Recently, the Telegraph ran a news story headlined Scientists Find Secret To Writing A Best-Selling Novel.
My first thought on reading the headline was Well, there’s a genie that’s never going back in the bottle. It wasn’t even negative so much as…cynical.
I could pretty accurately predict the premise of the news story, because at my day job I work in a field that is very heavily analytical, a field in which analytics has created a revolution in the last five years. I work for a not-for-profit entity in their fundraising unit, though I don’t do any fundraising myself. I manage operations for an office full of people who essentially perform one of two tasks:
— Providing actionable intelligence on potential donors that fundraisers are about to go hit up for cash.
— Discovering new sources of money by finding new potential donors.
When people began to apply analytical thought to the latter task, the whole industry changed. The idea was that you could look at, say, a sample of donors who had given over ten thousand dollars in a single gift, and from that sample draw characteristics that you could use to comb your database for similar people who hadn’t yet given you money. That’s grossly simplifying things, but at its most basic, that’s what it is. You could look at the way a donor moved through a set process from identification to solicitation to “reward” (anything from a thank-you letter to their name on a building) and predict how other donors would react to the process. They do this in the entertainment industry all the time, too — they pick a demographic and then figure out what that demographic wants to see. And if they don’t make something that demographic wants to see, they at least package something else so that it looks like it in a film trailer.
So I could see where analytics plus publishing was going: market-driven novels written to specification, new analytics divisions in publishing houses, and the homogenization of the novel. After all, the tools are already in place — your ebook reader comes with a host of analytical functions you may never even see.
But then I took a step back, because essentially that’s what I’ve been doing with extribulum all along — finding out what my readers want and giving it to them. There is a fine line between “give them what they want” and “give them something that looks like what they want” — not to mention “tell them what they want” — but essentially, I’d been doing market research all along anyway. The difference is, of course, that I’m not industrialised; extribulum dominates zero industry sectors.
Still, I was interested enough, and newly unafraid enough, to have a look at the article, and I’m really glad I did. Because what’s awesome to me is that these scientists, employing “statistical stylometry”, figured out that “bad prose doesn’t sell”.
Here are the elements of a good book according to the stylometry: complex prose (“heavy use of conjunctions”), descriptive prose (“large numbers of nouns and adjectives”), and thoughtful narration (“verbs that describe thought processes”). The analytics didn’t address the topics of these books, whether they had sad or happy endings, who their characters were, or what their plots were — just the use of language involved, the frequency of certain forms of word and what those frequencies indicated.
Now, on the one hand, this does rule out writers whose style of prose may not rigidly fit the statistics that Science has laid out for our convenience. And that may mean that writers who are experimental, who write in dialect or who have different things to say in different ways, may be in peril when it comes to publishing. On the other hand, I think it means we’ll have a few less true stinkers becoming bestsellers because a publishing house pushed them, and more books making it on their own merits.
So it is a genie, and it’s definitely not going back in the bottle — but where it does end up going we don’t yet know, and it’ll be fascinating to find out.