Estimating the Demand Elasticity of Cinema Attendance

Estimating the Demand Elasticity of Cinema AttendancePaul N. Willis

Introduction and OverviewThe film industry is a market that is characterised by a relatively large amount of monopolistic power, where cinemas have the freedom to set prices to maximise revenue. This income is distributed between cinemas and the companies that produce the films, typically the studios. Very little published research has been carried out on measuring the demand elasticity of cinema-goers. This is perhaps due to the vast level of heterogeneity and innate subjectivity involved in these transactions. The last notable study that attempted to do so was conducted by S. Cameron in 1985, who concluded that cinema attendance was relatively inelastic to price and that cinemas were not maximising revenue as their ticket prices were too low.1

This conclusion is supported by subsequent data that shows that between 1987 and 2010, the inflation adjusted price of a cinema ticket rose from $5.8 to $8, whilst during the same period attendance per capita decreased only slightly from 4.5 to 4.42,3.

During this time, there are periods where price and attendance show a negative relationship, such as between 2002 and 2010. However, there are also times when the data shows a positive relationship, such as between 1995 and 1998 where attendance increased despite a simultaneous increase in price. The value of using a traditional regression analysis to account for external factors would be close to null with data of this type as there are many significant factors involved that are simply immeasurable. In order to reach any kind of accurate conclusion on consumer behaviour, a more innovative methodology is needed.

1. Cameron, S, “The Supply and Demand For Cinema Tickets: Some U.K. Evidence”2. Statista. Number of Movie Tickets Sold & Ticket Prices in the U.S. and Canada from 1980 to 20123. The World Bank. Inflation, GDP deflator (annual %) & Population, Total

The Orange Wednesday campaign was introduced in 2003 and allowed anyone who used the mobile network Orange a ‘buy one get one free’ offer to use at the cinema on Wednesdays. Since 2003, Wednesdays have gone from the least popular weekday to attend the cinema to the most popular.4 This offer is not entirely restricted to Orange customers, as the ‘buy one get one free’ voucher can be forwarded and shared between peers, and so in order for someone to get a half price cinema ticket, a consumer simply needs to know someone who uses the Orange network. Due to this drastic decrease in the price of cinema tickets, a relatively accurate portrait of consumer demand can be obtained by identifying the consumption patterns before and after 2003.

From this data, conclusions on the behaviour of specific types of consumers can be identified. For example, are younger audiences more or less willing to pay for a cinema ticket than older audiences? Is a consumer more or less willing to pay to see a film in its first week of release compared to its sixth week? These questions have significant strategic implications for the cinemas and the studios and it is in answering these questions that is the primary focus of this study.

MethodologyThe data used consisted of grosses of films in the UK weekly top 20 from 2000 to 2010. This sample identified how much each film took on each day of the week. The source was the Rentrak Company, who are industry leaders in collecting film market data.

For every week in the time period, the ratio was calculated of what each film grossed on the Wednesday compared to what it grossed on the Tuesday, and then the median of each year was plotted.

The offer was introduced in late 2003, but given the time it would take for this information to diffuse to consumers, the effects would be increasing year on year from 2004.

As such, we would expect the data to look flat or downward sloping if the consumer group was inelastic to the price change, as people would have not reacted significantly to the price decrease. The data would show an upward slope if the consumer group were price elastic as more people would have decided to visit the cinema on this day compared to before the price reduction;

4. Orange. Orange Wednesdays Data.�

Weeks where films had previews, (when the film has a limited release prior to its official release) were excluded from the data set as these weeks are characterised by vast fluctuations of grosses between days. The top 5% and bottom 5% of ratios were omitted from the sample set also, so as to further reduce inaccurate anomalies.

Films were categorised in terms of what age certificate they had received (U, PG, 12, 15, 18) and what week of release they were in. This was to identify how the consumer patterns of different groups varied towards different films.

ResultsThe collated data of all films in the sample show an upward slope, with the data spiking in 2005, before returning to a gradual increase afterwards. This suggests that audiences are demand elastic and that consumers are affected by changes in ticket prices.

The year on year change in elasticity can be seen as follows;

Year

2000 0.9832

2001 1.0021

2002 1.0075

2003 0.9764

2004 1.0692

2005 1.3838

2006 1.1737

2007 1.1869

2008 1.2106

2009 1.2348

2010 1.2505

Median of Wednesday to Tuesday Ratio

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The data was then categorised by which week the film was released in. The results show that the earlier the film is in its release schedule, the more sensitive to price changes consumers are. As films continue their run, consumers become less sensitive to the price reduction.

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This shows that when a film is first released, increased prices cause more of a reduction in the people who will go to the cinema than if a film is later on in its run.

An explanation for this behaviour is that when a film is first released there is a greater proportion of people who are undecided and are convinced one way or another based purely on the price of a ticket.

This has significant implications for the pricing strategies in the film business, not only from the profit maximisation view, but also with regards to inter-business negotiations, as the percentage of revenue that a cinema will pay to a film studio is much higher in the first few weeks of a film's release compared to later on in a film's run. Therefore, a decrease in price would benefit studios more than it would cinemas. This is discussed in more depth later in the study.

Films were then categorised by what age classification they received. It should be noted that this method is giving a comparative representation of consumer behaviour. For example, if a film received a 15 rating compared to an 18 rating, the consumer was not necessarily aged between 15 to 18. However, due to specialised marketing campaigns, films in each age bracket are targeted at specific age groups and as such this will increase the likelihood of each age rating attracting consumers of that category.

The data showed that consumers for U rated films were the least elastic to the price change. This suggests that adults taking young children to go and see film's care less about price than any other consumer group.

Consumers for films rated PG were more sensitive to changes in price, with the price decrease causing a larger proportion of people to buy tickets.

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Films rated 15 were the most price elastic, followed by films rated 12, suggesting that teenagers react most to fluctuations in ticket prices.

For films rated 18, consumers reacted less to the price change, showing an upward trend until 2007 and then decreasing significantly in the three years that followed. This suggests that the demand elasticity for this consumer group is relatively low.

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Executive Implications

This heterogeneity in the price elasticity of consumer groups has significant implications that can be used to shape strategic behaviour within the film industry. For example, the fact that different age groups had different price elasticities demonstrates that profits could be increased if a different price were charged to each consumer group. As the data shows that audiences for films with a U rating were the least elastic, charging this consumer group more for cinema tickets would result in higher profits. This explains why recent increases in the cost of a ticket due to 3D cinema have increased the performance of children's films, with U and PG rated films consistently having successful 3D releases.5

Due to consumers being more demand elastic to films earlier on in their release, offering discounts and promotions for newly released films would have a positive increase on revenue also, as the data shows that more people are on the fringe of deciding to go to the cinema during this time than any other.

The higher demand elasticity of newly released films gives implications on the revenue split between studios and exhibitors also. As studios take a larger percentage of revenue from films in their first few weeks of release, an increase in ticket prices would be disadvantageous to the studios as less people would be inclined to buy a ticket for a newly released film. This would not have as much of an impact on exhibitors who take a larger percentage of revenue for films later on in their run. This information would help in leveraging the bargaining power of the studios in the case of any proposed increase of ticket prices, ensuring that studios would be compensated with a higher share in revenue were such an increase to take place.

Perhaps the most significant implication of this study however, is in the fact that the behaviour of different consumer groups varies to such a large extent. This suggests that this analysis could be applied to other sectors of the film industry where uniform pricing is present, such as within Home Distribution. If such differences in the attitude to prices were found between consumer groups in this sector, this would give massive advantages to the studios, giving them the ability to adapt pricing strategy and increase revenue significantly.

5. Box Office Mojo. 3D Movies at the Box Office.�

References1. Cameron, S, 1986. “The Supply and Demand For Cinema Tickets: Some U.K. Evidence”. Journal of Cultural Economics. 10[1] pp.38-622. . Statista. Number of movie tickets sold & Ticket prices in the U.S. and Canada from 1980 to 2012, www.statista.com/statistics/187073/tickets-sold-at-the-north-american-box-office-since-1980www.statista.com/statistics/187081/average-ticket-price-at-north-american-movie-theaters-since-19803. The World Bank. Inflation, GDP deflator (annual %) & Population, totalwww.data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZGwww.data.worldbank.org/indicator/SP.POP.TOTL4. Orange, 2013. Orange Wednesdays. www.orange.com/en/sponsorship/films/Orange-Wednesdays5. Box Office Mojo. 3D Movies at the Box Office. www.boxofficemojo.com/genres/chart/?id=3d.htm

Annex

Table 1.1 Collated Data

Table 2.1 Categorised by Week of Release

Year

2000 0.9832

2001 1.0021

2002 1.0075

2003 0.9764

2004 1.0692

2005 1.3838

2006 1.1737

2007 1.1869

2008 1.2106

2009 1.2348

2010 1.2505

Median of Wednesday to Tuesday Ratio

Week of Release

1 2 3 4 5 and 6 7 and 8 9 and 15

2000 0.9563 0.9566 0.9875 0.9922 0.9817 0.9821 1.0495

2001 0.9757 0.9922 0.9739 0.9943 0.9882 1.0045 1.0417

2002 0.9824 0.9881 1.0142 1.0113 1.0009 1.0369 1.0449

2003 0.9458 0.9754 0.9477 0.9885 0.9908 1.0005 0.9943

2004 1.0679 1.0642 1.0776 1.0491 1.0738 1.0603 1.0679

2005 1.1261 1.1349 1.1657 1.1539 1.1637 1.1771 1.1198

2006 1.1681 1.1954 1.1788 1.1779 1.1753 1.1578 1.1556

2007 1.1856 1.2213 1.1794 1.1823 1.1962 1.1495 1.1386

2008 1.2148 1.2415 1.2229 1.1826 1.2205 1.0740 1.0681

2009 1.3032 1.2915 1.2422 1.2294 1.1355 1.0678 1.1562

2010 1.3407 1.3282 1.2252 1.1802 1.1654 1.2428 1.0903

Table 3.1 Categorised by Age

Graph 1.1 Collated

Age RatingU PG 12 15 18

2000 0.9828 0.9818 0.9769 0.9891 0.9791

2001 0.9606 0.9531 1.0071 1.0197 0.9896

2002 0.9959 0.9748 1.0002 1.0280 1.0149

2003 0.9448 0.9478 0.9761 0.9793 0.9885

2004 0.9557 0.9787 1.0671 1.0998 1.1177

2005 1.0835 1.0795 1.1694 1.1715 1.1250

2006 1.0877 1.1261 1.1877 1.2038 1.1820

2007 1.1040 1.1340 1.1797 1.2315 1.2396

2008 1.1033 1.1795 1.2053 1.2640 1.0500

2009 1.1928 1.2055 1.2401 1.2538 1.0350

2010 1.1177 1.1881 1.2849 1.3333 1.0566

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G raph 2.1 Release Week 1



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Graph 2.4 Release Week 4

Graph 2.5 Release Weeks 5 and 6

Graph 2.6 Release Weeks 7 and 8

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Graph 2.7 Release Weeks 9 to 15

G raph 3.1 Age Rating U

Graph 3.2 Age Rating PG

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Graph 3.3 Age Rating 12



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Estimating the Demand Elasticity of Cinema Attendance

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