When we think of analytics, we think of marketing campaigns and funnel optimization. Analytics can seem a little overwhelming, with so many charts and lots of new features. How can we use analytics for design insights?
The best thing about analytics is that they can show us what people do on their own. The worst thing is that analytics don’t tell us much about context, motivations, and intent. Like any kind of data, there are limitations. But that doesn’t mean analytics aren’t useful. Working with analytics is about knowing where to look and learning which questions you can reasonably ask. Read More
This is the first edition of my new column, Data-Informed Design, which will explore the use of data to inform UX design. Data, in many different forms, is changing how we think about ourselves and the world. And, for better or worse, it is definitely changing our experience with technology—from great new mobile apps that we can use to monitor our health to incremental improvements on our favorite Web sites to those annoying ads that follow us everywhere.
In my column, I’ll describe how to use different types and sources of data to create better user experiences and how to achieve some balance—so data isn’t driving decisions. There are three key topics that I’ll cover:
Starting at a high level, I’ll look at why you would want to use data, some misconceptions around data and UX design, and discuss a process for incorporating any kind of data into your decisions.
Then, I’ll move on to considering various data sources such as analytics, A/B tests, social-media sentiment, and various types of quantified data from UX research.
I’ll also describe how to use and analyze data, including metrics and measurement frameworks, as well as presentation tips and visualization tools.
Despite all the talk about data-informed design, there is not much agreement on what data really means for a product or service’s user experience. That might be because teams don’t yet have a shared language for talking about data, or because access to data is uneven or siloed, or perhaps because team members have different goals for the use of data.
At its core, data-informed design can be difficult to define, because there is not even agreement on what counts as data. We tend to think in dichotomies: quantitative and qualitative, objective and subjective, abstract and sensory, messy and curated, business and user experience, science and story. But the more I work with data and the more familiar I become with the data-science community, the more inclusive my definition of data becomes. Read More