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Dimensions 101: Understanding & Utilizing User Characteristics & Attributes in Analytics

There are two types of data in most website analytics solutions – metrics and dimensions.

Most “digital” professionals are familiar with and report on metrics (such as unique visits and pageviews), but those that take advantage of “dimensions” can obtain a far more detailed and nuanced understanding of their audience as well as the trends that may be influencing them.  In the long run, this can prove far more useful to enterprises than using metrics alone.

While the ultimate aim herein is to help Web professionals more fully understand and utilize the user data being reported in their analytics solution (editor’s note: the term dimension may differ between systems), it is best to simply start at the beginning with a broad definition, as well as an example, for those just getting started.

DEFINING DIMENSIONS

Dimensions are essentially attributes of website visitor data. Let’s say, for example, that a woman between 35 and 44 years old from Houston (using an Internet Explorer browser that runs Windows on a desktop device), selected an organic listing on the Bing search results, which she found by searching for a particular keyword phrase such as “healthy meals.” From there, she arrived on a website’s homepage.

This information, all of it (gender, source/medium, city, age browser, device, operating system, keyword and landing page) is routinely reported in website analytics solutions and can provide much needed context to various website metrics (such as sessions, pages per session, bounce rate, goal conversion rate, etc.) or any quantitative measurement being gathered. Ultimately, that is what dimensions provide – context.  

THE SCOPE OF DIMENSIONS

Think of dimensions as characteristics or attributes of website visitors and metrics as the behavior of those website visitors. If marketers can look at each of these data points simultaneously (or layered upon one another), however, the insights gathered can be far more valuable than exploring them singularly. Say for instance that an enterprise’s marketing department is interested in uncovering why users in one age group are outperforming others in relation to engagement metrics. One of the best ways to accomplish this is with the help of dimensions.

The manner in which both metrics and dimensions are collected, processed, reported (and even analyzed) are very different (the latter, for example, being done by cookies in most instances).

The end result, at least in the case of most analytics solutions (the popular Google Analytics included) is that not every metric can be combined with every dimension because each dimension and metric has what is known as a scope.

A scope is a characteristic of each dimension and metric, and each dimension or metric can only have one scope. In the case of Google Analytics, data is organized into four scope types: user data, session data, hit data and product data (e-commerce). Scope, and its relationship to both dimensions and metrics, can be somewhat confusing to understand for newcomers.

Learn more about why scope is important and its opportunities and limitations at wsm.co/scope411.  
 

MAKING USE OF DIMENSIONS

Most analytics solutions provide some default (predefined) dimensions to help their users explore and understand more about who their users are in the context of their digital behavior and the goals of the business. These predefined dimensions are available in various (but not all) reports and are ready to use after a sufficient amount of data has been collected.

While underutilized by most responsible for analytics initiatives, they are capable of providing an immense amount of data; marketers just need to start using what is available.

There are instances when the available predefined dimensions do not provide the information that is needed or required however. In this scenario, being able to create a custom dimension is the answer. Custom dimensions make it possible to combine analytics data with non-analytics data. It is possible, for example, to store the gender of signed-in users in a customer relationship management (CRM) system, and combine this data with analytics data to see pageviews by gender.

THE NEXT-LEVEL OF ANALYTICS

While this has been but a brief overview, realize that the use of dimensions can take digital analytics efforts to the next level – a level that can provide practical insight into the performance of not just a website but of a brand’s broader marketing and advertising initiatives. Use the available data thoughtfully and strategically and find a deeper and more profitable understanding of users.
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