Custom Rates & Metrics in Google Data Studio

by Chris Wright

Data blending on Data Studio dashboards is a powerful feature that allows you to create your own custom rates, including custom metrics and completion/drop-off rates between event-based interactions. Rates are a great way to provide a quick, context-rich measurement of key user interactions with your site.

Why Create Your Own Custom Rates Using Data Blending?

Sometimes you need to create a calculated metric that includes a numerator and denominator that each need to have a separate filter (or one needs to be filtered and one doesn’t). An example would be sessions that reached a certain shopping stage in the case of a funnel drop-off calculation, or sessions that experienced an evented error in the case of an error occurrence rate. This isn’t possible with the existing “Create Field” functionality because if you apply a filter it is applied to both the numerator and denominator in your formula. That isn’t very helpful if you need to apply a different filter for each part.


The Blended Data functionality in Data Studio allows you to create your own metrics that require a different filter on each side of the formula. This allows you to calculate things like rates, drop-off/completion totals and %, and more.

How do I do it?

To show you the workflow we will work through making a metric that calculates event occurrence rate. This example is using an Event Action that tracks Contact Us click events. In this example we will be dividing the unique errors, filtering for the Contact Us event, by all sessions to show how often the Contact Us event is fired in all sessions. This could be a useful metric to track over time if your site is lead generation based or provides help desk support, however, the workflow is broadly applicable to any of the use-cases described above.

  1. Create a Scoreboard

2. Click “Blend Data” in the Data tab of the Scoreboard, select “Unique Events” as your metric. Beside the metric name click “AUT” and give the metric a new name “Unique Contact Us Clicks” (this isn’t completely necessary in this use case but will come in handy with other use cases where you are using the same metric on each part of your data blend).

3. Click “Add a Filter” at the bottom of the column, give it a meaningful name (“Contact Us”) and configure the filter to isolate for the Contact Us Event Action. In our example it would be Event Action equal to Onsite Click. This metric will now only have totals for this event action because it is filtered.

4. Once your filter is complete and saved. Click “Add Another Data Source”, choose the same data source as you have for the first column then select “Sessions” as your metric.

5. Name your Blended Data in the top right corner, name it something meaningful like “Contact Us Data Blend”. Click “Save” in the bottom right corner. Your scoreboard will now have “Contact Us Data Blend” as it’s data source. The only metrics included in this data source are the ones you just finished specifying, Unique Contact Us Clicks and Sessions.

6. Data Studio is pretty smart. In this case it already knows what you are trying to do and has made a calculated metric based on your data blend. To verify that the formula is correct click on “AUT” beside the metric name in the Data tab. It’s important to do this as Data Studio will sometimes misinterpret your intention and make a formula that isn’t what you were expecting. It relies on the order you selected your metrics in the data blend to create the formula. You should give your metric a new name to better describe the  data that is included. In this case, I labeled it “Contact Us Rate” and clicked “Apply”.

That’s it! The workflow is pretty simple and the utility of this technique is pretty broad. I have used this to create funnels with drop-off/completion rates, filtered calculated metrics and, of course, filtered custom rates.

Here’s some ideas to get you started, in each of these use-cases you need to filter an event in at least one half of the formula so blended data is needed:

  1. Signup Success Rate – Signup Success Event / Signup Submit Event
  2. Login Success Rate – Login Success Event / Login Submit Event
  3. Form Submission Rate – Form Submission Event / Form Views
  4. Downloads Per User – Download Event / Users

Good Luck. I’d love to hear what use-cases you come up with. Contact me on LinkedIn if you have any questions.

Chris Wright

Analyst

As a Web Analyst at Napkyn Analytics, Chris uses digital data and visualization to tell the story of our clients' performance, helping them make decisions to move from insight to action.

See more posts from Chris