The biggest difference between Web journalism and print journalism is that on the Web both publishers and advertisers have some idea about what readers are actually doing, and this naturally ends up informing both the commercial and editorial sides of what we do. Thanks to e-readers, similar analytic power is coming to the world of books:
Scribd is just beginning to analyze the data from its subscribers. Some general insights: The longer a mystery novel is, the more likely readers are to jump to the end to see who done it. People are more likely to finish biographies than business titles, but a chapter of a yoga book is all they need. They speed through romances faster than religious titles, and erotica fastest of all.
At Oyster, a top book is “What Women Want,” promoted as a work that “brings you inside a woman’s head so you can learn how to blow her mind.” Everyone who starts it finishes it. On the other hand, Arthur M. Schlesinger Jr.’s “The Cycles of American History” blows no minds: fewer than 1 percent of the readers who start it get to the end.
Oyster data shows that readers are 25 percent more likely to finish books that are broken up into shorter chapters. That is an inevitable consequence of people reading in short sessions during the day on an iPhone.
One difference between books and periodicals is that since the book publishing industry has never been based on advertising, getting people to actually read books has never been a particularly important part of the book industry. The point is to sell books. A beloved book might be passed around between friends and family, checked out of libraries, re-read every two or three years, or whatever. Alternatively, some new founding fathers biography might be bought as a gift for thousands and thousands of people who leave it on the coffee table for a few months without ever really reading it. It’s the latter scenario where the publisher actually makes money.
At any rate, articles on the use of analytics in media production are supposed to feature some hand-wringing about the nature of the creative process. But I think only uncreative people use data to stymie creativity. And certainly that’s an option. The more you know about your audience, the more precisely you can implement a strategy of “precisely copy what’s working elsewhere.” But a person who wants to innovate can also take advantage of data to do so. After all, suppose you want to try some new things. Wouldn’t you like to know which of those things works so you can iterate?
