Google Analytics Reports consist of metrics and dimensions.
Analytics Metrics are numbers that are used to measure characteristics of dimensions. For example, the characteristics of the Source / Medium dimension may include:
Usually, metrics appear in reports as columns.
We’ll go through the definitions of the basic analytics metrics including:
A page view happens when a user visited one of the web pages on your website. When this user continue visiting a second web page of your website, then the page view count becomes 2. Your website’s total page views can be calculated by adding the page view count of all the users.
The similar term to page view is unique page view. For example, user A visits your website’s page 1, and closes his/her web browser. Then this user repeated this same action 4 more times all within 30 minutes. Note, the user has always only visited the same web page over and over. He/she generated 5 page views through his/her visits, but the unique page view count stays at 1.
|User||PV Page 1||PV Page 2||Unique PV|
A user visits your website, regardless of how many pages he/she has viewed, and the session count is 1.
We’ll go through some examples to demonstrate how sessions are counted. Let’s consider the first scenario.
Let’s consider the second scenario.
Note, sessions are sometimes also known as visits.
Unique User – Google Analytics used to call this metric Unique Visitor in the reports.
When a user visits your website for the first time and views one of your web pages, the web analytics tool (installed on your website) sets a new cookie on the user’s web browser (e.g. Chrome). The unique user count is 1.
Technically, unique users in Google Analytics (or most of any other web analytics tools / reporting) is the count of all the unique cookie ids within a given time period.
Several hours later this user visits your website again (through Chrome). The web analytics tool remembers this user from the cookie that was set in the first place. The unique user count is still 1.
Still on the same day, the user visits your website through a different web browser (e.g. Firefox). The web analytics tool finds no previous cookie has been set on Firefox, and counts this user as a unique user.
Now your web analytics reports will show 2 unique users, even though it is the same user who has visited your website multiple times on the same day but through different web browsers.
In analytics reporting, Unique users (unique visitors) have to be associated with a time period, i.e. monthly unique visitors, weekly unique visitors, daily unique visitors.
For example, consider the case below:
|Time Period||Who Visited Your Site|
|Month 1, Week 1, Day 1||Gordon, Brian, Adrian|
|Month 1, Week 1, Day 2||Gordon, Brian, Tom|
|Month 1, Week 1, Day 3||Gordon, Tom, Steve|
|Month 1, Week 2, Day 1||Gordon, Brian, Tom, Steve|
|Month 1, Week 3, Day 1||Gordon, Adrian, Brian, Tom, Steve, Lisa|
Let’s count the number of daily unique visitors:
Count the number of weekly unique visitors:
Count the number of monthly unique visitors:
When a user visits your website for the first time and views one of your web pages, the web analytics tool (installed on your website) sets a new cookie on the user’s web browser (e.g. Chrome). The unique user count is 1, and the new user count is 1.
Several hours later this user visits your website again (through Chrome). The web analytics tool remembers this user from the cookie that was set in the first place. The unique user count is still 1, and the new user count is still 1.
Now a second user visits your website for the first time. Your web analytics tool sets a new cookie on the user’s web browser (e.g. Firefox). This second user contributes toward a new user.
Now the total new user count (of your website) is 2.
When a user visits your website for the first time and views one of your web pages, the web analytics tool (installed on your website) sets a new cookie on the user’s web browser (e.g. Chrome). The unique user count is 1, and the returning user count is 0.
Several hours later this user visits your website again (through Chrome). The web analytics tool remembers this user from the cookie that was set in the first place. The unique user count is still 1, and the returning user count becomes 1.
Page Views / Sessions is a ratio. Let’s demonstrate how we normally use this ratio.
For example, an ecommerce website’s page views / sessions on any normal day is between 8 and 9 (e.g. 8.2). This range should become a benchmark for this ecommerce website’s user behavior.
After making considerable changes on the website i.e. by cutting down the steps in the purchase funnel for users, on the next day the page views / sessions ratio has become 6.7. When the ratio goes up or goes down more than 1.0, it isn’t considered normal. But as long as you understand the reasons behind the ratio change, it is acceptable.
Another example is yesterday the page views / sessions ratio decreases considerably (e.g. from 6.7 to 5.0, i.e. the change is larger than 1.0 again) when they were no critical changes on your website. You may want to look into each major traffic sources’ page views / sessions. You may find one suspicious traffic source (e.g. advertising channel X). The day before yesterday, its page views / sessions was 6.0, but all of a sudden yesterday its ratio becomes 1.5. Now you can suspect yesterday advertising channel X has sent your website garbage (or bot) traffic.
Let’s explain time on site with an example.
A user visits page A at 21:10:00. He/she visits page B at 21:10:20, and then visits page C at 21:10:50. The user has no further action on your website.
This user’s time on page A is calculated by 21:10:20 – 21:10:00 = 20 seconds.
The user’s time on page B is 21:10:50 – 21:10:20 = 30 seconds.
But the user’s time on page C is unknown. The exact time when the user exits your website altogether (by closing the web browser) is not recorded by your web analytics tool.
The time on site is calculated by time on page A + time of page B = 50 seconds.
The average session duration (avg. session duration) is calculated as:
Avg. Session Duration = Time on Site / Sessions
Assume your site has 3 sessions, each lasted for (i.e. time on site) 3 minutes, 4 minutes and 5 minutes – which is a total of 12 minutes. Then the avg. session duration is 12 / 3 = 4 minutes.
Let’s explain bounce and bounce rate with an example.
A bounce happens when a user lands on one of your web pages without any subsequent actions, and then leaves your website (by closing his web browser). Now the bounce count is 1, and session count is 1.
A second users lands on one of your web pages, clicks to open another page on your website. Then he/she leaves your website altogether. This user’s bounce count is 0, and session count is 1.
Now your website’s bounce count is 1, and session count is 2. Bounce rate is calculated by bounces over sessions.
Bounce Rate = (Bounces / Sessions) x 100%
So bounce rate is 50%.
A conversion is an action that is performed by users on your website.
For example, on a website, registrations can be defined as conversions. Your web analytics tool can be configured to track users’ registrations. Your website provides a form for users to fill in their personal details such as name and email address. The user completes and submits the form. He/she is taken to the next page which says “Registration Completes”, and now the registration count is 1.
In Google Analytics, you can track a goal conversion by setting up Goal Tracking.
Dimensions are the attributes of users to your website. For example:
When a user visits your mobile website, he /she may have the attributes and values below:
Usually, dimensions show up in reports as rows.
We’ll go through the definitions of the basic analytics dimensions including:
A page (or web page) is usually the smallest unit of dimension in web analytics. A page is identified as a URL (or web address).
For example, the homepage of domain example.com usually is:
www.example.com/ or www.example.com/index.php
For example, the category page can be:
www.example.com/fruits/ or www.example.com/fruits/index.php
The metric page views can be associated with a page. e.g. Web page “A” has 30 page views yesterday.
A landing page is a web page. For a user’s session to your website, a landing page is the entry point for that user, and can be associated with a traffic source (e.g. direct traffic, or Google organic search, or etc).
The metric sessions can be associated with a landing page. e.g. Landing page “A” (m.example.com/fruits/) has 50 sessions yesterday from all traffic sources.
The metric sessions can be associated with a landing page by traffic source. e.g. Landing page “B” (m.example.com/oranges/) has 20 sessions yesterday from direct traffic.
Within all types of landing pages, one type is called a squeeze page. The main objective of a squeeze page is to convert a user, i.e. Make users perform a specific action on the landing page (squeeze page). The action may include signing up a new account, subscribing to a newsletter, or any other actions.
An exit page is the very last page a user visits before he/she leaves your website (by closing the web browser).
A traffic channel is a group of multiple traffic sources all in the same category. The major (and most commonly used) traffic channels are:
Traffic sources are the sub-categories of traffic channels. Let’s demonstrate with the examples below.
Campaigns are usually used on advertising as a sub-level dimension. For example, under Google paid search you may name a campaign “brand-keywords” and name another campaign “generic-keywords”. Another example is Facebook advertising in which you may name a campaign “college-students”.
You can find out how Campaign Tracking works and how it can be setup in Google Analytics.
Keyword data is usually recorded in your web analytics tools when a user visited your website from a search engine.
Users can come through organic search or paid search. When you have property “tagged” your paid search URLs with keyword data, your keyword data will appear in your web analytics reports for those users who come through paid search.
Users who come through organic search (especially Google or Bing), your web analytics tools may not show any keyword data. This does not mean the users didn’t search with any keywords on Google or Bing. Search engines such as Google and Bing in most cases serve search results pages in https protocol, which have restricted the keyword data being passed through to most web analytics tools.
For your reports to make any sense, a metric has to be assigned to a dimension. For example, within a specified date range:
You can create custom dimensions and custom metrics and have them showed up in Google Analytics reports.
Content on Gordon Choi’s Analytics Book is licensed under the CC Attribution-Noncommercial 4.0 International license.