How AI Can Invigorate Publishing

  • 10 August 2017
  • Expert Insights

So many books, movies, TV series, and multimedia adaptations published by so many studios, publishing houses, and individuals: there have never been so many stories to tell. The pipeline is full; inboxes are overflowing. And audiences are clamoring for … well, what are they, in fact, clamoring for? That’s what publishers and studios need to know.

It’s a great time to be a content creator, but it’s a fraught time to be a content producer. Even with so much demand and potential supply, the risk of failure is greater than ever. Choosing the right property, developing, marketing and selling it—just getting noticed by the right audiences—is so much more difficult today. Decision making is all the tougher and the downside of a bad bet is all the steeper. You can only invest so much of your financial and human resources; you’d better make the right call. One acquisitions editor at a Big-Five publishing house remarked that his company is better off if he stays home all year and draws a salary than if he makes a mistake and signs up a flop.

Editors can browse bestseller lists and sales stats and figure out what sells. But sales stats don’t tell us much about why an individual book sells; and, as most publishing people will say, every book is its own distinct, special thing. Publishing is an industry that prides itself on success via human insight, experience, and plain old gut instinct. Of course, a hit was never made solely on instinct; but today there’s even less reason to go it alone without the support of modern, data-crunching, computer muscle.

Rafal Olkis/123RF

By now everyone acknowledges the power of Big Data to inform decisions and help match consumers with products. Who hasn’t noticed Amazon’s section of related and competitive products when shopping? Who hasn’t shivered at those mind-reading pop-up product ads, such as hotels for a future holiday destination. (How did they know?)

But only in recent years could publishers access reliable competitive sales figures in order to estimate a new title’s possibilities. In the publishing industry, big data’s promise has been only partially met. Although many industries broadly employ data analytic tools, publishing’s use of data has mostly restricted itself to book sales and distribution. That’s about to change, for these reasons:

Authors, Inc. is applying cutting-edge StoryFit(™) technology to uncover fascinating insights into a work’s structure, style and appeal. When these analytical results are compared to a manuscript submission or acquisition, publishers can see just how on- or off-target it is; get a clear sense of what it would take to improve the property’s aim; see how the perfect audience is likely to engage with it; and bolster the decision making process for a greater chance of meeting goals.

Now, with the support of LSC Communications, StoryFit(™) will expand to produce ever more sophisticated analyses and deliver them directly to LSC customers through integration with the its digital platform, helping publishers select and market their products with the support of data intelligence. Here’s why our technologies offer competitive value to publishers as well as the film and television industry.

[1] Artificial Intelligence—AI—is growing up. Although there’s still an aura of science fiction to the image of AI (and how appropriate is that, publishers?) the reality is that AI applications can reveal in amazing detail aspects of rich content that were hidden from us. When analyzed across thousands of published or produced works (both hits and flops), the results show patterns that can guide us toward commercially successful future investments and away from, well, the other kind.

We human readers more readily see comparisons in themes (a girl on a train, a space adventure), but machines see commonalities across genres, such as variations in rhythm, mood, and language, that can turn out to be critical to whether readers embrace a book or abandon it.

[2] Discoverability—making individual titles findable by individual buyers—is now scalable. The same natural language processing that fuels the StoryFit(™) analytic engine can deliver powerful, updatable keyword sets for an entire backlist with the same perspicacity that has until now required a human touch, and in far less time.

[3] Audience matching—profiling the prime audience for a book—has a sharper set of tools. Thanks to Big Data and a decade of online and digital reading, the industry has gathered vast amounts of detailed data about its readers. StoryFit(™) harnesses that data and uncovers which groups of readers are likely to respond to a book under consideration. One tool for this job looks at comparable titles: “comps.”

What, really, does it mean when a book is called “the next Gone Girl?” That there’s a manipulative protagonist? That it has the same perspective-switching style as Nick and Amy? StoryFit(™) adds those nuances to get at the heart of what makes one book “like” another. More than just a comparison, StoryFit’s(™) “faceted comps” show why a comp is, in fact, comparable. These insights help everyone make marketing plans and design decisions with a crystal-clear view of the target.

[4] The path to profitability—from acquisitions to sales—has a smoother ride.

CEO of Authors, Inc.

StoryFit(™) technology reduces the expense, in time and dollars, of correcting mistakes. (For example, a property that needs a lot of revision to make it timely, targeted, or trendy; one for which there’s no clearly defined audience, or that turns out to be too much like, or not enough like, the competition.) Decision makers can make better decisions with powerful data and insight at their side, adding fuel to the creative talents (and yes, instincts) of the publishing team to deliver a winner.  Humans, not algorithms, are still in the driver’s seat, but thanks to AI, the road has fewer potholes.

AI in service to creativity is a human-controlled operation that can spur, not stifle, inspiration. It can reveal that gem in the submissions inbox that might have been overlooked, or it can simply support, with sophisticated data, the publisher’s gut feeling that this book is going to be big. Publishers are still taking a risk, but it’s a data-informed and -supported risk. And there’s always room for that rare entry in the race (how about Harry Potter?) that speaks so powerfully to millions of readers, and becomes, in turn, its very own data model. Great stories are still being told; AI can help find and successfully publish them.

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