How To Find Things In Google Analytics Without Losing Your Mind

Michael Wiegand Sep 4 2018

How to find things in Google Analytics - A businessman searches for his web analytics reports

If you’re not in GA all day every day, it can be difficult to know where to look for things. And it’s even more intimidating given that Google changes their navigation pretty regularly and renames their reports on occasion too.

Here are a couple tried and true methods I’ve found to get what I need out of Google Analytics without going insane.

Search, Don’t Browse

So why don’t you hunt and peck through the navigation tree?

Well, for one, Google changes it every six months or so. New reports are coming into it, and some reports are moved into different sections. It’s a mess.

For two, even when they’re not changing the navigation itself, they’re renaming reports or metrics that you used to rely on.

How do you get around that? If you think about what does Google does best, they’re a search engine.

So there are two ways to search in Google Analytics:

One is by searching for report themes on the left-hand search. This search works best with one- or two-word queries. Typing “Location” or “Mobile Device” or “Source” will surface the most relevant reports for those topics.

Search is your best friend.

The second search method is still in its infancy, relatively: Analytics Intelligence. You ask it a sentence-long question, and it attempts to find you an exact data point to match your query instead of making you jump into a report.

Try out Analytics Intelligence.

When it first launched, it was hit or miss in terms of finding answers, but this feature has gotten a lot better of late.

Segment for Specifics

The next best way to find what you need in Google Analytics is creating advanced segments.

Searching only works if your question is straightforward, like:

“How much organic traffic did I get in July?”

For more complicated questions, like…

“How many organic visitors who landed on the widgets page went on to buy more than $150 of product?”

…that’s where you’ll need to build a conditional segment.

Add a new segment to compare against All Users.

Make sure you keep a naming convention so you can find them later.

Skip down to the Advanced section.

The sky’s the limit here, and once you’re done building this segment, you’ll be able to use it in the Google Analytics UI and also import it into Google Data Studio!

A couple of caveats on segmentation:

  1. User-focused segments only allow you to look at 90-day windows. If you need to look at a broader date range, use Session-focused segments instead.
  2. You’re more likely to encounter data sampling when you segment, so look for the yellow shield next to the report title and know that your data is directional when sampled, not exact.
  3. Note that your segments are unique to your Google login. However, if you share a login with multiple stakeholders, be sure to stick to a naming convention so that your colleagues know what your segment is about.

Separate the Data from GA’s UI

Sometimes the best way to find what you need in GA, you need is to liberate the data from its UI.

You can either export to Excel and use pivot tables instead. It’s tried and true. We all have so much experience with the Microsoft Office suite. Why not re-contextualize the data in that environment.

Most reports in Google Analytics can be exported in .csv format.

Microsoft Excel. Hello, darkness, my old friend.

Alternatively, check out Google Data Studio’s new explore feature.

Adding a new explore session starts in the lower right-hand corner.

Search for your Google Analytics data source, or create a new one!

You get all the power of Google Data Studio's visualizations with every dimension and metric from Google Analytics.

What’s the benefit of getting outside of GA?

For one, you can typically avoid data sampling by getting out of the platform. Google Analytics samples so that it can present reports to you faster at the UI layer. But if instead you’re doing an export of canned reports or using the API to pull only a few metrics at a time (which Google Data Studio does), you can get at unsampled data more often.

The second benefit is you get to build your UI and visualization for the data instead of being limited to the defaults. That allows you to tell better data stories that would otherwise require you to pull together multiple stock GA reports.

Conclusion

Hopefully these three tips will help all of you – either GA beginners or GA power users – to stop fiddling with the UI when you’re trying to get at what you want: data and insight.

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