When facing a challenging business problem or difficult strategic question, it can be hard to know where to start. In these situations, companies and organizations often turn to established strategies to create more value for their customers and improve their own positions in the market. For many companies, design thinking is a new and innovative method for solving problems and gaining new knowledge; for some, it is even considered “risky” because its popularity as a go-to process for creating innovative solutions is relatively new. But design thinking’s origins actually date back further than you might realize, pre-dating Aristotle. When you get down to it, design thinking is just the scientific method adapted for the purpose of creating of products, services, and experiences rooted in human experiences.
Discovery and hypothesis-based problem solving
Let’s go back to middle school for a moment, when we all probably first learned about the scientific method. In a nutshell, the scientific method emphasizes experimentation, discovery, and inductive reasoning. You start by making observations, often through the use of experiments, and combine the results of those experiments with existing facts. From these observations and experiments, you create informed hypotheses about how the natural world works. Then you test those hypotheses through additional experiments to see how accurate they are and if they can enter the realm of theory, law, and fact. If the observational data don’t support a hypothesis, you abandon it and explore what else may be supported by results. Overall, you seek to find new solutions by understanding what is already there.
“Science, at its core, is simply a method of practical logic that tests hypotheses against experience.” – John Michael Greer
Design thinking is the scientific method expanded to include observation and discovery of human behavior, the emotions behind those behaviors, and using that data to create innovative solutions to complex business problems. It can work really well in concert with the scientific method. Where the scientific method excels in understanding objective and quantitative data, design thinking offers a way to collect and understand subjective and qualitative data, such as customer wants, needs, as well as personal histories and experiences. These types of data are especially helpful in the earliest stages of a project when a lot may be unknown, even what problem you are trying to solve. It’s at this beginning stage that hypotheses are formed from the data and customer insights collected. [Read: The Robots Haven’t Won Yet (And Other Reasons I’m a Design Thinker)]
Just like with the scientific method, you continuously test and validate (or invalidate) your hypotheses and ideas during a design thinking process. Instead of Petri dishes and pipettes in a lab, you may use “in the field” ethnographic observational tools such as a one-on-one interviews, fly-on–the-wall observations, and diary studies. Once you have a small collection of hypotheses validated based on a deep, inductive understanding your customer, you are ready to solve the right business problem.
Most professionals, particularly those in service industries, don’t rely on the scientific method in the way scientists have been trained to do. Design thinking can seem like a new and risky process to companies and organizations because they don’t recognize the similarities between design thinking and its well-established relative: the scientific method.
“The scientific method is nearly perfect for understanding the physical aspects of our life. But it is a radically limited viewfinder in its inability to offer values, morals, and meanings that are at the center of our lives.” – Huston Smith
Building a better fall jacket: Design thinking in action
The scientific method relies on testing a hypothesis within the parameters of the problem that the team is attempting to solve, and design thinking helps the team understand those parameters. By simulating the conditions in the desired context, the hypothesis can be either invalidated or supported, and new knowledge about the problem can be created. The parameters of the experiment simulate real world conditions as understood through design thinking, and make the results of the experiment more credible.
By way of example, let’s say a company that sells outerwear wants to create the best fall jacket of all time. The design team could take several approaches to decide what that jacket should be. They might look at popular jackets throughout history and emulate the features that made them successful, but that would only result in designing a jacket for customers of the past, and the customers of the present and the future could reject it. This is a typical “let’s do it the way it’s always been done” approach that rarely results in breakthrough solutions that delight customers.
If the team approaches the question using only the scientific method and without a design thinking mindset, they’d attempt to design the best jacket through a process of experimentation without necessarily having any customer insights upon which to base a hypothesis. They could set the parameters for what they want the jacket to accomplish and come up with hypotheses for what jacket designs could meet those needs, but they would not have any validation that those needs actually exist.
Maybe they assume the jacket should be waterproof for rainy days, and that it should be insulated for warmth. It should have plenty of pockets and have a detachable hood to keep the wearer’s head dry. They also assume that it should be reversible, durable, and colorful. They design a jacket that fits those parameters and conduct experiments that prove its capability, but when the jacket is released, it is widely panned as clunky and ugly. This approach fails because the team is hypothesizing with limited data and designing experiments based on unverified assumptions about what their customers desire. How could they discover what their customers actually want or need in a fall jacket?
By instead beginning the jacket design process with the principles of design thinking, the design team can create something that truly connects with their customers. Before hypothesizing or making assumptions about what jacket designs might sell, the design team begins with research to understand customer needs. They might conduct in-the-moment interviews with people shopping for jackets, observe people working outdoors in fall weather, and research emerging social trends. Through this process, the team is able to empathize with customers and can understand them as people rather than data points.
The design team also works to define their parameters. They discover, for example, that people buying fall jackets in the wet and temperate Pacific Northwest prioritize waterproofing and detachable hoods, whereas people in the Northeast prefer jackets with more insulation because their region is dryer and colder. The design team learns that, rather than a one-size-fits-all solution, there are market opportunities for several jacket designs. They also identify new customer needs that weren’t present when jackets of the past were designed: pockets have changed to accommodate what people carry with them today, including smart phones, e-readers, and tech accessories.
Through ideation and rapid prototyping, which is design thinking’s “experimentation,” the team can create educated hypotheses and test which concepts best meet the parameters established. The end result is a jacket designed for the needs of actual people based on data gathered through a design thinking-oriented process. Because they worked with actual customers and weren’t constrained by assumptions anchored in the past, the design team’s solution hypotheses are more meaningful and accurate.
Where the “design-tific” method works
Many businesses successfully use design thinking already. When Airbnb was struggling financially in 2009, the company used design thinking strategies to reconnect with their customers. They discovered that many of their listings weren’t catching the attention of potential guests because their owners had done a poor job photographing their spaces. They decided to step in and help the owners improve the quality and accuracy of their photos. They experimented with different strategies for how to represent their spaces until they found what worked. They applied the scientific method’s iterative, quantitative approaches along with design thinking’s human-focused discovery and found a solution to their problem that worked for everyone. Within a week, their customer-centric solution had doubled their revenue.
Humanitarian efforts have also benefited from combining the scientific method with design thinking. The Deworm the World Initiative used rigorous ideation and iteration to develop its strategy of increasing the quality of childhood education by combating parasitic worms. With the goal of improving the school attendance and test scores, DWI first tested several “obvious” solutions (additional textbooks, replacing textbooks with flipcharts, more teachers) before they discovered that treating worms in children had a much more significant impact, reducing absenteeism by 25% and raising incomes after leaving school by 20%.[note] 1. MaCaskill, W. (2015). Doing good better: How effective altruism can help you help others, do work that matters, and make smarter choices about giving back. New York: Avery[/note] If DWI had not been open to learning from the failures of their earlier hypotheses, they would not have experimented with the techniques that eventually led them to solve the right problem and achieve success. By following the rigors of the scientific method and applying design thinking’s focus on the human experience, they were able to push beyond the obvious and find an actionable, if unexpected, solution.
By approaching a complex problem or question with a design-thinking-meets-scientific-method approach, companies and organizations can reliably and accurately serve their customers with the right products, services, and experiences.