Big
awards. Favorable books reviews. Great endorsements. Huge book publisher. Great social media platform. We believe some combination of these things –
coupled with word-of-mouth for a well-written book – serve as the foundation
for a formula on hitting the best-seller lists.
Not
exactly, says a recently published book.
The Bestseller Code: Anatomy of the Blockbuster Novel by Jodie Archer
and Matthew L. Jockers.
The
authors created a groundbreaking algorithm that they believe tells us not only
how and why we buy and read what we do, but it can predict which books will be
best-sellers. Okay, maybe not predict a
specific book’s fate, but it can compare it to those that have become
best-sellers and give you a score on its potential to be a best-seller.
All
of this sounds fascinating but it just fails too many tests to become some kind
of gold standard for handicapping the next best-seller.
For
instance, little in the book takes into account factors such as:
·
Author’s
credentials
·
Publisher’s
pull to get reviews
·
Size
of ad/marketing campaigns
·
Resources
to promote the book
Patterns
only exist for so long. Once one trend
gets used up, another takes its place.
The
nation’s mood, demographics and economy change over time, and these will
influence what gets published and purchased.
The
biggest reason a book becomes a best-seller, in my view, is because the author
previously hit a list. A big buy-in and
expectation comes with follow-up books until that author releases a clunker,
and loses some goodwill.
The
next biggest reason a book is a best-seller, in my view, is there’s a big,
savvy, far-reaching marketing machine and PR push behind the book. How can you ignore a book that gets on TV, is
heard on radio, has bloggers discussing it, has ads on Facebook and receives
reviews from PW, Library Journal, and
Kirkus?
The
authors go so far as to suggest that a computer, given all of their research,
could possibly write a bestseller. They
wrote: “Given our research on novels
using algorithims, we are often asked about our interest in a machine producing
a novel. It is natural to wonder what
programmers might be able to do if given access to all of the data that we have
compiled for our work on best-sellers.
We trained our computers to detect and measure the presence of several
thousand ingredients that are essential to best-sellers. It might be an attractive idea to take all
this data and develop, new scripts that build novels from our set of
variables.”
The
authors initially looked at 28,000 features or variables to examine a book for
comparative analysis. They settled on a
core set of 2,799 data points that they believe are genuinely predictive.
So
who are these authors and why are they qualified to say anything on
bestsellers? Jodie Archer was an
acquiring editor for Penguin UK, earned a Ph.D., and worked for Apple as their
research lead on literature. Matthew
Jockers is an Associate Professor of English at the University of Nebraska –
Lincoln. He directs their Nebraska
Literary Lab. His text-mining research
has been profiled in The New York Times,
LA Review of Books, and The Sunday Times of London.
The
authors also noted what doesn’t seem to work if a book is to be a best-seller –
all things fantastical and other worldly.
Tell that to J.K. Rowling, but she appears to be an exception.
“Perhaps
it is fair to speculate that the portion of the American public that actually
reads fiction likes to read more or less about itself,” say the authors. “To us, it seems like readers enjoy seeing
their own possible realities dramatized.”
Surprisingly, the bestseller DNA that the authors uncovered shows sex, drugs, and rock and roll
each, thematically, represent a tiny percentage of best-selling novels.
“Contrary
to what you might expect, given the prominence of sex in TV, movies, and the
media,” writes the authors, “the U.S. reading public of the past 30 years has
demonstrated a preference for other topics.
The mix of topics that tend to dominate contemporary best-sellers
suggests a reader who wants books to be something different from the lowest
common denominator.”
The
authors also notice that at the core of the best-selling narrative in the current era is
realism. They don’t see books about far-away topics for people who are nothing like us as being popular.
So
what else did they notice of their thorough analysis of New York Times best-sellers?
“The
model showed that symmetry in a plotline, and a clear three-act structure, used
to indicate that readers will find a novel pleasing, and we also saw that a
carefully manipulated emotional ride, can lead to high global sales. But without an understanding of style, no
author will make it to the list, even with the right themes, and a driving
plotline.”
Their
machine was able to accurately identify that 80% of the books that had made the
best-seller list should be best-sellers.
That means any book fed into the machine - and there were thousands – it
could accurately say which one’s a best-seller.
That sounds powerful but I’ll believe it if they can take books being
released in 2017 and tell us – before they hit a list – whether or not they make
it.
We
know there are general patterns that successful books or authors follow. The more that follow such a pattern, the more
likely that they will be successful. But
if everyone does the same thing, they can’t all break through. It still takes a creative author with an
interesting background, a Big 5 publisher behind her, and a fat marketing
campaign to position a book for success.
If the book’s not well-written and fails to get good word-of-mouth it is
likely to die out.
The
Bestseller Code was interesting and shows us where we’ve been. Imagine a new code will develop over the next
decade. And then another and
another. Go write the book you believe
in – and the rest will take care of itself.
The minute you seek to copy some formula you lose your edge, your
uniqueness, your writing soul.
All-New 2017 Book Marketing & PR Toolkit
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