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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