I recently discovered six Google Analytics tips that have completely transformed the way I use the software. I had only recently started using Google Analytics on a casual basis.
I’d log in, look around, and then leave with only a hazy understanding of what was going on with our traffic. To put it another way, I was only scratching the surface of what was possible.
We recently completed an all-day intensive Google Analytics training session, during which I was shown a plethora of features that I was not using and was unaware of.
Prior to the training, I was primarily using Google Analytics to monitor daily and weekly blog traffic trends. However, the features I’m about to show you enable me to see:
Detailed visitor demographic data, as well as how the behavior of specific types of users can be compared to “typical” visitors
- How our traffic relates to our objectives and how visitors progress through our goal funnels
- Our conversion paths, as well as the (often winding) path that visitors take from the first action to conversion
- Unusual traffic patterns and the underlying causes of abrupt changes
- How do current traffic trends compare to previous periods?
That knowledge will be passed on to you in this post! Here are six Google Analytics tips you should be aware of right now.
When creating custom visitor segments, use audience data.
Hopefully, you’re already using custom segments to categorize your visitors based on demographic information like age, gender, and location.
However, if you don’t use the data in the Audience reporting views to create custom visitor segments, you’re missing out on a wealth of information about how specific users interact with your site.
Needless to say, until recently, I had no idea I could do this, which is why the first of my Google Analytics tips is my personal favorite.
To begin, navigate to the Audience reporting section’s Interests Overview (Audience > Interests > Overview). As shown in the figure below, this will give you a broad overview of the other three Interests reports: Affinity Categories, In-Market Segments, and Other Categories.
Based on this data, we know that nearly 8% of all visitors across all sessions are classified as “technophiles” – people who have a strong interest in technology. We can also see that nearly 5% of In-Market Segment visitors work or are interested in Financial/Investment Services.
We’re getting a better idea of who our typical visitor is, but we’ll dig deeper before creating our custom segment. Next, we’ll look at Age and Gender data by selecting the following reports from the Audience reports’ Demographics section (Audience > Demographics)
This data indicates that the majority of our visitors are between the ages of 25 and 34, and the graph below shows that men visit our site far more than women (not surprising, given the gender bias in search)
So, after some investigation, we discovered that many of our visitors are:
- 25 to 34 years old
- I am very interested in technology.
- Work in the financial or investment services industry
How to Create Google Analytics Custom Segments
With this information, we can create a custom visitor segment to track against goals (more on this shortly). To accomplish this, return to Audience reporting and select the downward-facing chevron to the left of “All Sessions”:
Then, using the demographic data from above, we’ll create our custom segment. For this example, we’ll also include information from the Other Category report within Interests Overview, which in our case was Arts & Entertainment/TV & Video/Online Video:
All that remains is to name and save your custom segment. You can also try out this advanced custom segment to see how many visitors fall within these parameters.
Rather than being limited to a broad overview of all pageviews or sessions, this segment can now be compared to other visitor traffic to gain insight into how different types of visitors behave in comparison to one another.
This process can take a while (or even fail outright) depending on your sampling size, specified date range, and the number of advanced segments you’re already using, so you may need to go back and make some adjustments before your custom segment saves correctly.
BONUS GOOGLE ANALYTICS TIP: After you’ve created your custom segment, you can refine it further by including the date of these visitors’ first session within a specific date range.
This cool feature was only introduced a month or so ago, and it allows you to fine-tune how you track specific visitors. This can be extremely beneficial for remarketing campaigns.
Put a monetary value on goals
My second Google Analytics tip is about goals. You should definitely use Google Analytics to set goals. If you aren’t, you’re essentially looking at meaningless metrics like pageviews and time on site. However, you should not only set goals; you should also assign a monetary value to them.
Consider the following Goal Flow report from WordStream’s Google Analytics account.
As you can see, it is fairly simple. We get the majority of our traffic for this goal from Google, with strong direct traffic coming in as our second-largest source.
If you’re new to Goal Flow reports, the red areas to the right of the second and third stages of the goal funnel steps are known as “funnel exits” – visitors who failed to perform the action we want them to at a given stage, which in this case is to sign up for a free trial.
So, why should you give your Goal Flow a monetary value? Because it’s impossible to know how much money you’re losing on each lost lead that exits the funnel until you assign a monetary value to your goal flow.
Have you ever noticed the “Page Value” metric on your traffic report? This is where you can see a page’s financial value in relation to its goal value and the position it holds in your conversion paths. These values will be zero if you haven’t assigned a value to your goals.
Assume you give a goal a value of $25. It’s important to note that the values displayed in this column will not simply be $25 or $0 – the page’s role in assisted conversions is also weighted in the Page Value column, so these values will vary depending on the page and its role in one or more conversion paths.
How to Assign Goal Values in Google Analytics
To assign a value to a goal, go to the Google Analytics Admin section (accessed via the top menu) and select “Goals”:
You’ll now see a list of your objectives. When you select one, an interface will appear in which you can specify the monetary value you want to assign to the goal.
The precise value of a goal will vary depending on several factors, but it is generally recommended to undervalue the goal’s worth. Stick to a lowball estimate until you have a better idea of what each conversion is worth to you financially.
You can begin to see (in real financial terms) how much money you could be losing with your current goal flow by estimating the value of a lead and assigning this value to each goal. This may prompt you to consider whether your goal funnel is sufficiently optimized.
Do you need to take any additional steps? Allow users to easily return to previous sections of the funnel with additional navigation. Remove a simple thing that’s hurting your conversion rate. All of these questions may arise if each goal is assigned a monetary value.
Investigate Your Top Conversion Paths
Unfortunately, visitors to your website do not always behave as you would like. Wouldn’t it be great if prospective customers saw your ads, visited your website, and then made a purchase all in one sitting?
That rarely happens, which is why understanding your conversion paths is critical – especially in today’s advertising landscape, where people rarely complete a purchase on a single device, let alone in a single session.
Examining your top conversion paths in Google Analytics provides an intriguing glimpse into user behavior – and the often-complicated route many visitors take from the first action to the ultimate conversion.
Conversion Path Analysis in Google Analytics
Go to the Top Conversion Paths section of the Conversions reports (Conversions > Multi-Channel Funnels > Top Conversion Paths) to see these visitor journeys. By default, you’ll see the top 10 conversion paths, with the option to expand the number of rows displayed.
The most effective conversion paths in this example are fairly standard (two direct visits, an organic search leading to a display ad, three direct visits, etc.), but some other conversion paths are a little more unconventional. Two display advertisements? A direct search that results in a display ad? Two natural searches?
You can also display the top conversion paths by MCF Channel Grouping Path and map these results against the Keyword (Or Source/Medium) Path, which can reveal additional information about how each of your channels is performing:
Create Intelligence Events
It’s critical to monitor your site’s performance on a regular basis, but chances are you won’t notice significant differences from one day to the next. But what about those strange anomalies that make you look twice? Those huge traffic spikes (or drops) that defy explanation? This is when Intelligence Events enter the picture.
Google Analytics Intelligence Events is a feature that allows you to set custom parameters to monitor for unusual site activity and send alerts to designated account managers.
A 200 percent increase in traffic on a given day, for example, would be considered unusual, and Google Analytics would record the data surrounding this event and notify you of it.
You might think that monitoring your usual metrics would reveal such a significant increase in traffic to your website, but this may not be the case. Let’s look at a recent example that we came across.
On April 19, we received an Intelligence Event alert indicating a 216 percent increase in traffic to a specific page. The alert also provided information about the source of much of the traffic (in this case, California), as well as the metric associated with it (one of our conversion goals).
Shouldn’t this kind of spike stick out like a sore thumb? That’s what we assumed. We were, however, mistaken.
As shown in the graph above, overall traffic for that day appeared to be low – nothing out of the ordinary for a Saturday, when our traffic tends to be lower than on weekdays.
We would not have known that we received more than 200 percent more traffic to that page if we had relied on a cursory glance at our Pageviews report, and this spike would have gone completely unnoticed.
How to Configure Google Analytics Custom Intelligence Event Notifications
Google Analytics will notify you of unusual site activity, such as the type of anomaly in the example above, by default. However, don’t rely on Google to notify you when something significant occurs. Create your own Intelligence Events instead.
To begin, navigate to the Intelligence Events reporting section and select the “Custom Alerts” tab from the right-hand menu. Then click on “Manage Custom Alerts.”
Then, click the red “+ New Alert” button. This will bring up the interface where you can create your own Intelligence Events.
In this section, you’ll define a set of parameters that will trigger custom Intelligence Events notifications. As you can see, you can customize the views to which the alert conditions apply, the time period, and how notifications are delivered – either via email (to one or more specified addresses) or via email and SMS text alerts (note that SMS alerts are only available to Google Analytics users with United States-based cell numbers).
Examine Previous Traffic Trends
Many Google Analytics users are only concerned with current traffic trends; however, identifying patterns based on previous traffic can provide valuable insights into how traffic changes over time. Using the Compare to Previous Period tool in the date range dialog box is one of the best ways to investigate this data:
Once you’ve specified the desired date range (as well as the previous period to compare it to), you can use this filter to see how your traffic compares from one time period to the next – in this case, April 12 to May 12, and March 12 to April 11 (the previous period):
Notice how the valleys of one plotted line (April 12-May 12 in blue) correspond to the peaks of the other (March 12-April 11 in orange) and vice versa? This is due to differences in the days of the week specified when comparing recent traffic data to data from a previous date range.
How to Customize Date Ranges in Google Analytics
For example, if you applied this filter to a weekly view, you might expect a Monday-Sunday week view to exactly mirror the previous period – but it doesn’t. Instead of the previous week’s corresponding days, Google Analytics defaults to the number of days in the specified period. Let’s see how this works in practice:
The first time span is from Monday, May 5, to Friday, May 9. This covers the entire five-day business week for the specified time period. However, when we select “Compare to Previous Period,” Google Analytics pulls data from the five-day period immediately preceding the first date range rather than the previous five-day work week, resulting in the (misleading) graph below:
However, if we specify a custom date range (with the days of the week perfectly aligned), we’ll see that the two graphs are nearly identical. You must enter the desired date range manually in the relevant fields, rather than clicking on a start day and letting Analytics fill in the blanks:
As you can see, there is still some minor variation in the traffic – nothing out of the ordinary, and certainly nothing like the significant variation seen in the misleading graph above.
Include Annotations in Your Reports
The last (but certainly not least) of my Google Analytics tips is about good housekeeping. Perhaps you aren’t the only one in charge of monitoring your Google Analytics account.
If this is the case, you’ll need a way to keep track of what happened and when. Was a media mention to blame for the massive increase in traffic? Is it possible that the slump coincided with a less-than-successful email campaign? Annotations can help you remember anything in Analytics.
Annotations are simple notes that can be added to an Analytics reporting graph to explain traffic increases or decreases, notify other account managers of promotional campaigns that launched on a specific day, and pretty much anything else you want to make a note of directly within Analytics.
Annotations are represented by speech bubble icons at the bottom of an Analytics graph. Notice how many of the peaks in our Pageviews graph have annotations in the figure above.
How to Use Google Analytics Annotations
Simply click on the downward arrow tab icon immediately beneath the graph to read the annotations. You’ll see a list of all annotations made during the specified time period, as well as who made the annotation and their email address.
You can control who sees what by setting annotations to “Public” or “Private.” To make a new annotation, simply click “Create new annotation” on the right, above the email addresses of existing annotation authors:
Whew! That concludes our top six list of Google Analytics tips. If you haven’t already, I strongly advise you to use these techniques to gain a better understanding of your traffic, conversion funnel, and visitor behavior on your site.