As the author of four books, I pay close attention to the publishing world, especially with respect to its use of emerging technologies. Brass tacks: I often doubt wonder if traditional publishers have truly embraced the Information Age. While I understand the resistance, can’t data analysis and business intelligence help publishing houses improve their batting averages (read: select more successful books, reach their readers better, and avoid expensive mishaps)?
As the author of four books, I pay close attention to the publishing world, especially with respect to its use of emerging technologies. Brass tacks: I often doubt wonder if traditional publishers have truly embraced the Information Age. While I understand the resistance, can’t data analysis and business intelligence help publishing houses improve their batting averages (read: select more successful books, reach their readers better, and avoid expensive mishaps)? These are just some of the issues broached at O’Reilly’s Tools of Change for Publishing conference. This annual event bills itself as the place in which “the publishing and tech industries converge, as practitioners and executives from both camps share what they’ve learned from their successes and failures, explore ideas, and join together to navigate publishing’s ongoing transformation.” From a recent article reflecting upon TOC 2012:
If one thing was clear from this year’s TOC it’s that the publishing business is finally getting serious about data and analytics. This can mean looking at the granularity of day-to-day marketing strategies — such as when to send out a tweet to get maximum re-tweets on your social network (there’s an app for that), making quantitative assessments of the number of books a “library power patron” will buy based on their reading habits (one bought for every two borrowed), or the likelihood of a German to feel bad about downloading a pirated e-book from BitTorrent (not so much).
Years ago, most publishers selected books exclusively upon the recommendations of acquisition editors. AEs have been the gatekeepers, those coveted folks who somehow knew which books would be successful. Except many of them didn’t.
Bad Batting Averages
In fact, the number of misses by big publishers is pretty astounding. Stephen King received hundreds of scathing rejection letters before he proved himself a book-selling machine. Publishers passed on initial manuscripts of Chicken Soup for the Soul, a franchise that has reached tens of millions of people. John Grisham self-published his first book. I could go on but you get my point: relying upon hunches and intuition isn’t exactly a recipe for successful decisions, and book sales are no exception to this rule. Slowly, publishes are recognizing this fact and embracing analytics and Big Data. They have to; their margins are being squeezed and they have no choice but to adapt or die. While developing the perfect equation to predict book sales may be impossible (there are always Black Swans), no doubt publishers can benefit from a more information-driven approach to managing their business. After all, it worked for the Oakland A’s, right?
Simon Says: It’s Not Just About Previous Book Sales
Individual judgment will always matter in evaluating any business opportunity. No one is saying that machines, data, and algorithms need to completely supplant the need for human intervention. The data may tell us what, but it may not tell us why? Plus, there are always times in which it makes sense to bet big the other way–to ignore the data. Maybe 20 years ago, an author who sold 20,000 copies of a book might sell more or less the same number. These days, however, publishers are using new and often fuzzier metrics like an author’s (or prospective author’s)
- Twitter followers
- site’s Google PageRank or Alexa ranking
- RSS subscribers
- size of mailing list
- number of Facebook fans
- Klout score
- and others
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What say you?