My name is Gordon Choi. I’ve practically reviewed and/or implemented 100+ Google Analytics accounts for different websites. I’ve written a Google Analytics Book, “Gordon Choi’s Analytics Book”, which is mainly about how to use Google Analytics and its technical setup. I’m sharing the entire book’s content on my website.
Have fun learning Google Analytics!
As the most commonly used web analytics tool, we often hear people asked “How to use Google Analytics for websites?”
To further break it down, these are the questions:
Hopefully, the above questions can be answered by this Google Analytics Book.
Define the strategies on how you want the user behavior data of your website to be aggregated or segmented. Your setup strategies decide how you want the data to appear in the reports.
Google Analytics has a 3-level structure with Account, Property, and View.
An example is when you’re using multiple sub-domains, then you’ll set up in multiple Properties:
The second example is when you have uk.example.com, de.example.com, jp.example.com, and kr.example.com where each sub-domain represents a country-specific site. You’ll set up a Property for each country-specific site.
But how can you aggregate all the country-specific data? You can do it by applying multiple levels of Properties.
Each Property has a unique ID in the format of UA-XXXXXXXX-Y, and comes with its own Google Analytics Tracking Code (GATC). So with the second example’s 4 country-specific site, they’ll have Property IDs like this:
Now create a 5th Property which will be the “umbrella” level of all 4 country-specific Properties. Place the GATC of the 5th Property on all pages of all 4 sites, and place each of the 1st to 4th Properties on all the pages of their own sites.
The one major magical and powerful feature in Google Analytics is the Custom Reports. All the standard reports that are available in Google Analytics are great in which they show you the high level trending of each section (i.e. By country, by traffic source, by device, etc).
But you’ll always need more than that, and in most cases you’ll have to drill down the data with more granularity. i.e. You’ll have to segment, segment, and segment!
Google Analytics Custom Reports enable you to segment your data, and build new reports (that aren’t available to you).
There are more that can complement the Custom Reports.
Sometimes the standard dimensions and metrics provided by Google Analytics by default aren’t enough for your reporting or data segmentation. To compensate that, Google Analytics lets you create your own Custom Dimensions, Custom Metrics, and Calculated Metrics.
When you’ve created a new custom dimension, it will appear as a new second dimension in Google Analytics reports. This enables you the power to customize how you want to segment the data in your reports.
You can select two or more standard metrics from Google Analytics, and put them into a formula (with rules). A new “calculated metric” will be created based on your selected metrics and rules. In any custom reports you create, you can include your Calculated Metrics.
Google Analytics free version isn’t without any problem.
The first problem is data sampling in segmented data reports. Google Analytics starts displaying sampled data in reports when the data required for the specific report has exceeded the Property’s data size limit.
When data sampling happens, your reports start losing accuracy in detailed data.
An example is when there were 1,000,000 sessions in your selected date range for a report that was requested. Google Analytics would take only 100,000 sessions (10.00% of sessions) to calculate your report metrics. This will then be multiplied by 10 to achieve the totals which are the numbers that will be displayed in the reports.
So 90% data (or in many cases more than 90%) of the report is estimated numbers. That’s why you cannot expect too much accuracy from Google Analytics reports (especially segmented reports).
Experienced web analysts use Google Analytics reports when they require high level trending insights which are much more useful for online marketing strategy’s decision making.
The second problem is self referral data.
When a user came to your website (e.g. example.com) from a search engine (i.e. Google), then the traffic source should report “google / organic” which means Google Organic Search.
When the traffic source reports as “example.com”, then you know this data has been incorrectly accredited to “self referral”.
Self Referrals are mainly caused by client-side redirects or untagged web pages.
Many times an old page (URL) is replaced by a new page with a new URL, URL redirection on your website has to be implemented. One method for implementing URL redirect is by using client-side redirect. Client-side redirect doesn’t “pass” the original referral name (i.e. traffic source) to the next page. This causes Google Analytics to mistakenly pick up your website’s domain (i.e. example.com) as the referral, and then report it as the traffic source.
Untagged web pages mean when Google Analytics Tracking Codes are missing or misplaced on some of your web pages.
Gordon Choi has also written two other books:
Here are other resources (or guides) that was written by Gordon Choi:
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