Guide to Calculated Metrics in Google Analytics

A long awaited and quite awesome feature is finally here in open beta for all Google Analytics accounts: Calculated Metrics! As a long-time power user I’ve been longing for this for quite some time since I had to resort to spreadsheets or API-based solutions (like our Agency Dashboard tool) when I needed to calculate my own metrics based on the standard metrics GA has. When things like Revenue per User, Conversions per User or Site Searches per User where of interest there was no other option. Now there is, thanks to calculated metrics.

Calculated metrics allow you to compute metrics based of the standard metrics in Google Analytics in order to come up with data that better aligns with your measurement needs and helps you answer the important questions that drive action much easier. Once you create a calculated metric it starts functioning as any other metric in your reports – gathering data from the moment you define it onward. There are some differences, though, as you can use calculated metrics in custom reports and custom dashboards, but sadly not in Custom Segments (as of moment of writing at least). Also you’re limited to just 5 calculated metrics per view for free accounts (50 for Premium accounts). I guess this is one more reason to use up that view quota.

Read below on how to set up your first calculated metric and 10 awesome examples of calculated metrics you’d want to use.

How to set up a calculated metric in Google Analytics?


1.) Go to Admin, select the view you want to set the metrics for and click the “Calculated metrics” button:

Calculated Metrics

2.) Click on the “New Calculated Metric” button:

New Calculated Metric

3.) Set up your metric:

Revenue Per User Google Analytics

Now this is the harder part. First enter a name for your new metric. Make it short, but descriptive. As you can see GA automatically fills in the “External Name” field based on your input in the “Name” field. You can, however, type in this field directly as well, but bear in mind the restrictions that apply. The External Name can NOT be changed once the metric is created, while you can change the Name anytime you want.

Next you should select the proper formatting type. Available values are “Float”, “Integer”, “Currency”, “Time” and “Percent”. These should be pretty self-explanatory. Make sure to select the proper type though, otherwise it may lead to unpredictable or just plain wrong results.

Finally the most interesting part – the FORMULA. This field should include the fields you want to operate on and simple arithmetic operators. Just start typing and Google Analytics will suggest metric names based on what you’ve typed so far. In the example calculated metric above we have one of the very first metrics you’d want to have for e-commerce sites – revenue per user. The formula for it is {{Revenue}} / {{Users}} . Now let’s see some other great examples of calculated metrics:

10 awesome calculated metrics


1.) User-based Goal Conversion Rate

Name: User Goal CR

Type: Percent

Formula: {{Goal Completions}} / {{Users}}

This can be altered to work for just a specific goal. In the case of Goal 1 the formula would be:

{{Goal 1 Name (Goal 1 Completions)}} / {{Users}}

Don’t copy the above formula, as you’d need to use your actual goal name.

This can also be made to work for a set of goals, say 1 and 2 only:

({{Goal 1 Name (Goal 1 Completions)}} + {{Goal 2 Name (Goal 2 Completions)}})/ {{Users}}

Don’t copy the above formula, as you’d need to use your actual goal names.


2.) User-based E-commerce Conversion Rate

Name: User E-comm CR

Type: Percent

Formula: {{Transactions}} / {{Users}}


3.) Revenue per User

One of the best calculated metrics for e-commerce sites.

Name: Revenue per User

Type: Currency

Formula: {{Revenue}} / {{Users}}


4.) Avg. User Duration

Name: Avg. User Duration

Type: Time

Formula: {{Session Duration}} / {{Users}}


5.) Searches per User

Very useful for sites where search plays an important role:

Name: Searches per User

Type: Float

Formula: {{Total Unique Searches}} / {{Users}}


6.) Sessions with Search per User

Great for estimating in how many sessions user perform a search:

Name: Sess. with Search per User

Type: Float

Formula: {{Sessions with Search}} / {{Users}}


7.) Transactions per User

Name: Transactions per User

Type: Float

Formula: {{Transactions}} / {{Users}}


8.) Product Clicks per Session

If you have Enhanced E-commerce set up this metric will give you the average number of product list clicks per session. Can be altered to be user-based as well.

Name: Product Clicks per Session

Type: Float

Formula: {{Product List Clicks}} / {{Sessions}}


9.) Product Views per Session

While this is similar to #8, it includes product views that didn’t occur as a result of clicks on product lists, e.g. a user has landed on your product view page.

Name: Product Views per Session

Type: Float

Formula: {{Product Detail Views}} / {{Sessions}}


10.) Product Views per Transaction

If you have Enhanced E-commerce set up this metric will help you estimate how many products are viewed on average in order to complete a transaction.

Name: Product Views per Transaction

Type: Float

Formula: {{Product Detail Views}} / {{Transactions}}


We hope these metrics give you good start in using calculated metrics in Google Analytics. If you’re still wondering how they will appear in your reports, read on.


How to use calculated metrics?


As said in the beginning you can use calculated metrics in two ways – as a metric in a custom report or as a metric for a custom dashboard widget. Here is an example report configuration featuring some of the proposed 10 metrics above:

Custom Report with Calculated Metrics

The metrics you’ve created would be grouped under “Other” in the metrics drop-down.

Unfortunately we can’t use them for custom dimensions or in-report filters, but we hope this functionality will be implemented in Google Analytics in the months to come.

About the author

Georgi Georgiev

Managing owner of Web Focus and creator of, Georgi has over eighteen years of experience in online marketing, web analytics, statistics, and design of business experiments for hundreds of websites.

He is the author of the book "Statistical Methods in Online A/B Testing" and white papers on statistical analysis of A/B tests, as well as a lecturer on dozens of conferences, seminars, and courses, including as a Google Regional Trainer.

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