How design thinking can help improve analytics outcomes
Blog Posts:Mu Sigma
Published On: 29 December 2014
Design thinking, popularized by the design firm IDEO, is starting to catch on within the decision sciences realm, as pros see its potential impact on analytics consumption. According to Tim Brown, CEO and president of IDEO, it’s about, “matching people’s needs with what is technologically feasible and viable as a business strategy” – in other words, ensuring that data analytics solutions are realistic and executable.
A decision scientist needs to infuse empathy with engineering. However, that can be challenging considering our natural inclination towards the usage of statistical techniques and technology. Having a designer involved in analytics projects from the initial stages helps ensure that the right blend of sensibility, technical feasibility, business viability and consumer needs are considered through the project.
There are three critical phases where design thinking can positively impact an analytics initiative. They aren’t necessarily sequential, and there may even be some overlap – but at a high level, the phases are Motivation, Idea Refinement and Prototype Evolution.
Following traditional problem-solving approaches such as the Situation-Complication-Question-Answer (SCQA) model may cover the business problem at hand, but does not ensure that the consumer will be able to (or want to) consume the solution. This is where empathy comes in, i.e., putting yourself in the customer’s shoes and asking the right questions. This introspection often leads to a redefinition of the problem itself.
Bank of America’s “Keep the Change” program provides a good lesson in this context. Initially, the team set out to help customers increase their savings – but behaviors are difficult to change. Using design-thinking principles, the bank came up with a debit card that automatically rounded up each transaction to the nearest dollar, depositing the “change” directly into a savings account – making saving effortless. Along with goodwill, this created value for both customers and the bank. As a result of this new service, Bank of America claims to have won five million new customers, seven million new checking accounts and one million new savings accounts, all while helping customers build up savings totaling $500 million.
A decision scientist is on a constant journey to build the empathy muscle by developing and testing ideas iteratively. While solving a problem, putting together a discussion team including people not directly related to the problem will bring robustness to the solution. Role-play techniques will further help to understand the stakeholders, as it’s not always feasible to interact with the end users. This pushes the team to introspect and refine the problem statement.
Design thinking enhances this process by allowing the ‘fail fast and often’ mode, i.e., being creative in problem solving while molding with the iterative feedback.
Once ideas are shortlisted, the focus is to develop consumable solutions. Instead of overwhelming users with near-complete versions, start with simple prototypes with bare necessities to evoke feedback and get to the root of users’ wants, needs and constraints.
Two design students were required to set up a business during a course at IDEO. In a matter of weeks through rapid prototyping, user feedback went from, “This is crap” to “Is this app preloaded on every iPad?” The result—Pulse News—which received public praise from Steve Jobs, has been downloaded by 15 million users, and is one of the original 50 apps in Apple’s App Store.
Just like all other exercises, design thinking requires perspiration. So what is your decision sciences team doing to incorporate design thinking into its everyday work?