(This piece was originally published on January 15, 2018)
By the middle of February, 80 percent of people promising to change their behavior at the start of the new year will have nothing more than six weeks of self-imposed misery to show for it.
At least, that’s what leading psychologists say the data shows.
There are many reasons people have a tough time keeping their New Year’s resolutions, starting with the tendency for them to set unrealistic goals that are broken out of the sheer frustration that comes from climbing the very steep hills required to keep them.
More fundamental is that most resolutions require that people break old habits — and form new ones.
And that takes time — particularly when the habits that people set out to break are typically well-ingrained over a period of years.
There’s data on that, too.
Researchers at University College London say that getting a consumer to change habits, on average, takes about 66 consecutive days of doing that new behavior — with a range of between 18 and 254 days, depending on the degree to which old dogs really do have to learn new tricks to make them stick.
It explains why many fitness trackers, gym memberships, home exercise equipment, or books or apps on physical and financial self-improvement bought at the start of the year begin to collect dust right around Valentine’s Day — just in time for a big romantic dinner.
People simply haven’t invested enough time in learning these new behaviors for them to become the new routine.
And, they haven’t seen enough of a reward to commit to continuing.
It also explains why some innovations in payments have been met with open arms by consumers and others given the cold shoulder. And why advances in the field of voice and visual, particularly when applied to authentication, are poised to become such a disruptive force across the payments and commerce landscape. And why those who enable it will be among 2018’s power brokers.
In fact, we’re seeing it happen in a variety of retail, banking and broader commerce pilots and prototypes today.
What Does It Take for a New Habit to Stick?
In 2010, two researchers at MIT’s McGovern Institute for Brain Research published a paper that shed new light on how the brain makes decisions to stick with or change routine behaviors. Their work was credited as the first to explain, scientifically, why routine behaviors — aka habits — are more or less on autopilot for humans.
Their study concluded that routines are shaped by how the brain’s neurons process information about the cost and benefit of switching things up — or not, and sticking with the status quo.
What was unusual about their work was the “real world” environment they created to run the experiment. So to do that, they used monkeys who are a lot easier (and cheaper?) to recruit than MIT kids.
But here’s the real trick. Rather than first training those monkeys to perform a specific task and then introducing change, the researchers introduced the monkeys to a series of experiences that mimicked the complexity of decision-making that humans encounter every day when going about their day-to-day routines. This method, they believed, was the best way to understand not only how habits were formed, but also what it would take to change them.
The monkeys were exposed to a sea of white and green dots, and rewarded when they looked at one of the green dots in the grid. Over the course of the experiment, through a series of 1,000 daily tasks, the researchers changed how the white and green dots were presented to the monkeys.
They observed that the monkeys changed their behavior — in this case, looking at the green dots — when the cost of doing so was minimal. In this experiment, the cost was measured as the time and distance required of the monkeys to move their eyes to find a green dot and receive a reward. Shorter distances to a green dot drove a consistent change in the monkey’s routine deviating from their previously acquired habit of going to other green or white dots.
In 2015, these two researchers went back into the lab to dive deeper.
What they found not only supported their initial findings — that the shortest distance between two points resulted in a change in behavior — but that rewards would not change that behavior if the brain’s neurons imputed that the cost of the change was too high.
In other words, big rewards don’t produce lasting changes — or even any change — if the behavior change was too great — even when the monkeys were offered a reward if they did made such a change.
The researchers concluded that brains make real time judgements about tradeoffs where changing habits isn’t the result of dangling a big reward in front of, in this case, the monkey. The application to the human brain? That money or rewards or the offer of free stuff may not be enough to persuade someone to change a habit.
The bigger the change required of a person, the more likely that considerations related to time, convenience and other intrinsic benefits will weigh more heavily on their decision to switch or stand firm.
And where decisions about routines and habits are already hard-wired and made reflexively without people thinking too deeply about making — or changing — them.
Why Changing Consumers’ Habits Isn’t About Changing Them
Of course, we’ve seen evidence of this hard-wired behavior play out in our own payments and commerce stomping grounds over the years.
For 50 years, consumers have been trained to produce a plastic card of some kind at the point of sale to pay for something. They know they can do that anywhere they shop — the biggest of the big stores and the smallest of the mom-and-pop shops — and that their card will be accepted and will work.
It’s why they might have grumbled at first about dipping and not swiping when EMV was first introduced, but two and a half years later still produce a card at checkout in the store. That’s despite an inconsistent checkout experience at the in-store point of sale — where dipping can be a short or long wait, and where swiping and not dipping may still be the checkout protocol.
And it’s why mobile wallets — except for a few noteworthy examples such as Walmart and Starbucks, that each have ubiquity across all of its stores — have struggled to get any meaningful traction despite this inconsistent EMV checkout experience.
The switch — that uncertainty about whether the merchant has or doesn’t have NFC at the point of sale — comes at too much of a cost, at least now. It requires too much of a shift in consumer behavior.
What’s consistent is that the consumer produces the same plastic card that has worked year in and year out for more than five decades.
That’s also why investments are being made to improve the form factor of a physical card — whether it is introducing contactless capabilities or creating cards with biometric IDs — and are being piloted by the card networks and issuers. And why innovations that introduce more intelligence into the digital versions of those accounts that require little to no change of the consumer are highly valued by consumers, merchants and issuers.
It’s also why mobile order ahead has seen such speedy growth.
Over the last five or so years, consumers have increasingly used their mobile devices and apps to discover and buy things. Mobile order ahead eliminates the pain of standing in line at the coffee shop, fast food joint or sandwich, salad or pizza place, and rewards the consumer at a minimal cost. By ordering ahead using their phones, customers are simply applying their online shopping behavior to a new, and very habituated, use case.
It’s why I believe that remote payments will ultimately change the in-store point of sale experience. Making that shift delivers a reward that’s much bigger for the customer, without requiring a wholesale shift in consumer behavior to achieve those gains. It’s just another use case for digital order ahead.
It’s why consumers default to Amazon when they buy online, and why the eCommerce giant now owns north of 40 percent of all online spend, based on Q3 2017 retail sales data. It’s easy to start and finish on Amazon, as consumers have been trained that they generally find what they need when they visit.
It’s also why consumers are increasingly comfortable using their voice to “ask Alexa” to help them buy things — in their cars, on their phones, at the office, in the kitchen, in the laundry room and even in the bathroom. It’s easy and convenient, uses an interface that is ubiquitous — their voice, and with Echo Show, visual — and comes to them from a trusted commerce intermediary.
It’s what Google is hoping Google Home and “Ask Google” will do for them.
In 2016, consumers “asked Google” to perform at least two trillion searches every year. This year, Google reported that 15 percent of those searches were for brand-new search terms, but down from the 20 to 25 percent they claimed in 2007.
Consumers have other places now to “ask” for things — including social networks and Amazon for commerce. Research that we did in 2015 on this topic then reported that nearly 60 percent of all searches related to commerce started in Amazon. It wouldn’t surprise me if that number was well into the 60 percent range today.
But, like Amazon and Alexa, Google — with Google Home — is allowing third-party hardware and solutions providers to leverage its voice commerce ecosystem — and is courting merchants who have an interest in competing with their biggest retail rival.
This Is the Human Brain on Habits
Today, voice technology is being used mostly to remove friction from routine tasks — asking Alexa (mostly) or Google Home to turn lights on or off, lock and unlock doors, play music, find out how many cups in a quart while cooking, adding items to a shopping list, turn washing machines on or off and now, um, even flushing the toilet. It’s a way for consumers to use what they all know how to use — their voices — to do a series of things that they’d perhaps use their voices to ask someone to do for them, anyway. Instead of asking Mom to turn on the lights or Dad to turn off the television, Alexa or Google Home can just make it happen.
But what if instead of producing a key to lock and unlock the front door, a peephole in that front door was a camera that used iris scanning to allow access only after the owner or other authorized users presented themselves? And used voice biometrics to confirm that the request to lock or unlock the door was coming from an authenticated user? And if that front door authentication was integrated with the various commerce use cases associated with the variety of connected devices inside that smart home?
We’ll have to wait and see what Amazon has in store for Blink, the Boston-based outdoor security camera company it bought last year. And Echo Show, the device with a screen and television, now that Prime and Alexa are available through Apple TV.
Or if, as we saw at CES last week, rearview mirrors in cars were used to authenticate consumers via iris scans when they sat down in the driver’s seat, and preauthorized them to make purchases at the places they might want to buy things on their digital drives — gas stations, QSRs, parking garages.
Or, if as the folks at Amazon are fond of saying, check-in does, in fact, become the new checkout at retail. And the combination of voice and visual in stores becomes how consumers are authenticated when they enter the store, or at some point in their shopping journey before checkout. In each of these hypothetical use cases, authenticating the consumer, staging her for a secure, digital commerce transaction, was done in the normal and ordinary course of her day-to-day routine.
Locking and unlocking the front door.
Looking into a car rearview mirror.
Talking to someone in a store.
Looking at a screen.
Now think about the endless number of use cases where consumers today speak to or look at something in the course of enabling a commerce transaction — and how innovations in authentication, digital commerce, and tokenization could eliminate checkout friction by, well, making checkout part of check-in.
By triggering secure ways to pay digitally into doing what comes naturally to consumers.
Looking. Talking. Asking a question.
Voice and visual biometrics have the great potential to build on that idea and make commerce a secure, contextualized experience for consumers wherever there’s a connected device and an opportunity to do business. And not just in a retail setting. Smart people have been working on adapting very complicated technology to use cases in banking, healthcare, insurance and government, to name but a few.
The beauty is that in each case, they build on old habits — not new ones — to provide significant gains across the ecosystem.
It’s been proven that’s good enough to get monkeys in the lab to change their habits.
My guess is that it’s going to be good enough for their more evolved and smarter primate cousins, too.