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Google Analytics has long been on the lips of everyone who works with the web. The fact is that, precisely because it is a tool almost taken for granted, many still limit themselves to wandering around among some reports, picking up some interesting signals from time to time, by merely observing what emerges from the water more than noticing the iceberg beneath the surface.

If you’ve always wanted to understand Google, deeper Analytics, When…, today I would like to give you some concrete ideas.

A note before starting: the data you find in the screenshots are related to the Google Analytics demo account. A fully configured statement, made available to all users by Google and associated with the – genuine – data of the Google Merchandise Store. It is an excellent starting point for those wishing to replicate what we will see in the subsequent lines.

The Topic Of This Post

  • 1 Dimension and metrics
  • 2 Primary dimensions
  • 3 Secondary dimensions
  • 4 Deep in the reports
  • 5 Custom dimensions and metrics
  • 6 The problem has no data

Dimensions and metrics

Everything you see in Google Analytics is made up of dimensions and metrics. Understanding their role will help us juggle different and valuable features hidden in plain sight.

Dimensions are attributes of the data. For example, the “Page” dimension shows on the table that the URL stands for Uniform Resource Locator. Colloquially called a web address refers to a web resource such as a site, page, or file of the page. The “City” can report “Turin” and so on. 

Metrics, on the opposite palm, are quantitative measurements. Numbers, to put it simply. The “pages/session” metric reports the average number of pages viewed for each user session. The metrics are shown in the columns.

If you seem almost, you will discern that the columns in many reports are now divided into three blocks: metrics related to the acquisitionbehavior, and In web marketing, conversion means when a user takes a specific – measurable – action that is important to your business. Examples are access to the site, the visit. Also, in this case, Analytics refers in a broader sense to the inbound path.

Because dimensions and metrics together define a context. By crossing the measurements (inline) with the respective metrics (in the column), we can obtain answers to specific questions. On the other hand, knowing how much the bounce rate is, as a number in itself, without relating it to the page on which it occurs (or the user’s source, or the type of new / returning user) would make little sense, do not you think?

Primary dimensions

We are used to browsing in Google Analytics, switching from one report to another, without even paying attention to it. However, it is essential to focus on one aspect: each message has its primary dimension, shown at the top of the table.

For example, the report “Acquisition”> “All traffic”> “Source / Medium” reports as main dimension just “Source / Medium”. So far, so good.

In conjunction with the main dimension, a report can, in some cases, show the ability to switch to another central dimension. In the case of our example, we can choose “Source,” “Medium,” “Keyword,” or any other of the dimensions present on the platform by clicking on “Other.”

Secondary dimensions

One of the most exciting opportunities in Google Analytics is the use of secondary dimensions. Put; you can add an extra dimension to almost any report to break down the data provided by the central dimension. Let’s take an example?

In the report “Public”> “Mobile device”> “Overview,” I usually find the overview (in fact) of the categories of devices that users have used to access the site. This report shows “desktop,” “mobile,” and “tablet.”

To add a secondary dimension, all that is needed is to click on “Secondary dimension” in the table header and choose from a vast list of what I find interesting for my analysis. Let’s say my choice falls on the secondary dimension “Source.”

By applying the new dimension to the report, the table will extend and, together with the device category, I will also find the specific traffic source. By sorting this table by “Users” in descending order, at this point, I will be able to see how the highest number of users is coming in from a desktop device, from the “google” source, followed closely by users coming in from mobile, always coming from “google.” “

Choosing the right secondary size is not at all obvious. It all depends on the question we have in mind – because traffic data analysis always comes from a specific question – and sometimes, it is possible to get the same answer through multiple reports.

It is interesting to underline that the use of secondary dimensions goes well with the advanced search filter of the table. Suppose usually, “searching” on the table through the box visible at the top right; that filter on the main dimension. In that case, when we add a secondary dimension, we can also filter the latter, maintaining absolute control over the information to be shown in the table.

Deep in the reports

Of course, for all those reports that allow clicking on one of the items listed in the line – for example, the “Acquisition”> “All traffic”> “Source” report. I can click the “google / organic” source/medium – You can further restrict the amount of data shown. And apply at this level, let’s say “deeper” my secondary dimension, with or without a filter.

Custom dimensions and metrics

It is worth mentioning an aspect that is advanced in itself but removes some doubts for those who begin. Even if Analytics is a complete tool for analyzing the traffic data of large and small projects, as is done in all those cases where we need to obtain precise information that does not match a dimension or metric already present on the platform. ?

Well, the question-answer is very simple:  you can create custom dimensions and metrics.

For now, we will only mention it, saying that this requires putting both hands-on Google Analytics and the site code, defining specifically methods and objectives for the collection of this tailored information.

Custom dimensions and metrics come in quite handy when you have a CRM.CRM (Customer Relationship Management) typically refers to software for managing relationships with customers and prospects. CRM stands for Customer … or CMSA Content Management System, often abbreviated to CMS, is software that helps users create, manage and modify the contents of a site … from which we want to collect information on the user’s use of our content.

For example, you may want to know how many times a user on your site has interacted with a product configurator, coming to compose a price. Price is the amount of money required for a product or service. In a broad sense, the price is the sum of all final amounts equal to or greater than n-hundreds of euros. With due care, you could work with your web analyst and developer on the best strategy to collect this insurance information and pass it to Google Analytics through customized dimensions and metrics.

The problem has no data.

As I like to say, in web analysis, the problem does not have harmful data: the problem is not having data. Proceed blindly. Or you are trusting your belly, which isn’t exactly the best. Today, Google Analytics represents the most complete (and at hand) tool to approach more aware data analysis.

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