achieved. As long as the ticket price can
be determined freely, an extension of the facility should to be aimed for as long as
the distance between cost function and turnover function is largest. In this case, only
a temporary extension would be recommended since P 2 > P1 > P3 an extension
would yield a profit (P3), but this would be smaller than that achievable by
constructing a temporary stand.
In the following, two situations are examined where an OCOG must decide on the
ticket prices and where the underlying conditions are closer to reality. The first
situation is one that would take place well ahead of the Olympics at a time when the
OCOG can freely decide on the ticket prices and the capacities of the venues can
still be changed. The second situation occurs a few months prior to the Games when
an OCOG has determined ticket prices but can still expand the stadium capacity by
temporary stands (Organizing Committee Atlanta 1998: 117-118, 123).

3.4.1 Model in the case of free price decision and free capacity
The OCOG should keep in mind that changes in the ticket price will influence the
number of tickets sold. To make the reflections less complex in the following, the
cost function is simplified by assuming that all costs are fixed. The maximum profit
is at the climax of the turnover function.
                                                                                  17

The concept of the price elasticity of demand is the theoretical framework for
altering the demand with differing prices. This is the ratio between a relative price
change and the relative change in the number of tickets sold which this price change
causes.
Two sports venues are analysed. The capacity (C1) of one is below, that of the other
(C2) above the number of tickets for profit maximisation (x max). Since prices can be
determined freely long before the Olympics, two prices at an elasticity > 1 (p 1, p2)
and two prices at an elasticity < 1 (p3, p4) will be selected as examples.

Figure 6
Price elasticity > 1
               Price
               Turnover
               Costs




                             b     a
                 p
                  1
                 p
                  2                    a
                                   c


                                                                 quantity
                              C1           xmax           C2
Figure 7
Price elasticity < 1
               Price
               Turnover
               Costs




                                                  e

                                       e              d
                 p
                   3
                 p                                d
                  4
                              C1           xmax       C2        quantity
18

If the OCOG pursues the objective to fill the facilities the limited number of seats is
the attribute of action. The ticket price is determined by the point where the quantity
forming the capacity limit (C1 or C2) intersects the price/sales function.
If the OCOG pursues the objective of maximum profit, the profit-maximising price
is selected and the number of tickets sold results from the price/sales function. The
ticket price is determined by the point where the quantity forming the maximum
profit intersects the price/sales function. The OCOG does not suffer a conflict of
interests as long as the capacity limit of the sports facility is to the left of (e.g. C1) or
directly at the maximum profit quantity (x max). Then a filled venue always means
achievement of the maximum profit possible.
If, however, the capacity limit is to the right (e.g. C 2) of the maximum profit
quantity (xmax) the situation is different. A full venue then means that the maximum
profit cannot be achieved or, if maximum profit is pursued, some seats will remain
empty.
At an elasticity > 1 (Figure 6) the sales of tickets will rise with a falling price. Here
the quantity effect overcompensates for the price reduction. A price reduction is
sensible if only few tickets can be sold for facilities with large capacities such as
qualifying rounds in football. Whether a price reduction will fill the entire facility
(b) depends on the capacity (C1 or C2). If the number of tickets leading to the
maximum profit (xmax) is smaller than the number of tickets which would have to be
sold to fill the venue (C2), an OCOG pursuing maximum profit should fill it only
partially (a). If the venue is filled at an elastic demand, the recommendation would
be to increase the price to the point where the venue can just be filled (c). In this
case, it should be checked whether it would not be even better to leave the price
unchanged and satisfy the high demand by increasing the capacity through
temporary stands or extensions. However, as mentioned above, each extension leads
to increased costs. The distance between turnover function and cost function is then
the criterion for deciding whether to expand capacity.
At an elasticity < 1 (Figure 7), sales of tickets also rise with a falling price.
However, the quantity effect does not compensate for the price reduction. At a non-
elastic demand it is recommended to increase the price until the profit-maximising
number of tickets (d) is sold or until the facility is filled (e) in the case of a low
capacity (C1). Here it must also be considered whether a capacity increase would not
be more profitable.
For OCOGs which pursue maximum profit the following applies when determining
ticket prices: If the capacity of a facility (C2) is larger than the profit-maximising
sales quantity of tickets, seats remain empty (a, d). If the capacity limit lies below
the profit-maximising sales quantity of tickets, the price is changed until the demand
meets the capacity (b, c, e). Alternatively, it must be determined whether a capacity
expansion would serve to increase profit.
The fact that it is the aim of a profit-maximising OCOG to maximise the sum of all
profits and not only the profits from individual venues must be considered as a
higher priority than the price decision for each sports venue. Therefore, the OCOG
                                                                                     19

could prefer hosting two sports parallel to each other in a single hall without offering
the profit-maximising number of tickets for each type of sport over the choice to
erect another hall. In this way, although the OCOG might make a deficit for one
sport, the sports venue itself can be utilised for the other type of sport in a profit-
maximising way.

3.4.2 Model in the case of fixed prices and limited capacity
Once the price has been determined, the OCOG loses the price as a attribute of
action. International ticket sales start very early. For some venues an OCOG might
therefore be able to increase capacities to a certain extent through temporary stands
when there is the case of an unexpectedly high demand. This will become
increasingly important, as is already the case in Sydney where it has been decided to
determine the number of permanent seats in the venues exclusively by the expected
follow-up benefit. Therefore huge temporary stands have been erected for the
Games.

Figure 8
Turnover and cost functions of entrance tickets at fixed prices


    Price
    Turnover
    Costs
    Profit
                                                        T2
                                                         x
        P2


                                               T1
                                                    x
        P1
                                           n




                               n
                                          io




                          ctio
                                       ct




                   t f un
                                      un




                Cos
                                   erf
                                ov




                                                              Price
                               rn




         p
                          Tu




          1
               Free tickets
          O                   Capacity           C1      C2           quantity
                                                              x
                               Temporary extension            1



Figure 8 shows the turnover function at a fixed price (p1) which is relatively low.
The OCOG can only change its profit by the number of tickets sold. Here, there is a
20

demand up to the quantity x1. Considering the fixed cost step for the temporary stand
the largest distance between turnover function and cost function up to x1 needs to be
determined. In the given example, the temporary expansion is worthwhile since
profit P2 is larger than P1 when the existing capacity is utilised. The turnover
revenue of the temporary construction is larger than the additional costs involved.

4. Conclusions
There appears to be overwhelming evidence that, practically, scientific methods,
techniques, and investigations can contribute to optimise the revenues generated by
ticket sales. The knowledge required to estimate a price/sales function seems to exist
in techniques such as multidimensional scaling and conjoint measurement. These
techniques represent state-of-the-art knowledge in marketing and are employed at
major corporations world-wide to optimise revenues from all kind of products. The
pricing model embedded in the aforementioned techniques can be used to consider
examples with different prices, capacities, fixed cost steps, or different sports. For
example, it has been shown that changes in the seating capacities of venues and
prices strongly influence the revenues an OCOG obtains from ticket sales. However,
these changes were caused by luck and not the result of scientific inquires as MDS
and Conjoint offer - thus making them independent of lucky strikes. Also, the
aforementioned calculations were not focused on opportunity costs or institutional
constraints which have been mentioned in section 2. Only the preference of the
OCOG to maximise the revenues from tickets and the relationship with the
preferences of potential spectators has been considered.
Looking ahead, the internet will provide a huge opportunity to enhance the
marketing of tickets - especially once the pricing potentials for the various events are
identified, exploited, and managed. Additionally, the European Union will make
country contingencies of tickets unnecessary, e.g. in Spain there was no longer an
exclusive ticket agent. Spanish spectators had to order their tickets internationally.
Good information politics can be expected to increase demand and spectators'
willingness to pay. The OCOG may react by increasing the prices due to the fact that
venue capacities are limited. This may create conflicts with the general
"environment", as such pricing will accelerate a polarisation into "rich" and "poor"
spectators. The prosperous segment of a society will be able to afford to watch the
Games in a stadium, while the poorer segments will watch TV, assuming that TV
remains free of charge.
Note, however, that a solution to this seeming "discrimination" would be to use the
lotto-system, similar to those employed in Munich „72 and in Moscow „80 (Preuss
2000: 182) for a fair distribution of tickets. One part of the tickets could be sold at
higher prices due to market demand which is dependent on their ability to pay. The
other portion could be distributed via lotto to "winners" which is independent of
their ability to pay. The OCOG could participate in the rising lotto income.
                                                                               21

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