Do you get the feeling apps are getting dumber? They are, and that’s a good thing. Behind the surprising simplicity of some of today’s top apps, smart developers are realizing that they’re able to get users to do more by doing less. A new crop of companies is setting its sights on changing the small behaviors in your life, hoping to reap big rewards.
They’re using the best practices of interaction design and psychology to build products with your brain in mind. Here’s how they’re doing it:
Be a Feature
Famed venture capitalist Fred Wilson insists that successful mobile products need to do just one thing well.
App designers often forget the speed and attention constraints people experience while using their products. Testing your app in the office, while it’s connected to wi-fi and is the focus of your attention, hardly represents the hectic, real-world conditions experienced by most users. Mobile services not only compete for our attention with the other umpteen things we could do with our smartphones but also have to vie for our focus with the many offline distractions associated with life on the go.
For example, Voxer‘s simple walkie-talkie interface gives new functionality to the smartphone by replicating the “push to talk” experience within the app. Its few options give users limited functionality but focus on the one thing the app is built to do — send short audio messages.
Let’s admit it, we in the consumer web industry are in the manipulation business. We build products meant to persuade people to do what we want them to do. We call these people “users” and even if we don’t say it aloud, we secretly wish every one of them would become fiendishly addicted.
Users take our technologies with them to bed. When they wake up, they check for notifications, tweets, and updates before saying “good morning” to their loved ones. Ian Bogost, the famed game creator and professor, calls the wave of habit-forming technologies the “cigarette of this century” and warns of equally addictive and potentially destructive side-effects.
When Is Manipulation Wrong?
Manipulation is a designed experience crafted to change behavior — we all know what it feels like. We’re uncomfortable when we sense someone is trying to make us do something we wouldn’t do otherwise, like when at a car dealership or a timeshare presentation.
Yet, manipulation can’t be all bad. If it were, what explains the numerous multi-billion dollar industries that rely heavily on users willfully submitting to manipulation? If manipulation is a designed experience crafted to change behavior, then Weight Watchers, one of the most successful mass-manipulationproducts in history, fits the definition.
Much like in the consumer web industry, Weight Watchers customers’ decisions are programed by the designer of the system. Yet few question the morality of Weight Watchers. But what’s the difference? Why is manipulating users through flashy advertising or addictive video games thought to be distasteful while a strict system of food rationing is considered laudable?
Right now, someone is tinkering with a billion dollar secret — they just don’t know it yet. “What people aren’t telling you,” Peter Thiel taught his class at Stanford, “can very often give you great insight as to where you should be directing your attention.”
Secrets people can’t or don’t want to divulge are a common thread behind Thiel’s most lucrative investments such as Facebook and LinkedIn, as well as several other breakout companies of the past decade. The kinds of truths Thiel discusses — the kinds that create billion dollar businesses in just a few years — are not held exclusively by those with deep corporate pockets. In fact, the person most likely to build the next great tech business will likely be a scrappy entrepreneur with a big dream, a sharp mind, and a valuable secret.
Where are the Secrets?
I believe secrets about human behavior, which provide insights into the way people act even though they can’t tell you why, are levers for creating user habits and competitive advantage. These kinds of secrets are also relatively cheap to uncover but can be the basis of massive enterprises.
Once, only large companies had the resources to discover monetizable secrets. Throughout the twentieth century, companies like GE, Dupont, Chrysler, and IBM specialized in discovering the optimal form of physical goods and their insights lay largely hidden in the discipline of industrial design. For these companies, uncovering secrets required massive R&D investment to find the best way to create a better, cheaper, or faster product.
If you’re like me, you’ve had enough of the Facebook IPO story. For tech entrepreneurs struggling to build stuff, the cacophony of recent press is just more noise. That’s why when my friend Andrew Chen posted an insightful analysis of Facebook user data, I was happy to get back to learning from what the company did right instead of debating what its bankers did wrong.
Chen calculated Facebook’s historical ratio of daily active users (DAU) to monthly active users (MAU) and the stats are startling. Since March 2009, when the earliest data is available, approximately 50% of Facebook users logged in daily.
As other technology companies struggle to maintain DAU to MAU ratios of 5% or less, Facebook’s numbers appear stratospherically high in comparison. But what is equally surprising is the consistency of that ratio over time. Despite periodic user revolts in reaction to changes in the site, the ratio remained strangely stable. In fact, the number has risen over the past year and is now hovering at 58% as of March of this year.
It’s as if Zuckerberg has steered the company by this golden ratio. Which raises the question: is there some wisdom here regarding this ratio as a predictor of Internet success? Obviously, there are no guarantees and starting cutting edge tech companies will always be risky business. But, assuming you have a solid business model, there are good reasons to believe that if there is one metric to focus on while building your business, it’s the percentage of users who come back daily as expressed by this ratio.
It’s time to abolish the reference check. The unpleasant process of calling up a job applicant’s former boss to gab about the candidate’s pluses and “deltas” is just silly. Maybe if we all just agree to stop doing it the practice will go away, like pay phones and fanny packs. Instead, I’ve learned a better way to hire that leverages a universal human attribute—namely, the fact that we’re all lazy.
What’s my beef with reference checks? They don’t accomplish the job we intend them to do. In a startup, you can’t afford to hire B-players. But reference checks, which are intended to do the screening, fail to eliminate these candidates who are just so-so. This happens because the person giving the reference has no incentive to say anything but good things about the candidate. Telling the whole truth, warts and all, could expose the former boss to a defamation lawsuit. But legal action aside, no one likes to speak poorly about an ex-colleague. It’s bad karma and just feels icky.
Instead of asking a reference to call you and spend an awkward half-hour chitchatting about pretty much nothing, try a technique I’ve come to call it the “average-need-not-apply” method. Though I’m not sure who invented it, the approach was taught to me by Irv Grousbeck at Stanford.
Rather than using conventional feedback loops, companies today are employing a new, stronger habit-forming mechanism to hook users—the desire engine.
At the heart of the desire engine is a variable schedule of rewards: a powerful hack that focuses attention, provides pleasure, and infatuates the mind.
Our search for variable rewards is about an endless desire for three types of rewards: those of the tribe, the hunt and the self.
In advertising, marketers reinforce a behavior by linking to the promise of reward. “Use our product,” they claim, “and you’ll get laid”; it’s the gist of many product pitches from soap to hamburgers.
But online, feedback loops aren’t cutting it. Users are increasingly inundated with distractions, and companies find they need to hook users quickly if they want to stay in business. Today, companies are using more than feedback loops. They are deploying desire engines.
Desire engines go beyond reinforcing behavior; they create habits, spurring users to act on their own, without the need for expensive external stimuli like advertising. Desire engines are at the heart of many of today’s most habit-forming technologies. Social media, online games, and even good ol’ email utilize desire engines to compel us to use them.
Note: This post originally appeared in Techcrunch. I’m proud to have co-authored this post with Katy Fike, PhD. Dr. Fike is a gerontologist, systems engineer and Partner at Innovate50, a consulting firm helping companies create products and services for the 50+ market
As web watchers, entrepreneurs, and investors search for the next big thing, they’d be wise to focus on innovations that can be easily adopted by technology novices. A recent string of companies, including Groupon and Pinterest, have found success outside the early-adopter digerati by building products simple enough to be used by just about anyone. Designing with tech novices in mind can mean the difference between staying niche and going mainstream. Here are three principles for designing software for people Silicon Valley too often disparagingly calls “normals.”
What’s It For?
Don’t tell them “how it works” or “what it is” and certainly don’t tell them how wonderful your company is. Just tell them in big, uncluttered, blatantly obvious terms what your service is for. Novice users need to know when your service would be useful in their lives.
Take a look at Twitter’s homepage for new users. It says simply, “Welcome to Twitter. Find out what’s happening, right now, with the people and organizations you care about.” Same story at Facebook. “Facebook helps you connect and share with the people in your life.” Brilliant! Now the tech novice knows, in no uncertain terms, when and why these sites would be useful. Twitter is for knowing what’s happening and Facebook is for connecting and sharing.
Recently, my mom came for a visit. She read my blog and discovered her son has a crazy habit of running barefoot. After some convincing, she begrudgingly accepted my rationale, especially after I showed her that a nice Jewish professor at Harvard said it’s ok.
But on one morning, as I was about to walk out the door, my mom stopped me with a tight grab to the arm reminiscent of my childhood. “It’s bad enough you run outside with bare feet but you look ridiculous running with these cheap shmatte gloves.” She always had an eye for spotting the quality of apparel and she correctly identified my Wal-Mart bargain bin gloves, which I bought for $2 per dozen.
“Why are you wearing these things?” she exclaimed. “You must be cold! Let me get you a nice pair of warmer gloves. You’re cold, right? Is that the reason?”
“No,” I said. “It’s not.”
She tried again. “It must be a fashion thing then. The kids are not wearing shoes on their feet but they’re wearing gloves on their hands.” This time she was sure she’d deduced the reason. “So at least let me buy you some nice quality gloves from Bergdorf. You want to be in with the times, I get it. Is that the reason you wear gloves when you run?”
Reading Leena Rao’s recent article on Techcrunch about the personalization revolution, you get the sense that the tech world is waiting for a bus that isn’t coming. Rao quotes well-known industry experts and luminaries describing what needs to happen for e-commerce to finally realize the promise of personalized shopping, a future where online retailers predict what you’ll want to buy before you know yourself.
Ironically, Rao and her pundits are missing the zooming race car that’s speeding by them as they wait for the personalization bus to arrive. That racecar is Pinterest and the new breed of startups marking the beginning of what I call the “Curated Web.”
The promise of personalized e-commerce began over 10 years ago with technology pioneered at Amazon. It was then that the mental dye was cast for what eCommerce personalization would look like, an algorithmic solution for matching customer to products. Web watchers came to expect that someday all online retailers would have such algorithms on their sites and the dream of personalized commerce would finally be realized.
For over a decade, startups took their best shot at making this apparition a reality. Companies like Hunch tackled the data collection piece of the equation, asking users endless survey questions to determine their tastes and preferences. Google’s Boutiques.com tried to crack the challenges of structuring the data associated with personalized shopping recommendations. Ultimately, these attempts failed.