This is the first of a two-part series – click here to read part two.
Once, data was the domain of data scientists and analysts. Now, technology has forged ahead to give us improved data processing and software and this means that it’s possible for us to access and analyze all manner of data to enable better informed decisions. Of course, web analytics have been around for a good while and the software of choice is Google Analytics. This free resource can be invaluable in helping you to make design-centric choices which are based on what the users are already doing on a site.
It’s worth remembering before you approach analytics that data that’s compiled by software and analyzed by humans is open to bias and interpretation. While hard facts may be presented by software, this still has to be understood by you. Additionally, you should remember that there’s always going to be a certain amount of ‘signal bias’ – “the bias of omission, inclusion, and emphasis.”
This means that you should consider the kind of data that’s being presented. So, social media data might tell you a completely different story than website data as essentially, all of the data is not objective, but crafted by human design.
Data for design
In design you’re likely to be using one of two types of analysis:
- Quantitative – numerical information which demonstrates the ‘who, when and where’
- Qualitative – non-numerical that demonstrates the why or how
You will be using quantitative data to look at who has visited the site, where they arrived from in the world, what devices they used and whether they came from organic or paid search, or social and so on.
However, what the data won’t tell you is the why and this is where qualitative data comes in. Qualitative information helps to give you some perspective and to understand why a user took action.
For example, say you’ve designed an ecommerce site. The site has gone down well with users in general, but you’re finding that users are dropping off on product pages or the shopping cart. Qualitative data can show patterns and trends that suggest it’s the design of the page that’s prompting people to leave the site before completing a purchase.
Check out the graphic below from Statista, which sets out the most common reasons why shoppers don’t continue on to make a purchase.
So considering that it’s likely that your visitors will abandon the cart for one or more of these reasons, how do we determine which ones? We use Google Analytics to create goals and study the data that comes in using the funnel visualization tool. This allows you to view clearly where visitors are dropping off after placing items in the cart so that you can make design changes in order to increase conversions.
Using goals and funnels
You can use goals and funnels to look at any aspect of your site where you want to measure user flow through the website.
Goals allow you to:
- Track how well the site meets objectives
- Locate where in the site your objectives are not being met
- Monitor overall performance of objectives
Once goals are set up, you can use the powerful reporting features that Analytics offers in the Conversions and Behavior flow sections to see how users progress along the sales funnel, where they leave it and where they come back into it.
Goals is of course just a small, but powerful, section of Google Analytics and it’s likely that you’ll want to track other things to understand how your users are navigating the site. Using basic tracking you can answer this and more.
- Where the user entered the site and where they left
- What path they followed through the site
- What technology they used
- What content they engaged with
- Whether they were referred through social or another site, or search
However, as Petras Baukys points out, much of this data is geared towards marketing and as such, we only really gain superficial insights from this. With this in mind, we can use “virtual page hits” to set up Analytics manually in order to track when users perform a specific action.
So if you want to know if your users are opening up a certain tab, or looking through gallery or even product items, then you can add a line of code to the tracking script that tells Analytics to track it as a page hit, as set out by Petras below:
Ga (‘send’, ‘pageview’, ‘/your-custom-pageview’);
This can be used for any action that you like, to inform you more comprehensively on how your users behave on the site.
Figuring out intent
Data can tell us many things, but what it can’t inform us about is user intent. With this in mind, if you can use A/B testing and real life test groups to further inform the design then you should. This is especially the case for times when you have to justify design changes to stakeholders who may not be familiar with the principles of design and user experience.
It’s very difficult to do this with just Analytics which are open to interpretation, so A/B tests can give you very clear results when carried out correctly. You can also use in-page analytics to measure how much interaction there is with links and clickable elements on the page. As you can see in the image below the in-page analytics extension allows you to view any web page which you have Analytics access to within the browser.
Using the bar at the top, you can also compare by date range and using segmentation. So on this particular page, we can see that the Services tab isn’t getting as much attention as we’d like, so this would prompt a redesign of the navigation system.
Next time in part two we’ll be looking at bounce rates and what they mean to your design.
Kerry is a prolific technology writer, covering a range of subjects from design and development, SEO and social, to corporate tech and gadgets