So former publishing person Jodie Archer and analyst Matthew L. Jockers have developed an algorithm to isolate features common in bestsellers, and will reveal their new formula in their soon-to-be- published work, The Bestseller Code.
What does the algorithm (and the public) like?
Young, strong heroines who are also misfits (the type found in The Girl on the Train, Gone Girl, and The Girl with the Dragon Tattoo). No sex, just “human closeness.” Frequent use of the verb “need.” Lots of contractions. Not a lot of exclamation marks. Dogs, yes; cats, meh. In all, the “bestseller-ometer” has identified 2,799 features strongly associated with bestsellers.
What Archer and Jockers have done is just one part of a larger movement in the publishing industry to replace gut instinct and wishful thinking with data. A handful of startups in the US and abroad claim to have created their own algorithms or other data-driven approaches that can help them pick novels and nonfiction topics that readers will love, as well as understand which books work for which audiences. Meanwhile, traditional publishers are doing their own experiments: Simon & Schuster hired its first data scientist last year; in May, Macmillan Publishers acquired the digital book publishing platform Pronoun, in part for its data and analytics capabilities.
I must admit that on reading this article I mentally reviewed some of my unsold proposals to see if any of them featured young female protagonists who preferred hugs to sex and were particularly needy. (Not much luck there, I’m afraid.)
The authors state that this will enable editors to point to the algorithm in order to justify the purchase of a book by an unknown author. (A book, that is, featuring a young female protagonist who prefers hugs to sex and is particularly needy.)
The editor’s higher-ups, of course, will likewise point to the algorithm in order to justify turning down an otherwise fine work that does not feature a young female protagonist who prefers hugs to sex and is particularly needy.
And in fact they’re much more likely to point to the algorithm as a reason to fire the editor, or at least the editor’s assistant (assuming they haven’t fired her already). Why should literary taste enter the equation? After all, there’s already an algorithm that tells them which book to buy, and that would be the one that features a young female protagonist who prefers hugs to sex and is particularly needy.
When I teach Taos Toolbox, I spend a bit of time going into literary formulas such as the Hero’s J0urney and Lester Dent’s Master Plot Formula. And while I point out that a lot of good stories have elements drawn from these formulas, once you start employing formulae by rote you stand the chance of your writing becoming, well, formulaic. As for instance if every work features a young female protagonist who prefers hugs to sex and is particularly needy.
So if publishers take this algorithm to heart, and buy lots of works featuring a young female protagonist who prefers hugs to sex and is particularly needy, the books (those featuring a young female protagonist who prefers hugs to sex and is particularly needy) will start to seem, I dunno, just a little bit repetitious.
So the publishers will overbuy one particular type of story, as happened recently with urban fantasy, and has happened in the past with horror and bodice-ripper romance and historical family sagas, and then there will be a crash, and nobody will want to read books featuring a young female protagonist who prefers hugs to sex and is particularly needy ever again. (Until a really good one comes along.)
I suspect that the people best profiting from this new formula will be agents, because they can market new works highlighting young female protagonists who prefers hugs to sex and are particularly needy, and just market the shit out of those books until the bubble bursts, and then it’s on to the next formula for all concerned.
Maybe some of those editors will even be re-hired.
Oh, wait, no. Never mind. That won’t happen even in a fantasy.
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