Scatter Plots in Analysis Workspace
Last week, I wrote about how to use the new Venn Diagram visualization in Analysis Workspace. Now I will discuss another new Analysis Workspace visualization – the Scatter Plot. This visualization should be familiar to those in the field and has been available in Microsoft Excel for years. The purpose of the scatter plot is to show two (or three) data points on an x/y axis so that you can visualize the differences between them. In this post, I will continue using my blog as an example of how the scatter plot can be leveraged.
Scatter Plot Visualization – Step by Step
The first step in creating a scatter plot visualization is to create a freeform data table. This normally means adding a dimension and a few metrics. I would recommend starting with two metrics that you want to see plotted against each other. Here you can see that I am looking at my blog posts sorted by popularity and also added Visit Time Spent:
Once I have this table the way I like it, I can drag over the scatter plot visualization and then highlight the two columns to see this:
In this case, I am seeing the views of each blog post on the “x” axis and the time spent on the “y” axis. Blog posts that have a lot of views will appear on the right side of the visualization, while those with fewer views will be on the left. At the same time, those with more time spent in the visit will be near the top and those with lower time spent will be near the bottom. Blog posts with the most views and the most time spent will be in the upper-right quadrant. You can hover your mouse over any of the scatter plot points to learn more about it. For example, if I want to see what the best item is at the top-right (in green), I can hover to see this:
In this case, my post on Merchandising eVars seems to be the one viewed the most and with the most time spent (probably because Merchandising is a tricky topic!).
Most web analysts use scatter plots to identify improvement opportunities. For example, if you are plotting products, cart additions and orders, you can see which products have a high number of cart additions, but a low number of orders and figure out ways to take action on that. In this case, I may look for blog posts that have a large amount of time spent (which may mean that they are engaged with the content), but a low number of views. In this example, I might hover over the purple circle and see this:
This may indicate that I need to promote this report suite tweaking blog post more to get it more views.
When using scatter plots, there are some ways you can customize what you see in the visualization. If you want to flip the x/y axis, you simply reverse the metric columns in your freeform data table. If you want to see percentages instead of raw numbers, you can do this in the settings as well. You can also choose whether or not you want to see a legend in the visualization.
Finally, if you want to plot an additional data point, you can add a third metric to your freeform data table and the scatter plot visualization will modify the size of the circles to reflect the size of the new data point. For example, if I add Average Page Depth to the freeform table, the circle size will reflect the average depth associated with each blog post. Now I can see that my Merchandising post seems to be more of a “one and done” reading versus other posts that appear to be viewed concurrently or with other website content.
Seamless Adobe Analytics Integration
One of the best parts of Analysis Workspace and its visualizations is how seamlessly it works with the other aspects of Adobe Analytics. Last week, I showed how you can apply segments to Venn Diagram visualizations and the same is true for scatter plots. But the integration doesn’t end there. Imagine that I look at some of the visualizations above and ask myself, “which types of blog posts do people view the most and spend the most time on?” While the above visualization helps me differentiate the different blog posts, I tend to write a lot of posts and that can make it difficult to see the big picture. To conduct this kind of analysis, I can use SAINT Classifications to associate a “Blog Post Type” with each blog post. In my case, my blog posts tend to be either about Adobe Analytics features, types of analyses you can do, implementation best practices, etc. So if I put each blog post into one category or type using SAINT, I can get a much higher level view of how my blog is performing. Here is a sample of what my SAINT file might look like:
Once this is done, I can repeat the above steps to create a scatter plot, but this time, instead of using the Blog Post Title dimension, I will use the Blog Post Type dimension (classification of Blog Post Title) and re-build my scatter plot. This allows me to see fewer data points, since all of my blog posts have been grouped into a small number of types:
This new scatter plot allows me to see that blog posts focused on implementation best practices and analyses tend to get the most views and have the most time spent. Posts around product features are next, but have a drop-off in the time spent. I can also see that posts on Analysis Workspace have a low number of views and time spent, but I attribute that to the fact that those posts haven’t been around very long and I would expect that category to move closer to the pink circle (Adobe Analytics product feature posts) over time. Finally, I can see that my posts about training classes and my miscellaneous posts that are a bit different don’t seem to get as many views or time spent. This combination of SAINT Classifications and the scatter plot allows me to learn things that I could not have easily surmised by looking at the scatter plot of the individual blog posts above.
As you can see the combination of pre-existing Adobe Analytics features and the new Analysis workspace visualizations can be extremely powerful. Since they are easy to build, unlimited and have no additional cost, I suggest that you try them out with your implementation. Enjoy!
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