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All rights reserved. This is a work of fiction. Any resemblance to actual persons, living or dead, events, or locales is entirely coincidental. This book is for you. I am constantly inspired by your expertise, good humor, and love for the written word. Anne Messitte, for your faith in me. I will forever be indebted to you. Niall Leonard, for your love, support, and guidance, and for being less grumpy. Valerie Hoskins, my agent—thank you for everything every day. Kathleen Blandino, for the pre-read, and for all things Web.

Joanna: It's great to finally have you on after we met earlier this year. Your SEO's pretty good, too. Chris: I definitely have very the clickbait-y titles.

Joanna: You do, and we're going to get into that, because I've talked before about how I don't write to market but we're going to talk about this a bit later. But first of all I wanted to just find out a bit more about you.

Why do you care about data science as well as writing? Tell us a bit more about your own background and how these topics connect in your own life.

Chris: It's really integral to who I am professionally. Back in , I worked in collections at a credit union making just above minimum wage and really disliked the job. I was desperate to escape that situation. Right around that time, the iPad was released. I had this epiphany where I looked at the iPad and I realized that the people who could make applications for the iPhone and the iPad were going to make a ton of money, because in the next five years, everyone would have one.

I invested several thousand dollars I didn't have, and I used that money to download a new Mac and the software that I needed to teach myself to develop these applications. And if you fast forward just a couple of years, I ended up moving to San Francisco where I got a job at a start-up, and this is where I first started learning about data science. What I didn't know at the time is San Francisco is actually the real tech hub of the world, I thought it was Silicon Valley, but everybody from Uber to Fitbit, to PayPal, to Square, to LinkedIn, they're all within a couple of miles of the same financial district, either north or south of Market.

When you're going to lunch, everybody wears their hoodies or their t-shirts like uniforms, there's the PayPal guys and the Square guys and everybody likes to talk about what they're working on. What people were working on in and was data science. The company that I landed at, CellScope, we created a microscope that attached to your smartphone so that you could record an ear exam and then broadcast that to a doctor.

Then how do we get from an app to diagnose your ear stuff to books? Chris: At the same time that I'm learning all this data science, which is fascinating for me, I'm a JC dropout, I never finished my college degree and I'm surrounded by people with doctorates from Stanford and Berkeley.

I began to realize that site was using data science, so that what I saw was not the same thing that a co-worker might see or a friend might see, or my family might see. site was dynamically generating content for every single user that came to their site. That started to lead me into how is data science being used for writing. It occurred to me, site has been doing this quietly in the background for almost 15 years, where they're gathering all this data.

I started to realize that if I could use the system they had created, I might be able to sell more books, and unsurprisingly, I guess, it worked. Joanna: We'll get into that in a minute, so let's just stick with some definitions. What is machine learning, so people know that?

I mean, many people have heard about the algorithms, and of course, we don't know what the algorithms do necessarily, but we can take some educated guesses. What is machine learning, algorithms, all these types of things? Chris: Machine learning is exactly what it sounds like, it's machines that can learn.

They're capable of learning new things. You teach a computer program what it's supposed to know about, and then it gets smarter and smarter at whatever that thing is.

The best easy example I can think of is if you've used Facebook and if you've uploaded a photo, you know, how it draws that little box around your face? How does it know to do that?

How To Use Data Science To Write And Sell More Books With Chris Fox

How does it know where the face is in the image? That's machine learning. This is what eyes look like. This is what your mouth looks like. And if you find all of them and they're in this arrangement, you found a face.

Joanna: I know some people will be getting lost already but we're going to try and hold it together. Now, site also kind of hire out, don't they? Because I use site S3 to host this podcast for example, and S3 like the cloud hosting, and in fact, I think a massive proportion of site revenue now comes from hosting a lot of the internet on the site service. But you can now hire their machine learning services, can't you, in the same way that I use their service. What's the difference between what they use for books and what you can actually rent yourself?

Chris: site is at its heart a data company, and they've built a number of tools. So think of this like a toolbox that a data scientist could pick up where I could make my own hammer and I could make my own wrench.

Or I could pay site to use the things they've already created, including the hardware where you need to do all the number crunching. There's a lot of very intense CPU work needed on these computers. site spun off this division, site Web Services, which we used at the company, the start-up that I worked at as well, to host all our stuff. They really have made it accessible so that almost anybody. If they've got a data scientist or two, a teeny little startup can use what they've created and start figuring out again, almost anything that you can imagine — how to diagnose ears being the tip of the iceberg.

Joanna: I don't program, but I would love to use the site AI stuff to try and figure out how we can automate your system, but let's go further into that. Let's first talk about how does site differ from iBooks, Kobo, Nook, other retailers in how they use this type of machine learning?

Chris: iBooks, Kobo, and Nook, what they all have in common is they work very much like a real brick and mortar bookstore. A person is deciding what books they're going to promote, where those are going to show up and how long they're going to be there, and that's very carefully curated by those companies.

If you have a relationship with Apple, the people at iBooks are great, then they're going to help you out and they're going to put your book where it can be seen by its target audience. What site does, it's more of a meritocracy where their data science is figuring out which books are selling. And then in the background, they're showing those books to more people without you needing to do anything at all. site is much more adept at using data science to locate the target audience for any given product, not just books, but everything from surfboards to televisions are sold using the same algorithms.

Joanna: My ears pricked up there and I know people listening will as well. So sharing information about your book or pushing your book without you having to do anything?

Chris: Yes. It's already in site and a lot of people are going to a lot of effort to try and sell more books by pushing traffic, for example, to the system. How can we use the AI, you use the phrase, how can we train the site system to sell more books for us, without us having to do so much marketing?

Chris: This is easier to do if you've written a book to market, but it's not necessary to write a book to market to make this work.

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What you want to do is figure out, with as much accuracy as possible, who your target audience is. And when you start selling your book, the number of sales is not nearly as important as who you sell your book to, because each of those sales to site represents a customer profile. If you can convince them that people who voraciously read in your genre are going to love this book and you sell a couple of hundred copies to people like that, site's going to take it and run with it.

You've now successfully trained them about who your audience is because you used good data and now they're able to easily sell your book. If, on the other hand, you and your mom downloads a copy and your friend at the coffee shop downloads a copy, and people who aren't necessarily into that genre are all downloading it, site gets really lost and confused. The beauty of how this works is that as an author, all you really need to do is figure out who your audience is and sell a few books to them, and site will do the rest.

Joanna: Yeah, and again, everything you were saying is like yeah, figure out who your audience is. I just want to come back there to something like perma free. I've been doing this since really, when site KDP went international, before then, it was only for Americans, back in the old days, but the permafree, first in series, has been an evergreen marketing tactic that a lot of people have used, myself included. I know some people are now saying maybe that has fallen off.

Others are saying it hasn't. What we know with the permafree and when we promote the permafree is that a whole load of people who pick up free books will pick up the permafree. Are you saying that a permafree with a BookBub on a free book is a bad thing because even though you might shift 10, copies, maybe 9, of those people are not the people that you want the algorithm to know about? Chris: Exactly, and you can test this yourself.

Let's say you've had a BookBub in the last six months, how long was the tail? Because if it was the right kind of data, you're going to see that your book is sustaining it's sales rate, it's going to keep selling once it hits a certain level. But if you have the wrong type of audience that will manifest both in your Also Boughts, where you'll see that you have cookbooks mixed in with your romance novels or whatever genre it is that you're writing, and I think that that can be really dangerous because once that data is polluted, if you give away those 10, copies to a bunch of random people, site will never again have any idea how to sell your book.

Joanna; Actually. I had a look today at something.

E L James – Provocative Romance

And what would be the best ways to do that according to the system that you're mentioning? Chris: Absolutely. Make sure you're hitting whatever your target audience is and you're going to get a trickle of sales.

It sailed all the way up to number in the store, because that 99 cents kinda removes the barrier of download, and a lot more people are going to pick it up during that window. Joanna: I've heard a few things recently that make me really on the edge of removing my permafree and it's kind of like ooh, it's difficult because we tend to like the things that worked for us in the past.

But one of the reasons to talk to you is to challenge myself around what I do. I'd like to think I'm a futurist and we'll talk about that, but gosh, I find myself sticking with things that have worked in the past. Whereas we both know how fast things change in this market.

Chris: It's really difficult. I faced the same dilemma. I went wide with my books in, I want to say, April of And I had a permafree, first in series, and it did pretty well. And that was working for a while. Joanna: Of course, I hate to hear that, because I am one of these people who is really into going wide. Mainly because in , I got laid off along with loads of other people in the GFC, and I swore never to rely on one company again. Now, I'm thinking that I will write something new, like a different series that would be in Select, as opposed to pulling my other books.

Because I'm very committed to Kobo, who sponsored the podcast, and iBooks, and I think we, on a much bigger level, we should be investing in building an ecosystem, and also in other countries, some of these other stores are stronger.

So how are you feeling? Right now, you've pulled them as you say. Is that just a short term strategy? Chris: It is. It is absolutely a short term strategy. Like you, I'm terrified of having all my eggs in one basket, which is why I went wide with my books originally.

It's scary because site could decide tomorrow they're killing off KDP or they're cutting our royalties in half or whatever they're going to do, and if you're solely reliant on them, you're really in trouble. This gets back to the data science, why I'm willing to do this short term risk. I actually learned it on The Creative Penn, putting that on the front and back matter of my book.

So by selling a ton of books on site in the short term, I'm building up this really engaged, large list of people that I can use at places like Facebook or Google for advertising. When I'm prepared to go back wide, now I've got this massive audience that I can take with me. And that I think makes it worth it, but it is still a little scary because you never know what's going to happen with site.

Joanna: Exactly. A lot of this has to do with back list and having a number of books. I sent it to a line editor who gave it a quick look and sent it right back, and then a week after that, it was live on site. The goal there was to write a book that would sell really well.

I was gambling that it would get into the top 1,, which it successfully did. It got all the way up to number , sold plus copies the first day and it stayed there for months. That book sustained it's rank for far longer than I expected it to, and when it started to fall, I put up the sequel and the same thing happened.

Joanna: Why did you write those books? Let's get into the write-to-market thing. Chris: I chose this genre because it's an intersection between where I know there's a large, voracious audience of readers.

They're going to go ahead and download this book in droves because there's so many of them out there and they love science fiction, and it also is something that I enjoy writing. Growing up, I watched a lot of science fiction movies, I've always read sci-fi books.

I knew that if I could find that intersection, if I could get to a point where I was writing something that had a massive audience, my book being very similar to other books that were in that genre already, I'd have a really high likelihood of selling to the same audience of people that liked the books that were already out.

I'm going to explain how I followed your thing in a minute. How do you find a sub-category or a smaller niche within the site ecosystem? What are the things to look for in order to find a voracious readership? Chris: What I do is I start looking at the rankings of the number 1, the number 20, 40, 60, 80 and books. You can tell based on where those books are ranked, how many books in the genre are selling.

If the number one book is ranked in the top in the store and so is the 20th book, then you've found one of the hottest genres on site. If you find that by the time you get down to number 40, the rank is dropping off sharply, that suggests that not enough books are being produced in that genre and it might be a great place for you to jump in and make a name for yourself.

They're kind of an edge of horror, they're a bit of supernatural. They literally don't fit anywhere. But this London Psychic Trilogy, I just can't find a place for it. I went through and identified the things about my books that were interesting. Kim Carpenter. The Lucky One. Nicholas Sparks. The Best of Me. Lone Wolf. Jodi Picoult. Where We Belong. Emily Giffin. The Casual Vacancy. Those in Peril. Wilbur Smith. Sweet Addiction. Maya Banks.

On the Island. Tracey Garvis Graves. The Look Of Love: The Sullivans, Book 1. Bella Andre. The List. The Great Escape. Fiona Gibson. A World I Never Made. James LePore. Beautiful Bastard. Christina Lauren. Shadow of Night.

Deborah Harkness. The Last Boyfriend. Nora Roberts. Gabriel's Inferno. Sylvain Reynard. Her Last Letter.

Nancy C. The Help. Kathryn Stockett. Me Before You. Jojo Moyes. Cheryl Strayed.

PDF Fifty Shades Trilogy (Fifty Shades of Grey / Fifty Shades Darker / Fifty Shades Freed)

Suspicion of Innocence. Barbara Parker. Gabriel's Rapture. The Innocent. David Baldacci. The Marriage Trap. The Virgin Cure. Ami McKay. Dan Brown.

Lover Reborn. The Marriage Mistake. The Litigators. John Grisham. Before I Go To Sleep. Sadie Matthews. From This Moment On: Moving from welfare to work The federal program that subsidizes child care for low-income workers in Michigan was shaped during the welfare-reform era of the mids , when policymakers from the Clinton administration on down were looking for ways to push more people who relied on public assistance into the job market.

And so what became an evolving program of block grants made to individual states began to take hold, with most of the money coming from the federal government and most of its administration and distribution left to the discretion of individual states. This at a time when research is revealing the enormous brain development that happens in children between birth and age 3, and its implications for later learning. The state has among the lowest income caps on families that are eligible, and offers among the lowest reimbursement to providers for care.

The Citizens Research Council, along with Lansing-based Public Sector Consultants, produced a report on state policy options to improve the outlook for our youngest, poorest children. Of the child-care program, it recommended more investment by the state, among other changes. But little has changed. The cap was recently raised to percent, still one of the lowest levels in the country. By way of comparison, as of , California admitted families making up to percent of the federal poverty threshold.

A boy runs in for a quick hug from his teacher before heading out for more play. With the announcement in May that the state was taking in less tax revenue than previously projected , the subcommittee that oversees the child care program is thinking more frugally these days, approving only the minor expansion from percent of federal poverty to percent.

Phil Potvin, R-Cadillac , chairman of the Appropriations Subcommittee on the Department of Education, said the state deserved credit for funding a state expansion of preschool for 4-year-olds in the previous two years.

But doing something similar for even younger low-income children will have to wait for bluer skies and fatter tax collections. And raising the eligibility threshold to percent, he added, is progress.

We did something. One of the saddest stories I know. Bridge reporting helped make the case for expanding the program to policymakers and the public. Blank said 22 percent of mothers of children under 3 in Michigan work in low-wage jobs and would generally qualify for child-care subsidies nationwide.