E-Book Market & Sales – Analysis Pool

On this page you can find a collection of all my statistical analysis and research regarding the Kindle ebook market and sales. I’ll keep the page updated.

How E-Book Sales Vary at the End / Beginning of a Month

The E-Book Market in Numbers

Computing and Tracking the Amazon Sales Rank

Typical Per-Page-Prices for E-Books

Quantitative Analysis of Top 60 Kindle Romance Novels

Mathematical Model For E-Book Sales

If you have any suggestions on what to analyze next, just let me know. Share if you like the information.

How E-Book Sales Vary at the End / Beginning of a Month

After getting satisfying data and results on ebook sales over the course of a week, I was also interested in finding out what impact the end or beginning of a month has on sales. For that I looked up the sales of 20 ebooks, all taken from the current top 100 Kindle ebooks list, for November and beginning of December on novelrank. Here’s how they performed at the end of November:

  • Strong Increase: 0%
  • Slight Increase: 0 %
  • Unchanged: 20%
  • Slight Decrease: 35 %
  • Strong Decrease: 45 %

80 % showed either a slight or strong decrease, none showed any increase. So there’s a very pronounced downwards trend in ebook sales at the end of the month. It usually begins around the 20th. Onto the performance at the beginning of December:

  • Strong Increase: 50%
  • Slight Increase: 35 %
  • Unchanged: 10%
  • Slight Decrease: 5 %
  • Strong Decrease: 0 %

Here 85 % showed either a slight or strong increase, while only 5 % showed any decrease. This of course doesn’t leave much room for interpretation, there’s a clear upwards trend at the beginning of the month. It usually lasts only a few days (shorter than the decline period) and after that the elevated level is more or less maintained.

Analysis of Viewers for TV Series

I analysed the number of viewers of all the completed seasons for the following tv shows: Fringe, Lost, Heroes, Gossip Girl, Vampire Diaries, True Blood, The Sopranos, How I met your Mother, Glee and Family Guy. The data was taken from the respective Wikipedia pages.

My aim was to find simple “rule-of-thumb” formulas to estimate key values from the number of premiere viewers and to see if there’s a pattern for the decline of a show. Below you can see the main results from the analysis.

Result 1: Finale vs. Premiere

The number of finale viewers is about 85 % the number of premiere viewers.

Result 2: Average vs. Premiere

The average number of viewers during a season is about 83 % the number of premiere viewers.


Result 3: Decline Pattern

The average number of viewers during a season is about 93 % the average number of viewers during the previous season.


This last result implies that the decline in popularity is exponential. If the average number of viewers for the first season is N(1), then the expected number of viewers for season n is: N(n) = N(1) * 0.93^(n-1). We can also express this using a table:

Average season two = 93 % of average season one

Average season three = 86 % of average season one

Average season four = 80 % of average season one

Average season five = 75 % of average season one

Average season six = 70 % of average season one

etc …

Of course, this is all just the sum of the behaviour of all the analyzed shows. Individual shows can behave very differently form that.

Analysis: Size and Loading Times of Blogs

In the fast paced online world people are not so patient as in real life. Accordingly, having a large home page size and loading time can negatively affect your blog traffic. Studies have shown that the greater the loading time, the higher the bounce rate. To find out how well my blog performs with respect to this (feel free to use the results for your benefits as well), I did a analysis of 70 blogs. I used iWEBTOOLS’s Website Speed Test and OriginPro for that. With the tool you can analyze ten webpages at once, but note that after ten queries you have to wait a full day (not an hour as the website claims) to do more analysis.

The average size of a blog according to the analysis is 65.3 KB with a standard error SE = 3.0 KB. Here’s how the size is distributed:


The average loading time at my internet speed (circa 600 KB/s) is 0.66 s with the standard error SE = 0.10 s. Here’s the corresponding distribution:


Note that the graph obviously depends on your internet speed. If you have faster internet, the whole distribution will shift to the left. My blog has a home page size of 81.6 KB. From the first graph I can deduce that only about 24 % of home pages are larger in size. My loading time is 0.86 s, here only about 22 % top that. So it looks like I really have to throw off some weight.

Here’s the loading time plotted against the home page size:


In a very rough approximation we have the relation:

loading time = 0.009 * size

In other words: getting rid of 10 KB should lower the loading time by about 0.1 seconds. Now feel free to check your own blog and see where it fits in. If you got the time, post your results (if possible including URL, size, loading time, internet speed) in the comments. I’d greatly appreciate the additional data. For a reliable result regarding loading time it’s best to check the same page three times and do the average.

Quantitative Analysis of Top 60 Kindle Romance Novels

I did a quantitative analysis of the current Top 60 Kindle Romance ebooks. Here are the results. First I’ll take a look at all price related data and conclusions.


  • Price over rank:


There seems to be no relation between price and rank. A linear fit confirmed this. The average price was 3.70 $ with a standard deviation of 2.70 $.


  • Price frequency count:


(Note that prices have been rounded up) About one third of all romance novels in the top 60 are offered for 1 $. Roughly another third for 3 $ or 4 $.


  • Price per 100 pages over rank:


Again, no relation here. The average price per 100 pages was 1.24 $ with a standard deviation of 0.86 $.


  • Price per 100 pages frequency count:


About half of all novels in the top 60 have a price per 100 pages lower than 1.20 $. Another third lies between 1.20 $ and 1.60 $.


  • Price per 100 pages over number of pages:


As I expected, the bigger the novel, the less you pay per page. Romance novels of about 200 pages cost 1.50 $ per 100 pages, while at 400 pages the price drops to about 1 $ per 100 pages. The decline is statistically significant, however there’s a lot of variation.


  • Review count:


A little less than one half of the top novels have less than 50 reviews. About 40 % have between 50 and 150 reviews. Note that some of the remaining 10 % more than 600 reviews (not included in the graph).


  • Rating over rank:


There’s practically no dependence of rank on rating among the top 60 novels. However, all have a rating of 3.5 stars or higher, most of them (95 %) 4 stars or higher.


  • Pages over ranking:


There’s no relation between number of pages and rank. A linear fit confirmed this. The average number of pages was 316 with a standard deviation of 107.


  • Pages count:


About 70 % of the analyzed novels have between 200 and 400 pages. 12 % are below and 18 % above this range.