Not so long ago, great design was considered its own validation. No one ever focus-grouped Dieter Rams’ creations, nor was frog’s “Snow White” design language for Apple a result of extensive ethnographic research. These designs were beautiful and simple, and they worked. But the rapid globalization and digitalization of the ’90s meant that design solutions were being adopted by an increasingly diverse audience. In the face of new cultures and continents, not to mention rising market fragmentation, designers could no longer rely on intuition alone. And neither could businesses. Designers needed new methods for informing their designs with an understanding of the specific end-user at hand, and for justifying their designs to a newly-engaged business world, accustomed to the recommendations of corporate strategists and financiers.
Chief among these was ethnography, a technique drawn from anthropology and the social sciences as a means to formalize the study of behavior, itself long a part of the design process. Ethnography yields rich generative insights for designers by inviting us to suspend our value systems and observe, as transparently as possible, the context in which our designs will operate.
It’s a powerful technique, but it’s simply not enough. Ethnography breaks down at the moment we ask not just for depth of knowledge, but breadth. Anyone who’s struggled to conduct a massive ethnographic study across multiple time zones can tell you this firsthand. While ethnography facilitates the generation of ideas in relation to specific users and use scenarios, it leaves us clueless as to which among these will satisfy a wider audience. Ultimately, we need complementary methods that scale more effectively and validate our work in a way clients can understand. What we need is quantitative research.
The idea of instituting an empirical approach to design research is hardly new. In particular, the 1960s saw the emerging field of computer science making its imprint on design research theory. MIT political scientist and artificial intelligence researcher Herbert Simon argued for a “science of design,” which would be “a body of intellectually tough, analytic, partly formalizable, partly empirical, teachable doctrine about the design process.” Companies like frog, IDEO, Smart Design, and Ziba have labeled themselves strategic design firms, promising clients (in the words of Wikipedia) a bridge between “innovation, research, management and design” by making design decisions “on the basis of facts rather than aesthetics or intuition.” It’s time we started living up to it.
But how? Just as ethnography borrowed heavily from academia while applying a looser, more liberal lens, quantitative research can be similarly engaged. When individual observations can be contextualized within a data-driven knowledge of the market at hand, designers can have the best of both worlds. And there are many analytical tools that work well in this context. Segmentation analysis can be used to challenge thinking around current and prospective users, sorting consumers into salient, sometimes unexpected groups that hold together based on survey data – groups that defy traditional demographic segments can be linked by more relevant factors, such as behavioral patterns or attitudes towards technology.
Another method, factor analysis, can be used to sort through a list of product criteria, suggesting which are related to each other in the user’s mind (size and weight, for example) and which represent independent ideas (perhaps device size versus perceived value). Regression analysis can explain how strongly each of these factors will impact a consumer’s purchasing decision, helping to prioritize design considerations in a cost- or time-limited project. By exploring and quantifying the chain of considerations that influence a complex consumer decision, we gain insight into how each step impacts the next, yielding breakthroughs from formerly intractable problems.