Email marketing benchmarks by industry (in 3D)

In a discussion on I just stumbled over some interesting benchmarks that have been published by MailChimp a while ago. Email senders have always been keen on comparing themselves to others in order know where they stand. The reported data, although from 2010, might be a good reference for small enterprises. From a consultant perspective, it would also be interesting to know if there are industry groups that perform similarly good or bad. Well, have a look at my fancy little email marketing benchmark cube…

Industry benchmarks in 3D:

(If you got a WebGL compatible graphics card & browser and Javascript activated (I recommend Firefox; Chrome doesn’t show the labels),…

… Usage: Drag the mouse to rotate the cube, use your mouse wheel to zoom in and out, and the right mouse button allows you to change the field of view.)


  • The width of the cube, i.e. the x-axis entitled “[pc2] OPENS&CLICKS“, represents a combined measure for open and click rates: A value of three is superb, minus one is bad. The y-axis (height) is a combined measure for hard and soft bounces. The z-axis (depth) called “[pc3] UNSUBSCRIBES” represents the unsubscribe rates for the different industries.
  • Something to the scale: all values are mean-centered, i.e. 0 represents the average performance for the corresponding measure. Going to -1 is under average and to +1 over average correspondingly. Take for example “Other”, which ranks highest for the open and click rate measure: it also has average bounce rates (pc1 close to 0) and unsubscribe rates slightly above the average (pc3 close to 1).
  • You might have noticed the half transparent plane. This marks the mean average for the combined measure of open and click rates. Therefore, industries on the left of it underperform, whereas the intersecting ones have rather average open and click rates. Finally, those few industries on the right of the plane rank above the global average.
  • The colors represent the five clusters that I found (plus two outliers “Other” and “Social Network and Online Communities”). Clusters should have rather homogenous pc1, pc2 & pc3 values within themselves, and rather heterogeneous values in comparison to one another.

Some findings: