Calculations Behind The Historic Category Feature
With the release of the Historic Category Feature, Publisher Rocket offers advanced analytics on Amazon Categories. In this article, we’ll cover some of the calculations going on behind the scenes for those curious about how these metrics were generated.
Important: To use and benefit from Rocket, you don’t need to understand the nitty gritty of where this data comes from. Rocket is designed to take this data and make it easily understandable for everyone. These details are just for data geeks like us who want to see the inner workings of this feature.
Users of Publisher Rocket can access the Historic Category Feature by going to the category search section, then clicking the “Insights” button. If you would like your own copy of Publisher Rocket, you can purchase a copy here.
Once insights is clicked, Rocket taps millions of data points for market data on the Amazon store to generate a card about that specific category. This card includes Amazon category statistics, growth over time, an overview, insights, and the category graph.
Each of these datapoints are aggregates of collected market data. Below is a description of how these are calculated, such that Rocket users can best understand how the data is sourced. If you are not yet a Rocket owner, but you would like to access these analytics yourself, you can get your copy here.
For each of the calculations above, rocket pulls many data points and compiles them to produce a meaningful metric. It’s important to note that Rocket only pulls data from a category’s bestseller lists, in order to show authors the tactics used by the most popular books in that category.
Aside from the Sales to #1 and Sales to #10 metrics, the eight statistic boxes at the top of the page are calculated with rolling averages across the category’s bestsellers. This helps minimize the impact of a single best seller on these metrics, making them more robust and representative of the genre as a whole. For example, if a single author were to list their book at $100, the price average would be minimally affected, as that would only be one datapoint out of many. Below, each of these boxes is defined.
Sales to #1: The number of sales a book needs to make in a day to reach #1 in that category.
Sales to #10: The number of sales a book needs to make in a day to reach #10 in that category.
Kindle Unlimited %: The percent of books in a category’s bestseller lists that are enrolled in Kindle Unlimited. Note: This is not applicable for audiobook or bookcategories, as only Kindle books can be enrolled in Kindle Unlimited
Large Publisher %: The percent of books in a category’s best seller lists that were released by publishers of a significant size.
Average Price: The average amount charged for a book in a category’s best seller lists. This is broken up between independent authors and large publishers, to better show different pricing strategies between these two groups.
Average Age: The average amount of days since publication for a book in a category’s best seller list. This can help indicate the level of churn in a category, as well as the freshness of content on its bestseller lists.
Average Rankings: The average number of stars out of five for a book in the category’s best seller list. Note: This is averaged by book, so a book with two rantings and a book with five thousand ratings are weighted the same.
Average Page Count: The average number of pages for a book in the category’s best seller list. If a selected category is an audiobook, this shows the duration in minutes.
Large Publisher List: When hovering over the “info” icon on the large publisher box, the most prominent publishers in a category are displayed. For Rocket’s metrics, publishers are considered large if they continually show up in Amazon’s best seller lists. Small publishers who do not often appear on the best seller lists are grouped in with independent authors.
Historic Category Graph
In the historical graph, the number of sales of a category’s best seller list is displayed. This can be considered as the category’s size/popularity, and indicates whether more or less sales are occurring within the category each month. Only a category’s top thirty books are considered when producing this metric. A trendline is applied with linear regression to the monthly datapoints which is then used to generate the monthly growth rate.
It’s important to note that categories may see a high amount of volatility if a massive bestseller enters or leaves their list. For example, if a major author releases a book within a smaller category, that category’s datapoint will be significantly impacted by that single book’s sales. Similarly, if a major book were to be removed from a category, that could also significantly affect the category’s size. Rocket is programmed to detect major category swings caused by large bestsellers, and adds a notification in the overview section if this is suspected of occurring.
In the overview section, Rocket estimates a category’s growth per month, then assigns a label of rapidly declining (<-20%), significantly declining (-15% to -5%), relatively flat (-5% to 5%), significantly growing (5% to 15%), or rapidly growing (>20%). In addition, Rocket identifies how large the category is compared to the rest of Amazon based on monthly sales, so users can better understand its profitability as well as the competition level. In this section, Rocket also warns users if the category has high variation, which can be caused by a handful of bestsellers rather than the category as a whole.
Unique Category Insights
While datapoints are useful, their interpretation is far more important to make actionable data driven decisions. In this section, Rocket detects anomalies within the category compared to the rest of Amazon, then notifies the user. For example, if a category has lower prices, Rocket will notify a user so that they can update their own pricing strategy.
Categories can have a maximum of three unique insights about their behavior, and these insights are specific to the individual category.
Often, it is difficult for authors to find niche categories pertaining to their work. With the Related Categories button, Rocket makes this easy by generating similar categories. Rocket determines related categories based on Amazon market data, which helps authors find ones they may have easily missed from all across Amazon.
The Publisher Rocket team is dedicated to bringing authors more advanced analytics about how they can better position their book in the Amazon Marketplace. Stay tuned for additional insights that can better help planning which books to write, how to better market already publisher books with keywords and categories, and how to become more aware of general market trends to benefit your business.
If you do not already own a copy of Rocket, but would like to try these analytics out for yourself, you can purchase a copy here.