Columnist David Fothergill shows how data visualization can unlock insights about AdWords performance you may not have gleaned otherwise.
Data visualization is one of the most powerful tools available if you want to explore and understand your data, whether it’s on a small scale or at a scale which qualifies it as “big data.”
In this post, I wanted to run through some of the fundamental elements of data visualization and illustrate why these concepts start to reveal insight once combined.
I’ll use a very simple set of data with some fairly logical conclusions in order to focus on the effect of different techniques, avoiding adding any unnecessary complexity.
A simple scatter plot example
For the purpose of this post, let’s consider a scatter plot approach for a modest set of AdWords keyword data. My fictional dataset consists of data for ~700 keywords for a period of one month, with fields reflecting cost, clicks, conversion and revenue metrics.
As a starting point, let’s plot the cost per click (CPC) vs. the revenue per click (RPC), represented on the x and y axis respectively:
All very nice, but doesn’t really tell us too much. What we can draw from this is that the relationship is fairly wide-ranging, with some keywords delivering much more in the way of ROI, and some keywords in the bottom-right corner which appear to be unprofitable.
Adding context using segmentation
If ever you want to try and add some useful context to a dataset, then segmentation is a really nice, elegant way to achieve this. Instantly (assuming you’ve applied a relevant segmentation), you’ll start being able to compare and assess patterns/trends across different groups, which is often the starting point heading toward the insight that will be useful.
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(Some images used under license from Shutterstock.com.)