Tracking the performance of a business website is absolutely vital to understand how well the website is or is not generating leads, traffic, and sales. One of the most powerful tools to track the performance of a website is Google Analytics. As of today, there are so many Google Analytics Dimensions and Google Analytics Metrics that it seems to get confusing.

Google Analytics offers a significant amount of information or data on a website with several reports that provide insights into the website’s performance. As Google Analytics continuously improves its reporting abilities, it is essential to track specific metrics to identify weaknesses, strengths, and opportunities of a website.

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What Are Dimensions in Google Analytics?

A Google Analytics Dimension is an information attribute. By investing one’s time to understand the dimensions, one can learn a lot about the customers, along with the language they speak, the place where they are from, the gadget used to visit the website, and so on. 

What Are Metrics in Google Analytics?

Now, let us understand what Google Analytics Metrics is. Google Analytics collates information or data about a website and the traffic that the website generates in real-time. This data is then displayed as a group of reports – each group with widgets that are fixed to track specific Google Analytics Dimensions and Google Analytics Metrics.

What’s the Difference Between Metrics and Dimensions in Google Analytics? 

Google Analytics Dimensions are qualitative data that is non-numerical in nature. One can consider them as “categories.” Google Analytics uses these categories to organize the data it collates. Google Analytics Dimensions comprise the following:

  • Device Category
  • Country
  • Medium
  • Campaign

For example, if a 35-year-old woman in Spain accesses a website through an Instagram ad from on her smartphone, then the dimension attributes are as follows:

  • Gender: Female
  • Age: 35-45
  • Location: Spain
  • Source/Medium: Instagram/paid_social
  • Device Category: Cellphone

Google Analytics Metrics are distinct elements of a specific dimension that can be measured as a ratio or also a sum. They are quantitative measurements of the information or the data that is tracked by Google Analytics. Google Analytics Metrics comprise the following:

  • Sessions
  • Conversion Rate
  • Session Duration
  • Goal Completion

Thus, metrics help to analyze data; however, they are obsolete without the dimensions against which data can be compared.

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10 Most-Tracked Google Analytics Metrics

Following are the 10 Google Analytics Metrics that are absolutely essential to track:

  • Users

Users are individuals that visit a website. There are two categories of users - new and returning. New users are people who have never visited your site before and returning users are the people who have visited your site before.

  • Bounce Rate

If a user visits a website and leaves without any interactions on the site, then additional requests are not sent to the analytics server and such interactions are termed as a bounce. Thus, the bounce rate is the total percentage of all sessions that have bounced on a website.

  • Sessions

Sessions are defined as a group of user interactions that occur with a website within a specified time period. A session is initiated once a user visits a website and is terminated after 30 minutes of the user being inactive, or if the user comes to the website through one campaign, leaves, and then joins through another campaign.

  • Average Session Duration

The average time that a user spends on a single page is termed as average time on page. By observing the average time, one can gauge the user engagement level with the website.

  • Percentage of New Sessions

Percentage of new sessions displays the percentage of the total amount of user sessions received from new users to a website. This Google Analytics Metric aids in tracking two performance goals, which are the retainment level of the users and progress with attracting new visitors to the website.

  • Sessions By Channel

The channel groupings of Google Analytics helps to classify the traffic so that one can track the performance of specific channels, such as paid, organic, social, direct, e-mail, and so on. The number of sessions that are attributed to each channel grouping is termed as sessions by channel.

  • Pages Per Session

The number of pages that a specific user views during a session are termed as pages per session. If one observes an increase in the pages per session, this ideally means that the users are quite engaged with the content being displayed on the website as they are navigating through more pages on the website.

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  • Goal Completions

The goal completions metric are defined as the how many times a website visitor completes a specific goal on a website. 

  • Pageviews, Unique Pageviews 

In Google Analytics Metrics, pageviews refers to the number of views for each page. Pages that are viewed by the same user more than once are also taken into count. The unique pageviews count the number of times a page is viewed in a particular session. 

  • PageViews By Page

PageViews by Page is a metric that displays the total number of pageviews that each page of a website has received within a given timeframe.

  • Source/Medium and Channels

In Google Analytics Metrics, the source refers to where the traffic to a website is being generated from, for example, a search engine, where a user specifies a URL address and goes to a website or chooses it from a bookmark. The medium metric is a general category of how the user visits the website, for example, through non-paid traffic from search engines, ppc, and so on.

Examples of Dimensions in Google Analytics

Following are examples of various categories of Google Analytics Dimensions

  • Source/Medium Dimensions: In simple terms, the source dimension is the name or the origin of the website being referred to, for example, facebook.com, Google, Bing, or Yahoo. The medium dimension is the type of website that is being referred to. Basic traffic mediums comprise cpc, organic, email, referral, and none.
  • Campaign: The campaign dimension either refers to the Google adwords names or the names of the UTM campaigns for custom marketing campaign tracking.
  • Page: The Google Analytics Dimension, Page refers to the URL of the page that is being viewed.
  • Products: The product's dimension is a metric that has product-level scope. For example, product, product SKU, product category, product brand, product list name, transaction ID.
  • Browser: The browser dimension is the viewport size of the browsers of a user. The browser size displays the viewport dimensions in pixels and the formatting is in terms of height * width, for example, 960×1920.
  • Country: Analytics offers several geographical dimensions, such as country, continent, city, and so on.,
  • Language: The language metric refers to the language of the user visiting the website. Google Analytics depends on the user going to the device settings to change to a specific language. 
  • Device Category: This Google Dimension Metric refers to the device from which the user is visiting the website.

Predefined and Custom Dimensions

The standard dimensions available in Google Analytics are termed as predefined dimensions. Predefined Google Analytics Dimensions are used by default while creating reports. Examples of predefined dimensions are age, gender, location and the device category of the website visitors.

Custom Google Analytics Dimensions are defined by the individuals who manage the website, generally with data that is not available by default in Google Analytics. For example, additional data such as CRM data and logged in user phone call data to set up personalized dimensions can be imported from other sources. Custom dimensions offer a better control over the reports and enable one to analyse the values that are important to them. 

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What Are the Limitations of Google Analytics?

Following are some of the stark limitations of Google Analytics:

  1. Recording spam and bot traffic: Not all machines that load a specific website are being operated by humans. There are several bots constantly crawling websites for different reasons. Such bots can twist or skew the data to a large extent. Bot traffic is not good for analysis as bots use websites in a very different manner than human users.
  2. The time spent on a website: The metric time on site has always been difficult to calculate for Google Analytics. When a page is loaded by a user, the time taken for the page to load is sent to Google's servers and once the next page loads, the time is recorded. Google then does a quick calculation to understand the time the user spends on the first page. This works well until the user comes to the final page. Google has no way to track the time the user spends on the final page of a website.
  3. Measuring all the users: Google Analytics functions by loading a snippet of javascript code on the website pages. Once the page loads, the code then sends across a string of data to the Google servers to be processed. However, not all browsers enable the execution of javascript codes.
  4. The need to customize: To measure any interactions occurring on a website, there is a requirement to customize the tracking to suit individual needs as Google Analytics has no idea of what's important to a website owner.
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Conclusion

Google Analytics offers a significant amount of information for the use of digital marketers to use, but the most essential step that marketers have to take is to set up suitable goals for their website. As more businesses go online, there will be no dearth of websites competing for the customers' attention. Thus, the importance of Google Analytics is set to increase in the universe of Search Engine Optimization (SEO). Enroll in Simplilearn's PG in Digital Marketing course which will provide you with right skills and in depth knowledge of digital tools and techniques to kickstart your digital marketing carrer.

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Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

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