Why should we make our research more qualitative?
A suggestion for user research teams inside companies to expand their methods
Design and Qualitative methods
Designing a good product means facing great challenges, starting with brainstorming to eventually building the prototype. The design process is a very dynamic one, since it consists of a sequence of divergent and convergent processes until the prototype is built.
The challenges of the design process, more than often, concern the usability of the product for the target user group and its overall feasibility. One of the pillars of the design process is User Research. It is commonly grasped inside companies that User Research is the collection and analysis of user’s data based on quantitative methods that produce metrics. And sometimes it is satisfactory for companies to only rely on that. However, academic (Dourish, 2016) and industry research (Churchill, 2017) groups have shown that a more qualitative approach to the user research methodologies has the potential of producing great value in the design process.
By “qualitative approach” I refer to the collection of complex and thick data which concern emotions, experiences, stories, motives of the users (Wang, 2013). This is achieved by looking at the user through an “ethnographic lens” (Churchill, 2017), to better understand the context and biases that affect their experiencing of the product.
Quantitative vs. Qualitative methods
Many would argue about the position of qualitative methods used in research in the design process. It is important to know that these methods can provide good and valuable insights on their own, but combined with quantitative data, they produce insights with strong foundations based on numbers.
By this, I suggest that these two methods should be complementary. Quantitative methods provide data sets, numbers and metrics which give objective insights, while Qualitative methods are of good use in exploring contexts and relationships (Wang, 2013).
Its good to know the big picture of Big Data, but to make a difference, it is essential to know the Thick Data, which are found on a more personal level. Instead of questioning about how and when, it is the why that makes for customized experience and satisfied users.
References:
Tricia Wang. (2013). Big Data needs Thick Data.
Dourish, Paul. (2006). Implications for Design. Conference on Human Factors in Computing Systems — Proceedings. 1. 541–550. 10.1145/1124772.1124855.
