Success in Books: A Big Data Approach to Bestsellers

Burcu Yucesoy, Xindi Wang, Junming Huang and Albert-László Barabási
EPJ Data Science
7:7
April 6, 2018

Abstract

Reading  remains the preferred leisure activity for most individuals, continuing to  offer a unique path to knowledge and learning. As such, books remain an  important cultural product, consumed widely. Yet, while over 3 million books  are published each year, very few are read widely and less than 500 make it  to the New York Times bestseller lists. And once there, only a handful of  authors can command the lists for more than a few weeks. Here we bring a big  data approach to book success by investigating the properties and sales  trajectories of bestsellers. We find that there are seasonal patterns to book  sales with more books being sold during holidays, and even among bestsellers,  fiction books sell more copies than nonfiction books. General fiction and biographies  make the list more often than any other genre books, and the higher a  book's initial place in the rankings, the longer the book stays on the list  as well. Looking at patterns characterizing authors, we find that fiction  writers are more productive than nonfiction writers, commonly achieving  bestseller status with multiple books. Additionally, there is no gender  disparity among bestselling fiction authors but nonfiction, most bestsellers  are written by male authors. Finally we find that there is a universal  pattern to book sales. Using this universality we introduce a statistical  model to explain the time evolution of sales. This model not only reproduces  the entire sales trajectory of a book but also predicts the total number of  copies it will sell in its lifetime, based on its early sales numbers. The  analysis of the bestseller characteristics and the discovery of the universal  nature of sales patterns with its driving forces are crucial for our  understanding of the book industry, and more generally, of how we as a  society interact with cultural products.

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