Tuesday, January 7, 2014

Using the Motion Chart for Multivariate Analysis

So, we've all seen them by now: the infamous Motion Charts made famous in a now iconic TED talk, given by Hans Rosling. Rosling used it to show how you can present a great deal of data in a clean and meaningful way: on a two dimensional plane that displays more than just two dimensions.

Collecting meaningful data has always been the most challenging step in generating actionable visualizations. However, digital analytics data has made a wealth of free and pertinent information available. We can use this to see exactly what dimensions affect web site traffic, and formulate insights based on them. Sites that function as E-commerce storefronts are a bit less concerned with general traffic, and are more so concerned with sales. Therefore, it's worth examining the conversion rate a bit more in detail.

Take a look at the recent conversion rates of an actual, but unidentified site against the following metrics: bounce rate, pages viewed per visit, aggregated daily visitors, and average time spent on the site per visitor.

For a more traditional view, click the bubble data point, check the box marked 'trails', and change the x-axis to the 'time' option. Now hit play and adjust the speed. You will see how the conversion (sales) rate changes over time, by the number of overall web site visitors, number of pages viewed per visit, and visitor bounce rate. While it would be good to get an R-squared value of a specific metric to the dependent variable (conversion) this would only give us a numeric, abstract statistical understanding of what factors affect conversion, most profoundly.

However, the best way to use the motion charts is to disregard the time variable as an axis label. Only then can we see how all four dimensions affect conversions simultaneously. The multiple dimensions are represented by the x-axis, the y-axis, the size of the bubble, and the color of the bubble, at any given point. This gives us a much more rich interpretation of the factors that influence conversion.

But what about our clients? After all aren't we reporting for their benefit? Chances are, they don't care what the R-squared value of any one variable is. But a visual representation like the motion chart can go a long way to show just how much of an influencing factor a particular metric is to conversion.

Motion charts can no longer be generated from the GoogleDoc gadget function. This motion chart was generated in R using the Google Chart Tools interface and googleVis package.

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