It's another week of lockdown, and pretty much everyone I know is now working from home, and pretty much everyone is just waiting for the virus to die down.
So, today, let's talk about a few interesting things.
Over the past several weeks, as well as many times in the past, we have had a discussion about whether news should be free during a crisis.
In the early days, I felt the same way about this as many others, in that I felt that newspapers could do this as a nice gesture, and it wouldn't cause much harm.
But then two things happened. First, publishers started doing it more and more, thereby undermining their own efforts. And secondly, I started seeing a trend where people stopped appreciating that the news is free, and started demanding it. And today I see people getting angry at newspapers when they don't offer free news.
This is a terrible future, and I have become very committed to doing everything I can to reverse this harmful pattern that I now see for our industry.
In my latest Plus report, I talk about this in much more detail, and also why free doesn't work in the long run.
So take a look at: Why is it important that we do not give news away for free during a crisis?
Speaking of this, Lucinda Southern over at Digiday wrote a good article where she interviewed Schibsted about their subscription growth during this crisis and concerns about future churn.
It's an excellent article: 'Opening the paywall is not an option': Schibsted sees subscriptions mini-boom".
While the current crisis is obviously bad for a lot of people, I am very excited about the increased role of data and math in journalism. We have seen so many brilliant articles (and a few really bad ones) that have tried to make sense of the COVID-19 data.
But what excited me about this the most is what we can learn from this in other areas, and I want to give you an example of this:
One of the newspapers who are doing a very good job at presenting the data is the Financial Times. And one of the graphs they have put together is this one:
I love this graph so, so much!
Mind you, there are the usual caveats that all COVID-19 data is inaccurate due to the difference in testing, classifying and even what countries actually report, but that's not my focus here.
The reason I love this graph is because of how it works. Instead of just creating a graph and mapping it out over time like you would usually do, what the FT has done is to index the time to zero at the point where each country hit 100 cases.
And then the x-axis is showing days from that point forward.
This creates a remarkable graph that is so many times easier to understand and to compare than any other form of graph.
Let me zoom in so that it's easier to see:
Because all the data has been set to start at the same time-index (when each country hit 100 cases), you can clearly see which countries are performing better than others.
It's the same when we look at deaths, where they are doing the same thing, this time indexing the x-axis at "number of days since 10th death".
It makes it so much easier to compare how each country is doing and what their trajectory is.
And this isn't just useful for this specific story. We can use this technique for all kinds of things.
For instance, forget about COVID-19 for a second and just think about your normal analytics where you are trying to figure out how each article performed.
Today, you are most likely just using a normal analytics system to do this, which will tell you which article had the most views over a period of time.
But this isn't very useful, because it doesn't really help you to understand how each article actually performed.
Let me give an example.
Imagine that this was also how you measured pageviews (or even better, the number of page-reads). So instead of just a total view per month, you mapped out the views each article got from the day it was published.
Like this example of three articles, compared to all the other articles published that month:
Obviously, the above illustration is just something I quickly put together (using the same graph as before), but look at what this is doing.
Imagine that each one of the graphs illustrates the performance of each article. Look at how amazingly easy it is to see how one was doing compared to all the other articles you posted last month.
You can instantly see which articles worked and which didn't.
And it isn't just about articles. You can use this technique for anything.
Think about something like subscriber analytics. Today, what most publishers do is to track how many new subscribers you got each month and how many people churned.
And, I mean, that's fine, but it doesn't tell you anything about the trajectory or the loyalty of each subscriber.
So imagine that you instead measured your subscribers by indexing the time to zero from the day they subscribed, and then you measured the cumulative increase in articles seen since that day.
What you might see is something like this:
Imagine that each graph is one subscriber. Now we see that three of them are really good subscribers. They keep coming back (the total number of articles seen keeps going up), and they read a lot of articles.
But we also see that we have three subscribers who are not really that engaged. Look at the person in the lower-right corner.
This person subscribed, but didn't really read that many articles, and after only 8 days, he simply stopped coming back at all.
This person is guaranteed to churn the next time his subscription is up for renewal.
This is one of the most powerful ways you can do analytics in a much clearer way. In fact, imagine if this was the standard way you did analytics.
Indexing your x-axis so that zero is when 'something started' regardless of the actual date is such a powerful and yet simple way to compare things.
A funny thing about all this working from home is to see how different media companies have been dealing with this.
For instance, Nick Robinson from BBC Radio posted this picture of his working from home setup.
This picture was particularly amusing to me because it's very similar to how I record my podcast(and yes, I know, I need to record another episode).
Of course, the big TV studios have been trying very hard to keep up appearances, so they have installed really elaborate home studios for their hosts.
Here, for instance, is Savannah Guthrie, co-host of NBC Today, where they have partially converted her bedroom at home into a television studio.
And here is a picture of what that looked like on TV:
Another TV station that did this was Sky News, who created these home setups for their hosts.
And finally, we have another example from Fox News, who are also working from home, even though they just spent the past several months telling their readers that it's not something to worry about.
This is kind of amazing.
Of course, I also can't help but think about this in relation to YouTubers. This is 'normal' for a YouTuber, and many YouTubers have created similar home studios somewhere in their homes. And you can go to YouTube and find tons and tons of videos of professional YouTubers showing their viewers how they actually created their home studios. Here is one of many examples.
I tweeted about this a few weeks back, when I wrote:
Essentially, what is happening to the world is that everyone is suddenly experiencing what it means to be a YouTuber.
YouTubers are self-employed, and most work from home every day trying to produce the best videos for their audiences around the world.
To do this, they create 'creator spaces' in their own homes, they communicate with people online all day, they work asynchronously to be more productive, and are really efficient at what they do.
Of course, the difference being that since YouTubers do this permanently, their 'home setups' are way better designed.
But, we are all YouTubers now ;)
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"Thomas Baekdal is one of Scandinavia's most sought-after experts in the digitization of media companies. He has made himself known for his analysis of how digitization has changed the way we consume media."
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