What is the Difference Between Dimension and Metric in Google Analytics
Let’s settle this Google Analytics dimension VS metric debate — once and for all. What is the difference between metric and dimension in Google Analytics, what is a “dimension” in Google Analytics, and incontestably — what is a “metric”?
Which dimensions are present by default and which can be added as secondary, which metric types exist and how are these terms related. Let’s find out in our new article!
Difference Between a Dimension and a Metric in Google Analytics
So, where do you usually meet both dimensions and metrics in Google Analytics? The answer is — everywhere. These two terms go hand in hand and inhibit literally every dashboard, every table and every Google Analytics report that you can imagine.
On top of having default dimensions and metrics provided by Google Analytics, you are also presented with an opportunity to create custom descriptions of your own. Now, knowing that dimensions and metrics are universally irreplaceable when it comes to data analytics, let’s find out what they are and how are they related.
In general, the term dimension is used to describe a measurement of something in a particular direction like height, width or length. A term metric is used to symbolize a system for measuring something, be it litres, inches, centimetres or something else.
And here’s the deal — the difference between metric and dimension in Google Analytics is that while they both describe data, they do so in different ways. Dimensions describe data qualitatively, meaning they use words and characters. Dimensions are used to showcase the quality of your items, say purchase statuses like “sold” and “on hold”, office departments, colour options, user segments or cities to identify which data category are we talking about.
Now metrics in Google Analytics are used to describe data quantitatively — with numbers. Metrics describe how many conversions, clicks, page views — you name it — has a page acquired, how much money did each office earn during the last quarter, or how many items have been sold online and in-store.
In the next paragraph, we are looking into what is a “dimension” in Google Analytics and what is a “metric” more specifically, and if you’d like to know more about data analysis differences, check out a killer article right here — Qualitative and Quantitative Analysis: Main Differences.
What is a Dimension in Google Analytics
As you have found out from the previous section, dimension is a descriptive characteristic or attribute of your data. Dimensions show us where, what, why, with whom and how do your observed items operate, giving you the ability to describe them in any way you find possible.
Exit Page, Screens, Browser, Campaign, Landing Page, City — are all examples of default dimensions that are pre-made and available in Google Analytics to you. Dimensions arise in all of your reports, but different dimensions are applied to different types of data, so don’t expect the same dimensions set for each chart or table. Employ dimensions to arrange, segment, and structure your data.
Google Analytics also allows you to create custom and secondary dimensions that carry additional types of data you receive through Data Import, Analytics API or the tracking code.
What is a Metric in Google Analytics
Now the metrics in Google Analytics provide numeric value to those dimensions you’ve read about earlier. Metric is the number that stands right to the Country, Browser, or any other dimension you’ve chosen to review with a table.
Answering your what is a “metric” in Google Analytics? question, metrics in Google Analytics are quantitative measurements of your data. Metrics in Google Analytics can be displayed as sums (total amounts) or ratios (proportions to the whole).
Pages per Session, Screenviews and Average Session Duration are all common examples of metrics in Google Analytics. And since you clearly love data the way we do, check out this latest article of ours on a similar topic — What is the Difference Between Source and Medium in Google Analytics.
Knowing how to use the right terminology within the data analytics space gives you a great opportunity to dig deeper into the meaning of things, avoid technical mistakes and simply shine a ray of knowledge when an opportunity presents itself pretty 😉
Read our next articles on Google Analytics so you are even more data-driven than you were before reading this article:
Originally published at https://insightwhale.com on August 21, 2020.