The First Issue Boom

Fast out of the gate

#1 issues in comics are all the rage. They’re the jumping-on point for new readers and, as such, publishers tend to heavily promote them. There is also, as explained with trademark cynicism here, still a tendency for investors to buy up #1 issues on the assumption that they will eventually be worth something.

The speculator bubble, which burst in the ’90s, still flickers briefly into life every time a new #1 issue is released. This combines with the publishers’ promotion to fans who might pick them up casually to create a huge boom for each #1 issue of a new series. However, there is also the tendance for sales to immediately fall away after that first issue. You could almost say that the entire philosophy of Marvel and its Distinguished Competition is to publish as many #1 issues as they can, and hang their fortunes on the few that don’t shed a huge amount of readers. In fact, in 2011 DC Comics relaunched their entire line with 52 new #1s, essentially erasing whole decades of continuity to capitalise on the craze for #1s.

To illustrate just how reliant the publishers are on this method, I created an alluvial diagram to show many new #1 issues appeared in the best-selling lists, as compiled by Comichron. First, though, here is a diagram showing how volatile the comics market actually is:

Click for interactive version

Click for interactive version

As you can see, a disproportionate amount of the comics published by DC and Marvel (DC made it a point to only publish 52 comics concurrently during 2013) that made it into the top 10 were #1 issues.

That said, as illustrated by the following alluvial diagram, the vast majority of new series, which had launched the previous month with a #1, failed to crack the top 10 the following month.  Each vertical black line represents a month of 2013, and the split tracks that follow indicate how many went on to crack the #1 the following month and how many failed to do so.

Colours not accidental

Colours not accidental

Fallacy of the first?

As can be clearly seen, the vast majority of the series launched with a #1 in the top ten failed to crack the top ten afterwards, as they were displaced by reader apathy and a fresh wave of #1 issues and more were no longer being published by December than even the new #1s published that month.

In fact, in a number of months, not a single comic from the previous month’s #10 made it into the next one. Even the mighty Superior Spider-Man, otherwise a permanent fixture in the top 10 since its launch in January, failed to reach the top 10 in  April and September.

A few caveats, however:  Some of the  comics published (as can be seen by the dataset I used, reachable through the picture embedded below) are what are known as ‘limited series’ and were events scheduled for a few months at most. The following are the limited series books, which make up a large proportion of the books no longer being published:

  • Age of Ultron
  • Kick-Ass 3
  • Forever Evil
  • Infinity
  • X-Men: Battle for the Atom

Nevertheless, I believe my alluvial diagram has proven that DC Comics were correct to entirely relaunch their line with #1 issues in 2011. Now the struggle for them is to find comics that can actually hold onto their readers, and not force them to do so again in a few years’ time.

Click to see the cleaned up data

Click to see the cleaned up data

How was it done?

Initially, I scraped the excellent Comichron site for their comprehensive sales figures. I used Outwit Hub, a great and easy to use data scraping tool, to grab the first 100 lines of the tables for each month in 2013, the latest data they had available. From there, it was a simple matter of cleaning the data to only contain the data I required, then arranging it in such a way that it could be put into an alluvial diagram.

I chose to use Raw to create the diagram, since it’s so simple but versatile, perfect for quick visualisations. If you’re looking for a tutorial on how to use it effectively to create an alluvial diagram, read my fellow Interhacktive Laura’s great tutorial here.

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Posted in Data Stories

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