Boxoffice November 2016

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GUEST COLUMNIST > WHAT WOMEN WANT METHODOLOGY Movie Clusters In order to better understand the preferences of different segments of moviegoers, we first construct an audience-based network of the movies released between January 1, 2014, and September 15, 2015. To build the network, we take advantage of the Movio Media Research platform. This platform contains data describing the demographics and moviegoing history of over eight million moviegoers throughout North America, and it provides studios, distributors, and advertisers with a variety of tools to develop a better understanding of how movie audiences behave. In particular, Movio Media’s Similarity Rating provides us with a measure of the audience similarity between each pair of movies under consideration. This rating is based on a similarity score, which is an estimate of the likelihood of the size of the audience intersection for a particular pair of movies given the size of each movie’s individual audience as well as the size of the total population. The similarity score is given by: Where p1, p2, and p12 are the proportions of the population that saw movie 1, movie 2, and both movies, respectively. The similarity rating is calculated by normalizing the similarity scores so that the similarity rating varies between zero and 10, and the average movie’s best match has a rating of five. Once we have a matrix of pairwise similarity ratings, we create the graph by placing an edge between each movie and each of its 10 most similar movies. Each edge is weighted by the similarity rating between the pair. Using these edges as an affinity matrix, we apply a spectral graph partitioning algorithm4 to break the set into k clusters, selecting the value of k that maximizes the modularity of the partition. We then create subclusters by applying this algorithm again to each cluster, once again selecting the number of clusters using a maximum modularity condition. However, if the resulting modularity would be less than 0.2, the partitioning into subclusters is unlikely to add much information, so we leave the cluster intact. Audience Once we have constructed a network, we would like to profile the audience for each of the clusters in order to better understand the differences between the various segments of the moviegoing population. To create these profiles, we take a sample of one million moviegoers from the Movio Media Database who watched at least two of the movies in the network. The sampling is performed so that the age and gender distribution of the resulting population is consistent with the general moviegoing population described in the latest MPAA Theatrical Market Statistics Report. The sample has an average age of 40, is 52 percent female, and watched an average of 8.0 movies in the database at a rate of 6.4 movies per year (the frequency is greater than the number of movies / the total time, as not all members were in the program over the entire period). Preferences An important component of each moviegoer profile is the moviegoer’s preference for each cluster or subcluster of movies. In order to estimate this preference, we take an empirical Bayesian approach, using a Beta (a, β) prior, with ai =pi, βi =1-pi, where pi is the proportion of total movie views associated with the i th cluster or subcluster. This ensures that we only assign strong preferences to moviegoers for which we have a large amount of information, and therefore we assume that moviegoers who have only seen a couple of movies are most likely to have average preferences. About the Author Bryan Smith has a B.S.E. degree in biomedical engineering and mathematics from Tulane University and master’s and Ph.D. degrees in applied mathematics from Northwestern University. He heads all research in statistics, science, and analytics at Movio, concentrating on the development of new products that use Movio’s global moviegoer database to generate analytical insights. Movio is the global leader in marketing data analytics and campaign management software for cinema exhibitors, film distributors, and studios. The Movio Media technology offers the most powerful and most accurate real-time film market research platform in the United States. www.movio.co 30 BoxOffice ® Pro NOVEMBER 2015
WHO DOESN’T GO TO BLOCKBUSTERS? 310 movies released between 1 Jan 2014 and 15 Sept 2015, clustered by audience intersection After eliminating movies with very small audiences, our resulting movies dataset contained 310 movies released between January 1, 2014, and September 15, 2015. The first pass of the clustering algorithm described in the Methodology section resulted in 10 clusters. The 10 clusters are described as: tentpole, animation, tough action, drama, comedy, horror, indie pop, art-house, faith-based, and Bollywood. A second pass of the clustering algorithm was then applied, which resulted in the breakdown of the tentpole, drama, comedy, and indie pop clusters into subclusters. Once we had identified the clusters, we evaluated each moviegoer’s preference for each one. Tentpole was the most popular cluster, with an average preference value of 37 percent, meaning that the likelihood that the next film they will see is a tentpole is roughly two in five; followed by animation (16%), drama (14%), comedy (14%), and tough action (11%). The most popular clusters. The likelihood is approximately 2 in 5 that the next film an average moviegoer sees will be a Tentpole. THE MAP TO THE STARS TENTPOLE TOUGH ACTION ANIMATION HORROR BOLLYWOOD COMEDY INDIE POP ARTHOUSE DRAMA FAITH-BASED NOVEMBER 2015 BoxOffice ® Pro 31
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