As digital tools proliferate and their capabilities multiply, one thing remains constant: what people don’t have more of is time. Our time is, arguably, one of the most precious resources we have on this planet.
Over the past eight years, as a part of our research for our annual Collaboratives programs, which focus on large cross-industry challenges, we have been asking consumers and companies to describe for us their desires and beliefs about new digital experiences. While their responses have varied, one common thread has run through every field study, survey, and interview we’ve conducted: no one wants digital tools that waste their time, while everyone wants digital experiences to offer time well spent. Too many apps, URLs, and devices do the opposite. There are two false premises that hinder the time value of digital experiences.
Two Myths About Creating Valuable Digital Experiences
Myth #1: Creating more “moments” for customers will lead to more value.
Your customers already have a wide range of digital solutions vying for their time every day. People value memorable moments when the time is right, but they don’t need more moments with digital technologies. What they do need is help orchestrating their lives so that their activities fit together in ways that make digital interactions feel like time well spent.
Myth #2: Building frictionless digital experiences will increase engagement.
The premise of an omnichannel strategy is that creating a seamless experience across channels boosts engagement. But today, a frictionless transfer between say, a bank call center and a branch location, is table stakes. It’s not enough to create engagement. People engage when smart tools understand them, their situation, and the jobs they want done. Companies that don’t understand that are wasting customers’ time.
How to Create More Valuable Digital Experiences
So, what should companies actually be doing to help customers feel like it’s worth their time to engage with your digital platforms?
Instead of “moments,” focus on “modes” — the primary way people get things done today. A mode is a mindset and set of behaviors that people get into temporarily; for example, we can be in work mode one moment, and mommy/daddy mode the next, then back to work mode, and then on to dinner-making mode.
For solution providers, modes provide context. When digital tools can ascertain what mode a customer is in, the intelligence of the tools grows. When people use solutions that recognize their modes, they become more empowered, feel like the tool understands their situation, and feel like their time is well spent. Both Apple and Samsung phones now support “focus mode,” and when the users turn on the feature, they’re telling the tool what mode they want to be in — not what mode they want the phone to be in. Tesla is famous for its modes, including Ludicrous (a 10% acceleration boost), Plaid (a step up from Ludicrous mode), and Dog mode (a climate control feature for people who need to leave their dogs in the car) — each of which taps into a different mindset and set of behaviors for drivers.
The future of digital engagement centers on such digital context — the data smart tools create and use, as well as the personal data ecosphere that people build to get jobs done in unique situations. As individuals incorporate more smart tools into their personal data ecosphere, companies that understand digital context can vastly increase people’s abilities while not negatively affecting their limited time. To do so, they need to understand these four different types of experiences their tools can yield, ranging from not-smart (technology that just wastes your time) to genius (technology that ultimately feels like time well invested).
The Continuum of Intelligence in Digital Experiences
Not-Smart Experiences
Some digital experiences not only don’t get the job done; they’re functionally frustrating. We’ve all encountered this: the faucet that sprays water at the wrong time or not at all; the red light where not a single car crosses; the “smart” thermostat that turns on the heat even when it’s hot outside; the app that doesn’t work, or worse, collects data solely to splatter you with ads. These tools do not care about individualizing, and they negatively affect digital context by not using data for your benefit (if at all), and in the end just waste your time.
No-Data Experiences
Of course, many tools people use today lack intelligence; they don’t collect or use data. Think of the classic jobs-to-be-done example of the drill, which is “hired” for the job of making a hole. No data, no context, and no problem — it gets the job done. Because they are data-deficient, such tools do not understand context and have limited individualization — you can change the size of drill bits, for example — and only do one job well. Such tools do not contribute data to the ecosphere, but can still be highly useful as long as they indeed get the job done.
Smart Experiences
People increasingly expect many daily activities to be smart: earbuds that you can talk to, doors that open automatically, thermostats that adjust to home use, TV apps that remember what you were watching last, and the list goes on. Smart means the tool: 1) Uses data to get the customer’s job done, 2) Senses and responds at the point of use for the person and supports digital context, primarily through modes. 3) Remembers individual preferences and does more jobs for customers. In short, smart tools benefit customers by being information-rich.
Genius Experiences
Eventually, almost all smart tools will become part of a genius platform. Genius experiences understand digital context through AI/machine learning, and don’t just sense and respond, but go one step further to anticipate people’s needs and desires, vastly multiplying the potential jobs to be done. These offer not just incrementally better smart experiences; they create something akin to superpowers. For example, ChatGPT can write a 1200-word article on digital customer experiences that speaks to time well spent. DALL-E 2 can create a photorealistic image of an intelligent car from a description in natural language. These are genius-level applications. Note how in this figure the three axes are all logarithmic in nature, increasing the potential jobs to be done exponentially.
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The world is moving toward more genius platforms. For example, genius platforms are starting to transform the car industry. Who wants to carry a key fob? Shouldn’t your car already know you and other drivers in your family? Shouldn’t it adapt to current driving circumstances? Shouldn’t it offer the best route for your destination — taking into account not just traffic, but your mood, preferences for back roads, say, and your desire for a certain soft drink, which it ordered ahead at your favorite burger shop because it knows your routine? Now that’s genius!
You can’t get to this level, though, with any number of independently smart products; it requires a platform that is context-filled based on each individual’s personal data ecosphere. Such platforms create an ecosystem across devices, capabilities, and companies that work together on behalf of individual customers, supporting them in home, at work, and across their lives. Alexa, for example, already has over a hundred thousand “skills” — each one fulfilling one or more jobs to be done. Amazon, in typical fashion, is shooting for hundreds of millions of skills for its platform.
So what separates genius platforms from smart products? It’s not just about exploiting artificial intelligence and machine learning, facial recognition and emotion detectors, adaptive technologies, predictive analytics, and so on. Genius platforms couple embedded intelligence with a relentless drive to understand and add value to individual, living, breathing customers in whatever situation they find themselves.
While smart products sense and respond to customer requests and established needs, genius platforms anticipate what the customer wants or needs in advance. While smart products customize outputs to the individual, genius platforms individualize offerings to the particular set of jobs the individual wants to get done in whatever mode they’re in at a particular point in time. And while smart products give customers some control over their circumstances, genius platforms use contextual clues to read a situation and figure out what to do on behalf the individual. And then they do it.
No-data tools can still be the right choice in many circumstances, but they rarely go beyond “time saved.” The intelligence embedded within smart tools can more readily offer “time well spent.” They will become the norm — especially as they are increasingly integrated into a platform that is, well, pure genius. And people may very well find that genius experiences become time well invested.
