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Analytics and Data Prep Create Box Office Magic

Michael Singer
Director, Product Marketing, Oracle Analytics

Long before Moonlight won the Academy Award for Best Picture or Casey Affleck was chosen as Best Actor in Manchester by the Sea, adaptive analytics helped film producers and investors determine the risks and rewards of these and hundreds of other films last year.

The secret to choosing a winning film lies within the numbers gathered from historical movie metadata (such as genre, cast and crew, MPAA rating, production companies, and distributors) and aggregate financials (including production budget, box office, and DVD and Blu-ray sales, not to mention licensing to streaming platforms such as Netflix and Hulu). Independent filmmakers and their financiers often look at such data and use that as the basis for investment.

The analytics behind these numbers are run by Bruce Nash, who founded Nash Information Services 20 years ago. The company operates The Numbers, which provides free information on box office and video sales, and OpusData, which includes a database of movies, metadata, people, and companies involved in the film industry, as well as all time-period data such as daily, weekend, and weekly box office and weekly video sales.

When we talked, Nash described, Moonlight—which was produced for $1.6 million—as a longshot because the main ensemble was generally unknown. By contrast, he described Manchester by the Sea as a lower-risk film because it had recognizable stars and an $8.5 million budget. However, Moonlight has performed better at the box office, earning nearly twice as much as Manchester since taking home the Best Picture award.

“As well as doing in-depth analysis for individual clients, we license OpusData for use on partner websites and applications. They might merge our information with their own proprietary data to help them identify which projects to invest in and which ones to avoid,” says Nash.

We also asked Nash about the effort it takes to prepare data for his clients who may need the information as it happens.

Oracle: What type of data prep does your company have to engage in to make sure the information is clean and reliable?

Nash: Basically, we sanity-check everything! The back end is, in effect, a massive rule set that we expect all the data to abide by. At the very simplest level, this might be something like “today’s box office total for movie X is normally yesterday’s total plus today’s daily box office.” But over the years, we’ve added many rules that collectively keep the data in shape. For example, “movies tend not to have sequels unless they’re profitable” is a rule that can be used across the entire data set to tell us quite a lot about how movie financing works.

Oracle: To what extent do your clients know how to set up and use the data to begin their visualizations?

Nash: That depends on how we supply the data. For data extracts, it’s a matter of loading a .csv file into your favorite analytical tool. Our API comes with a replicator that makes it easy to mirror our database in real time into another database system. Generally, even clients with high-end needs can be up and running on day one.

Oracle: Are there data sources that your company has to deal with that are either proprietary in nature or sensitive in their function? How do you deal with this type of situation?

Nash: All of the data we sell ultimately tracks back to information that is publicly available, although we might be doing a huge amount of number-crunching between data gathering and estimating such as total video-on-demand rentals for a particular film. We do use proprietary data from our clients [film producers] to build and fine-tune our models, but that data and the model itself are not made public.

For an interesting analysis of the Oscar-nominated films, check out my colleague Chris Garcia’s data visualizations.

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