Have you ever watched a great movie and marveled at the script, only to learn that your companion thought it was terrible? Unlike math or engineering, writing feels subjective, which makes it seem hard to evaluate objectively. But might that intuition be misguided?
Wharton marketing professor Jonah Berger is cracking the code on what makes a good story. He and two colleagues Olivier Toubia, marketing professor at Columbia Business School, and Jehoshua Eliashberg, emeritus marketing professor at Wharton devised a way to measure language to determine what makes some narratives more successful than others. They compared movies, TV shows, and academic papers, and the results were published in a study titled, How Quantifying the Shape of Stories Predicts Their Success.
Berger joined Knowledge@Wharton to talk about the study. Listen to the podcast above or read an edited transcript of the conversation below.
Knowledge@Wharton: What inspired this study, and how did you go about measuring something as abstract as language?
Jonah Berger: We are constantly consuming narratives. We are reading books, we are watching movies, we are reading articles online. But were also creating stories. When we make a presentation, when we give a talk, even when we write an email, we are creating content thats like a story. We started to wonder, why are some of these things more successful than others? Some movies are blockbusters and some tank. Some books are bestsellers and others arent. And some written content and online articles are just so engaging we cant put them down, but that doesnt happen for everything. What makes a hit? Why does something succeed while others fail? And can we use language to understand that at a deeper level?
When we make a presentation, when we give a talk, even when we write an email, we are creating content thats like a story.
Knowledge@Wharton: What was the key takeaway?
Berger: I think the key takeaway is theres a science here. Often, we watch movies and think its just some magic, creative process where things gel together and theres no way to understand whether it will succeed or fail. Thats not exactly right. It feels like magic. Certainly, when were watching a great movie or reading a great book, we are caught up in the narrative and dont think about anything else. But theres a science. We can understand the science of stories, of content more generally, by understanding the progression of ideas. By using tools that have recently become available through computational linguistics and natural language processing, we can shed a light on some questions that might otherwise seem impossible to uncover.
Knowledge@Wharton: You measured three things: speed, volume, and circuitousness. Can you explain those and what you found out about them?
Berger: Think about traveling in a car, for example. Sometimes you drive faster, and sometimes you drive slower. What does it mean to drive faster? It means you cover more distance in the same amount of time. In an hour, you go a few extra miles when youre traveling at a greater speed.
Well, it turns out that we can say the same thing about stories, content, or narratives. Think about a story as a set of ideas that are unfurling over time. Maybe theres a wedding, for example, and theres the first scene in a wedding, and theres another scene in a wedding, and theres a scene after that about something else. We can measure the distance between those ideas, how similar or different they are.
Just like a car can go faster or slower in the same amount of time, a narrative, a story, or a piece of content, can go faster or slower as well. It can talk about two things that are very closely related, or it can move from one thing to another thing thats not so much related. You can see this in textbooks, for example. Imagine opening up an Earth science textbook from your high school years, and theres the beginning of a chapter that relates very much to what happens next in the chapter, which relates very much to what happens next in the chapter. If instead, you went from the beginning of one chapter to two or three chapters down, it would obviously be further away because the content is less relevant.
It turns out that there are these neat tools called embeddings that allow us to take text, embed it in a multidimensional space, and measure things like distance. We can take the first chunk of a movie, for example, and the next chunk of the movie, and look at whether theyre closer together or further apart. Are they traveling faster, moving before unrelated ideas, or are they traveling slower, sort of plodding from one idea to the next?
Volume is the same sort of idea, but less moment to moment and more general. If you think about a story, some stories cover a lot of ground. They cover so many different things that arent necessarily related. Other stories are a bit more narrow. They cover a small set of things that are closer together. In addition to measuring the speed, you can also measure the volume. Imagine a story having a set of points. We can take all of those points together and ask, are they closer together or further apart? For example, if someone goes for a 4-mile run, they could have gone four times around a 1-mile track, or they could have gone on one 4-mile loop.
We can understand the science of stories, of content more generally, by understanding the progression of ideas.
Circuitousness is, how direct are the ideas? Are we moving through the shortest path possible through these points, or are we taking a more indirect or circuitous route? If you think about the numbers of a clock on the wall, the fastest way to go through those twelve points would just be to go around the circle, hitting each of them in turn. But you could also go from twelve to six to one to seven to three to nine, and so on. Youd cover the same points, and the volume would be the same. Its still all inside that clock, but it would be a much more indirect route.
Its the same thing with stories, right? Do stories take a very linear path from idea to next idea to next idea, or do they double-back, touching on similar things that theyve touched on before, before moving on to unrelated ones in the future? We measured each of these three things in everything from movies and books as well as television shows and academic papers.
Knowledge@Wharton: What can marketers learn from these findings?
Berger: Whether were a marketer or a leader, whoever we are, we are constantly creating content. We may not think of ourselves as speakers or writers, but we spend a lot of time speaking and a lot of time writing. In fact, almost everything we do on a daily basis involves language either producing it through writing or speaking or consuming it through reading or listening. As marketers, as leaders, as others, these findings really help us think about how to better lay out the content whether that content is a presentation, an argument, a speech in a way that will impact the audience. Should we try to cover a lot of ground or relate the ideas more closely to one another? If were covering the same ground, should we use a very direct path or more of a spiral, where we go back to the same ideas again and again to deepen the understanding around those things?
I think these findings have implications for content engineering. If Im Netflix, for example, and Im trying to figure out which movies to green light, or Im trying to figure out whether a certain book is going to make The New York Times bestseller list, these tools can do that. Indeed, our research shows that we can predict how successful movies and TV shows and academic papers are going to be. But I think these findings also have really useful implications for us. We think about telling stories as something we do for fun, but we are constantly telling stories. We are constantly laying out narratives to explain a set of ideas, and this work has some clear implications for how we should lay out those ideas.
Almost everything we do on a daily basis involves language either producing it through writing or speaking, or consuming it through reading or listening.
Knowledge@Wharton: In this study, you covered specifically movies, television shows, and academic papers. But I am curious about another channel that you did not study, which is social media. Posting on social media has become a very convenient form of mass communication for individuals and brands. What do you think you could apply from this study to social media?
Berger: Academic papers are not the same as TV shows or movies, and thats exactly why we wanted to look at them. Because while we can measure the speed and volume and circuitousness of both movies and academic papers, the features that matter, and the way they matter, are very different. While speed is good for movies and TV shows, its bad for academic papers. While volume is good for academic papers, its bad for TV shows.
To think about your social media question, we need to think about what were hoping the language, the ideas, the arguments were outlining will do. Are we hoping theyll be fun and engaging and interesting and stimulating? Then speed can be good and volume may be bad. In other cases, if were trying to impart knowledge, if were trying to get people to understand something or convince people of something, a different set of those features might be valuable.
Were actually doing some work right now on social media. One question we had is, think about yourself as a brand or an influencer. Not only is there sort of a speed or volume or circuitousness of one piece of content, but you can think about the volume across multiple pieces of content. Today, Im going to post about X. Next week, should I post about something similar or different? Should I post two buckets of things, or should I post one? How should I think about laying out my content strategy to increase the impact of that content? Were taking things like influencers and brands, and calculating things like speed and volume over time across the content they produce to look at how different ways of laying out ideas may be more or less impactful.
Knowledge@Wharton: Is there anything else about this study that is particularly salient for marketers and advertisers?
Berger: Marketers and advertisers are constantly creating content. Whether its ads, whether its content marketing, and as salespeople, were making sales pitches. And these findings have some clear implications for how we should lay out our ideas. For example, when were pitching something, should we jump from benefit to benefit, or should we focus on one thing rather than going into many? In ads, should we try to talk about everything a product can do, or stay a little bit more focused? As marketers think more and more about content marketing across a variety of platforms, I think these findings have some clear implications for improving content marketing, making ads more effective, and helping salespeople sell a bit better.
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The Science of Blockbusters: What Makes a Good Story? - Knowledge@Wharton - Knowledge@Wharton