Lesson 3: How Sales Data Is Shared With Labels

So we know labels earn revenue. And we know they earn revenue from potentially dozens if not hundreds of DSPs and physical retailers. So one may ask themselves, how in the world do labels understand not just how much they have earned but also what for.

The answer is something most royalty specialists will agree on: we excel.

Sometimes we look at data in Apple’s Numbers or simply Notepad. But generally, we look at billions and billions and billions of lines of data in Excel.

Sheets of data

When revenue flows, so do the spreadsheets. Distributors, physical retailers and DSPs will detail why they are paying you with spreadsheets specifying (hopefully) as many data points as possible such as how much you have earned, for which track or release, for which format, from which source, from which territory and on what date.

Each row in a spreadsheet will represent a usage, with each column in the spreadsheet detailing a relevant data point. A common spreadsheet format are the familiar Excel formats, such as .xls, .csv and .txt. The different columns in these formats are delimited by a chosen character, allowing the different data types to be easily opened and reviewed in Excel. Sometimes you may still need to go through the trouble of using a Text To Column formula to display the separate columns of data, which is what we defined as one of five essential excel tricks every royalties specialist should know.

Below is an example of such a format. In fact, this is the format that Spotify uses to send usage data to the master rights holders. (All names & values are fictional). We can see how each line of data represents a stream or collection of streams with the same data points. And how each column stores a data point for all the usages.

Some important data points displayed in this file, that you hope to find in most other statements, are:

• The Source - Which DSP did this revenue come from? Spotify specifies themselves as the sender in Cell D2.
• Format - Was it a download, an ad-funded stream, a premium stream? Spotify highlights this in column C, with the code P referring to a Premium Stream and the code A referring to an Ad-Funded Stream.
• Territory - In which territory the usage happened.
• Release Title - Which product was sold? And in the case of a stream, as part of which release was the Track streamed?
• Catalogue Number or Barcode - This is a unique identifier assigned to each release by the label or distributor. This allows for efficient mapping of sales data to a specific release in a database.
• Track Title - If it’s a track specific sale, which sound recording was streamed or downloaded?
• ISRC - Short for International Standard Recording Code, this is a unique identifier assigned to each sound recording by the label or distributor. DSPs will use these unique identifiers when transferring data that highlights how much a specific track has earned. This allows for efficient mapping of sales data to a specific sound recording in a database.
• Artist - Which artist performed the Track or Release that was sold?
• Units - How many times was this specific track or release sold or streamed?
• Net Amount - For any usages coming from this source, for that rights type, from that territory and for that Work, how much is owed to the label?

Many sources will send you data in an Excel format. However, every source will have their own data points and construct these data points in a different way, meaning statements from any source will need to be read in a slightly different way.

Billions and billions and billions of lines of data, really?

Yep, really. Whilst selling a million LPs would have been seen as a huge success just 20 years ago; in a digital age, having your song streamed a billion times is not unheard of. It meant that the amount of data that labels and publishers need to process has skyrocketed in the past decade. Thankfully, it’s not just up to the royalty specialists and their Excel program to process and analyse this data. Powerful royalty platforms exist, built specifically with this usage in mind, to enable label and publishing businesses to efficiently process all of this data. More on this later.

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