
istockphoto / gorodenkoff
A central, frequently touted promise of big data and advanced analytics is that they can personalize offerings so deeply and effectively that they offer each consumer precisely what that person wants at any given time. But observers of recent trends and directions in movie-making, especially by streaming platforms and content developers that rely on such big data, suggest the real outcomes have gone the opposite way, producing far more generic offerings that can apply to everyone.
Although many platforms might be involved in this development, Netflix—with its more than 300 million subscribers, global reach, cutting-edge recommendation algorithms, and vast production capabilities (e.g., it released more than 100 original titles in one recent year)—represents the primary source. Accordingly, it is home to a remarkable number of what might be called “algorithm movies.” These films feature storylines that are easy for viewers to follow, even if they might be doing household chores or browsing social media at the same time as they watch the movie. The sound is designed to be relatively flat, so that people can hear what’s happening whether they are listening through headphones, surround sound speakers, or their mobile devices. Visually, the colors are bright, and feature a lot of yellow (attention grabbing but not gendered), but the images offer a low contrast style. The actors featured tend to be generally appealing but not necessarily top-tier, award-winning stars.
Also known as ambient programming, such algorithm movies aim to appeal to anyone and everyone, regardless of their consumption situation or personal characteristics. Thus, they cannot precisely match any individual consumer’s preferences. In turn, their quality generally gets assessed as middling. They’re usually not terrible, but they also are not breaking any new ground or prompting a creative revival in film. In this sense, like a gourmet cheeseburger, the basic offering is familiar, comforting, and satisfying, offering slightly varied combinations of ingredients without ultimately altering the recipe.
Such content is based firmly and powerfully in the expansive data that Netflix gathers from tracking consumers’ watching habits. The company obtains an estimated 700 billion bits of data, produced by any interaction that a user has with the platform, every single day. Its analyses of these data inform its attempts to ensure the greatest number of consumers are watching the greatest number of titles over time, which it works to achieve by presenting viewers with recommendations and nudges to watch the same content.
In line with these uses of big data to inform content development, Netflix also gauges performance in unique ways that leverage its distinctive market research capabilities. Rather than traditional box office receipts, it counts the total viewing hours that all users devoted to a particular title, then divides that number by the title’s run time. Thus it obtains an average viewing rate that offers a precise indicator of the broad appeal of the content, though again without specifying why each viewer watched or what was uniquely appealing about it.
An ongoing question though involves what algorithm movies really reflect. Is the content getting generalized, because the content creators are seeking the widest audience possible? Or are consumers, who peruse movies while also doing other things, growing more bland in their preferences, such that they are seeking out the gourmet cheeseburger instead of more experimental fare?
Discussion Questions
- Do algorithm movies represent an effective use of big data analyses? Why or why not?
- How would you answer the questions that conclude this abstract?
Sources: Paul Hoad, “Bland, Easy to Follow, for Fans of Everything: What Has the Netflix Algorithm Done to Our Films?,” The Guardian, August 28, 2025.



