Interpreting analytics data in UX design

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More and more, we're using website and app analytics data to complement our research-led UX design and evaluation approach here at Inviqa. This complements our qualitative research to help us better advise our clients on how to make their digital channels perform best for their customers.

Google's is the most common analytics packages we use (and free up to a limit), but Adobe Analytics, Webtrends, and other A/B testing suites out there share similar features, meaning that, no matter what platform you use, your research insights can be enriched by numbers.

How to apply analytics data in UX

At the start of a project, gaining data helps gain a deeper upfront understanding of customers and their behaviours. We use this knowledge to fill gaps in our Discovery research to help us be more confident that we are solving the right (customer and business) problems with our designs. Recently we put this data to good use in the redesign of a University's homepage, using IA to help us validate our understanding of the top tasks people were coming to the University's website to perform.

Measuring and getting some hard stats helps us see the true effectiveness of our clients' digital channels.

Similarly, once we are confident of the tasks customers want to (and are expected to) perform using websites or apps, measuring and getting some hard stats on these tasks helps us see the true effectiveness of our clients' digital channels. We are then in a prime position to identify areas for improvement, or ways to optimise their design and develop ideas for more strategic longer term content and features.

So, with these aims in mind, what do we aim to find out from analytics, and how do we make it a useful addition to our UX research, strategy, and design toolkit?

Discovering user needs, requirements, and behaviour

As part of initial Discovery research, finding out as much as you can about both your existing and potential audiences' current digital performance is crucial to making future improvements. To complement and focus your qualitative research there's a wealth of analytics data which allow us to spot trends, and interesting patterns of behaviour in our clients' audience to design for:

Identifying top tasks

Do customers take routes and complete the goals you believe they're after? What tasks are completed?

As any well versed UX designer knows, identifying the top tasks users are trying to achieve will help you create experiences they will flock to and love. By sifting through the wider analytics, you can begin to understand UX requirements around the content, features, and functionality to include and optimise for in your new designs.

  • Which goals or end points do people complete or end their visit doing? Set up multiple goals and track completion rates, and track different funnel performances
  • Which pages attract the most new visitors?
  • What do people most commonly return to do?
  • What are the biggest search terms for customers coming to your website?

Content requirements

Do you suspect that reams of text and stock imagery aren't getting you the conversion rates and levels of engagement you desire? Are you struggling to stand out in a tough SEO market?

Having a better understanding of how your content helps your customers complete their tasks is crucial in keeping you relevant and attracting audiences to your product. With that in mind, it's  worth keeping your content strategy updated. Provided you already have content in place, or new content you're testing out, it's worth setting your analytics package up to identify:

  • Preferences and levels engagement for different types and topics of content. Look at key metrics such as time on page, click-through-rates, and conversion/task completion rates
  • Split your conversion funnel into cohorts based on content types and measure the difference. Try looking at your top-performing content (in terms of SEO landing visits), as well as other traffic sources

Device adoption and usage

Are you noticing an increase or change in devices used by your audience? Are you struggling to make the case for a mobile-first approach? Are you unsure whether customers behave differently when not on desktop? Do you have pages or content that's underperforming on a particular channel?

Being in a position to confidently present the numbers on how and when customers are using different channels and devices allows you to achieve much greater buy in to your design approach and help achieve decisions easier. Aim to identify things such as:

  • Do people buy all products (or complete all tasks) through all devices? Try separating your goals based on device or screen width
  • Which pages are not optimised on which devices? As you analyse your conversion funnel, check to see whether certain devices or screen widths lead to unusual levels of drop-offs
  • Make decisions on which browsers to support, or the number of viewports to design and optimise for. Try identifying what browsers or devices your audience tend to use. It might be worth checking changes in device adoption over time as well

Understanding holistic user journeys

Beyond online channels, where are customers coming from? Do they behave differently depending on the channel they came in on or go out to?

At the wider customer journey level, begin to understand where your users are coming from, the most effective conversion channels, how effective your CRM efforts are, and where and when leave your website or app:

  • Get a better idea whether people are looking for information or trying to complete a more specific action
  • Rate the effectiveness of offline marketing campaigns or communication strategies (check which offline channels bring in the most traffic)

Measuring your current UX performance

Again, we find analytics is very complementary to a qualitative investigation of our client's audiences' user experiences, particularly when your new designs are live, or if you've been asked to help understand your clients' current website or app performance.

This time around, the focus of your analysis should be on understanding what you need to fix, and demonstrating how well customers can achieve what they set out to do (rather than trying to figure out what or how they come to do things). With that in mind we commonly look to evaluate:

User journey performance, funnel analysis, and task completion

Are people following the routes and journeys you've created? How well is content along these journeys working? Do people complete end-to-end journeys on the website (and if so in the way you imagined them to do so?) What causes people to behave differently?

Think of ways to improve your designs to better provide routes to the content or functionality people are trying to use (e.g. more quickly, at a higher percentage, with greater value):

  • Have changes made to navigation structures and labelling made a change?
  • Will reducing, combining, or splitting steps along the user journey make an improvement?
  • Do changes to overall look and feel, content, and tone of voice better engage and help people to complete their journey?
  • Do features and functionality along the journey help people to better finish their task?

Problem pages and layout optimisation

Are the stats indicating you have a page from which everyone exits your site? Can you find out any more about why? Perhaps time on page is incredibly high, but what does this mean and, more importantly, do changes you make to this page make a difference in conversion rates, or a reduction in phone calls?

Analysing site-wide metrics (e.g. drop-off rates, time on page) to look for any stats that stand out, particularly for a certain content section or on a particular device is a useful technique to identify where immediate fixes might need to be made.

Can updates to the overall experience improve performance, engagement, and/or completion rates? Try measuring key metrics (e.g. time on page, drop-off) specifically focused around any changes you made within a page e.g. around calls to action, layouts, page navigation, or the general visual design and branding.

Want to dive deeper?

Using analytics to identify user needs and evaluate UX design success is only the beginning; there are a range of quantitative and qualitative tools out there that can help you be more data-driven.

With increasingly powerful analytics tools and packages, (e.g. Google tag manager and other remote-study or analytics tools released each week), now more than ever there's no excuse to not make the most of data for the wider UX good!

Here are some other resources to help you gain maximum value from the insights your data and analytics can provide: