Churn Rate Graphs

Wish a live internet video broadcast, there is usually one key metric: viewer count.

How many people are watching now? And how about now? And now?

The number changes over time, meaning we can easily express how the number changes over time with a time-series line graph. You can see if you are getting an influx of new viewers or if people are tuning out.

There’s a critical flaw in this viewer count number, however. If your broadcast is hovering around 1,000 viewers and most people are captivated and sticking around, the line graph will look very similar to a graph representing the same quantity of viewers with new viewers tuning in at the same rate that people are leaving. Those are two very different scenarios that could look exactly alike on a graph.

Live Viewership with Churn Rate

In broadcast television, the rate at which viewers come and go is called “churn rate”. This variable can be added to the viewership line graph, for example by plotting new connections and disconnections relative to the viewer count. The wider the line, the higher the churn rate.

Churn Rate Sparkline

This can also be applied to sparklines, where small, unlabeled graphs can show not only the trend in viewership count, but also the churn rate (as illustrated by the thickness of the colors.

Tags: sparklines

This entry was posted on Saturday, July 3rd, 2010 at 2:11 am and is filed under datarealization. You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.