Understanding Users Through Data

Quantitative researchers (BI, Data Analyst) had highly benefited from web analytics and quickly found their way to the heart’s of stakeholders. How about UX researchers?

Whether Qualitative (non-numerical) or Quantitative (numerical) analysis – the main aim of research is to help business leaders make better decisions. In that respect, little has changed since the days before Internet.

Qualitative researchers keep giving the answers to “Why,” whereas quantitative to the question of “How much.”

Quantitative researchers (BI, Data Analyst) had highly benefited from web analytics and quickly found their way to the heart’s of stakeholders.

Qualitative researchers (UX, Customer Research) are still mostly working on a small scale, often offline, extracting insights from testers in a lab or randomly met people at coffee shops. They do it not because they are coffee addicts, but because analytics provide oversimplified view on customers’ behaviors and they have to get their job done.

Ability To Answer “Why” With Analytics Is Limited By Data Collection And Segmentation

But the change is coming. Already, out of 7.9 Zettabytes of data in existence, over 70% is unstructured data, by individuals. Together with Big Data, context fell through the gap between qualitative and quantitative research, making it possible to answer the most important question: “What?”

As in, “What is important?” What to do next?

Failing to understand what’s really important, and therefore what to do next places UX researchers in an awkward position of trying to plug a leaky pipe without all the tools and data. A lack of understanding results in a loss of revenue.

Research Methodologies And Questions They Answer

As ability of web analytics to digitalize customer experience grows, the polarisation of research into qualitative and quantitative will diminish.

Linkedin Profiles With “Qualitative” And/Or “Quantitative” Keyword

Versatile researchers (or research teams) that can embrace the volume of data and sift through the noise using both empathy and statistics this represents a gold mine. Why empathy? Because qualitative analyses beat algorithms (someone would have to write them first!) in making sense out of complex customer behaviors.

Humans Are Still Much Better At Understanding Complex Data Than Algorithms

Why statistics? Because not everything is worth making sense out of. Quantitative analysis helps researchers keep the focus on business problems.

There’s a great article called “Qualiquantive” on Medium that goes into details of that approach.