Predicting movie grosses: Winners and losers, blockbusters and sleepers
TL;DR: In this paper, the authors predict movie grosses: Winners and Losers, Blockbusters and Sleepers, and predict the box office performance of each movie based on its box-office performance.
Abstract: (2000). Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers. CHANCE: Vol. 13, No. 3, pp. 15-24.
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TL;DR: In this paper, the authors discuss critical practical issues for the motion picture industry, review existing knowledge on those issues, and outline promising research directions, focusing on three key stages in the value chain for theatrical motion pictures: production, distribution and exhibition.
Abstract: The motion picture industry has provided a fruitful research domain for scholars in marketing and other disciplines. The industry has high economic importance and is appealing to researchers because it offers both rich data that cover the entire product lifecycle for many new products and because it provides many unsolved “puzzles.” Although the amount of scholarly research in this area is rapidly growing, its impact on practice has not been as significant as in other industries (e.g., consumer packaged goods). In this article, we discuss critical practical issues for the motion picture industry, review existing knowledge on those issues, and outline promising research directions. Our review is organized around the three key stages in the value chain for theatrical motion pictures: production, distribution, and exhibition. Focusing on what we believe are critical managerial issues, we propose various conjectures---framed either as research challenges or specific research hypotheses---related to each stage in the value chain and often involved in understanding consumer movie-going behavior.
481 citations
Proceedings Article•
01 Jan 2006TL;DR: The main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release.
Abstract: The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job.
354 citations
TL;DR: Using data from a popular movie website, a metric of a purchasing population's propensity to rate a product online is introduced and it is found that it exhibits several relationships that have been previously found to exist between aspects of a product and consumers' propensity to engage in offline WOM about it.
Abstract: The emergence of online communities has enabled firms to monitor consumer-generated online word-of-mouth (WOM) in real-time by mining publicly available information from the Internet. A prerequisite for harnessing this new ability is the development of appropriate WOM metrics and the identification of relationships between such metrics and consumer behavior. Along these lines this paper introduces a metric of a purchasing population’s propensity to rate a product online. Using data from a popular movie website we find that our metric exhibits several relationships that have been previously found to exist between aspects of a product and consumers’ propensity to engage in offline WOM about it. Our study, thus, provides positive evidence for the validity of our metric as a proxy of a population’s propensity to engage in post-purchase online WOM. Our results also suggest that the antecedents of offline and online WOM exhibit important similarities.
238 citations
Proceedings Article•
02 Jun 2010TL;DR: This paper uses the text of film critics' reviews from several sources to predict opening weekend revenue and describes a new dataset pairing movie reviews with metadata and revenue data, and shows that review text can substitute for metadata, and even improve over it, for prediction.
Abstract: We consider the problem of predicting a movie's opening weekend revenue. Previous work on this problem has used metadata about a movie---e.g., its genre, MPAA rating, and cast---with very limited work making use of text about the movie. In this paper, we use the text of film critics' reviews from several sources to predict opening weekend revenue. We describe a new dataset pairing movie reviews with metadata and revenue data, and show that review text can substitute for metadata, and even improve over it, for prediction.
208 citations
Book•
18 Jun 2004
TL;DR: The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis.
Abstract: DATA ANALYSIS AND DECISION MAKING is a teach-by-example approach, learner-friendly writing style, and complete Excel integration focusing on data analysis, modeling, and spreadsheet use in statistics and management science. The Premium Online Content Website (accessed by a unique code with every new book) includes links to the following add-ins: the Palisade Decision Tools Suite (@RISK, StatTools, PrecisionTree, TopRank, RISKOptimizer, NeuralTools, and Evolver); and SolverTable, allowing users to do sensitivity analysis. All of the add-ins is revised for Excel 2007 and notes about Excel 2010 are added where applicable.
170 citations
References
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TL;DR: In this article, the authors consider the relationship between the market and the critic and their reviews in the entertainment industry and propose a method to understand the relationship. But few marketing scholars have considered the relationship among the two.
Abstract: Critics and their reviews pervade many industries and are particularly important in the entertainment industry. Few marketing scholars, however, have considered the relationship between the market ...
831 citations
Posted Content•
TL;DR: In this paper, the authors explore two alternative economic explanations for the role of stars in motion pictures, namely, informed insiders signal project quality by selecting an expensive star, and the "rent capture" hypothesis, i.e. that stars receive their marginal value.
Abstract: The purpose of this paper is to explore the role of stars and other potential informational signals in the movie business. In the first part of the paper, we explore two alternative economic explanations for the role of stars in motion pictures. The first approach is a signaling view; namely that informed insiders signal project quality by selecting an expensive star. The second approach is the 'rent capture' hypothesis, i.e. that stars receive their marginal value. These two approaches have different implications regarding stars' pay, movie revenues and return on investment. The second part of the paper contains an extensive empirical investigation of a sample of movies produced in the 90's. Univariate analysis seems to show that star-studded films bring in more revenues than other films. However, regression analysis only supports the notion that any big budget investment increases revenues. Sequels, highly visible films and 'family oriented' ratings also contribute to revenues. However, when we measure return on investment, we find that stars or big budgets are not associated with profits; if anything, low budget films seem to do better. This supports again, the 'rent capture' hypothesis. We identify some additional variables that are associated with profitable films.
516 citations
TL;DR: In this paper, the authors develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data, which is intended to assist motion picture exhibitor chains retailers in managing their exhibition capacity and in negotiating exhibition license agreements with distributors studios.
Abstract: The primary objective of this paper is to develop a parsimonious model for forecasting the gross box-office revenues of new motion pictures based on early box office data. The paper also seeks to provide insights into the impact of distribution policies on the adoption of new products. The model is intended to assist motion picture exhibitor chains retailers in managing their exhibition capacity and in negotiating exhibition license agreements with distributors studios, by allowing them to project the box-office potential of the movies they plan to or currently exhibit based on early box-office results. It is also of interest to practitioners in other software industries e.g., music, books, CD-ROMs where the distribution intensity is highly variable over the product life cycle and is an important determinant of new product adoption patterns. The model and its extensions are of interest to academic researchers interested in modeling distribution effects in new product adoption, as well as forecasters looking for ways to leverage historical data on related products to forecast the sales of new products.
We draw upon a queuing theory framework to conceptualize stochastically the consumer's movie adoption process in two steps---the time to decide to see the new movie, and the time to act on the adoption decision. The parameter for the time-to-decide process captures the intensity of information intensity flowing from various information sources, while the parameter for the time-to-act process is related to the delay created by limited distribution intensity and other factors. Our conceptualization extends existing new product forecasting models, which assume that consumers act instantaneously on the motivating information they receive about the new product. The resulting model is parsimonious, yet it accommodates a wide range of adoption patterns. In addition, the stochastic formulation allows us to quantify the uncertainty surrounding the expected adoption pattern. In the empirical testing, we focus on the most parsimonious version of the modeling framework. BOXMOD-I, a model that assumes stationarity with respect to the two shape parameters that characterize the adoption process. The model produces fairly accurate early forecasts using at most the first three weeks of data for calibration, and the predictive performance of the model compares favorably with benchmark models. We propose extensions of the basic model that account for more realistic non-stationary distribution intensity patterns---including a “wide release” pattern that relies on intensive distribution and promotion, and a “platform release” pattern that involves a gradual buildup of distribution intensity. Finally, we present an adaptive weighing scheme that combines initial parameter estimates obtained from a meta-analysis procedure with estimates obtained from early data to produce forecasts of box-office revenues for a new movie when little or no box-office data are available.
An important finding from the empirical testing is that motion picture box-office revenue patterns display remarkable empirical regularity. We find that there are only three classes of adoption patterns, and these can all be represented within the basic model by using a two-parameter. Exponential or Erlang-2 probability distribution, or a three parameter Generalized Gamma distribution. We also find that cumulative box-office revenues can be predicted with reasonable accuracy often within 10% of the actual using as little as two or three data points. However, our attempts to predict revenue patterns without any sales data meet with limited success. While the scale parameter can be estimated reasonably well from a historical database of parameter values, we find that it is considerably more difficult to predict the shape parameters using historical data.
The parsimony we seek in developing the model comes at the cost of several limiting assumptions. We assume that the time-to-decide subprocess and the time-to-act subprocess are independent, which may not be the case if decisions on continued exhibition by retailers are endogenously related to box-office revenues over the life cycle. In the basic model formulation, we also assume that the time-to-act process can be represented by an exponential distribution, which may not always be the case. While we provide some empirical evidence to support these assumptions, further research could relax these and other assumptions to enrich the basic model, although this would entail some loss in parsimony.
498 citations
TL;DR: In this article, two alternative explanations for the role of stars in motion pictures are presented: informed insiders signal project quality by hiring an expensive star, or stars capture their expected economic rent.
Abstract: This article presents two alternative explanations for the role of stars in motion pictures. Either informed insiders signal project quality by hiring an expensive star, or stars capture their expected economic rent. These approaches are tested on a sample of movies produced in the 1990s. Means comparisons suggest that star-studded films bring in higher revenues. However, regressions show that any big budget investment increases revenues. Sequels, highly visible films and "family oriented" ratings also contribute to revenues. A higher return on investment is correlated only with G or PG ratings and marginally with sequels. This is consistent with the "rent capture" hypothesis. Copyright 1999 by University of Chicago Press.
467 citations
TL;DR: In this paper, a hierarchical Bayes formulation of the Poisson model is employed to predict the first-week viewership of new movies in both domestic and several international markets, where the number of screens, distribution strategy, movie attributes such as genre, and presence/absence of stars are among the factors modeled to influence viewership.
Abstract: This paper attempts to shed light on the following research questions: When a firm introduces a new product or service how can it effectively use the different information sources available to generate reliable new product performance forecasts? How can the firm account for varying information availability at different stages of the new product launch and generate forecasts at each stage? We address these questions in the context of the sequential launches of motion pictures in international markets.
Players in the motion picture industry require forecasts at different stages of the movie launch process to aid decision-making, and the information sets available to generate such forecasts vary at different stages. Despite the importance of such forecasts, the industry struggles to understand and predict sales of new movies in domestic and overseas markets.
We develop a Bayesian modeling framework that predicts first-week viewership for new movies in both domestic and several international markets. We focus on the first week because industry players involved in international markets studios, distributors, and exhibitors are most interested in these predictions. We draw on existing literature on forecasting performance of new movies to formulate our model. Specifically, we model the number of viewers of a movie in a given week using a Poisson count data model. The number of screens, distribution strategy, movie attributes such as genre, and presence/absence of stars are among the factors modeled to influence viewership. We employ a hierarchical Bayes formulation of the Poisson model that allows the determinants of viewership to vary across countries. We adopt the Bayesian approach for two reasons: First, it provides a convenient framework to model varying assumptions of information availability; specifically, it allows us to make forecasts by combining different sources of information such as domestic and international market-specific data. Second, this methodology provides us with the entire distribution of the new movie's performance forecast. Such a predictive distribution is more informative than a point estimate and provides a measure of the uncertainty in the forecasts.
We propose a Bayesian prediction procedure that provides viewership forecasts at different stages of the new movie release process. The methodology provides forecasts under a number of information availability scenarios. Thus, forecasts can be obtained with just information from a historical database containing data on previous new product launches in several international markets. As more information becomes available, the forecasting methodology allows us to combine historical information with data on the performance of the new product in the domestic market and thereby to make forecasts with less uncertainty and greater accuracy.
Our results indicate that for all the countries in the data set the number of screens on which a movie is released is the most important influence on viewership. Furthermore, we find that local distribution improves movie sales internationally in contrast to the domestic market. We also find evidence of similar genre preferences in geographically disparate countries. We find that the proposed model provides accurate forecasts at the movie-country level. Further, the model outperforms all the extant models in the marketing literature that could potentially be used for making these forecasts. A comparison of root mean square and mean absolute errors for movies in a hold out sample shows that the model that combines information available from the different sources generates the lowest errors. A Bayesian predictive model selection criterion corroborates the superior performance of this model. We demonstrate that the Bayesian model can be combined with industry rules of thumb to generate cumulative box office forecasts.
In summary, this research demonstrates a Bayesian modeling framework that allows the use of different information sources to make new product forecasts in domestic and international markets. 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. Hence, the motion picture industry should use both product-specific and market-specific information to make new movie performance forecasts.
357 citations