A closer look at 4 simple yet effective uses of analytics data to improve user experience for designers and UX specialists that save so much time.
Whenever I work for a new website to improve UX, I get asked the same question "What are the most important metrics that inform UX?"
This question is not easy to answer because it depends on the overall website's objective and are related to your brand, what you want to achieve and current situation.
Also, another issue with analytics is that it can become a huge black hole of interesting data without any actionable insight. There are lots of free analytics software with massive amounts of data, which means you get distracted looking at those that I define "vanity data". Most of the times, analytics is seen as "let's have a look at how the website is doing" kind of task. This can lead to huge distractions and waste of human resources, so at the end, even if they are free, these software cost you in terms of resources.
This is especially true for analytics beginners, who are easily impressed by the amount and diversity of data. In particular, they get stuck with the 3 following issues:
Number of metrics: so many things can be measured, which one are really tailored to your brand?
Metrics definitions: so many metrics are similar to each other, think of, for example, bounce rate and exit rate, sessions and users. Which do best answer a specific questions?
Dashboards: which dashboard do you need to answer your questions?
This last point in particular is the main problem. Many UX researchers and designers are looking at the dashboard as a tool rather than as an helping hand.
"Here is the complete overview of the website".
Sounds familiar? So familiar and so pointless.
I would recommend to step back for a second and think of what you really need in a dashboard, which analytics data, which metrics are best fit to support your work, to support the users' funnels, web design and the final aim of your website.
Luckily, I have been in so many of these situations and I know exactly which uses you can make of analytics data and in this article I share 4 uses with you.
In summary these are the four uses of analytics for UX specialists, web designers and web developers:
Analytics for spotting issues
Analytics for experimentation
Analytics for investigation
Analytics for usability
Use number 1: spotting issues
Web designers and UX specialists often work with conversion rate optimisation specialists, developers and data analysts when they need to launch a new website, refresh a page or add a new feature. UX teams' performance in many companies is measured by the ability of increasing engagement and they need analytics data to measure it. So the analytics guys will send weekly reports to UX teams to monitor performance.
Rather than sending reports with pre-determined metrics, how about we add a section for spotting issues and drive further investigations?
So the type of weekly dashboard I would recommend to be shared among the teams would look like this one:
Main website goals: a look at how the website is doing on a very high level, for example number of conversions, leads vs the desired number of goals. This last point is very important. It's useless to look at the number without benchmarking it against the goal you want to reach.
Micro conversions: these are the supporting actions, those that support the main goals. For example, funnel reports, percentages of drop-outs, visiting a specific page, clicking a particular button/ link, completing a form; again, as above these goals need to be compared against those you WANT to reach;
Metrics: pure analytics data with metrics to ascertain that the actions are actually happening as intended. These are very helpful to UX, developers and web designers to identify issues.
Descriptions: for those not familiar with the terms, a short description of what the metrics and what it measures.
Here is the template you can use for your weekly reports.
In this table I suggest using scroll depth as opposed to avg. time on page for measuring if users read your content. To know more about scroll depth and how to track it, check my short guide.
Use number 2: Experimentation
UX and web designers can look at the table above and identify issues related to the main goals and supporting conversions. Normally conversion rate experts will do this job and recommend the other teams on how to implement something and fix an issue. However, also UX, web designers and developers can have a look at the table and formulate hypothesis on what needs to be changed.
What role does analytics play in this process? You can use analytics data to prove your hypothesis wrong and right.
Experimentation included various steps and here is what I normally use to test my hypothesis:
The investigation step: in this initial phase, I look at the dashboard and compare the numbers and conversions with what I need to achieve on a weekly basis. For example, if I only have 1,600 signs-ups against a weekly goal of 2,000, I know that this issue needs closer attention.
Hypothesis formulation: I might have an idea of what is going wrong with the sign-ups on the landing page, but my idea is just an opinion not validated by data.
Testing: in this step I set up an A/B test to validate or invalidate my opinion on a small percentage of traffic. I can test the current version of the landing page vs a new version with my changes included and measure it using the same analytics metrics as in the table, namely scroll rate a d page views.
Results: I can let the experiment run for 2 weeks and gather enough amount of data to support or contradict my opinions. If I am right, I can implement the changes to 100% of the traffic. If not, I learn from the data and develop a different test.
Having briefly explained the experimentation, now we need to find out what exactly is causing issues to my website. In other words, what do you need to experiment?
You can try to make quick experiments by looking at your measurement dashboard to identify quickly issues. However, you need to go deeper in your analysis because even if you find that lower signs-ups are due to a decrease in page views, you still don't know what caused the decrease of page views.
So in the next stage you need to identify typical analytics issue categories.
Use number 3: Investigation
This is the third use of analytics data you can make as an UX specialist to avoid wasting time and efforts in trying to find what to look at.
In my experience, I normally look at these issues categories when investigating more on a website:
1. Traffic issues
In this analysis we want to determine if the decrease in page views (for example) is due to a specific traffic source by using custom dimensions. You can do this in Google Analytics for free. Traffic sources can be organic traffic (Google, Bing), referrals, direct traffic, specific campaigns or email traffic.
If you use Google Analytics, select your property, then you can go to Behaviour> Site Content> All Pages to open the report on traffic to pages.
Then click to "Secondary dimension" and type "source" in the bar to open the drop down menu and filter the traffic to those pages by source only to see where the traffic si coming from:
The next step is to insert the page URL in the search bar down on the right.
Now you trigger a report with traffic sources for the page you need, eliminating all distractions and you should do this within 1 minute. It's that simple.
Remember, this investigation into page views was just an example of what you can achieve with Google Analytics. If you want to find different data you can also look at the Landing Page report.
2. Technical issues
In this investigation we want to find out if you have a technical issue on a page by checking the number of events.
The Events report lists all events on your website that have been tracked. If you select one Event you can see all metrics associated to it and find out whether there is an issue.
How do you know if there is an issue? The report can show lower than normal events or unusual metrics data.
Go to Behaviour> Events> Top Events and then click on the event you want to check.
Now compare total events with metrics:
3. Navigation issues
With this report you can check whether buttons, clicks and other events related to navigation have some issues.
Note: to enable this report I would recommend to track buttons click with Google Tag Manager. Luckily, I have written a guide on this.
Alternatively, if you can't track clicks to buttons, you can simply check a different report on Google Analytics called Navigation summary.
Go to Behaviour> Site Content > All Pages.
This report details from which website pages users came before visiting the page of interest and where they went after visiting that page.
4. Content issues
With the following report you can find out whether new wording on landing pages, product pages and other types of pages important for your brand can bring any benefit.
Note: to see these stats you need to enable one of the following advanced functions:
Enhanced Link Attribution on Google Analytics.
Enhanced Link Attribution with Tag Manager.
Both options deliver you exactly the same report. In both cases you are able to see where people are clicking, which call to actions they click, if a variation of a word makes any difference in clicks within the same page and for links that look the same.
Google assigns each link a unique ID.
Benefits of enhanced link attribution:
You no longer need to add any tracking code to elements of your site. Google does it automatically for you.
You can find in-pages analytics of your site and visually analyse clicks on the website.
You see a colour map of your links with percentages of clicks.
Enhanced Link attribution on Google Analytics
To set up enhanced link attribution on Google Analytics you need to follow these two simple steps:
just go on Admin> Property > Property setting> Use enhanced link attribution > Save.
Add the Google in-page Extension
Once you have done these two steps correctly, you should see a visual report on your website that looks like this:
Use number 4: analytics for usability
When we test the website for usability, we normally want to know if people are finding what they are looking for quickly and easily. For example, let's assume people are coming to your website to buy sport shoes. How do you know if they find them quickly on your website?
You need to jump into your users' shoes (mind the joke:) and use the website as they would.
You can check three options to make sure there are no pitfalls (and to find some):
1. Search terms report. Assuming you have in your website a search bar to look for products (if you don't, you have to add one right now) check search terms report. In Google Analytics you go to Behaviour> Site Search > Search Terms. Here you find all the search terms people are using to search within your website. What do you need to check here?
Two things: first of all, why are they using the search bar? Is this what you want them to do? Your sport shoes link is right there on the home page, maybe they haven't seen it?
This should ring a bell and fix that issue.
Second, the search report informs you of the terminology people use, so you can align your content to their own terms. Some people use "shoes", for example, while other might use "trainers". Did you add this to your terminology?
2. Form completion report. Let's say you have a form people need to complete. You want them to download an e-book, subscribe to your newsletter, create an account.
Check the Events report . (go back to the Investigation section on to to do this).
This report tells you shy people abandon completing a form, there might be some usability issue there and you need to understand why.
Analytics data is becoming more and more important than ever before. Web designers, developers and UX specialists should consider using quantitative data to inform their own job, validate their work and use numbers for their own performance checks.
You can use analytics data to do the following:
Spot issues related to conversions
Investigate usability performance
Make tests to put your hypothesis to test
Learning analytics is a daunting task for marketers because of the amount and complexity of data and dashboards, but not for UX. If you follow these steps you will increase your UX efforts, or at least you will surely increase your knowledge of analytics and understand the value.
What's covered into this piece is telling you how to check UX using analytics data. And in this post for SEMrush I explain how improved UX also drove more organic traffic to a page.