Long before computing become personal, “reader response” and “reader expectation” were all the rage in literary criticism. As far back as the 1960s, people such as Roland Barth and Stanley Fish explored the role of reader experience as the expression or meaning of a literary work. For instance, Fish’s Surprised by Sin: The Reader in Paradise Lost, argues that—despite Milton’s stated intention to “justify the ways of God to man”—the reader sees the Devil an ingenious rebel while God is a dull despot who has fixed the game.
Today we have the genre of User Experience, or UX, in the mix of what makes a tool or process more salable or more usable. (Please tell me why the letter X is de rigueur for naming things in technology!) My own thinking is that User Experience is a blend of user expectations and the actions your tool or process requires for a successful outcome. All users come to the interface (which is actually a unique grammar), with a set of expectations derived from their experience and knowledge. Your tool or process then presents a necessary “work stream” to accomplish the intended result: the value or payoff of the tool or process.
Here’s a marketing example: Book and tablet buyers both have expectations and some knowledge about what they want to buy. Their actual experience with the thing they want to buy falls within certain ranges. If you are selling, you might ask, “Has the prospect used my product before?” And “What does my prospect expect from my product?” Big Data has allowed sellers to collect prospect information from a conglomeration of sources and use it to shape a context (an adaptive interface) tailored to the prospects’ experience as they show initial interest in a product or solution, become aware of what might meet their needs, investigate their options, and then choose to buy one product or service instead of others.
On the performance or usability side, engineers construct the User Experience as the work stream, which they—given their own experience and knowledge—love to chart as a linear process, when almost always it is not. Engineers would do well to shape the user experience for new technology to account for both user expectations—derived from experience and knowledge—and for all variations of user activities required to execute a tool or process successfully. These are best understood as a gestalt, a collection of nonlinear cognitive impositions and reactions on the part of the user.
Your tool or process has no meaning for the user unless it can anticipate user expectations and ease the user experience. The goal is to provide a set of cues (words and images) that are familiar enough to allow the user to complete the task. Marketing communications, technical documentation and the user interface provide these cues. They assume things like the ability to read and comprehend language, the recognition of meaningful symbols and the aptitude to learn that certain actions create desired results.
Big Data and neuroscience research are helping us to develop better analytics and tactics for sales and customer service as well as guiding the developers of technical documentation and user interfaces. Still, we should be careful to think of any new development or approach as a magic bullet that will complete our understanding of customers’ and users’ experiences with interfaces and other things such as social networks. David Brooks provides the kind cautionary advice I like in two recent columns about the limitations of Big Data and new developments in neuroscience.
As thinking people, we should temper our amazement at even the most astounding new technology and science and heed old wisdom:
That which has been is what will be,
That which is done is what will be done,
And there is nothing new under the sun.