Multivariate Display: What Your Car Says About You

Having always had an interest in automobiles (particularly premium ones), I thought it might be a good idea to incorporate this interest into my multivariable display.  With the competition between brands at an all-time high and marketing at its most fierce, I decided to cut to the chase and research the types of people who are actually drawn to these very different premium/luxury car brands.

I began with an affinity map, because I felt it provided for the best layout and enough space for drawings; on the two axis, I placed average age and income.  I then drew each respective logo in the appropriate place with regard to these two variables.  Next, I used color to illustrate the percentage of males versus females who purchase the brands, male being shown in blue and female in pink.  Lastly, I wanted to include the prevalence of college education among these supposedly affluent sets (and if this has any correlation to the types of cars they buy).  To depict this, I outlined the accompanying text in one of three colors: red if 0-40% of buyers are college educated, orange if 40-70%, and green if 70-100% of buyers went to college.

The statistics certainly say something about who each brand appeals to.  On one end of the spectrum is Cadillac, the with the oldest average age (57), lowest average income, and least college education.  Conversely, Audi buyers are much younger (35 on average) and more affluent (with an average income of about $154k).  Mercedes-Benz and Porsche buyers are the wealthiest, bringing in $164k and $188k, respectively, with older average ages, and the majority having been college-educated.  Interestingly, because Saab is on the lower end of the luxury spectrum, buyers seem to be very well-educated, young, and affluent.

In the beginning, knew how many variables I wanted to include, but struggled with how to incorporate all of them into one graph.  Ultimately, color proved to be the greatest help in conveying the information.  While not 100% visually accurate (it is difficult to decipher exactly how many males vs. females, for instance), it provides a good background for those who care about car buyer demographics.  My "aha" moment occured when I finally formulated my plan, realizing I could grab the viewer's attention with detailed drawings and bright colors, but to have each element serve a purpose.