The Movie Industry as a Marketing Model for Forecasting Sales Performance
Research by Pradeep K. Chintagunta
"Sure things" flop. Small-time films soar. Despite
the best efforts and big budgets of the studios, marketing
remains an inexact science, and movie moguls cannot always
predict how their products will fare at the box office. Recent
research can help clarify their marketing departments' cloudy
crystal balls.
One challenge of predicting new product performance in global
markets - in this case, movies - is the overabundance of competing
information sources. Additional challenges arise when products
are launched sequentially. Information becomes available at
different points and requires new forecasts to be generated
at each stage.
Pradeep K. Chintagunta, a professor at the University of
Chicago Graduate School of Business, and Ramya Neelamegham
of the Amrita Institute of Management in India, examine these
issues in their study, "A Bayesian Model to Forecast
New Product Performance in Domestic and International Markets."
Using the movie industry as a framework, the authors developed
a statistical approach that forecasts what movies will perform
well in which countries, particularly during the first week
of release.
"The movie industry is ideally suited to studying whether
information in one market can be used to predict success in
other markets," says Chintagunta.
Furthermore, the authors' methodology can be applied to almost
any industry that uses estimates. In the domestic market,
the model could predict launches in different states or regions,
perhaps to forecast video sales of a movie using data from
the stage performance. Internationally, it can be used in
any product category where there is a sequential launch. Examples
include Citibank's sequential launch of credit cards in the
Asia-Pacific region and the launch of the nicotine patch first
in Ireland and then the United States.
The Making of a Successful Model
Chintagunta and Neelamegham's model works as follows. Movies
are first released in the United States and Canada before
they are released in other countries. As a result, information
on the performance of a new movie in these regions can be
used to make forecasts for overseas markets. Information from
the United States and Canada is combined with the historical
performance of previously released movies in those international
markets to arrive at a forecast for new movies in those markets.
Choosing 35 movies, the authors analyzed their data for the
United States, Australia, Brazil, Canada, France, Germany,
Italy, Japan, Mexico, the Netherlands, Spain, South Africa,
Sweden, and the United Kingdom. The last 13 countries represent
more than 80 percent of the overseas box office for American
films. In most countries, average first-week viewership almost
doubles that of average overall weekly viewership, so the
authors focused on forecasting first-week performance.
Chintagunta and Neelamegham gathered much of their data from
the domestic and overseas box office receipts reported in
Variety Magazine from January 1, 1994 to May 26, 1996. In
addition to revenue, the data included the number of screens
showing a film, the number of weeks of the run, the rank,
the number of first-run engagements, and the name of each
film's foreign-rights holder and local distributor. To obtain
information on attributes such as genre, the presence or absence
of major stars, critical reviews, and Motion Picture Association
of America (MPAA) ratings, the authors turned to the Cinemania
Index.
The Making of a Successful Movie
Decision makers in the film industry need forecasts at five
stages, so Chintagunta and Neelamegham tested their model
at each stage. When first evaluating the market, information
is linked to a historical database of previous performance
releases in international markets. Studios use forecasts at
this point to help them pick a movie and the markets for release.
After the movie has been produced, characteristics such as
genre and stars are known, and managers look for pre-launch
forecasts to help formulate the distribution strategy. The
third stage is the pre-domestic launch, at which point the
authors incorporated the distribution strategy.
This forecast helps executives plan marketing activity over
the movie's life cycle and make decisions about the international
launch. After the domestic release, domestic performance data
can be used to hone the forecast further. Finally, the authors
make a fifth forecast just before international launch, using
all possible information, to fine-tune strategic decisions
and assess the competition.
Plugging existing data into their model, Chintagunta and
Neelamegham determined that the key factor in a movie's success
in the United States is, as expected, the number of screens
showing the movie.
"This finding seems to apply to international markets
as well," says Chintagunta.
Big-name stars have a significant positive impact, especially
in the United States, Japan, and South Africa. Local distributors,
who provide greater attention and support, ensure increased
viewership internationally. It is a different story in the
domestic market, write the authors, where "the power
and financial muscle of major studios boosts increased viewership."
In terms of genre preferences, the United Kingdom, Canada,
Australia, and Italy prefer action movies; Japan and Korea
prefer thrillers; and audiences in the United States, Sweden,
Germany, and South Africa are partial to romances.
"What this means," explains Chintagunta, "is
that it is possible to create non-geographic groupings of
countries for movie marketing based on their relative preferences.
The way the groupings came out was somewhat of a surprise."
To test the model's accuracy in making specific predictions,
Chintagunta and Neelamegham developed first-week viewership
forecasts for 10 movies: The Net, Waterworld, Dangerous Minds,
12 Monkeys, Heat, Get Shorty, Seven, Toy Story, Jumanji, and
Nine Months. Comparing their predictions to actual viewership,
the mean absolute percentage error (MAPE) turned out to be
45.2 percent for the United States, 44.5 percent for Canada,
and 43.3 percent across all international markets.
The markets with the largest MAPEs were Brazil at 69 percent
and the United Kingdom at 64.5 percent. Chintagunta and Neelamegham's
estimates were much closer for Japan and Germany, at 21 percent
and 22.4 percent, respectively. Admittedly, these estimates
could stand improvement.
"One of the reasons for the current performance levels,"
explains Chintagunta, "is that we do not have access
to information on other variables influencing movie viewership,
such as advertising, production budgets, and release schedules."
Compared with other marketing models, however, Chintagunta
and Neelamegham's model is a great improvement from the guessing
game played by the studios. Because their approach not only
combines different types of information sources, it also works
with incomplete information, allowing for predictions early
in the movie-making process. As more information becomes available,
accuracy improves. Finally, the prediction it gives is an
estimated range, rather than a specific point estimate, and
more accurately reflects forecasting errors.
Chintagunta and Neelamegham's research, then, should prove
useful to movie executives who are planning and finalizing
their marketing activities and negotiating contracts.
"Our results underscore the theme that each movie is
unique, as is each country-and viewership results from an
interaction of the product and the market," Chintagunta
writes. "Hence, the motion picture industry should use
both product-specific and market-specific information to make
new movie performance forecasts."
From Movies to Videos and Even Camcorders
As the industry struggles to understand and predict sales
of new movies in domestic and overseas markets, Chintagunta
and Neelamegham's research is much needed. In fact, says Chintagunta,
some of the decision makers he interviewed for the study have
asked him to implement customized models.
Chintagunta, however, is moving ahead to new areas of study.
Applying his methods to technology products such as digital
cameras and camcorders, the wide range of products his model
can be used for is clear.
Today more than a hundred models are available in the market.
As new products are rapidly introduced, firms need to develop
forecasts for these models.
"Technology products seemed to be a natural next step,"
says Chintagunta. "If one looks at a category like digital
cameras, for instance, we started off with just a couple of
models a few years ago."
The work is complicated, he notes, by the fact that technology
product attributes are evolving constantly and the attributes
must be determined before forecasting sales performance.
In addition, because technology products such as digital
cameras are a relatively new category, consumer preferences
are evolving as well. Looks like Chintagunta will be busy
for some time to come.
