Here, I intended to show that while the value being measured is basically level overall, the desirable cohorts within it are trending drastically downward. As such, it gets the job done, but there's an important reason this isn't the right graph for real-world applications.
Stacked area, as perceived
The issue is that our eye tends to compare the thickness of strata at a diagonal based on the apparent slope. This is exacerbated by the fact that our datapoints are pretty far apart in terms of pixels. This on-the-bias bias might be okay for real-time APM products with their closely-spaced samples, but not for general business metrics.
Stacked columns cut out the bias. It's easy to scan and see that the top cohort is growing as a proportion of the total. While the data hasn't changed, the graph is more accurate… but, it seems to lack a sense of velocity.
Switch to a non-stacked column graph (and decrease the maximum value on the Y-axis) and here's what you get. To my eyes, this immediately conveys velocity—and highlights outliers—while avoiding the visual bias of the area chart. But, you might lose a sense of the overall contour of how these columns sum.
There are some other options that could convey velocity, outliers, contour of the sum, and performance of each cohort, without suffering the bias of the stacked area chart. For instance:
- a multiple-column chart for the cohorts (like my last example) overlaid with a line chart to show the overall trend
- a column chart showing the total, decorated with inset pie charts to show the cohorts (though pie charts have their own issues)
- or some kind of stockchart
Lesson learned, and I will be avoiding the sexy stacked area chart for my cohort visualizations.