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For the past few years, people have talked about the potential of what is commonly known as the "Spotify for News" model. There are thousands of opinions and hundreds of articles by people speculating about its potentials (and pitfalls).
Only a few weeks ago, Danny Sullivan over at Search Engine Land contemplated the creation of "Google News all-access":
I'm taking that name from Google Play Music all-access. That's Google's program where it charges consumers $9.99 per month to listen to whatever songs they want. Behind the scenes, that revenue gets shared to rights holders according to some mystery formula but one that's clearly good enough that many participate in.
I want Google to do the same for news. Maybe it launches Google News all-access that allows people for $10 per month to read any content in Google News they want. Maybe Google keeps a small amount of this to manage the program. The rest is divided up among the publishers.
The problem with this is that the all-access model is extremely complex. With music, the value to you as a listener is roughly the same. You go to Spotify, start a playlist, and whether you listen to Rihanna, Nickleback or Frank Sinatra, it conveys the same amount of value. The difference being your individual taste in music.
Music also has a much longer lasting value, with songs being played for years after they have been released.
For content it's an entirely different reality. An article from Buzzfeed that people read while on a break conveys an entirely different level of value than an analysis published by the Economist. And the length and longevity of each article varies greatly as well.
But the largest issue with all this talk about an all-access model for news is that people don't understand the complexity of this 'mystery formula' that pays out to the publishers. What works for one publisher doesn't work for another, even when they are similar in concept.
So, in this article we will demystify this formula, and I will explain just what goes on when an all-access model calculates how much each publisher is paid. I will illustrate how nuances between different segments of people can dramatically alter your ability to make a profit.
I will also illustrate how the size of the market and the size of the supply (number of publishers involved) might not have the effect you hoped for. And finally, I will illustrate when it works and when it doesn't.
In other words, let's really crunch the numbers.
Obviously, analysing the profit margins for an all-access news model can't be done without data. But since we do not have a scalable all-access model yet, we have to invent one. To do this, I set up a test case, based on a number of assumptions concerning the type of publishers involved and segments of people using it.
So, I started out defining 10 different types of publishers (segments) that represent a wide field of publishing, both traditional and digital natives. And, I then matched this against 10 segments of user behavior across these 10 publishers.
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