Know it alls vs. Learn it alls
“Know it alls” don’t find the answers, but “learn it alls” do.
Chances are you’ve seen a Nest thermostat before. You may even have one at home. Back in 2011, Nest broke new ground with the launch of the very first “smart” thermostat. It revolutionized the HVAC industry and smart home sector. That part is well documented. The back story, not quite as known, is that the thermostat almost didn’t make it to market – at least not the way we’ve come to know it.
Early in its development, Nest did what any company trying to solve a hardware problem might do: It handed the project off to a team of brilliant engineers. Their challenge: Create a thermostat capable of saving homeowners energy and money all at once. Within eighteen months, the engineers delivered: A beautiful, stainless-steel orb built on machine learning and artificial intelligence. It was a brilliant little device, unlike anything that had come before it.
But how do you get people to pay for it and welcome it into their homes?
So Nest did something unusual, at least by industry standards: It assembled a highly diverse team made up of design experts and marketing executives to work together with the engineers during next-stage development. Together, this team would have to solve the final challenge: Changing consumer behavior.
Imagine for a moment you’re the engineer sitting on that team. You’d have every right to be a “know it all.” Because let’s face it: What could the marketing guy sitting across from you possibly have to say about AI or alternating currents? You and your engineering group built this thing from the ground up, imagining every possible technical challenge and solving around it.
But that’s not how the Nest team approached it. They didn’t come at it as “know it alls.” They approached it as “learn it alls.”
“Know it alls” have all the answers – or at least they think they do. “Learn it alls” realize they don’t have all the answers – but they know they might find them, if they listen closely enough.
As you’d expect, these marketers didn’t know much about machine learning, but it turned out they knew a thing or two about creating products that people would love. And that’s the story of how Nest’s signature design – the little green leaf – came to pass: The engineers were solely focused on building a device that would save energy. So that’s what they did. But the marketers understood that saving energy wasn’t enough – sure, people liked the idea, but not enough to actually commit to its practice.
To change habits, the product needed a behavioral trigger, something that could act both as a stimulus and a reward. That’s where the leaf would help. When homeowners got into the habit of energy conservation, the leaf magically appeared on the panel, a digital badge recognizing and rewarding their eco-consciousness. The leaf became a point of pride, something people began to talk about around the dinner table and among friends and neighbors. Families competed to see who could score the most consecutive days with the coveted leaf. Suddenly, conservation was cool.
The early designs, produced by engineers, leveraged artificial intelligence to make the product. But it was the later design, informed by the marketers, that leveraged human dynamics to make the product lovable.
“Know it alls” don’t have the answers. “Learn it alls” do.
And if Nest didn’t try to tap into their team genius and learn from one another, it just might have ended up making the world’s smartest thermostat that nobody ever wanted.