Reader Analytics Is No Silver Bullet

Expert publishing blog opinions are solely those of the blogger and not necessarily endorsed by DBW.

data, e-reading, ebooks, analytics, readersThe title of this post may surprise some readers. Why is the author, who is the founder of reader analytics company Jellybooks, saying that reader analytics is not a silver bullet that solves all the acquisition, discoverability, marketing and sales problems of publishers? Well, Jellybooks is based on the principles of openness, transparency and user trust, and we will be honest: reader analytics is a highly refined tool—made possible by the rise of the ebook—but it cannot magically predict how many units a particular title will sell.

Is reader analytics therefore worthless? It certainly is not! It is still a great marketing tool that can measure the engagement between audience and book, show what kind of reader engages with a book, and how and when they read it. It is a tool that helps authors, agents and publishers better reach, target and expand a book’s potential audience.

So why is reader analytics not an accurate sales prediction tool? Well, in a nutshell, people buy books for all sorts of reasons, and reading them is not the only reason why people buy them. If people buy books for a purpose other than reading, then reader analytics, quite naturally, will struggle predicting sales based on causes that are unrelated to reading.

In an earlier DBW post, I discussed eight reasons why people buy books. This was not an exhaustive list, but an attempt to highlight the many different motivations people have for buying books. After all, publishing is a fairly large industry, generating more than $100 billion in annual revenues worldwide.

There are two dominant reasons in trade publishing that can lead reader analytics to underestimate the sales potential of a book:

First, the endorsement of highly influential literary critics, celebrities or gatekeepers can propel sales of a book well above any level reader analytics could predict. Even in today’s fractured media landscape, a glowing, full-page review in the New York Times can still draw immense attention to a book. In other words, the opinion of one single person, or that of a small group of highly literary people, can push a large number of people toward buying a book.

The same trend applies to the judges who decide on major awards, like the Nobel Prize for literature, the Man Booker or the Pulitzers. Reader analytics cannot predict how such people form their opinions and which books they will pick. Rather, reader analytics is a tool to measure the genuine reader engagement of a group of “normal readers and thus predict a book’s organic word-of-mouth potential. This includes, to some extent, also the probability that a book will be picked up by a book blogger or book tuber who promotes a book primarily based on whether they think it will appeal to their followers rather than the book’s literary merits. In fact, some of them might use reader analytics in the future to help filter the quality ones out from the abundance of content being published.

Second, some books acquire a kind of “status position.” They are considered to be the greats of literature, included in mandatory school and college reading lists and the like. They are suddenly discussed in elite circles. Just think of Thomas Piketty’s Capital in the Twenty-First Century. People buy these books to display them on their shelves without necessarily reading them, and because they don’t read them reader analytics cannot predict the additional sales that come from these sorts of “status” purchases.

There is also the case in which reader analytics may overestimate sales, at least initially, because readers devour the book, but the cover is too poorly designed or the book is too poorly marketed and readers end up not discovering the book. However, these are solvable problems, and it is here where reader analytics really highlights to the author, agent or publisher that the content itself is great and the reader engagement fantastic, but that the marketing and sales efforts needs improving. This is where reader analytics can really shine so as to ensure that the book achieves its maximum sale potential.

So, reader analytics is not a magical formula that will tell you how many units a book will sell or at what price. Rather, at its simplest, reader analytics is a tool to help people understand other people. It helps authors and publishers better understand readers’ behavior, so they can better serve them.

Reader analytics still requires human interpretation, though it has been constructed to be so simple to use that any author, publicist or marketing specialist can use it. It was not designed to be used by data scientists or audience insight experts. It is instead a tool for anybody, and bit by bit we at Jellybooks are rolling it out so it will one day be available to everybody and for all sorts of different applications and purposes.

Right now it is still used mostly by very large publishers. This is primarily for reasons of cost, as it’s still in the range of £500 to £950 per book to undertake reader analytics (and usually involves multiple books being tested at the same time). This is equivalent to $700 – $1,400 per book, but that is for the most time-consuming measurement model, which uses advance reader copies and focus groups. Newer models that can be used for retail copies and other scenarios are currently being tested.

Coming up, we will discuss “Who Is Afraid of Reader Analytics?” and in June I will report on some of the results from our current non-fiction reader analytics campaigns.

Earlier posts in the data-smart publishing series:
“The Internet of Bookish Things”
“Reading Fast and Slow – Observing Book Readers in Their Natural Habitat”
“Start Strong or Lose Your Readers”
“What Books Have the X-Factor? Measuring a Book’s Net Promoter Score”
“Men Are from Mars, Women Are from Venus, But What About Readers?”
“How Does Age Affect Reading?”
“8 Reasons Why People Buy Books”
“Data Vs. Instinct – The Publisher’s Dilemma”
“It’s the Cover, Stupid! Why Publishers Should A/B Test Book Covers”
“Foreign Rights and Reader Analytics”
“The Great Amazon Page Count Mystery”


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One thought on “Reader Analytics Is No Silver Bullet

  1. Michael W. Perry

    There’s another factor that I discovered when I read Frank Capra’s marvelous autobiography, The Name above the Title. At one point the talented movie director described a problem he discovered with the test audiences for a film he was wrapping up. Small audiences in a small theater loved it. When he expanded the test audience to a larger one in a larger theater, they disliked it.

    Since the latter was how the film would most often be shown once released, he worried. He couldn’t figure out why that difference existed. On impulse, he showed the film to a large audience, doing nothing more than remove the first reel. The audience liked it, so it was released that way and did well.

    One problem with book metrics is that they’re typically testing the audience for a finished book. There’s little that can be done at that point if the equivalent of a first reel—say the early chapters—doesn’t appeal. Test readers, if used, may be like that smaller audience. They may not represent the wider public well.

    And yes, book subscription metrics may show that an inordinate number of readers were bailing out early. But that doesn’t explain why they were doing so and doesn’t demonstrate that matters would improve if those early chapters were dropped. The entire book might prove just as unappealing.

    One solution for the better endowed publishers would be to create multiple drafts of a book, each taking a different approach. Similar groups of readers representative of its larger audience would then be asked to rate the book. The one that rated the highest would probably be the best bet for publication.

    That sort of pre-publication reader analytics might work. It would, at least, be better than trying to discover what’d gone wrong after a book has been released and isn’t catching the benefit of readers telling others about it. All too often, reader analytics can be like like trying to discover the cause of a train wreck.

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