If there's one entrepreneurial topic that's difficult to generalize about, it's business models. What applies to a local restaurant--and the way it makes money--doesn't necessarily apply to an auto manufacturer or a software-as-a-service startup.
But in 2016, there's a way that most business models are potentially connected. The connection is a concept innovation expert Robert Tercek, in his new book, Vaporized, calls "dematerialization." Don't let those eight syllables throw you: Just think of how Uber and Lyft have replaced ownership of a physical automobile with access to transportation on demand. In other words, the material object--the car--has (figuratively) vaporized.
Of course, Uber and Lyft have been around for several years now. "But what's less obvious and less discussed is the gradual 'uberization' of everything you can possibly think of, from laundry and grocery shopping to education to leasing clothing," says Tercek.
The continued "uberization" of everything is one of several new or emerging business model trends you'll see more of in 2016. Here's a short list, assembled from conversations with Tercek and other experts.
1. Adding data collection as a revenue stream.
If you're wondering how this could be the case for say, a traditional manufacturer, Tercek recommends asking yourself and your top team a few simple questions: What part of my business consists of information? Can it be delivered separately from a physical product? And if so, how can I learn from it and monetize it?
The answers to these questions might surprise you. If you make clothes, you might digitize all of the cleaning info on neck tags into an app, which would help you learn about your customers' cleaning problems. If you make and install windows, you might find a way for your installations to capture solar data. The overall idea is to re-imagine your business around actionable data. What can you learn from your customer base that you--and perhaps other companies--would find useful?
For example, Tercek points out that automakers are now seeing how Uber's information capturing--about consumers, regions, traffic patterns, you name it--has value on its own. That basic insight, he believes, is the first step in a sweeping overhaul of their strategy. Think of it this way: Five years ago, you could buy a new car, and there was a good chance that once you left the lot, the automaker lost the chance to learn anything about your driving habits.
Those days are over. From this point on, there'll be enough software in cars--and other objects formerly thought of as strictly hardware--to capture data that can yield potentially lucrative insights about customer behavior. Your car itself may even become "uberized," packed with software that will alert online populations about when you're not using the vehicle--allowing you, the ostensible owner, to monetize it by lending it to other drivers. Tercek can see something similar happening with pricey, seldom-used consumer equipment like lawn mowers and power tools.
2. Facilitating collaboration between machines and humans.
So let's say an automaker--or a dishwasher maker or any manufacturer--becomes adept at capturing data about its users. And let's say that data becomes a useful piece of research for a project team tasked with innovating around an existing product. How can this project team mesh insights from the data with the social intelligence that humans bring to decisions?
That's a question more and more businesses will be puzzling over, as more and more customer data becomes available to them. Brian Uzzi, professor at Northwestern's Kellogg School of Management, believes that in 2016, you'll see new business models aiming to address the challenges of meshing human and machine learning. He calls this category of business model "thought partnerships."
"For business leaders, managers, experts, and everyday people, machines will be an extremely important part of augmenting our decision making," he says. As a big-picture illustration, he points to how on the game show Jeopardy!, human contestants can blow opportunities if they're nervous or low in confidence after a few wrong answers--but machines like IBM's Watson don't have that problem. "They do not get stuck or anchored to things in the past," he says.
Likewise, he says, humans have a tendency to let the visual cortex--the part of the brain that rapidly processes what we see--influence decision making. The visual cortex can help you survive in a jungle, but the downside is that it can also lead you to rely on problematic biases in, say, the hiring process. A machine-based evaluation of job candidates--which can overlook things like a person's appearance, skin color, and gender--can help employers detect those biases.
Of course, first visual impressions do matter in the business world--which is another reason Uzzi believes that you'll have a successful business model on your hands if you can find a way to blend machine and human knowledge in big decisions. Large human resources departments would pay handsomely for a tool like that. So would companies with countless team-based projects. For instance, you might have an instinct about which of your employees work best with one another, on small or large teams. But in the future, you'll be able to capture hard data about which employees are the best collaborators--and how it can change, depending on circumstances like team size, time of year, teammates, and the nature of the project.
For example: What if you had data about the way your employees use Dropbox? Imagine you knew which employees were the ones who always initiated a document--and which ones had the shortest waiting times before others jumped in to work on it? And further, which ones had the strongest track record of punctually finished projects? Uzzi sees 2016 as the year when new business models might help company leaders find information like this--and make better decisions based on it.
3. The end of the "business model" as a long-term proposition.
In a fast-changing world, where the manufacturers of today are reinventing themselves as tomorrow's software-as-a-service providers, there's a new-found need for leadership teams to constantly question their current business models--even if those current models are lucrative--for missed opportunities.
What's the best way to question your current business model? Ask yourself--with no mercy--if you really, deep down, know how your customers feel. Simple as it sounds, this question can lead to some stunning insights. In his forthcoming book, Small Data: The Tiny Clues That Uncover Huge Trends, branding expert Martin Lindstrom describes how while speaking at the annual executive retreat for Ansell--a global manufacturer of medical gloves and condoms--he handed a condom to everyone in the audience. Then he turned off the lights--and asked the execs to open their condoms.
One minute later, with the lights back on, he writes, not a single exec had been able to open the packaging in the dark--which is probably the most common situation their customers face when they need to use a condom. How could this happen--at a successful company, no less? Lindstrom says it sometimes happens when you're successful. "At large companies, you can look across different divisions. And all of them are fine-tuned machinery for one part of the puzzle," he says. "But often there's no one doing the holistic work of looking at the big picture."
So when it comes to questioning your business model in search of opportunities, think of your customers' emotional experiences. And ask yourself, honestly, if you're still in the dark about them.