What do we learn from bestseller regressions?

26 Dec

In The Bestseller Code, Archer and Jockers learn about the attributes of bestsellers by regressing whether or not a book is a bestseller on features of the book’s text. They find that topics like “human closeness” are prognostic of a book’s success. So is the author’s ability to focus on a few topics.

“It turns out that successful authors consistently give that sweet spot of 30 percent to just one or two topics, whereas non-bestselling writers try to squeeze in more ideas. To reach a third of the book, a lesser-selling author uses at least three and often more topics. To get to 40 percent of the average novel, a bestseller uses only four topics. A non-bestseller, on average, uses six.”

The Bestseller Code

The authors also conclude that “[t]wo notable sets of underperforming topics are all things fantastical and otherworldly.” This got me thinking about whether the insights stood the test of time (the regression doesn’t take account of evolution of readers’ tastes) or if the insights were right to begin with.

What is clear is that it is hard to interpret “underperforming.” One understanding of underperforming is that people don’t like books with topic X. Another is that people love books on topic X but because of that, there is a greater supply of books with topic X, making any book with topic X less likely to succeed (see here for a related simulation). As the authors write (in a slightly different context):

“Copycat publishing works just that way. After The Girl with the Dragon Tattoo, there was a vogue for publishing Swedish crime writers across the world.”

The authors seem to side with the former interpretation though the latter explanation seems likelier. Why does it matter? It tells us that we may learn more about readers’ tastes from modeling supply. And if the aim is to figure out the optimal strategy as an author, it is apt to consider both readers’ preferences and what is undersupplied in the market (assuming the cost of supplying is the same).

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