There’s a very simple first step to creating successful digital customer experiences: ensure that they are at least primarily driven by data and purpose rather than assumption and belief.
What exactly does that mean? You don’t have to look very far to find examples of purpose-driven design. It is enmeshed in our lives: the height and placement of the traffic lights at the intersection near your home; the Nordstrom salesperson who walks around the counter to hand you your purchase; the openness and minimalism of Apple stores; the warm cookies DoubleTree provides at check-in; the placement of the dairy case at the rear of the grocery store. These are more than aesthetic choices. Each of these experiences was intentionally designed with a specific and definitive purpose in mind – whether to maximize visibility, foster a sense of attentiveness and comfort, or drive sales.
The design strategy in each of these examples is also a statement of belief. Each is an assertion, deliberate or not, about both the design required to best serve a purpose and the people it serves. Someone believed seventeen feet was the optimal height for maximizing driver visibility of traffic lights; that coming out from behind the counter would enhance customer service and satisfaction; that a clean, simple look would invite consumers to try products; that the aroma of fresh-baked cookies would stimulate comforting brand associations; or that placing the dairy section at the rear of the store would increase average purchase value.
Some design beliefs are based on historical data. Most, however, are based on experience, imagination, and instinct. There’s nothing wrong with that. In fact, sometimes it’s the best alternative. Data (or time) simply don’t exist to support every design decision in advance. Even when historical data does exist, it’s directional at best and not always reliable. What was true yesterday may not be true today. Things change. Expectations change, behavior changes, and dynamics change. If the average size of vehicles altered substantially, the optimal height and position of a traffic light would likely change, as well.
Imagine if Apple had built its stores based on the prevailing best practices of the late 90s. The layout of its stores contradicted everything retailers knew and understood about store design at the time. In 2001 Bloomberg reported “I give them two years before they’re turning out the lights on a very painful and expensive mistake.” They would report again nine years later: “…Probably the highest grossing retail store in history.”
What about the dairy case placement, which still persists? Perhaps it was once true that forcing a customer to trek from one end of the store to the other for the two items they wanted was successful at increasing basket size, but can that still be the case? It’s almost a cliché to say that consumers today prize convenience above all else. Yet this grocery store design belief rejects convenience. It is wholly inconvenient. When that same customer can avoid the hassle of the store altogether and order milk and eggs from an app and have them delivered to his front door the same day from a competitor, the design belief has become antithetical to its purpose.
While not every design belief or assertion can be initially supported with data, they can be validated through testing and prototyping. As a customer experience agency, Hero works with a variety of leading B2B and B2C brands across site, app, mobile, community, and email experience design. An initial exercise we often conduct with clients for prototyping and test-based optimization focuses on probing and defining the underlying business purpose and design beliefs inherent in brand experiences. Every experience, touchpoint, asset, or gesture has a purpose – or should have one. And each, for the simple reason that someone created them, expresses an inherent belief.
We have developed a process to define the relationship between purpose and belief to drive optimization and innovation. It’s a simple five-step process but an incredibly effective framework to support data-driven design.