Expert publishing blog opinions are solely those of the blogger and not necessarily endorsed by DBW.
Last month, Mike Shatzkin wrote a blog post titled “Full text examination by computer is very unlikely to predict bestsellers,” in which he described how the claims of the creation of an algorithm that predicts bestsellers, as outlined in a new book The Bestseller Code: Anatomy of the Blockbuster Novel, are impossible.
While I agree in theory with Shatzkin that an algorithm alone cannot predict whether a book will be a bestseller or not, that isn’t precisely what The Bestseller Code claims, nor what our experience working with machine learning at Intellogo defines. What we aim to do is identify similar tones, moods, topics and writing styles to those books that are topping bestseller lists—as we can only do through algorithms—and, in this way, better understand the reading audiences’ desires. Machine learning allows us to do just that.
We use machine learning to read blocks of text from a specified set of sources, such as the books on a bestseller list, the books on a publisher’s forthcoming publication schedule, and potential acquisitions, in a matter of seconds and offers comparison and analysis (The framework of the search is defined by the user). Thus, the system can compare the current bestsellers, which represent current market interests, to any current titles a publisher is soon publishing in order to help identify where to focus marketing efforts.
Shatzkin states that we must consider the “consumer analysis, branding, or the marketing effort to promote the book” and claims that a better indicator of what might become a bestseller are the distribution numbers in the chain stores. But what is carried in a chain store is not dictated by market demand so much as the interests of the buyers. Any publisher that has received thousands of returns from said chain stores knows that system isn’t foolproof, either.
In this digital future, using machine learning platforms can provide publishers with opportunities to get real-time information about their readers, figure out what is working in the marketplace, and, perhaps, make the bestseller lists more of an accurate depiction of what readers want to read, not simply what is available.
Imagine a day when we take all our data about what people are reading and provide publishers (and authors) ideas of what people want to read, where to find those audiences, and better ways to reach them. This is the model that the film and television industries are already moving toward—with the help of Netflix and Amazon—so why shouldn’t book publishing take advantage of this market information? This type of decision support has not been possible up to this point, and publishers have often published books blindly, hoping that they would find the right audience and sell well.
Though “big data” can be a taboo subject when we talk about the romance of publishing, there are undeniable benefits to be had from using platforms that give publishers and authors information from which they can make informed decisions on how to invest their time and money.
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