Spy, Then Innovate

In A Primate’s Memoir, Stanford University neuroscientist Robert Sapolsky recalls how a generous army donation of surplus night goggles utterly transformed the field of carnivorology. The ability to see in the dark revolutionized how zoologists saw scavengers, predators and prey, which ultimately led to dramatic reversals in our characterizations of the animal kingdom.

Thus, Sapolsky writes, “Redemption of the hyenas. It turns out that they are fabulous hunters, working cooperatively, taking down beasties ten times their size. They have one of the highest percentages of successful hunts of any big carnivore. And you know who has one of the worst? Lions. They’re big, conspicuous, relatively slow. It’s much easier for them just to key in on cheetahs and hyenas and rip them off. That’s why all those hyenas are lurking around at dawn looking mealy and unphotogenic-they just spent the whole night hunting the damn thing and who’s eating breakfast now?”

That new instruments can enable new insights is hardly a revelation. The surprise is that these technologies have been better employed explaining animal behavior on the African savannah than exploring human behavior in more corporate environments. Just as night vision revealed the king of the jungle to be more scavenging bully than noble hunter, techniques such as network monitoring of software systems and video-based surveillance of employees at work can give innovators provocative perspectives on the predator, prey and scavenger relationships of their own customers.

For example, a medical device company (which understandably wants to remain anonymous) decided to surreptitiously videotape, with hospital administrators’ permission, how nurses actually used its prototype drug delivery system. The company quickly recognized that its product wasn’t being used the way it was supposed to be. Moreover, it discovered what kind of shortcuts the nurses would take-creative and otherwise-to get the system to work, and at what points they would either ask for help or simply give up. This information proved enormously helpful and led to a fundamental redesign of both the product and how hospital staffs are trained to use it. That, in turn, completely changed how the company marketed its systems to hospitals and nurses. The firm has yet to decide whether to make video surveillance an ongoing practice.

“Innoveillance”-think of it as “next-generation” market research-isn’t merely a function of bandwidth. Vision here means deciding what kind of innovation behaviors should be observed. What customer behaviors matter? How can technology give innovators a window onto the adoption and adaptation of their offerings? In practically every realm of innovation, opportunities exist for clever ways of capturing customer behavior in the wild. We’ll talk about privacy later.

Look at how software is now evaluated, for instance. Empirically, it’s clear that most software adheres to the “80/20” rule-that is, roughly 20 percent of the features and functions generate 80 percent of the usage. Similarly, approximately 20 percent of the code is responsible for 80 percent of the problems. These distributions are well known in software-engineering circles. Yet surprisingly, most Fortune 500 companies don’t really know the “80/20” drivers of their own software usage. While many firms track glitches, bugs and crashes, they’re frequently unaware of which features and functions are used most often. Indeed, even software innovators are remarkably ignorant of how their code is actually used.

Software innovators and their more introspective customers may be better off using “remote diagnostics”-spy technologies that capture workers’ activity in the natural environment-to anticipate customer use, instead of tabulating system crashes. How do users actually use new technology or follow new practices? What innovative new products, services and upgrades might these patterns suggest?

The same questions apply to products ranging from jet engines to automobiles to cell phones to personal computers to telecom networks to machine tools. Emerging infrastructures enabling remote diagnostics, combined with the ability to monitor and simulate usage patterns, create the corporate counterparts of night vision. Companies can actually choose what level of innoveillance makes sense for them. Innoveillance-generated insights are what will give innovators awareness of how their ideas are actually adopted. Market research, which is compiled by asking people in surveys or focus groups how they behave, blurs into jumbles of demographic statistics. Traditional maintenance becomes a medium for novel upgrades that serve no real purpose.

Do nurses, doctors, airplane pilots and white-collar workers want to have their keystrokes captured and their movements recorded? Probably not. Do questions of privacy and proprietary use have to be negotiated anew? Of course. Innoveillance represents yet another battleground where innovators and their customers will clash over the future of value creation. But the fundamental observation remains the same: the ability to intelligently discriminate between how people actually behave and how they are supposed to behave is critical to understanding how ideas spread. The marginal cost of providing that kind of vision is declining; the marginal value of having that kind of vision is climbing. You can’t see what you’re not looking for. Open your eyes.