Google Data Studio

Google Data Studio – Optional metrics serving the good of visualisation and analysis

Google Data Studio optional metrics is a nice feature providing possibilities to prioritize metrics comparing to others. Users decide to adapt visualisation regarding their live concerns.

But it is also a risk of overloading charts widgets, breaking visualisation design made with ergonomic hypothesis and KO limits…a kind of Pandora’s box of bad viz…

So, instead of overviewing what it is possible with this feature, let’s try to address real data visualisation and interaction needs, through 4 cases using optional metrics as the mean and not as the end.

Focusing by removing default metrics

Google Data Studio - Optional metrics - Focusing by removing default metrics

In this example, our website has 3 kinds of conversion and we wish to provide the big picture of trends by default, but also the possibility for readers to focus only on a part of these conversions.

let’s turn on optional metrics without additional metrics just to be able to remove part of default metrics to display the rest of them.

I like this first way to use optional metrics because it is the opposite of the goal of the feature. We transform “optional metrics” feature to “removable metrics” feature.
After all, if optional metrics is a risk for readability, let’s begin to use it to curate and focus on specific data.

Let’s see how to configure this tip is simple.

Adding raw numbers to bring data context for charts with relative metrics

In a lot of visualisation cases for performance follow-up, we don’t really need to display permanently raw numbers but only relative metrics like averages, rates or ratios.
In these cases, volumes are just here to confirm the representativity of relative metrics (what we are talking about).
Google Data Studio optional metrics are a good…option…😛…to create a seconde level of reading, providing data context with additional raw numbers.

As example, in the video below, we choose to display by default Google Analytics conversion rate by channel in a bar chart table. Then we add sessions and transactions as optional raw numbers for data context purposes.

Exploring data from macro to micro metrics and dimensions

Data exploration needs to be supported by strong methods if we don’t wish to be quickly lost by all varieties of analytics axis, especially with powerful tools offering huge possibilities.

Using optional metrics, in combination with drill down dimensions, can be a good way to provide a simple solution of exploration with limited possibilities, so for the good of understandability.

In the example below, we configure, in the same table chart, 2 levels of Google Analytics data to analyze user experience on landing pages.

  • By default we display pages with related bounce rate and average time
  • We can add engagement actions on the page (tracked as Google analytics events)
  • We can drill down pages by type of engagement actions to focus on micro conversions

Just combining optional metrics and dimensions drill down in a table, we build a mechanism to follow general performance as first reading, completed with micro conversions and related data.

Optimizing report spaces, Comparing side by side metrics on graphical charts

Visual comparison needs a lot of space in reports. Let’s use Google Data Studio optional metrics to compare a set of metrics freely, and so optimize used spaces.

Let’s create Create slide by side geomap charts clones with optional metrics.

PS: The short version of this post has been published on my Twitter account through a thread the 17th of September 2019: @wissi_analytics

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