When somebody gets it right, it’s important to say so.

So I’ll say it straight out—Apple’s iTunes Connect just hit a home run with its new reporting tools for indie authors. (iTunes Connect is the web site for managing your account as an indie author.)

Gack, who cares about reporting? That sounds so … dull.

Indie authors care about reporting. They care when they want to know how many books they’ve sold in the last day or week or month or year or lifetime. They care when they need to know how much they’ve earned.

Let’s be clear, those are the two main numbers indie authors care about:

• How many books did I sell?
• How much did I earn?

You’d think that all the major online retailers would make those two numbers easy to get. You’d think those would be the first thing the online retailers tell you.

Well, no, most of them make it hard to get that information. Some of them make it impossible.

But before we go complaining about who gets it wrong, let’s talk about what Apple has done to get it right. (And until last week, they were getting it horribly wrong.)

How Apple’s New Reporting System Works

You log in to the iTunes Connect web page and you see a page with a number of options. Click on the first one, “Sales and Trends.”

A page immediate appears that shows a graph of your sales over the past week. You see the total number of units you moved in that time period and you see the proceeds, in US dollars. (You can choose what currency you want for the proceeds.)

Scrolling down the page, you see a list of each book you’ve published, with the number of units of each one that you’ve sold in that time period. If you click the “Proceeds” tab, the graph changes to show your total earnings each day, and the list at the bottom changes to show the total earnings for each book. Simple and easy.

You may want to change the time-period for the report. No problem. You have several convenient ways to do that:

• Click one of the links: “Last 7 Days”, “Last 30 Days”, “Last Year”, “Lifetime”.
• Click on the calendar icons for the starting date and the ending date to manually set the reporting period.
• Adjust sliders to change the starting date and ending date graphically.

If you want to see how you’re doing in various territories (the iTunes store currently lets you sell your e-books in 51 different territories), you can click a tab to display your results by Territory.

If you want to see how your different categories of books are doing, there’s a tab to break out the results by category.

You can also see results for preorders.

It’s hard to see how the system could be simpler or better.

If you want your results in a spreadsheet, there’s a link to click that will take you to a page where you can choose the reporting period (annual, monthly, weekly, or daily). Then you just click the download button and you’ll download a text file with a table of data that you can load into a spreadsheet.

Authors tend to be obsessive about their sales numbers. Many indie authors log in every day to check their sales on various online retailers. Knowledge is power, and knowledge about your sales numbers gives you extraordinary marketing power. You can try a marketing tactic and measure in real-time whether it works or not. This is a huge advantage that indie authors have over traditionally published authors.

Apple’s new system is so simple and obvious, you’d think that every online retailer did something similar. But tragically, they don’t.

Let’s look at some of the other online retailers to see how they handle reporting.

Amazon’s Reporting System

Amazon makes it easy to view on the Reports page the total unit sales for the current month for each of your books for each of the online stores where your books are sold. This is nice, as far as it goes, but there are a number of shortcomings:

• You don’t see the revenue you’ve earned.
• You don’t see the total units sold for today (or yesterday or any day). If you want to know how many you sold today, you have to subtract the total sales for the month yesterday from the total sales for the month today. And if you didn’t write down the total sales yesterday, you’re out of luck.
• You don’t see the total units sold on all the retailers. There’s a combo box you have to change so you can see the different retailers in various countries—the US, the UK, Germany, France, Spain, Italy, Japan, India, Canada, Brazil, Mexico, and Australia. It’s a major hassle to manually click on each of these and write down the totals for each so you can get a grand total.

Amazon also provides you with the ability to download a spreadsheet with the results for previous months. These are not available until 15 days after the end of the month. And they give you the numbers, but they’re extremely inconvenient.

• Sales are broken out into groups of rows, where each group of rows contains results for a given country.
• There will be a row for each book with sales at the 35% royalty rate, and another row for that same book at the 70% royalty rate. But there’s no row that shows the total sales.
• Each row ends with the revenue to the author, using the currency of the retailer. But there is no exchange rate given, which means you can’t convert to one common currency.

The bottom line is that you can laboriously add up the various rows to determine the total number of units you sold for each book. But you can’t add up the rows to determine your total revenue. You don’t have enough information.

The Smashwords Reporting System

Smashwords gives you a dashboard where you can immediately see the total lifetime sales of each book. But it doesn’t show the total lifetime revenue.

If you want more detailed information, you can get it, but you’ll have to download it. And the time period of each report is a full quarter—three months. You can choose which retailers and which books you want a report for and click the download button. Then you get a large spreadsheet with numerous columns. With some work, you can find the columns you want, sort them by book, and then add up the results to get a total for unit sales and revenue for each book. It’s clunky, but it’s possible to get the results, which is better than Amazon’s system.

Barnes and Noble’s Reporting System

Barnes & Noble’s system has a Sales tab that shows you a bar graph of total units sold by month. The graph shows total units for all your books, so if you want to know the results for a single title, you’re out of luck. You can also see total units and total revenue for this month and last month, but this is not broken out for each book. You can also see yesterday’s sales, both in units and in revenue—but you can’t see today’s.

If you want more information than that, you can click on the “Monthly Sales” button to see sales data for any given month. There’s a table that shows sales for each day of month, broken out by title. But the totals at the bottom are for all your titles summed together.

You can download the same information for any month to a spreadsheet, but if you want total units sold and total revenue, you have to do some manipulation in the spreadsheet to get it. B&N only sells in the US and UK, and they do the currency conversion for you. It’s a hassle to get the total units and total revenue for each book, but it’s possible to get the information if you do some work.

What About Kobo?

I’ve not used Kobo directly (I get my e-books onto Kobo through Smashwords), but it appears that they’ve done a very good job of reporting sales to authors. I can’t tell exactly how good the system is. The info page on their web site displays an example page showing total units sold and total revenue earned all-time for all books. It appears that you can break that out by book. If you can break that out for any given time period, then that would be truly useful to authors.

I’ll probably create a Kobo account and upload my books to their store soon. Their system looks to be very author-friendly.

In my last blog post, Hugh Howey and the Tsunami of Cash, I talked briefly about the recent results posted by Hugh Howey and his collaborator “Anonymous Data Guy,” who analyzed in detail the sales of category best-sellers on Amazon. (The first study looked at about 7,000 books and the second study looked at about 50,000 books.) See all their results at AuthorEarnings.com. (Note that this site is no longer online.)

These results had been criticized by a number of people, so I thought it would be useful in my blog post to try to estimate the broad spectrum of indie author earnings using the 80-20 rule.

I was able to make rough estimates of the number of units sold by indie authors from the very top earners all the way down to the very bottom earners.

Hugh left a comment on that blog post, and so did Chip MacGregor, a well-known literary agent. Chip is a “no-BS” kind of a guy, and his comment was quite long and had some good questions (but also a couple of clunkers). Chip is a long-time friend of mine, and was my agent for several years, and I consider him one of the good guys. I didn’t want to simply bury my response to Chip in a comment.

I’ve decided to do a whole new blog post today just to answer Chip’s questions.

First, just to set the context, here is Chip’s entire comment, which I’ll answer line by line in the rest of this post.

As a guy who is supportive of authors self-publishing, I find Howey’s work interesting, but not earth-shaking, For the record, he looked at one day of sales, at one company, and admittedly guesstimated many of his numbers based on what friends told him. Um… Would your PhD program have accepted that, Randy? Do you think his sample size is adequate? Would you allow him to create a trend line from that? The two big questions that stick in my head after reading this report: Can we rely on Amazon marketing info to be accurate? And if so, why isn’t Amazon sharing information with him?

I know the people who are raising questions about the validity of the study are being hammered as Luddites, but I tend to think this needs a bit more study before it’s declared as gospel. I like your idea of applying the principle of factor sparsity to the data, but your suggestions seem pretty optimistic. Out of more than a million authors, the average number sold is about 300. (Nothing wrong with selling 300 copies, mind you, and if they were charging a couple bucks, they made themselves about $400, which is better than a kick in the head… but it’s not the windfall you seem to make it out.) I’m not sure why you state that “the average and median sales are not very useful,” Randy. Seems as though those are very useful to give context — as in, “There are more than a million authors on Amazon, and last year 150 of them sold more than 100,000 copies.” On the one hand, I celebrate the successes. On the other, you have to admit those are fairly long odds. Again, I hesitate to say  that, because everybody WANTS this to be true, and to have discovered the secret to making a lot of money at this crazy business.

I notice Hugh came on your site to say he personally knows “several others who sold multiple millions last year.” Um… this is the sort of thing that makes me wonder about his veracity. I guess I tend to doubt that he knows “several” who sold “multiple millions.” Several? Really? Even your quick data analysis doesn’t support that, Randy. Look, I’m a guy who has self-published books and done well, and who encourages the authors I work with to self-pub… but I don’t like the Amway-like atmosphere being promoted by people who want to make it sound like there are publishing fairies out there, waiting to sprinkle hundred dollar bills onto everyone. My two cents.

Randy sez: Now that you’ve seen Chip’s comment in full, I’ll repeat it line by line and respond to each logical unit.

Chip wrote:

As a guy who is supportive of authors self-publishing, I find Howey’s work interesting, but not earth-shaking,

Randy sez: I find it both interesting and earth-shaking. Here’s why. I knew that indie authors were doing well. I know many of them. I’ve seen the difference indie publishing makes in their lives. I know the kind of sales numbers they’ve been getting.

What I didn’t know is that indie authors (as a group) have just about reached parity with the Big 5 authors (as a group). That is, the set of indie authors Hugh and Data Guy analyzed are moving just about as many copies and earning just about as much money as the Big 5 authors they analyzed.

I don’t think anybody knew that. That’s why it’s created so much excitement.

Chip wrote:

For the record, he looked at one day of sales, at one company, and admittedly guesstimated many of his numbers based on what friends told him. Um… Would your PhD program have accepted that, Randy?

Randy sez: He’s now looked at two days worth of sales, and the two sets of results are in very reasonable agreement. The company he looked at was Amazon, which is by far the biggest player in e-books. But he’s now doing a study on B&N, and I think we’re all looking forward to those results.

As for his method of analysis, it’s more sophisticated than guesstimating based on what his friends told him. Hugh and other indie authors have been compiling data for years that allow them to accurately correlate a book’s sales rank with its actual sales. This is approximate, but it’s a very reasonable approximation, and the Law of Large Numbers tells us that statistical fluctuations will wash out pretty quickly as you get more data. And 7,000 books is a boatload of data. 50,000 books is even more.

As for whether my Ph.D. Program would have accepted that, let’s not be silly. I got my Ph.D. In quantum field theory at UC Berkeley. That’s a high standard of rigor, and it’s far beyond what people normally try for in real life.

From what I can see, Hugh’s calculations are well above the usual standard in the book industry. I’m looking at the BookScan report for my book WRITING FICTION FOR DUMMIES right now. For the last royalty period, BookScan underestimates paper sales by about 30% and it has no estimate at all for e-book sales.

So the real question is whether Hugh’s data increases our knowledge of author earnings. My judgment is that it does.

Chip wrote:

Do you think his sample size is adequate?

Randy sez: Yes, even for the first data set, which looked at about 7,000 books. For the second data set, it’s an embarrassment of riches, with around 50,000 books covering all categories, fiction and non-fiction. This is good stuff.

Chip wrote:

Would you allow him to create a trend line from that?

Randy sez: No, of course not. Chip, that was a bad question. Hugh isn’t analyzing the rate of change of things. He’s analyzing the state of the industry right now. In any event, you can’t create a trend line from one day’s worth of data. (Now he has two days’ worth, but he’s doing a static analysis, not trying to predict changes, so a trend line is really beside the point.) And of course, Hugh didn’t create one.

Chip wrote:

The two big questions that stick in my head after reading this report: Can we rely on Amazon marketing info to be accurate?

Randy sez: I didn’t understand this first of the two questions, so I emailed Chip to ask what it means. He emailed me back to restate it:

Since the bestseller lists at Amazon are largely seen to be a marketing tool, do we want to rely on them as a database for research?

Randy sez: OK, I see now. Chip is saying that many people believe that the sales rank for books isn’t strictly correlated to actual daily unit sales. Most people believe that Amazon uses other factors to determine the sales rank, and some of those factors might be Amazon’s marketing needs.

This means that it’s possible that the daily sales rank for a book would not be a good predictor for its daily unit sales. In that case, Hugh’s calculations with Anonymous Data Guy would be incorrect.

Fortunately, that is a testable question. Here’s how to test it mathematically:

All you have to do is look at the raw data for a large number of books. Each day, you look at the sales rank (which is public information) and you look at the actual units sold (this is private information that Amazon only tells the publisher). Since indie authors are publishers, they can easily compile this raw information and then any math person can model it.

I would model it as a Pareto distribution curve, S = C/(R**E), where:

• S = daily unit sales
• C = some unknown constant to be determined by the data
• R = the sales rank on the given day
• E = some unknown exponent to be determined by the data

So the mathematical solution is to do a least-squares fit to the data to determine the best values for C and E. Then do a chi-squared analysis of the fit to see how well the theory fits the data. This is easy to do. We could also compute variations from the best-fit. This would tell us the uncertainty in the calculations presented by Hugh and Anonymous Data Guy.

I don’t know if Anonymous Data Guy has done this calculation, but it’s not hard and it would answer Chip’s question. I have sent Hugh an e-mail about this issue.

Chip’s second question:

And if so, why isn’t Amazon sharing information with him?

Randy sez: You’d have to ask Amazon, but my understanding is that they hardly ever share any info with anyone. One thing indie authors like is that Amazon does give them up-to-the-minute sales information, which is a welcome change from the hassle it takes to get info from traditional publishers.

Chip wrote:

 I know the people who are raising questions about the validity of the study are being hammered as Luddites, but I tend to think this needs a bit more study before it’s declared as gospel. I like your idea of applying the principle of factor sparsity to the data, but your suggestions seem pretty optimistic. Out of more than a million authors, the average number sold is about 300. (Nothing wrong with selling 300 copies, mind you, and if they were charging a couple bucks, they made themselves about $400, which is better than a kick in the head… but it’s not the windfall you seem to make it out.)

Randy sez: Well, as I said in my post, the average and the median are pretty useless because they’re both dragged down by the great mass of unpolished writers. Two things are important:

1. How well are the top-performing indie author compared to the top-performing traditional authors?
2. Roughly how many indie authors are at each pay level?

So my blog post was aimed at guessing the answers to these questions. The answer is that about 10 indie authors are moving more than a million copies a year. That sounds pretty cool to me. Look at the other numbers in my post! There are opportunities here for a couple of thousand indie authors to be moving more than 10k copies per year. That makes it clear that the whole “outlier” thing is a myth.

Chip wrote:

I’m not sure why you state that “the average and median sales are not very useful,” Randy.

Randy sez: The reason is simple. We’re used to that pesky “bell-shaped curve” when talking about results. We know that the average man is about 5’9” tall, and the standard deviation is about 3 inches. We know immediately from this data that a 7 foot man would be exceptionally tall and a 5 foot man would be quite short. Note that both of those extremes are reasonably close to the average (and the median). So the average and median are useful numbers for understanding bell-shaped curve distributions.

But the Pareto distribution is wildly different. The top-selling author in my estimates was selling 7.5 million copies. The average author was selling just under 500.

If the top-selling author were as tall as he is rich, he’d be almost 17 miles tall!

That is the sense in which the average is not very useful for a Pareto distribution. The average gives us no information at all on what we should expect from peak performers.

I won’t belabor this, Chip, because I know you’re familiar with the Pareto distribution. You blogged about it recently on your own blog, in your article The Pereto Principle. (Aside from misspelling “Pareto,” it was a good article.)

Chip wrote:

Seems as though those are very useful to give context — as in, “There are more than a million authors on Amazon, and last year 150 of them sold more than 100,000 copies.”

Randy sez: No, the average does NOT give the correct context for a Pareto distribution. When you have a bell-shaped curve, you typically report two pieces of information–the average and standard deviation. Anyone who understands the bell-shaped curve then immediately understands the complete spectrum.

With a Pareto distribution, you also report two pieces of information, but they AREN’T the average and standard deviation! The two pieces of information you report are the earnings of the top-performer and the critical exponent (in my calculations I used .8613, which is the exponent for the 80-20 rule). Anyone who understands the Pareto distribution then immediately understands the complete spectrum.

Chip wrote:

On the one hand, I celebrate the successes. On the other, you have to admit those are fairly long odds. Again, I hesitate to say  that, because everybody WANTS this to be true, and to have discovered the secret to making a lot of money at this crazy business.

Randy sez: Everybody agrees that the odds of a major success are long. I said this in my blog post in October of 2012, Liars and Outliers in the Publishing World. Joe Konrath has said this many times, most recently in his blog last Friday, where Barry Eisler did a guest post and then Joe chimed in: Eisler – Publishing is a Lottery & Konrath – Publishing is a Carny Game. I don’t know of anyone who claims that every indie author is going to get rich.

Chip wrote:

I notice Hugh came on your site to say he personally knows “several others who sold multiple millions last year.” Um… this is the sort of thing that makes me wonder about his veracity. I guess I tend to doubt that he knows “several” who sold “multiple millions.” Several? Really? Even your quick data analysis doesn’t support that, Randy.

Randy sez: Actually, my analysis is consistent with Hugh’s statement. My calculations estimate that there are four indie authors who moved more than 2 million copies last year and ten indies who moved more than a million. I can think of several off the top of my head who sold more than a million last year, and at least two of them I’ve met in person. I’m sure Hugh knows a lot more of the heavy-hitters than I do. I don’t know who he has in mind, but it sounds plausible to me.

Chip wrote:

Look, I’m a guy who has self-published books and done well, and who encourages the authors I work with to self-pub… but I don’t like the Amway-like atmosphere being promoted by people who want to make it sound like there are publishing fairies out there, waiting to sprinkle hundred dollar bills onto everyone. My two cents.

Randy sez: I’m also opposed to the Amway mentality, and I’ve consistently pointed out on this blog that only a few authors will ever get super-rich. But let’s remember that with a Pareto distribution, we’re interested in the expected earnings of the top performer. The expected earnings of everyone else follows from that. So it’s REQUIRED that we talk about top-performers, even though this misleads people who want to think in terms of bell-shaped curves.

The blunt truth is that most authors won’t do very well. What I’m interested in is the spectrum of author earnings from the very top all the way down to the very bottom—how many authors are at each income level. That tells authors how to plan their careers (and it might keep a few people from quitting their day jobs prematurely).

My Pareto calculations are a first cut at answering that question. I hope to show more data soon.

Chip, thanks for your questions. It’s important to ask questions, because the issue of author earnings is important.

I think that we’ll get a fuller picture as Hugh and Anonymous Data Guy continue to analyze more data. I don’t think we’ll see a radically different picture as we get more data.

I think we’ll continue to see that indie author earnings are spread across an enormous spectrum, with a very few authors earning millions per year and hundreds of thousands who earn only a few hundred per year.

The key thing is that a couple of thousand indies are earning some tens of thousands per year. That’s the “broad shoulder” of the Pareto distribution, and it’s where most professional novelists will find themselves.

Bravo to Hugh Howey and to his collaborator, “Anonymous Data Guy,” for their recent series of articles at AuthorEarnings.com. (Note that this website is no longer online.)

Hugh and Data Guy have done a remarkable series of calculations that work as follows:

• Data Guy wrote a program to crawl through various best-seller lists on Amazon.
• Hugh already had data from many indie authors allowing him to correlate a sales-rank to an actual number of sales.
• Data Guy then used Hugh’s data to estimate author earnings.

You can read all the results at AuthorEarnings.com. We can summarize their results  as follows:

• Indie authors as a group are selling about as many units as the group of authors published by the Big 5.
• Indie authors as a group are earning about as much money as the group of authors published by the Big 5.

Edward W. Robertson has also done some recent interesting work to see what percentage of indie authors are doing well in various genres. 

 The Tsunami of Cash

We have been warned for years that indie publishing was producing a “tsunami of crap.” Indie books were supposed to be a vast wasteland of drivel, with perhaps a few “outliers” who were earning a lot of money. But most indie authors were claimed to be struggling along, earning on average just a few hundred (or possibly a few thousand dollars) per year.

Instead, the facts are now clear. Indie publishing is producing a “tsunami of cash” for indie authors. Yes, there are plenty of bad indie books, but the good stuff is easily found by readers. And good writing gets rewarded with money.

What Amazon Really Said

In this blog post, I’d like to look at a single data point that Amazon gave us on December 26, 2013. You can find it buried in this post on Amazon.

Here’s the data point, which has been widely misinterpreted:

“150 Kindle Direct Publishing authors each sold more than 100,000 copies of their books in 2013.”

Virtually everybody has read this to mean that a good indie author is moving about 100,000 copies per year. Which is good, but not great. After all, a good trad-published author moves millions of copies per year. So what’s the fuss about 100k copies? This would mean a total of 15 million copies sold for those indie authors.

The answer is that the top indie authors are moving a lot more than 100k copies per year, and those 150 authors are probably moving nearly 4 times as many units—around 58.5 million copies.

How do I know? I’ll explain how I know in the rest of this article.

The 80-20 Rule and Amazon

Most people have heard of the 80-20 rule, which says that roughly 20% of the people earn roughly 80% of the money.

The 80-20 rule is an example of a “Pareto distribution,” which you can read about on Wikipedia if you’re mathematically adept. I have discussed the Pareto distribution on this blog and in my e-zine in the past. (For a summary, see my blog post Liars and Outliers In The Publishing World.)

In my previous posts and articles, I’ve made a slightly different set of assumptions. I’ve worked with the so-called “Zipf Distribution,” which is a very simple version of a Pareto distribution. In this article, I’m going to work with the 80-20 rule, a slightly different version of the Pareto distribution which I think is closer to the real world.

The 80-20 rule is a good approximation to a lot of situations.

Let’s apply the 80-20 rule to the single data point that Amazon gave us and see what we can learn.

Mathematical Assumptions

Skip this section if you hate math.

We’ll make the following assumptions to create a very simple mathematical model, and then we’ll see what that model tells us. Please remember that we don’t claim this model represents reality perfectly. But if it approximates reality, then the model should give us valuable insights into the “Amazon economy” for writers. I am going to have to get a little mathematical here, so if you hate math, skip down just a bit.

• Assumption #1: Unit sales of books follow a Pareto distribution: sales of an author are inversely proportional to the author rank raised to a certain exponent. The equation for this is S = C/(R**E), where:
  • S is the unit sales of a given author
  • C is some unknown constant to be determined
  • R is the rank of the author among all the other indie authors
  • E is some unknown exponent to be determined
  • The operation R**E means to raise R to the power E.
• Assumption #2: We can use the 80-20 rule to compute the exponent E. The result is very well known:  E = log(4)/log(5) = .86135.  (Here, “log” means the natural logarithm.)
• Assumption #3: Indie author #150 sold about 100,000 units in 2013. We can use this to estimate the unknown constant: C = 7,488,300 units.

Estimates For the Top 10 Indie Authors

Now we can use our formula to estimate the unit sales on Amazon for ANY indie author. Let me emphasize, I’m talking here only about indie authors, so the rank we’ll use is the indie author rank. Note that some authors are hybrid authors—they work for trad publishers and they do some indie work. My model is just a simple model and it doesn’t account for this splitting of effort. So we can’t draw incredibly precise conclusions. But we CAN make some simple estimates that can guide our thinking.

I wrote a simple program to use our formula to estimate the sales for each of a large number of indie authors. I chose the number 600,000, because I know there are at least that many authors on Amazon’s Author Central. The exact number is not all that important, except when you try to calculate the average income or the median income for authors. (But neither of these is a very useful number to calculate.)

Here are the estimates for the top ten indie authors on Amazon:

• Rank: 1, Sales: 7488296
• Rank: 2, Sales: 4121828
• Rank: 3, Sales: 2906786
• Rank: 4, Sales: 2268803
• Rank: 5, Sales: 1872074
• Rank: 6, Sales: 1600000
• Rank: 7, Sales: 1401055
• Rank: 8, Sales: 1248831
• Rank: 9, Sales: 1128348
• Rank: 10, Sales: 1030457

 Holy cow! Do you see that? This model estimates that the best indie author is moving almost 7.5 MILLION units. That’s a lot more than 100k units. Yes, it’s just an approximation. But it shows us what the 80-20 rule is telling us, if we take the 80-20 rule seriously.

The model predicts that about 10 indie authors are moving more than a million units per year on Amazon.

 Estimates for the Top 150 Indie Authors

Now let’s estimate sales of selected indie authors in the top 150 (the folks selling more than 100k units):

• Rank: 10, Sales: 1030457
• Rank: 20, Sales: 567201
• Rank: 30, Sales: 400000
• Rank: 40, Sales: 312208
• Rank: 50, Sales: 257614
• Rank: 60, Sales: 220174
• Rank: 70, Sales: 192798
• Rank: 80, Sales: 171850
• Rank: 90, Sales: 155271
• Rank: 100, Sales: 141800
• Rank: 110, Sales: 130624
• Rank: 120, Sales: 121192
• Rank: 130, Sales: 113118
• Rank: 140, Sales: 106123
• Rank: 150, Sales: 100000

This is telling us that about 30 authors are selling more than 400k units per year. If we add up the sales for all those 150 authors, we find a total of 58.5 million copies, for an average of about 390,000 copies per author. You can see how misleading it is to assume that all 150 of them were selling 100,000 copies. The Pareto distribution is strongly distorted toward the top sellers.

 Estimates For The Rest Of The Pack

Finally, let’s look at sales of selected indie authors farther down in the pack:

• Rank: 200, Sales: 78052
• Rank: 300, Sales: 55044
• Rank: 400, Sales: 42963
• Rank: 500, Sales: 35450
• Rank: 600, Sales: 30298
• Rank: 700, Sales: 26531
• Rank: 800, Sales: 23648
• Rank: 900, Sales: 21367
• Rank: 1000, Sales: 19513
• Rank: 2000, Sales: 10741
• Rank: 3000, Sales: 7574
• Rank: 4000, Sales: 5912
• Rank: 5000, Sales: 4878
• Rank: 6000, Sales: 4169
• Rank: 7000, Sales: 3651
• Rank: 8000, Sales: 3254
• Rank: 9000, Sales: 2940
• Rank: 10000, Sales: 2685
• Rank: 20000, Sales: 1478
• Rank: 30000, Sales: 1042
• Rank: 40000, Sales: 814
• Rank: 50000, Sales: 671
• Rank: 60000, Sales: 574
• Rank: 70000, Sales: 502
• Rank: 80000, Sales: 448
• Rank: 90000, Sales: 405
• Rank: 100000, Sales: 370
• Rank: 200000, Sales: 203
• Rank: 300000, Sales: 143
• Rank: 400000, Sales: 112
• Rank: 500000, Sales: 92
• Rank: 600000, Sales: 79

Notice that the top 300 indie authors in this model are all moving more than 55,000 copies each.

And more than 2000 indie authors are moving more than 10,000 copies each.

Of course, the model also shows that there are at least half a million writers who are moving 370 units or fewer per year.

The 80-20 rule says that most people don’t sell very much. And it says that a certain select few sell incredible amounts.

My program computed a few other statistics of interest:

Total units sold:  about 292 million copies

Average sales per author:  about 486 copies

Median sales:  about 143 copies

The Amazon Economy

So the “Amazon economy” for indie authors is wildly different from Lake Wobegon, where all children are above average. In the Amazon economy, most authors are below average. For this set of numbers, about 88% of all authors are below the average.

Notice that the median and the average depend on how many authors there are. If we increased our estimate to a million authors, then the average would sink to 317 copies sold and the median would drop to 92 copies. But the sales of those people at the top wouldn’t change.

For some reason, many advocates of trad-publishing like to latch onto the known fact that average indie sales are low. This is true, but it’s inevitable if there are a lot of indie authors. There is only so much money to be made. The more authors you have, the lower the average gets sucked. The same is true for trad-published authors.

The average and the median sales of authors are not very useful numbers. What is useful to know is the sales of the top earning authors and the value of the exponent E. Once you know those, you have a very nice way to estimate the earnings for all authors at all ranks.

One clear result is that all those sales add up to a lot. Several hundred million units. There is a “tsunami of cash” coming to indie authors.

Some Cautions

Let’s be cautious here. The Pareto distribution is just an approximation to reality. The Pareto distribution is not reality itself. But it is probably a pretty good approximation, and once we make that approximation, we can make exact calculations. Those calculations are plausible, but of course they don’t correspond exactly to reality. That’s why we call it an approximation.

Furthermore, let’s be clear that we have made a model for unit sales of books, not for revenue. You have to work with what you have, and I’m working with just a single data point from Amazon—their statement that 150 indie authors each moved more than 100k books in 2013.

That’s not a lot of data, but it’s enough to get an approximate picture for all indie authors. If and when we get more data, it’ll be interesting to see how well the model holds up. I expect that the broad shape of the model will prove accurate, but there will probably be some surprises at both ends—for the top performers and the lowest performers.

 The Broad Shoulder

There will always be a few big winners and a large number who don’t earn very much. There is a “high head” and a “long tail.”

But the important point is that there is a “broad shoulder”—a set of writers who are not at the very top and yet are earning substantial money (thousands of dollars per year, or tens of thousands per year). For most of them, this is not enough to live on. But it’s enough to make their life better. That’s cool.

If we had more data, of course we could make a better model. We will always need models, because we will never have all the data.

The calculations we’ve done here would be similar for trad-published authors. The numbers would change, but the same sort of reasoning applies, and the economy is shaped in roughly the same way. There is a high head, a broad shoulder, and a long tail. 

The Future is Bright

As I have said many times, I’m not pro-publisher and I’m not anti-publisher.  I’m pro-author. And the good news is that the future is bright for indie authors. Bright, and getting brighter.