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How to add Google Analytics to Atlassian Add-Ons

31 January 16 Atlassian
How to add Google Analytics to Atlassian Add-Ons
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How to add Google Analytics to Atlassian Add-Ons

If you build Add-Ons, you might be surprised to find that you’re maintaining features that few users actually use. Adding Google Analytics to your Add-Ons will give you insights into which features are valuable to your users.

From time to time you might get first-hand feedback from users often it happens when there's a problem or issue. On the whole, though, do you even know which functionality is being used and how much? Is there a minor feature that users really value? Are there a handful of features that are often used in combination?

Whether you see unused features as unnecessary or something that needs to be better promoted, having analytics data will allow you to make better decisions. In the Adaptavist Product Team, we use analytics inside our Add-Ons and we thought it would be useful to share how to add Google Analytics to Atlassian Add-Ons.

Traffic and travel

We used Google Analytics (GA) in a test with Confluence Add-Ons. There are other analytics services that can track and report traffic data, but the Google option was seen as a tried, tested and well-documented option with well-established APIs.

Of course in normal circumstances, GA tracks and reports website traffic data. Our aim was to use this same facility for a plugin, and to keep the code associated with the plugin to an absolute minimum. As well as simply measuring traffic, we wanted to be able to see which parts of a plugin are used and how often.

Development and support efficiency

We felt that it was important to understand not just total user numbers, but where these users are travelling which parts of the plugin they're accessing most frequently. And, of course, it can also highlight the deserted areas of a plugin the features that people don't use. This can lead you to optimize your development and support efficiency by prioritising the busiest and most useful plugin features. However, the value is often in the more subtle cases diagnosing sticking points where users are trying to use a feature but not succeeding with it.

Spotting the patterns not individuals

It's important to note that no personal or confidential information can be obtained via an analytics service. The data gleaned shows patterns: individual use cases are not available for analysis.

To see the detail of the Confluence plugin test case and a technical view of how the Google Analytics tracking was implemented, read the full article on the Adaptavist Labs blog.

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