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.

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The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions

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.

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Predicting Movie Sales from Blogger Sentiment

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.

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A Statistical Measure of a Population’s Propensity to Engage in Post-Purchase Online Word-of-Mouth

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.

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Movie Reviews and Revenues: An Experiment in Text Regression

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.

Data Analysis and Decision Making

(2000). Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers. CHANCE: Vol. 13, No. 3, pp. 15-24.

https://pages.stern.nyu.edu/%7Ejsimonof/movies/movies.pdf

Ilana R. Sparrow

Papers

Predicting movie grosses: Winners and losers, blockbusters and sleepers

Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers