Capital Ideas Blog

Searching for a hit movie: Using the internet to predict what clicks

By Pradeep K. Chintagunta

From: Blog

This post originally appeared on the Kilts Center Faculty Blog

In the movie The Internship (involving two ex-salesmen and their internship at Google), Billy McMahon, the character played by Vince Vaughn, notes “Sometimes the long shots pay off the biggest.” While this statement is certainly true of movies – who would have predicted the multi-million dollar box-office performance of the Blair Witch Project? – it is also true of other new products. The main challenge facing marketers, of course, is the ability to come up with an accurate forecast of whether a product is going to be a hit or a bomb prior to its introduction or release. Yogi Berra is famously supposed to have said, “It’s tough to make predictions, especially about the future;” nevertheless, marketers invest in methods and processes that enable them to get a better handle on this issue. For example BASES (the Booz Allen Sales Estimation System), when used in the context of consumer packaged goods products, measures trial and repeat rates for products prior to launch and then adjusts these numbers based on variables such as awareness (generated via advertising) and distribution to obtain a forecast for the new product. Similarly, other methods have been suggested for products such as durable goods (the Bass model) and technology products. Most of these methods rely on “stated preferences,” i.e., what consumers feel they are likely to do in the future, combined with knowledge of what the firm intends to do to influence these decisions (prices, advertising levels, distribution, etc.).

One industry that is constantly putting out new products and hence faces the repeated challenge of forecasting is the movie business. As Chili Palmer, the character played by John Travolta in the movie Get Shorty puts it, “Rough business, this movie business. I’m gonna have to go back to loan-sharking just to take a rest.” Typically, movie studios commission tracking surveys that begin several weeks prior to the release of the movie to try and forecast box-office performance of the movie on release (see my paper “A Pre-Diffusion Growth Model of Intentions and Purchase,” published in the Journal of the Academy of Marketing Science, which describes this process and constructs a formal statistical model to forecast movie ticket sales). A challenge with this approach is that sample sizes tend to be limited, the respondents represent limited geographic coverage of the potential market, and stated intentions often do not translate into actual sales.

The Internet has allowed studios to potentially gather a lot more information about consumers’ perceptions of a movie prior to its release. In a recent article (see NBC news for a description or read the actual paper), researchers at Google describe how they make use of search patterns on Google to predict the box-office performance of new releases. For example, the number of searches provides a metric of the level of interest in a film. The researchers show that an index of total box-office receipts during the year 2012 correlates well with the total movie-related search activity. Interestingly, the researchers note that the release of high profile movies is accompanied by film-specific keyword search, whereas in periods when there are no high profile releases, most of the searches tend to be of a more generic nature – i.e., potential consumers looking for movie options. Importantly, the authors find a strong correlation between movie-specific search activities – search volume and search-ad click volume – in the seven-day period prior to release and opening box-office performance. Once a movie is released, general search volume is no longer significantly correlated with post-release performance; however, the search-ad click volume continues to correlate strongly, suggesting that metric is a better reflection of consideration and intent. The researchers conclude that for “film marketers, understanding these patterns can present a substantial opportunity. By adjusting search marketing strategies to these trends, marketers can either capture the attention of the ‘curious’ moviegoer, or deepen audience engagement with a blockbuster title.”

While the Google study certainly casts important light on the role of search, there are several other sources of information, both online and offline, which are correlated with box-office performance. In a recent study, my co-authors and I (see “Blogs, advertising and local-market movie box-office performance” with Shyam Gopinath and Sriram Venkataraman) find that a movie’s opening box-office performance is related to the level of pre-release advertising and the volume of pre-release blogging activity (as a measure of interest in the film or “buzz”). However the “valence” of the blogs (i.e., positive or negative sentiment) does not appear to correlate well with the opening box-office outcome. On the other hand, while advertising continues to be important post-release, the blog volume no longer matters; rather it is the valence – i.e., what moviegoers are saying about the film – that seems related to performance. Post-release, we also find that the valence of movie reviews of moviegoers (think comments on Yahoo!Movies and are related to box-office performance.

Bottom line: the Internet provides a wealth of new information about consumers’ reactions to products, which may be indicative of product performance. By carefully monitoring this information, marketers may be able to use their marketing resources more efficiently as well as effectively. Unlike George Valentin, the protagonist of the Artist who tries to resist the onset of the talkies —“I won’t talk! I won’t say a word!” (an Oscar-winning performance by Jean Dujardin), movie studios should be welcoming new sources of information that will enable them to better serve the viewing public – the customer.

This quote is an example of how it may be difficult to even predict the past – a casual search of the Internet reveals the somewhat apocryphal nature of the line. Others have attributed it to physicist Niels Bohr, Sam Goldwyn, Dan Quayle, and Mark Twain, among others.


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