Any marketer worth their salt knows how critical it is to measure the right performance metrics and to be able to trust that data implicitly. Nowhere is this more true than in digital marketing, where the expectation is for (nearly) flawless measurement across users, sites, campaigns, and devices.
In doing dozens of web analytics audits over the past year alone, Portent’s analytics team continues to see a lot of the same simple yet harmful mistakes again and again. Many of these issues are incredibly easy to fix. In some cases they literally involve nothing more than checking off a well-hidden box in the admin section of your platform.
Hopefully this post saves you the trouble of repeating these mistakes, or at very least helps you spot and fix these common problems in your own analytics before they come back to bite.
8 Common Google Analytics Mistakes
Under each of the listed common Google Analytics mistakes there is an action point, which will help you avoid them going forward.
1. Not using Google Analytics
This first on the list of common Google Analytics mistakes is not using it. For a strategy to be effective, it must be measurable. If it is not measurable, how do you assess whether or not your strategy was successful? Think of the M, in SMART.For Digital Marketing Agency in Hyderabad check Vivid Digital.
Google Analytics provides a simple and effective way to ‘actually count’ a wide range of strategic objectives. Thus, if you are not using Google Analytics, chances are you are not measuring your strategic objectives. If this is true then some doubt must be cast on your strategy.
Define your strategy, set the strategic objectives and measure those objectives with Google Analytics.
2. Using the list of Google Analytics metrics to define your objectives
Google Analytics has a vast array of dimensions and metrics. For the full list have a look here. This information can be compared, contrasted and data mined to death.
The question to ask is, “Is this information useful to my business, website or customers?” If not, you could spend a significant amount of time reporting on statistics that add no value to anyone.
First, decide what information is important to you and then open up Google Analytics so see how you can get that information.
3. Forgetting to filter your data
Unfortunately, the internet is full of robots getting up to no good. This nonsense data produced by these robots skews the real information by adding a whole lot of junk information.
Biased, skewed or tainted data is of no value because you don’t know if it is true. Information is only useful if it is accurate, so you need to filter your data to get what you need.
You can improve the accuracy of your data by setting up your account properly:
A. The first thing to do is create a new “View” which can store your filtered (think cleaned) data. Do not apply any filters to the default “All Website Data View” as Google Analytics will only store the filtered data.
In other words, once you filter data there is no undo button. To avoid this problem, it is good to keep a backup of the unfiltered data in the form of the “All Website Data View”.
B. Practically, click the Settings button in Google Analytics (bottom left, looks like a gear).
C. Select the “Property” that you want to use.
D. Select the “View” dropdown menu and select “Create new view”.
E. Give your new “View” a name, select the correct “Reporting Timezone”.
F. Select “Create View”.
G. Once the “View” is created, select “View Settings” from the main “Settings” page.
H Update the “Currency” displayed and check the box for “Bot Filtering”.
I. Select “Save”.
4. Counting yourself in your data
This is next on the list of common Google Analytics mistakes. One of the easiest ways to skew your Google Analytics data is by counting your website visits or actions. An example will show this best:
A. You visit your website 2 times a day and spend 15 minutes on the website for each visit. Your customers visit your website 20 times today and only spend 1 minute 30 seconds on the website for each visit.
B. If you count all the information, your visits and your customer visits, the data will look like this: 22 website sessions (visits) with an average session duration of 2 minutes 44 seconds.
C. If you count only your customer visits, the data will look like this: 20 website sessions (visits) with an average session duration of 1 minutes 30 seconds.
In this example, by including your own visits to your website, you have increased the average session duration by 1 minute 14 seconds. This results in a skewed belief that people are spending almost twice as much time on the website as they really are.
Filter your IP address. Here is how:
A. Type “What is my IP” into Google search. This will give you a number that looks like this: 196.192.100.01.
B. Click the Settings button in Google Analytics (bottom left, looks like a gear).
C. Under “Account” select “All Filters”.
D. Click the red “+ Add Filter” button.
E. Enter a name for the Filter.
F. For a single IP address
G. Filter Type: Predefined
H. Select Filter Type: Exclude
I. Select Source or Destination: traffic from the IP addresses
J. Select Expression: that contain
K. IP addresses: type your IP address
L. Select the “View” that created in point 3 above
M. Click “Add”
N. Finally click “Save”
O. For a range of IP addresses
P. Filter Type: Custom
Q. Select “Exclude”
R. Filter Field: IP Address
S. Filter Pattern: 196\.192\.100.[1-11] (For IP addresses 196.192.100.01 to 22.214.171.124)
T. Select the “View” that created in point 3 above
U. Click “Add”Finally click “Save”
5. Forgetting to set up goals
At the beginning of this article, we spoke of the importance of having a measurable strategy. This measurement would be defined by a set of objectives or goals. Goals can be: generate 20 leads in a month, have a whitepaper downloaded 100 times or generate R150 000 in online revenue.For more info check SEO Services in London.
Thus, Google Analytics goals enable you to see if your strategic objectives are being met as well as what is driving the effectiveness of these objectives. Without these goals it becomes very difficult to determine what is actually happening on your website or mobile application.
The majority of the Google Analytics reports allow you to filter data based on conversions (goal completions). This useful features enables you to determine the age, interest, location, gender, page, device or other dimension, which was associated with the conversions.
By compiling this information, you are then able to pull an accurate profile of the type of people who are most likely to complete a goal on your website. This information can inform future campaigns or assist you to adjust your existing campaigns to reach the correct target markets.
Set up goals on Google Analytics:
A. Define your goals as per your strategy.
B. Click the Settings button in Google Analytics (bottom left, looks like a gear).
C. Select the “View” dropdown menu and select the “View” that created in point 3 above.
D. Select “Goals” from the “View” menu panel.
E. Click the red “+ New Goal” button.
F. Select a predefined goal from the “Template” menu.
G. Click “Continue”.
H. Give your goal a name under “Goal Description”.
I. Select the “Type” of goal.
J. Click “Continue”.
K. Complete the details for the goal.
L.Under “Value”, click the button to define the ZAR value of a completion (try to calculate the estimated lifetime customer value https://en.wikipedia.org/wiki/Customer_lifetime_value).
It is very important that you add a monetary value to a goal as this enables you to estimate the actual worth of different customer profiles.
M. Finally, click “Save”.
N. Check the “Real-Time > Conversions” report to make sure that your goal is working correctly. If you are using IP filtering (as per point 4), turn off the IP filtering or use another IP address (your mobile phone with Wifi turned off) to ensure that your session appears in the report.
It should be noted that setting up goals and custom events can get quite technical. If you are not sure on the best way to implement a goal, speak to us or try find the relevant online tutorial.
6. Looking at average bounce rates
It is very tempting to look at the “Bounce Rate” in the “Audience Overview” report and use this as a measure of the website’s user engagement, but this one of the most common Google Analytics mistakes.
Unfortunately, this average bounce rate is essentially meaningless. Bounce rates are specific to individual website pages and must be viewed in context to draw meaningful conclusions.
Google Analytics says:
“A bounce is a single-page session on your site… These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Analytics calculate the length of the session.”To know more information on SEO Services visit Agropedia
So a bounce is a single-page session or a visit to your website by a person who only looks at one page. Now is a bounce a bad thing? It depends. On a contact page that only has your phone number and no contact us form, a bounce is perfectly acceptable.
The only thing a person can do on this page is view your number to call you, so “no subsequent hits” are required. However, if your contact us page has a form and you would like people to complete the form, then a bounce is a bad thing. Why?
Because you wanted the person to complete the form, “a hit”, and provide you with their query and contact details. Thus, a bounce rate is only useful for the specific page it relates to.
Use the “Behaviour > Site Content > All Pages” report to view the bounce rates for individual pages. Take a moment, view the actual website page and assess what it is that you are wanting a visitor to do on the page. In the context of this page, what is the bounce rate and how successfully are you engaging your website visitors.
7. Forgetting to look at different device categories
Mobile devices are becoming the primary device for most South Africans. Hootsuite estimates that 92% of adult South Africans currently use a mobile phone, 69% a smartphone and only 20% a laptop or desktop computer. In our own experience, we see more and more websites that have more mobile visits than desktop visits.
In general, a mobile phone visit (session) has a very different profile to a desktop visit. Desktop visits tend to be longer, have a higher page count, a lower bounce rate and higher conversion rates than a mobile visit.
Looking at these visits together, for instance, will lead you to the incorrect conclusion that mobile visits are longer and desktop visits shorter than they are in reality. To see the true data you need to look at the information separately.
Use the “Audience > Mobile > Overview” report to view your website data through the lens of a mobile phone, desktop computer or tablet.
8. Forgetting to check the location of your visitors
Lastly on the list of common Google Analytics mistakes is forgetting to check the location of your visitors. A few years back we spoke to a client who was very excited about the 30 000 visits that they were getting to their website a month. On the surface, this data looked very encouraging.
However, the true picture was not so rosy. This client only provided services in South Africa, but 95% of the website visits were coming from the Middle East. I am not sure how they created such a strong following in a country where they did not operate but they did. By segmenting this data, to only include visits in South Africa, it became clear that our client was only getting 1 500 visits per month.
In another example, have you seen Ashburn USA in your Google Analytics cities? This a web crawler operating out of an Amazon Web Services center in Ashburn. It would be best to exclude this data as it is not traffic from real people visiting your website. This can be done by excluding the location.
Thus, you need to be very aware of who your customers are and where they work or live to draw meaningful information from your Google Analytics data.
Try using a custom segment to view all the Google Analytics data by location. This is how:
A. At the top of any report click “+ Add Segment”.
B. Click the red “+ New Segment” button.
C. Under “Demographics > Location” select “Continent, Sub Continent, Country, Region, or City” as appropriate.
D. Select your matching criteria, we recommend “exactly matches” to avoid any funnies later.
E. Start typing your location in the text field. The exact name will appear in a drop down list as you type.
F. Select the correct location from this list.
G. Click “Save” in the top bar.
H. You will now be able to select your customer segment from the list of segment when you click “+ Add Segment”.
I.When the segment is applied you will be able to compare all the website data (in blue) against your location segment (in orange).
Need some help
Google Analytics has a good Help Centre to help you avoid some of these common Google Analytics mistakes. If you have any questions, this is a great place to start.
If you are wanting to hone your skills even further, try the Google Analytics Individual Qualification. This will certainly help you avoid some of the common Google Analytics mistakes
If you are wanting to talk to a person, give us a call. Our qualified experts will be happy to help you out with some of these common Google Analytics mistakes.