Redesigning Google Analytics from the Ground Up — How to Design User-Friendly Data Visuals
By: Zach Diamond
Tags: Analytics, Design, Data Visualization, Google Analytics, Data

Google Analytics is a tool with a high learning curve that focuses on powerful functionality and customizability. Here is a screenshot of a demo account.

As you can see, it uses an open sandbox design.

However, for newbies and veteran marketers alike, open sandbox designs are often overwhelming and can lead to questions that are not addressed by the design.

For example: which analyses should be performed routinely and which for special cases? How do I confirm a campaign’s effectiveness? Is the growth I’m seeing in my traffic good or bad?

The biggest weakness of this sandbox design is the underlying assumption that the user knows where to look for an insight and how to look for it. On the other hand, user-friendly data visuals assume almost nothing about the skill level of the user so, for today, we’re going to focus on how to build an effective one.

The first thing to understand is that user-friendly design requires a ground-up approach. Simply put, first we understand the user’s needs and pain points with GA. Then, using design, we try to resolve their pain.

Who are our users and what are their needs?

This is the first question that needs to be addressed for any user-friendly design. In order to do this, we first hypothesize about the needs of our core audience: marketers at small to medium e-commerce businesses.

To help put a face on our audience, we’ll call our hypothetical marketer Brendan. Brendan works with a small team on marketing and he gives weekly reports using Google Analytics metrics.

Brendan is interested in his website’s performance this week and has been monitoring his traffic with Google Analytics once a week. He wants a visual that allows him to accurately judge whether his metrics are going up, going down, or staying the same — in other words, which metrics are doing well and which are doing poorly.

Standard practice is for marketers to assess website performance by looking at how their metrics have changed during the last week and how drastic that change is.

So, we need our data visualization to convey 2 primary things.

The change of the default Google Analytics metrics (there are 9 of them) since the previous week.

The magnitude of their change.

Designing Visuals for the Needs of the User

In order to move away from the sandbox design, we need to look for effective data visualizations that tell strong narratives. In the visualization list from Storytelling with Data by Cole Nussbaumer Knaflic, the emphasis on change over short time periods suggests the use of text and slope graphs. (Slope graphs are essentially mini-line graphs that only show two time points.)

Let’s Start with Text.

Don’t underestimate text because it’s not “complex.” If text can communicate both change and magnitude effectively, why reinvent the wheel? Looking at one metric, text isn’t too bad. Brendan can clearly see and understand whether the metric has changed and by how much. Unfortunately, our real-life case involves 9 metrics.

This is visual overload. It’s pretty unreasonable to ask Brendan to piece together a narrative from 9 blocks of text about changing quantities. The idea of change is getting across, but the main issue is that nothing visually differentiates the magnitude of change. This means that it’s a lot more work for Brendan to interpret. After some deliberation, we settled on adding a visual aid and arranging it into a dashboard.

Not bad. With the addition of a small visual aid and some re-arrangement, we made a dashboard that helps Brendan with certain aspects of analysis. Brendan can now quickly scan and understand that these metrics belong in groups. The top group shows amount of traffic. The middle group shows type of traffic. The bottom group shows quality of traffic. Also, by adding an additional arrow for each 10% change, Brendan can get a much better sense of the magnitude of change. As such, Brendan can immediately compare the changes between grouped metrics and then begin hypothesizing based on the data shown.

For example, users have gone up by 2 arrows but sessions and pageviews have only gone up by 1 arrow.

So, the increase in pageviews and sessions is lagging relative to the increase in users. This suggests that new users are not exploring as many pages or making repeated visits as often.By adding a visual aid and grouping the information, Brendan is better able to hypothesize about the website’s performance. Now, we have a strong candidate for data visualization, and we haven’t even fully left the realm of text yet.

Now, Let’s Try a Trendline

To simplify things, we’ll isolate it to users, sessions, and pageviews. We want to see if this improves our dashboard.

It’s immediately obvious that trendlines have the clear advantage of being more visually pleasing and concise. They clearly show where the metric started and where it ended up. The slopes of each line seems to represent the magnitude of each change. This visualization seems well suited for weekly metric analysis.

Now, it seems natural for Brendan to look at that graph and say “Woo! Everything is increasing! Hooray!”

However, there is a hidden implicit assumption made that isn’t shown in a normal trendline. I’ll add it below.

After seeing this, Brendan might not be celebrating anymore. Notice how sessions and pageviews both had steeper slopes when going from 0 to last week? Since last week, their slopes have leveled off to the exact same slope as users. This graph suggests that newer users are visiting the site once, looking at a single page, leaving, and never coming back. That doesn’t sound too healthy from a marketing standpoint despite the fact that all of the metrics are experiencing growth.

Therein lies the weakness with using a trendline. While slopes excel in showing the absolute change from last week to this week, they do a poor job of showing the percentage change.

Even with the 0 segment added, and having a theoretical slope to compare with the current slopes, there is no clear indication of how much the slopes have leveled off. This forces Brendan to judge percentage changes by trying to visually assess the difference in slope between the dotted slope and the corresponding solid slope, further complicating analysis.

Text or Trendline? Which is More Useful to the User?

In designing our user-friendly data visualization, we need to choose which design is most helpful to Brendan.

These two visuals essentially tell the same story, and each one has pros and cons. Let’s look at both together to summarize their strengths and weaknesses.