# Kindle Vella - December 2022 - Survey Results

## Disclaimer

The data I'll be referring to was manually submitted anonymously by Kindle Vella Authors and as such we can make no warranty or guarantee regarding the correctness or authenticity of the data. You can read the full disclaimer regarding the data available here: https://www.book-genie.com/kindle-vella-bonus-breakdown . Readers should note that these are informal calculations gathered by unvalidated third party submissions.

None of the authors, contributors, administrators, owners, or anyone else connected with Book Genie, in any way whatsoever, can be responsible or liable for your use, reliance, or distribution of the information.

NOTE: Amazon has the authority to change the weighting of each variable at any point in time. Our findings shouldn't be considered outside the month measured. (December 2022)

## The Process

During the survey I asked Authors to report their results for each each series individually. The following list are metrics I focused on for this month:

“Bonus” - The bonus earned for a single series as reported in the Vella Dashboard

“Top 250 Faved Rank” - If the same series ranked in the Top 250 Faved at month end its position was recorded. Series that didn’t rank were marked with a “Did not rank” tag

“Royalties Earned” - The total royalties accrued for the month. Due to Amazon’s rounding in the dashboard, the results may be rounded to the nearest dollar.

“Total Episodes Unlocked” - The total episodes unlocked for the series in the month of December as reported in the Vella Dashboard

“Paid Episodes Unlocked" - The paid episodes unlocked for the series in the month of December as reported in the Vella Dashboard

“Episodes Released in December” - The published episodes released in December. Completed series were marked as 0.

When analyzing the bonus it is important to remember that authors are paid from a finite pool each month.

This means that the sum of all authors bonuses cannot exceed the total bonus pool announced for the given month. For example in the case of December the bonus pool was $1,000,000. Adding up every bonus that the authors received in December would therefore give you $1,000,000.

Since the bonus pool is finite, each author’s bonus would therefore be relative to the bonuses of all authors on the platform for the given month. In short, your bonus earnings aren't just dictated by your performance, but compared to the performance of all other authors on the platform.

This means there is no single formula that can be consistently used month on month to accurately predict your bonus. Monthly fluctuations can be influenced by both your performance and the performance of every other author on the platform. Changes in the average author’s performance, an increase in the number of series on the platform, and changes to the bonus pool can impact your bonus.

It should also be noted that episodes unlocked with free tokens are compensated within the bonus. However, we cannot accurately separate these earnings from the bonus.

We can look at each variable (e.g. Paid Episode Unlocks) that has been publicly stated by Amazon to impact the bonus and look at its individual relationship (Correlation) to the bonus to predict the weighting of each variable.

The correlation coefficient which I have used to compare each variable against bonus earnings is defined as a statistical measure of the strength of a linear relationship between two variables. Its values can range from -1 to 1. Indicating at -1 a perfect negative correlation and at 1 a direct positive correlation, with 0 indicating no correlation.

Simplified for the non-mathsy the closer to 1 the coefficient is the greater the relationship between that variable and the bonus. The closer to 0 the weaker the relationship.

For the statisticians reading I understand that there are other models we could use. However, given the time taken to reach out, compile, and write up this post I've opted to focus on linear correlation. In addition, based on the size of the data I was working with, I determined correlation coefficients were the most practical starting point.

## The Results

As per previous articles I will first show the results for relevant correlations **with the entire data set **. (NOT EXCLUDING OUTLIERS) With that being said the correlation coefficient of each variable to the bonus was as follows.

**Royalties Earned: 0.997****Paid Episodes Unlocked: 0.987****Total Episodes Unlocked: 0.982**

The next step is to remove significant outliers. Next I’ll list the correlations **for all bonuses less than $5000.**

**Royalties Earned: 0.719****Paid Episodes Unlocked: 0.744****Total Episodes Unlocked: 0.826**

Lastly **for bonuses less than $1000 **the correlation were as follows.

**Royalties Earned: 0.122****Paid Episodes Unlocked: 0.176****Total Episodes Unlocked: 0.225**

Readers of my November bonus results might remember that for November, royalties earned was by far the clearest indicator for bonus earnings. **The results above for the month of December would suggest that Royalties played less of an impact compared to both paid episodes unlocked and this months highest correlation, Total Episodes Unlocked. **This change could be due to the **smaller sample size **we had to analyze for December as we see Royalties remains the strongest of correlations with outliers included. Alternatively, this could **suggest a shift in how Amazon calculates the bonus.**

**Publishing frequency was analyzed against bonus earnings to determine its significance towards earnings, however, no relationship could be identified.**

The impact of a series being marked as complete on bonus earnings compared to an on-going series was analyzed. We found universally that series marked complete earned on average less than on-going series. Furthermore, the average series that did not publish an episode in December AND WAS NOT marked as complete earned a higher bonus than a series marked complete.

Lastly, I asked submitters to share how their bonus in December had changed from that of November. I’ve attached a chart below comparing those who saw an increase in their bonus, those who saw a decrease in their bonus, and those who had not received a bonus for the prior month.