The way ebook sales and earnings divide up between self-published authors, small/medium publishers, Amazon publishing imprints, and Big five publishers remained essentially unchanged: relative share shifted by less than 1 in every case. 2 for cal culating absolute sales numbers, the old, crowdsourced ae rank-to-sales curve ran a little high — it overestimated overall Amazon Kindle sales by roughly. The new methodology corrects that, letting us finally make authoritative, data-backed statements about how many total ebooks are being sold each day by each category of publisher, and overall by Amazon itself. And in the coming quarters, in addition to measuring how the pie gets divided up, our new methodology will also let us track whether the total size of the pie is shrinking or growing, and by how much. So how accurate is our brand new rank-to-sales prediction model? Lets put it this way: The ranking-based sales predictions it yields for randomly selected 300-book groupings taken from a separate, held-back validation dataset always end up matching the actual Amazon-reported total daily sales for the group to within 6 and most of the time to within.
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So we fired up some powerful computers, fed them all that raw data, and let them crunch the numbers. For our fellow geeks: we applied both old-school statistical curve-fitting approaches and more modern machine learning techniques, iterating our underlying numerical model until we zeroed in on the solution that yielded the best predictive accuracy. Taking advantage of a neat mathematical series-convergence trick (one whose applicability was no accident, because Amazons algorithms undoubtedly rely on it, too we ended up with a brand new, simpler, more elegant, and far more accurate rank-to-sales conversion formula for Kindle ebooks. For the non-geeks : Our data-science awesomesauce now tastes even better. Heres what the new rank-to-sales curve looks like: In retrospect, its striking how well AEs old, crowdsourced rank-to-sales curve (in black) matches our new data-derived one. . Graphically, the old ae curve ping-pongs back and forth between the new computed upper bound (shown in red defined by the higher number of daily sales required to first hit a rank when spiking up from a much lower sales baseline, and the new computed lower bound (shown. This new methodology revealed a few other interesting things about the current state of the publishing industry, too. As Data guy, ill short be presenting some of those findings in more detail onstage at the. Digital book world 2016 Conference, on March 9, 2016, in nyc. . But for the purposes of this report, our key takeaways: 1 for relative market-share percentages (the pie charts for which weve become known the old methodology proved to be absolutely reliable. The new rank-to-sales curve, generated from hundreds of thousands of points of raw Amazon-reported sales data, made almost zero difference.
This time we based it on raw, Amazon-reported sales data on the precise daily sales figures for hundreds of individual books from many different authors, spanning a period of many months. Our raw sales data included titles ranked in Amazons overall Top 5 — titles whose kdp reports verified that they were each selling many thousands of copies a day — and it also included books ranked in the hundreds of thousands — whose kdp reports revealed were selling less than a single essay copy. We combined that mass of hard sales data with a complete daily record of Amazon Kindle sales rankings for each of those books, pulled directly from individual AuthorCentral graphs. We ended up with nearly a million distinct data points in total. Why did we need so many data points? Because Amazons overall Best Seller Rankings arent a simple calculation based on each books single-day sales — they also factor in time-decaying sales from previous days as well. To reverse-engineer Amazons ranking algorithms, the more raw sales and ranking data we used, the more accurate our results would get.
Now for a deeper dive into this months report, starting with details about our new and improved sales-to-rank curves and methodology. Our previous Author Earnings fruit reports employed conversion formulas based on crowdsourced sales data. Dozens of authors, all of whom have access to essay their real-time sales numbers and overall best seller rankings, recorded their numbers in spreadsheets and shared the collective results. Over time, these rank-to-sales curves have been pressure checked and refined by other indies. On the whole, they were pretty accurate. And since the same curves were used for all ebooks, regardless of publishing path, we have been extremely confident with our pie chart percentages. But we thought it was time to roll out a new and far more rigorous approach for 2016, which will also let us double-check our old methodology. A brand-new rank-to-sales conversion curve, for this report, author Earnings threw out all of our previous assumptions. . We built a brand new rank-to-sales conversion curve from the ground.
The blue indie line shows where most of that increase is being funneled. Today, a quarter of all consumer dollars spent on ebooks in the us is spent purchasing indie-published ebooks. The most important graph for authors shows the rapidly diverging rate of ebook author income by publishing path. The big 5 publishers are now providing less than a quarter of the dollars earned by creatives for their ebook sales. Indies are taking close to half. As detailed in previous reports, higher prices and other missteps are a likely contributor to this accelerating trend, but the reality may be that major publishers simply are finding it difficult to compete with indie authors on diversity, price, quality, and frequency of publication, as this divergence has been. But as we can see, the transfer of market share in author earnings from Big five to indies did steepen significantly after the big fives 2015 reinstatement of agency ebook pricing.
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Two years ago, the first Author Earnings report revealed the growing market share of self-published ebooks. With data on hundreds of thousands of titles, it was suddenly possible to measure the relative sales and earnings power of ebooks according to publishing path. By sharing this data, we hoped to help authors understand the changing market in order to make sound decisions with their manuscripts. In the two years since, our quarterly snapshots have revealed emerging trends in the digital publishing world. Before we get into this months report, lets look at those trends, with our new February 2016 data points included.
Ebook market Share: 23 month trend. In two short years, the market share of paid unit sales between indie and Big 5 ebooks has more than inverted. The big 5 now account for less than a quarter of ebook purchases on Amazon, while indies are closing in. In the purple line above, we can see the decline in share of ebook dollars earned by Big 5 publishers. . Despite the greater profitability of ebooks over print books, some of these publishers have touted their shrinking ebook sales as a positive development. Meanwhile, we know from our own data (more on this later) and from Amazons press releases, that overall us ebook sales have actually gone language up in dollar terms.
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