Who’s Afraid of Reader Analytics?

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

data, e-reading, ebooks, analytics, readersThere are authors and publishers who fear reader analytics. This has been a fact since my first presentation on “Project Crowberry” in spring 2014, since followed by Project Honeyberry, Project Apricot, Project Pomegranate and soon Project Honeydew. Reader analytics is going from strength to strength and has been featured in the New York Times and The Guardian.

However, a recent polemical article written by the “The Biblioracle” (John Warner) for the Chicago Tribune has prompted me to take the issue by the horns once more.

Data has been a feature of book publishing for some time now. About 15 years ago, data became even more prominently used, as publishers could see sales data from competitors through then-new services like Nielsen Bookscan.

The fundamental function of a publisher is to make a decision on what to publish (curation). Like a venture capitalist, a publisher has to select among many competing and worthy prospects to select those that have the highest probability of delivering an economic return. Few books earn back their advances, and a publisher’s success and survival rely on a few outsized winners. This is as true for venture capitalists as it is for book publishers, record label executives, indie filmmakers and Hollywood moguls.

At the same time, a publisher has to spread its risk across a number of books, because nobody can predict which book will succeed. A venture capitalist invests in a portfolio of start-ups to make sure it contains the next “unicorn,” as it’s called, like Uber, AirBnB, Twitter or Facebook. Similarly, a publisher invests across a range of books to make sure it has the next DaVinci Code, Harry Potter, Wool or 50 Shades of Grey in its catalogue.

It is equally true that not every investment gets the same level of attention. Those prospects that perform better get more attention. And this is where metrics matter—what is performing well and has the highest potential and is thus most deserving of further investment. Marketing dollars for books and attention by the in-house (or external) publicity team has never been equally distributed, but now love and attention are increasingly based on data that measures engagement and performance rather than gut feeling.

This is where reader analytics comes into play, as it tells publishers which books are resonating and genuinely engaging the audience. Sales data can be misleading, just like unique visitor numbers can create an illusion of success among technology start-ups when what really matters is engagement, user loyalty and the all-important lifetime value per customer.

Moreover, every industry has built-in biases, and publishing is no exception in this regard. Instinct, on the one hand, reflects genuine knowledge of and experience in what works, but instinct is also rooted in non-scientific hunches and biases that may obfuscate cause and effect and mislead us. (This is why diversity has suddenly become such a hot topic in publishing.) Reader analytics has the potential to provide a more meritocratic approach by helping publishers decide what is really engaging readers.

However, the real fear is probably not the fear of data, but rather how the data will be used. Authors fear that publishers will refuse to publish a book based on data, but let’s remember that we are measuring reader engagement. What is more objective than whether people enjoy reading a book, are inspired by it or are informed and educated by it? Of course there are authors who argue that readers cannot be trusted to decide what to read, but need to be told by more informed gatekeepers. Ouch! You don’t get more elitist than that! If I want to read trashy romance, then let me read trashy romance! It’s my life, the reader says.

I think the fear is really more about data enabling machines to potentially make decisions that are based on strict and abstract logic rather than humans making the decisions based on a wide range of inputs. The world is messy, and rarely do we have data that gives a crystal clear yes or now, black or white answer. Reader analytics is no different. It helps make smart decisions, but it is not a silver bullet. Authors, being human, take the comfort from fellow humans with real-life experience and knowledge making an informed decision. We rely on other people’s empathy to understand our case.

Machines are seen as calculating and unemotional logicians—not human, in other words. They are tyrants made of copper and silicon, beings from another world governed by artificial intelligence, unshaped by human and emotional evolution. They are cold, abstract circuits, inanimate matter without soul and the experience of emotions. They are alien to us.

Thus, the fear of data is really the fear of algorithms in this brave new world that we are now entering, not just in book publishing, but in all walks of life—from self-driving cars to automated check-out registers and robo-surgeons.

Time to dust off those Isaac Asimov books!

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”
“Reader Analytics Is No Silver Bullet”
“Will an Open Web Liberate Reading Data?”

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4 thoughts on “Who’s Afraid of Reader Analytics?

  1. Madeline

    “I think the fear is really more about data enabling machines to potentially make decisions that are based on strict and abstract logic rather than humans making the decisions based on a wide range of inputs.”
    Yes. Only the fear is not groundless, or “fear of the machine”. Many of us have already had experiences in organizations where the humans who were supposed to base decision on the wide range of inputs instead, because of laziness, lack of time or insecurity, relied on the data alone. It is an easy out. It is also perfect CYA. If the wrong decision is made, just blame the data. How blissful to be absolved of responsibility for decisions! You are too optimistic in thinking that those humans are going to do a more holistic reasoning once they have your data. On small projects, maybe. When the data is readily available for all such decisions, unlikely.

    1. Andrew Rhomberg

      It is an essential attribute of any start-up founder to be an optimist, so yes, guilty as charged.

      I could think of my local government bureaucracy (Lambeth Council, South London, England) as an example where people use data as if data itself was a law.

      Data is supposed to be a *tool* and one should always stay firmly focused on the desired outcome/goal. In this regard humans are still superior to machines.



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