Querying and Authors.me. Finding Success in the Manuscript Submission Process.

First of all, when I was shopping Girlish, I went with the “wallpaper the internet with queries” technique. I queried editors and agents and entered emerging writers’ contests—I tried to put my manuscript in front of as many people as humanly possible. One agent I really liked only took submissions through something called authors.me, so I checked it out.

Authors.me is basically a dating service for agents, editors, and writers, and although the dream agent didn’t take me, it is the place where I connected with Skyhorse Publishing.

Here’s how it works:

First off, its’ completely free for writers. You upload all the pieces of your project: bio, hook, synopsis, outline, first 30 pages, and complete manuscript…

Read more at The Debutante Ball

Machine Learning & Essential, Actionable Insights for the Publishing Industry

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[vcex_heading text=”What we can learn from advanced algorithms and what they hold for the future” responsive_text=”true” italic=”true” font_weight=”500″ text_align=”left” font_family=”Open Sans” font_size=”30″ color=”#ffffff”]
[vcex_heading text=”BY MONICA LANDERS, CEO OF AUTHORS.ME” responsive_text=”true” font_weight=”700″ text_align=”left” font_family=”Open Sans” font_size=”14″ color=”#ffffff” css=”.vc_custom_1484600428455{padding-left: 100px !important;}”]

We began this company as a standardized solution to the laborious and inefficient methods of the traditional query process which is often painful for individual authors as well as publishers and studios. We’ve evolved this platform into a breeding ground for dynamic, transformational publishing technology that benefits every part of the industry. More than two years out, we have developed exciting technology that forecasts successful projects. After many conversations with industry professionals, we are more confident than ever that it can be changed for the better through technology and the essential insights it stands to receive.

Data-Driven Operations and Collection
Our platform is not only robust, it’s extremely effective.

Without getting into how the platform works (you can learn that here, here, and here), let’s look at the state of the industry’s submissions and publication statistics. According to writer Joseph Epstein, at any given moment 200 million Americans have a book they want to publish.  Digital Book World  surveyed writers and discovered that more than 60% submitted their work to a publisher or agent the previous year.

From that, we can estimate that anywhere between 125 million writers submit manuscripts to publishers and agents in the US every year (though anecdotal evidence from agents may suggest more like 20 million submissions every year3). This means each publisher/agent is receiving  between 3,000 and 20,000 submissions a year1. So, the likelihood that a given submission will be published is between just .25%2 at the conservative end and 15% at the optimistic end and, more importantly, the likelihood a manuscript will be rejected or ignored is up to 99.76%  

It’s a wonder anyone tries at all. But, the fact that they do means that there is real worth in trying to make the system work better.

With the AUTHORS.me platform, writers are 7 times more likely to be accepted and 13 times more likely to get positive, forward movement for their manuscript.  

For many writers the most frustrating part about the submissions process isn’t being declined.It’s not knowing where you stand or what is going on. While it is an ongoing process to get publishers and agents to use our workflow statuses to accurately represent when they review work, 39% of writers who submit through us know concretely that their work has been declined and just 56% in the lifetime of the platform are awaiting review. Considering that lifetime submissions on the platform nearly doubled in the past three months, that is a true improvement to the norm.


Transformational, Dynamic Development
Algorithm Progress & Essential Insights

For the past year and a half, our developers have been honing and deepening a patent-pending algorithm that delivers the probability an individual manuscript could be a bestseller. The underlying goal is to offer a product that helps publishers, agents, and production companies identify and act on lucrative properties more quickly and with increased acuity. Our belief is that this technology offers the industry transformational power driven by actionable insights.

As the work went on, we discovered that beyond just that singular determination, the program was able to identify strengths and weaknesses within a given piece of writing that could, by an editor or a writer, be turned into essential, actionable insights that both expedite and strengthen the editing process and go-to-market plan.

For example, a report may detect room for improvement in areas such as redundant phrasing, incomparable constructions, or explicit language use. Editing with specific actions or recommendations is far easier and less overwhelming. A manuscript that seemed like it just wasn’t working now has a dynamic road map for revision.

Likewise, the sentiment analysis and comparable literary archetype can, to an industry professional, become a keen market insight that allows for a faster, more objective method of finding comparable titles and, informed with a title’s less obvious but no less essential common characteristics, possibly expand the target audience. In an industry with a reputation for homogeneity both in representation and delivery, these kinds of tools bolster objectivity and in turn create a more diverse landscape.

With these insights in mind, we launched the first iteration of our Intelligent Editorial Analysis Reports in partnership with BookLife, a Publisher’s Weekly website that seeks to provide self-published authors with resources, community, and platform elevation.

The report uses the technology we have been developing for publishers and enterprise entertainment companies and delivers digestible, actionable insights on an individual manuscript. Anyone can upload a manuscript and receive feedback on elements of their writing from style and grammar to syntax and literary device implementation. It points out areas for potential revision as well as commendations for markers of “good” writing. It shows the writer the manuscript’s literary archetype based on sentiment analysis, and it also delivers a numerical evaluation of their manuscript in comparison to best sellers.

The road to get here was full of curious, fascinating experiments and realizations. Let’s look at a few of them.


We’ve analyzed thousands of books—bestsellers, mid-list titles, backlist classics, self-published books, and unpublished manuscripts—to develop and improve the algorithm. One of the measurement points is a sentiment analysis, which when translated into a plotted arc resembles the narrative arc of the story in question. In performing these thousands of calculations, we discovered that there are measurable differences in manuscripts down to the tenth decimal point.

That is how unique each piece of text is, and how quickly and easily a computer can prove it. Within those fractions of variation exist essential insights into tone, character, style — the possibilities for measurement and action are boundless and thrilling to data scientists and forward-thinking literary analysts alike.

Sentiment Analysis — Part of a Whole

Many teams are working on understanding NLP better and we’ve been able to incorporate into our systems training some of the smartest APIs available., such as Microsoft’s Watson program. In one of these tests, we asked Watson to analyze the sentiment arc for particular  parts of a book; specifically different characters and settings (it’s fascinating but not altogether surprising that an element can have a narrative arc, but that’s a story for another day.)

When we ran these results back through our own program and analyzing bestsellers, we found that the overall sentiment arc plays a much larger role in determining a title’s comparability to the standard bestseller profile than that of any one part of the book. Or, in simpler terms: a book is more than the sum of its parts. The romantic part of me somehow thinks we all knew this already, but this is documented, objective proof to that effect.

Data & Publishing
Finding Meaning in the Numbers

The larger point is that in all of this work, we are discovering objective key performance indicators of raw text that transform previously elusive, ephemeral qualities of writing into quantifiable, measurable, and meaningful data points.

With this information, editors and writers alike can optimize their own individual approach to their work. The industry and community at large can harness the raw power of Big Data to stake claim to their creativity and carve out pieces of the market that fit best, not just fit now.

We’re seeing that the increased prevalence and use of data in publishing doesn’t have to mean a withering competitive landscape, but instead a richer, more vibrant one where the bar is continuously raised, met, and transformed altogether.

1 There are roughly  6,080 traditional publishers and agents in the US.  [2014 SUSB Annual Data Tables by Establishment Industry]

2 In 2016, 311,723  books were traditionally published in the US. [International Publishers Association]

3 In our initial R&D, polled agents and editors who accepted unsolicited work reported an average of 100 submissions/week. 

Who Does it Better, Humans or Machines?

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Data analysis and data mining have been applied to traditional retail spaces for the past two decades. In recent years, some companies stepped into that space to cater to publishers and booksellers to benefit marketing and sales. And even more recently, the phrase “machine learning” has entered the conversation in that industry.

In a nutshell, machine learning “lets computers learn without explicit programming. In analysis, the technology uses algorithms that learn from the data, and, in turn, grow and change when exposed to new information, ultimately uncovering those all-important insights.”[Google]

[aesop_quote type=”pull” background=”#282828″ text=”#ffffff” width=”40%” align=”right” size=”2″ quote=”Computer science isn’t as far removed from the study of literature as you might think.” cite=” Inderjeet Mani ” parallax=”off” direction=”left” revealfx=”off”]

Could this technology be applied to the entertainment industry at the point of acquisition or as an enterprise quality control? The results across other industries who have adopted it (like manufacturing, retail, healthcare, travel, hospitality, financial services, and energy and utilities) are incredibly promising.

In “How Analytics and Machine Learning Help Organizations Reap Competitive Advantage,” MIT Technology Review in partnership with Google Analytics 360 Suite reported  that companies in the top third of their respective industries using data-driven decision making were “on average, 5% more productive and 6% more profitable than their competitors.”

So that’s not just an increase in their own profits and productivity due to implementing a new tool. That’s over their direct competition. But what could this technology do that it isn’t already doing for the entertainment industry?  

What if instead of waiting on sales reports to identify trends post-publication, content trends could be projected based on historic data?

What if machine learning could help publishers, agents, and studios not just identify consumer trends and habits, but predict content reception?

Consumer data is extensive, detailed, and can indicate not just what consumers are responding to, but why. Once those similarities are established, couldn’t a computer identify the less obvious but no less resonant comparable qualities, discovering fresh possibilities for titles new and old?

In short, it can.


To consider this in earnest, it’s important to understand that while books hold cultural weight, ultimately they are all data. But unlike the consumer data that, even formless, has proven itself so valuable, text has inherent rules and formations. And each deviation from one rule has its own set of rules and acceptable actions.

These rules are grammar, syntax, and archetypes (which are themselves the subject of data scientists’ attentions); those deviations are diction, literary devices, colloquialism and dialect, pastiche and tone. When book clubs discuss the obvious connection of the blue door to the heroine’s inevitable death, they are discussing the pattern identified in a semi-closed system. They are performing one tiny fraction of the computational potential of an algorithm trained on literature.  

Considering this perspective, it’s not difficult to imagine how a computer could evaluate and identify traits of a book. But could it identify a bestseller? (The short answer is yes, but it’s quite complicated,  according to Jellybooks founder Andrew Rhomberg)

At its core, that question belies the understandable fear that a computer will determine what is “good” and cast off the rest. But doesn’t that fear in itself really short-change humans in general? Like any tool, it is as strong as those who wield it and as multifaceted as them as well.

OK, so a computer can do it. But should it?


Many think, yes, it should. Take book discovery for an example.

Though now a foregone conclusion, book discovery was the concern du jour of data-minded publishers 10-15 years ago. How would we get readers to the books? How would the reader discover the books? As a result, a flurry of startups emerged that gave consumers new ways to discover, consume, recommend, and interact with their favorite stories. It wasn’t that the traditional methods of consumer discovery weren’t working, but they could work better and produce more consistent, profitable results.

The same logic can and should be applied to the systems in the entertainment industry that acquire and produce consumable media. There’s no lack of raw material, sure, but is the current query and acquisition system bereft of room for improvement?


Simply put: A human cannot read as much as a computer, because a human gets tired, distracted, hungry, etc. A computer does not feel, so it does not want or need. It simply does. And where computers finish, we pick up.

Humans are amazingly creative, observant, and resourceful. But at a certain point, your brain just can’t process any more data any more quickly.

A computer doesn’t have that problem. An algorithm doesn’t get a tension headache from the hours spent reading. Machine learning  software doesn’t tire of reading a story they think they’ve heard before and move on; it reads the entire book in a few seconds.



One benefit of a computer doing the analysis is that it can discover the latent aspects of a novel. So, while authors may have their own identifiable style, there are underlying components of a novel that a computer can see better than humans. And what we’re betting on is that underneath the facade of a book lies a story that connects a reader to that text. That’s what we’re uncovering.

So, could such a program recognize the outlandish genius of someone like David Foster Wallace, James Joyce, or Kaye Gibbons? I wouldn’t bet against it in the future; but that’s not entirely the point.

If we only adopt technology that feels familiar, safe, and ultimately just performs parlor tricks, we do ourselves and our industries a disservice.  We leave the possibilities of customization and adaptability of machine learning and artificial intelligence behind.

This does not necessarily mean that the goal of machine learning or artificial intelligence is to replicate human creativity (though it might at Wattpad). But instead amplify it, to embolden the editors, agents, and publishers with more data than they could ingest in a lifetime, distilled into actionable insights.

“In this digital future, using machine learning platforms can provide publishers with opportunities to get real-time information about their readers, figure out what is working in the marketplace, and, perhaps, make the bestseller lists more of an accurate depiction of what readers want to read, not simply what is available,” commented Intellogo founder and CEO Neil Balthaser in an op-ed in Digital Book World.

Imagine a day when we take all our data about what people are reading and provide publishers (and authors) ideas of what people want to read, where to find those audiences, and better ways to reach them. This is the model that the film and television industries are already moving toward—with the help of Netflix and Amazon—so why shouldn’t book publishing take advantage of this market information? This type of decision support has not been possible up to this point, and publishers have often published books blindly, hoping that they would find the right audience and sell well.

Though ‘big data’ can be a taboo subject when we talk about the romance of publishing, there are undeniable benefits to be had from using platforms that give publishers and authors information from which they can make informed decisions on how to invest their time and money.

Distinguished Google engineer Sagnik Nandy explained to the MIT Technology Review that where traditional analytics relied on the idea that people would access tools and already know what questions should be asked, based on the data.  

“But today, everything is changing so fast as businesses evolve, and there’s a lot going on that you might not be able to see,” Nandy noted.

The beauty of machine learning, as Nandy put it, is that extracts “all the information you’re not asking about. Once you have that information, you can generate insights even before a question is asked. That can be a huge competitive advantage.”

Whoever you are your competitors will implement machine learning and will take the advantage. The question entertainment companies should be asking themselves shouldn’t be “Will this threaten/cheapen art,” but “How can I implement this yesterday?”

In the end, this is just one more tool to add to the creative, entrepreneurial arsenal. As  computational linguist Inderjeet Mani so eloquently described in Aeon,

Those who resist the temptation to unleash the capabilities of machines will have to content themselves with the pleasures afforded by smaller-scale, and fewer, discoveries. While critics and book reviewers may continue to be an essential part of public cultural life, literary theorists who do not embrace AI will be at risk of becoming an exotic species – like the librarians who once used index cards to search for information.

Further Reading

Writing the Book on Artificial Intelligence: LBF Quantum’s Nick Bostrom” by Mark Piesing, Publishing Perspectives.

“Machine Learning and Bestseller Prediction: More Than Words Can Say”by: Chris Sim. Digital Book World.

Quantifying the Weepy Bestseller” by Andrew Piper and Richard Jean So Republic.

Direct Matching Added to Submission Platform

Matches Made in (Publishing) Heaven

On September 15, AUTHORS released Matching, its most transformative update to its submissions platform since launching last year. The update shows writers who among the participating agents and publishers their work matches.

One of the most frequently asked questions we receive from our writers at AUTHORS.me is ‘Who else can I submit to?. Our writers are eager to reach as many potential publishers as possible. And why not?” said David O’Brien, co-founder of AUTHORS.me. David communicates directly with the writers using the AUTHORS platform on a regular basis and fields their concerns. “But the question could, and maybe should be, ‘who should I submit to?’” Matching is the answer to such concerns.

Since launching the service last year, the company has aimed to not just streamline the submissions process for writers, publishers, agents, but to help make it a more efficient and enjoyable experience. This updated is an extension of that goal.

“A lot of the time, when a writer–myself included–asks who else they can submit to, they’re really trying to figure out everyone they can possibly submit to,” said O’Brien. “It’s an understandable desire, given the effort they’ve put into their work. Unfortunately, that thought results in publishers and agents having to wade through submissions they’ve said they explicitly don’t want or are at best poorly targeted. This makes it even harder for those that are submitting to the relevant companies to get noticed.”

The matching system addresses this industry-side concern as well. Publishers and Agents outline exactly what kinds of projects they are willing to review from genre and minimum word count to target audience and author qualifications.

“We want our writers to connect with the right agent and publisher, not just anyone. There has been a lot of thought and tech put into our process,” said O’Brien.  “We have streamlined a century old process down to 3 steps.”

Those steps are: Build. Match. Submit.

1. Build
Writers build their profile and project using simple forms. These get stitched together into a media-rich submission.

On the other side, agents and publishers input their criteria of what they are looking for from a writer.

2. Match
Once the projects’ minimum fields are completed, the matching technology gets to work.

The system grabs the writer’s data and cross-references it with the agents’ and publishers’ requirements. The results are matches that satisfy both sides of the submission equation.

3. Submit
The last step is the Submitting Process. Writers are given the option of following through on the matches and submitting their work. We encourage them to research the agents and publishers we present to them before we release the submission.

Since launching the update, submissions are up and we are hearing back from a lot of happy writers. The upgrade results:

  • Gives writers many more choices to submit their manuscript
  • Streamlines the process
  • Assures the writer and agent/publisher a higher probability of success
  • Keeps the cost to the writer at zero

The second most frequently asked question we hear from our writers is, “Why didn’t someone think of this earlier?” We are happy there is no answer for that.

Sentiment Analysis & Book Publishing

A few weeks ago, I talked to our developers about a phrase I heard them throwing around a lot: Sentiment Analysis. Once they finished their explanation, I immediately asked them to do a write up for the blog. This is fascinating! The people must know!

And…they gave me a blank stare, a kind smile and then promptly went back to work (as they should). So, with their guidance and fact checking, I’ve tried to translate their detailed, data-rich reports and updates for your reading pleasure.

So, to start off, what exactly is Sentiment Analysis and why are we talking about it? Simply put, sentiment analysis is one way of looking at books and is one of the analytic methods we use to analyze manuscripts. Technology can actually interpret the very life and breath within a manuscript.

How does that work?  

Sentiment Analysis 101

At a glance, sentiment analysis is fairly straightforward: text is analyzed and, using natural language processing, each part of the text is categorized positive–happy statements–or negative–sad/angry statements. Within each language, words can be determined positive (elated, kiss, jump), negative (smash, kill, cry), or neutral (the, a, road). Take these altogether and graph the results, and you can see the emotional arc of a text mapped out in a physical form, called a sentiment map.

Recent Sentiment Analysis Study —  Best Sellers

If you’re still with me, maybe you’re thinking: what can the sentiment map tell us about plot? We mapped out three extremely popular best sellers — Fifty Shades of Grey, The Girl With the Dragon Tattoo, and Gone Girl–to illustrate how these novels are constructed and why some books are said to be more surprising than others.

All three of these books buck the conventional trend of a high, happy opening and a high, happy, neatly wrapped up ending: they all have endings that are dramatically lower in sentiment than the highest point of the book or even the beginning. And, looking at the plot points for each, the graphs make sense.

*Spoiler Alert for all three novels*

1. 50 Shades of Grey  by E.L. James

Sentiment Analysis of Fifty Shades of Grey


Unlike most romances, the end of Fifty Shades is not happy.  You can see the sentiment taking a sharp decline in the last few pages as the book ends with the main character crying, swearing she never wants to see her lover again.

2. Girl with the Dragon Tattoo by Stieg Larson

sentiment analysis of Girl with the Dragon TattooThe major dip around 3/4th of the way through the
The Girl With the Dragon Tattoo is when Mikael is trapped by Martin Vanger and almost dies. The sentiment rises as Lisbeth frees him, and continues to rise as Mikael publishes the expose on Wennerstrom. The sentiment heads back downward through the end of the book as  Lisbeth goes to tell Mikael she loves him, only to find him with Erika Berger (Poor Lisbeth 🙁 hasn’t she been through enough?!)

3. Gone Girl by Gillian Flynn

Sentiment Analysis of Gone GirlArguably the progenitor of a recent trend in unreliable, potentially psychotic female central characters, Gone Girl’s graph is easy to follow. Consider: in the first half, Nick is looking for his missing/possibly dead wife Amy, during which time it’s revealed how much Nick cheated on her (a LOT) the seriously fatal state of their marriage, meanwhile Nick is named the number one suspect in her disappearance. So, the general downward spiral, errr slope, of the graph makes sense. The brief, sharp climb around point 60 is when Amy comes back and it turns out Nick won’t be going to jail, but the sentiment just plummets right on down because Nick is still unhappy, Amy is still terrifying and dangerous, and life does not seem to actually be getting better.

The final, brief peak at the end is when Amy tells Nick she’s pregnant, but even that is barely above neutral. In this world, news of Amy and Nick spawning is not really good news, and the sentiment analysis confirms that. And, additionally, this bit of information may give us insight to why so many people read this book and thought “WTF IS UP WITH THAT ENDING?!” It’s because most novels don’t end with a fairly neutral ending; so the reader is left feeling unsettled, as if  there is something missing that they can never recoup.


Had I convinced my technology-minded colleagues to write this blog, it may have ended with a discussion of linear regression, decision boundary, sentiment vectors. and macro-arcs. But since my background is in publishing,  I will end more philosophically. Thinking about editing and reviewing, this kind of technology is so exciting. It gives an editor another way to explain the pacing of a book to their writer. It gives a publisher the ability to look at the breadth of their work to see what kind of brand and niche they are establishing and where the holes may be that they could fill. It gives writers an ability to objectively see if the impact they are trying to achieve is actually coming across. And these are just a few of the possibilities. Sentiment Analysis and other machine learning actions are just additional tools in the hands of smart literary professionals. It’s a way to analyze across books and within books. And a way to look at thousands of books in the same time you or I can review one or two.


More on Sentiment Analysis:

 ACM Digital Library


The Right Genre: A Writer’s Dilemma

pexels-photo-92323-largeSo why is picking the right genre for your book so important and how does its success hinge upon choosing the right one? Let me illustrate this by talking about a movie.

I was in the mood for a movie last night so I went to my DVD shelves (I have five shelves of them) and scanned the titles. Like most movie buffs, I arrange my films by types or genres including Animated Features; Classics; Mysteries/Thrillers; Action/Adventure; Comedies; etc. You get the idea.

I selected Guardians of the Galaxies, which was under Sci-Fi/Fantasy section. Even though I find this movie to be offbeat and funny, I placed it in Sci-fi because that is what the main genre is. Could I have filed it under comedies? Not really.

I know when I watch a sci-fi movie, I expect to experience many of the elements one finds in a sci-fi space movie –cool space ships; futuristic tech; totally unrealistic distances traveled; bizarre planets; aliens bent on the destruction of all humans. Quick sidebar: Have you ever noticed the days of the weeks are never mentioned in space movies? NEVER. “Yes, Skywalker, we should reach the Death Star by this time Thursday.” I digress.

When you pick a movie or book by type, it’s all about expectations. Guardians is under sci-fi because I expect to see all the aforementioned elements. Hunger Games is Young Adult/Sci-fi (in a dystopian world) novel, so I expect to see characteristics of those genres prevalent in the story. Even though there is a love story in the book, you cannot label Hunger Games a Romance novel.

Genre is perhaps the number one qualifier for an agent or publisher to initially accept a work. If you query them by labeling your genre: Mystery/Romance/Sci-fi because your two young characters embark on an epic adventure to thwart an alien invasion and fall in love in the process while trying to solve a mystery, you’re in trouble and headed for the slush pile or rejection.

Agents and publishers want clarity. They have expectations and you need to meet those if you are to have any success with them and your future readers.

On AUTHORS.me, we give our writers the choice of numerous genres to pick from. Previously, we allowed our writers to select three, but after numerous consults with our partners, we have decided that 2 is plenty. Therefore, be precise. Do your research on the genre(s) you believe are represented in your work.

Imagine your book has been published and is in the bookstore. What one section will it be found? Make that your genre of choice. And when your book is made into a movie, I’ll be sure to put it in the right section as well.

Additional reading:

Let’s Get Personal – Writer Updates

At AUTHORS.me, we have been working hard at getting writers, agents and publishers connected. Call it our raison d’être. With over 150 book deals in less than 10 months, I would say we’re raison-ing pretty well. But it will never be enough. We want writers to get the most exposure they can muster in order to get read and possibly represented. So being the tinkerers we are, we are busy building something new.

We have started working on a great way for writers to promote themselves outside of AUTHORS.me but still keep all the work they’ve created on the platform connected. We are calling them Personal Pages. I know, great name right? These will be elegant representations of a writer, their body of work and their qualifications. Wait, isn’t that what we do with the AUTHORS.me app? Well, yes, but that content is inside an app seen only by agents and publishers who are in the app as well. What about the other agents and publisher yet to join AUTHORS? These Personal Pages will bridge that gap.

Think of it as your own website but without the hassle of generating content, learning WYSIWYG or CSS or web parts or whatever those templated services provide. We will generate the page from the content the writer created on the AUTHORS.me platform. Simple. Then like any other web page, you can share the url outside of the AUTHORS.me app. Cool. We love creating ways for writers to promote themselves professionally with ease.

We are still in the design and testing stage, but we encourage you to keep an eye out for further updates. Now if you will excuse me, I must return to my laboratory and join our team of tinkerers.

Diversity in Publishing: Writer Updates

We are a couple days from closing registration for our Diversity Contest and I want to share some thoughts about this. We launched the contest with one goal in mind, to give voice to those who are not being heard or read. Authors from diverse communities and stories with diverse subject matter and characters have struggled over the years, decades actually, in finding a place in the limelight of publishing. We hope to change that, not for the sake of change or some social agenda, but because we love diversity of thought. It makes us richer, smarter and more likely to understand each other.

The reason we chose children’s books as our platform for diverse books is because this is where the change can happen and continue on into our futures. Over the years I have purchased hundreds of books, many of them for my 5 kids. Because children’s books are simplified in message, it is a wonderful platform to begin learning about other cultures and ideas. And I’m not talking just about the kids.

Here is my solution to see more diversification of books and it begins with you. I challenge you to add just one book to your library in the next month whose subject matter, characters, or author is of or by people of diversity; a book that recognizes a diverse experience, such as people of a different color, gender, disabilities, body size, ethnicity, culture, and religion than you. You will be the better for it. The more books we buy, the more diverse books they will publish.

I look forward to reading the winners of our contest come September 6th. You can follow the winners here.

In other news, our Humor Us contest continues through August 31st. We need humor as much as we need diversity.

Discovery Doors are Open: Writer Updates

AUTHORS is all about seeing great manuscripts become great books. I love to read. Books take me places I never dreamed of. Now I am part of a company that helps to discover good books, which in and of itself is a dream come true. So when we made the decision to open the AUTHORS Discovery database to all our writers free of charge, I could only think about the flood of content coming our way. I always think about that Twilight Zone episode where the guy has a stopwatch that can stop time. So what did he use it for? He stopped time, then went to the library and gathered up hundreds of books he never had the time to read. I’m that guy. Where is that stopwatch?

Back to business. AUTHORS Discovery is where our agents and publishers find new content. They search for what they want to print and find you the writer. It’s the inverse of the world today where writers chase after agents and publishers. We love it. Our partners love it. And now nothing will keep our writers from entering in and getting discovered. Also, with more manuscripts entering Discovery, we expect to see lots more books deals and great stories enter the marketplace.

In other news, we have welcomed McArthur Gill aboard as our new Platform Engineer. He will help us make your experience on AUTHORS more efficient and enjoyable. I’ve been a writer, artist and vagabond most of my life, but I always had an affinity for the engineer’s mind. They look at the world different than I do and it fascinates me to no end. I look forward to McArthur helping me bring heady ideas into the real world and have them actually function properly. If we artist ruled the world, everything would look cool, but nothing would work.

Data Science, Happy Scientists & Publishing: Writer Updates

You know, as opposed to “mad” scientists. Did you ever ask how Google can be so wickedly accurate with their searches, sometimes populating the answer before you even finish typing the question? How about when you ask your phone a question and it gives you an answer? Well we have data science and the scientists who study it to thank for those and many other everyday applications, like image recognition, voice to text, price comparison websites, improved gaming, airline route planning, package delivery, and on and on. In truth, much of what we do today is impacted by the work of data scientists.

So what does this have to do with AUTHORS? Lots!

Over the past several months we have been hard at work building tools to analyze data, the text in manuscripts in particular. This is the heady work of data scientists. The more we understand the data (text) the better we will be at matching and recommending it to agents and publishers. We are hiring talented data scientists to work on our matching algorithms and much much more. Look at this little formula which can tell you the grade level of a book:

0.39 (total words / total sentences) + 11.8 (total syllables / total word) – 15.59

Really? Really. Data scientists examine massive amounts of data and outcomes to be able to predict what other data will do. Our guys at AUTHORS will be working on several things including making better matches and making recommendations to our partners and writers. This will make you the writer, agent, publisher happy with the outcomes.

When they are done with that, I am going to ask them to work on an algorithm that will help me predict how my fantasy baseball team will do.