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How to avoid being intimidated by quantitative UX metrics
How to filter out quantitative metrics that don’t matter
I’ve been intimidated by data several times in my UX career, but I never ran into analysis paralysis until I started looking at Google Analytics data.
The sheer amount of information, combined with unfamiliarity with making sense of the data, frustrated me with where to start.
And I’m not alone in this. According to John Ciancutti, Chief Product Officer at Coursera,
The tension [between design and data] is natural because it’s like “I don’t understand, it’s foreign, I’m not good at it”…As a designer, you are probably more capable than you recognize to raise great points around data, but you don’t know how to think about it yet because it’s not familiar.”
But there’s one thing that can help you immensely as long as you keep it in mind:
The goal of quantitative data is to answer specific questions about what’s happening in the User Experience.
If you know what you’re looking for, you can probably filter out most of the data that presents itself.
To understand these specific questions, let’s talk about a framework that uses both quantitative and qualitative data: UX Optimization.
Understanding UX Optimization
UX Optimization, by Craig Tomlin, is a process that combines the power of behavioral quantitative data with qualitative data coming from user testing to improve websites.
This process consists of 4 steps:
- Build appropriate user personas
- Check UX Behavioral metrics (from places like Google Analytics)
- Do User testing
- Compile Analysis/ Design Recommendations
To understand why we need to consult Behavioral metrics, let’s consider a scenario.
Imagine you’re choosing between two design alternatives, one catering to newer users and the other towards experienced users. Which one do you choose?
In most cases, you choose the one that’s going to serve our primary users. But to figure that out…
