Google Analytics Top Tips

Google Analytics is the de-facto industry tool for tracking your website’s performance against your business metrics. It’s a great way to find out how users are interacting with your website, where you’re attracting them from, and measure the effectiveness of your advertising campaigns.

Off the back of a couple of questions that I’ve had this week, I’ve thrown together a trio of tips to help you get the most from this brilliant tool.

Tip One: Removing insignificant data:

When you’re filtering a table, sometimes the number of results can be very large, especially if you’ve added a secondary dimension, or you may get results from pages that no longer exist, or are statistically insignificant.

To cut these redundant rows down you can click the “advanced” button next to the filter:
A screenshot of the Google Analytics filter bar

You’ll see the following screen: 

A screenshot of the Google Analytics advanced filter

If you click on “Add a dimension or metric” and choose one of the metrics (which are shown in blue in the list) you can filter out results that don’t meet a certain threshold:

A screenshot of the Google Analytics advanced filter with more rules applied

Here I’m using excluding results where there are fewer than 100 page views.

Tip Two: The Wonderful Pivot Table

Pivot tables are one of the most useful and under-utilised data layouts in Google Analytics. They’re a slightly more difficult concept to understand than a standard table but, once you do understand them, you’ll wonder how you ever lived without them.
Let’s say that you want to see which is your most visited page by country. You might be tempted to add “Country” as a secondary dimension of “Page” – while this would get you the results you wanted, it’s not a very elegant solution, and your results would be in an extremely long and difficult-to-interpret table.

Enter the Pivot table.

Pivot tables live within the icon group on the right-hand side above the main data table in most reports:

A screenshot of the Google Analytics table display buttons

Pivot tables allow you to break down one dimension by another – for example, you might want to see most popular pages broken down by country:

A screenshot of the Google Analytics pivot table

As you can see from the above screenshot – I’ve taken the “Pages” report and used the pivot table. The “Pivot by” button in the top left is the key. I’ve set it here to “Country”, and the metric I’m measuring is “Page Views”. I can also add a second metric to this if I desire.

Tip Three: Context Is King

One of the most important things to remember when examining data from Google Analytics is that context is king and understanding the key drivers behind the data is vital. For example, if I want see if there has been an uplift in users from a particular demographic, or for a particular area of the site, it’s important to examine the context of that. Firstly – we are a seasonal business – we have both yearly cycles, and also smaller cycles within the year. Before deciding on what to compare to, take a look at those cycles and ensure you’re running a like-for-like comparison. Take this view, which is number of visits to the site as a whole for the entire year:

A screenshot of a Google Analytics graph showing a year of site interactions

You can see here that there are a number of places where there are fluctuations. There’s a big jump where the new site launched, then there are smaller, weekly, cycles where usage drops off over the weekend. A slump in users around Christmas, and a huge jump around September, so not all periods are equal. If you were looking for an uplift for a specific page based on a marketing campaign, but happened to compare your period to the Christmas slump, you’d see a much larger jump in numbers than might actually be attributable to the campaign.

If I compare page views over a five-day period, and then use “Compare to previous period”, which will compare them to the previous five days, I get the following graph which looks like good news as we’re showing a strong upward trend.

A screenshot of a Google Analytics graph with an incorrect date comparison

However, this is a false positive, because the previous five-day comparison includes a weekend, and as we’ve seen above – user numbers dip during a weekend. If I run the comparison over the same days from the previous week I get a much flatter graph and truer numbers.

A screenshot of a Google Analytics graphs with a date range comparison

This is a contrived example, but it demonstrates the importance of understanding, not just the data itself, but also its wider context.

If you’ve found these tips helpful, and you’re interested in getting to grips with Google Analytics then James from the Marketing Team is running some Google Analytics half-day training courses through the year to give you some serious GA skills. Drop an email to to book a place.

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