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Google Analytics Motion Charts Tips

Intimidated by the slick moves of the new GA Motion Charts? Gaining insight was hard enough with a stable graph, you might be telling yourself – how can I find the trends in one that changes before my eyes? These quick tips will help you use the charts both within a larger analysis process, as well as offer insight on some of the trickier Motion Charts’ options.

The great thing about Motion Charts is that they allow for several distinct types of analysis: higher-level identification of trends, as well as exploratory analysis aimed at finding “buried treasure”: the less obvious trends, or the unexpected spikes in traffic or value for a dimension that lead to new marketing tactics.

We’ll start with some tips for using the Motion Charts at a higher level. Let’s say you are a Marketing Director, with no analyst in the department to make sense of the charts for you (a not-uncommon phenomena!) What essentials do you need before clicking the ‘Visualize’ button?

First off, its important to realize the distinction between the Motion Charts as compared to the more standard line, pie, and bar charts. In contrast to “bins” of data represented in a bar or pie chart segment, Motion Charts uses unconnected points to represent the values for various dimensions (Keywords, Traffic Sources), and metrics (Time on Page, Goal Conversion Rate) within the chart. Each point is one value for a particular time step. This means that some of the basic guidelines required of analyzing point pattern data become important.

First of these guidelines? Learning how to tune out random variation, or those spikes in the data that don’t suggest larger trends, is key. For example, here is one view of keyword data, followed by the view of the graph immediately following:

Notice how in the second shot, which represents just one day later, the bubble representing “internet marketing company” has shifted to an entirely new position? Focusing on this single jump takes you further from identifying the big trends at work. If it’s higher-level, comprehensive trends you seek, get used to tuning out these jumps, and focusing instead on bubbles that hover in the same general regions of the graph.

By default, the GA Dashboard segments the time period into days. But when looking for big trends its appropriate to change the time so that values are averaged over the week (Select the middle button next to the “Graph by” label. Doing this will make it easier to filter out the variation you don’t need to worry about.

Now for some analyst-level tips related to one of the features of the Motion Charts that might be less intuitive. Let’s start with an example. Here are two shots from the same graph at different points in time. Again, the data is keywords:

Notice how in the first shot, all of the values hover in a line above the X-axis? This formation continues for most of the period, except for a single day in which the bubble for one keyword, “pure visibility” moves to the very top. In the last example this was exactly the variation we wanted to tune out. This time, we’ll take notice of the spike (and perhaps look the traffic sources, visitor regions, or other dimensions for the date in question).

But what about all the other keywords? Should they be ignored? When doing a deep-dive analysis, there may be slighter, yet still noteworthy insight within their values. For instance, what if there is a slight trend toward higher or lower values for for variations containing a single keyterm? To a paid search analyst controlling a large budget, this sort of insight could suggest more (or less) use of the term in paid search marketing.

There are two ways to find this subtler differences, when the graph’s scale makes it difficult at first view. The first involves changing the scale from “Lin” (linear) to “Log” (logarithmic):

This above example is a time when changing the scale will allow you to focus on the more granular differences between the other words. A logarithmic scale is appropriate whenever the range of the values being graphed includes both very large and very small numbers – and those few large or small numbers make other values look virtually identical.

The second way to scale the data so that the big trends (those bubbles representing much higher values) is to use the search bar at the bottom of the grid to filter out the keywords or other items (Traffic Sources, etc) that are throwing the scale off. This example uses a regular expression to exclude the term “pure visibility”, both with and without the space between words:

Happy graphing with Google Analytics Motion Charts!

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