Our world is sentient. Websites watch where we look. Mobile applications keep track of our response times. Companies learn which buttons we like to press and which we don’t. With cameras, microphones, and thermometers, the human race is giving inanimate objects everywhere eyes, ears, and skin. And with all this observation, we’ve created a massive new layer of information.
Jonah Peretti, the CEO of Buzzfeed, knows that this layer of information can be used to test, learn, and iterate in rapid cycles. In this world, you can know, with some level of certainty, the way to craft the exact right title for an article — whether it’s investigative journalism or a cat video. “This isn’t possible in print, broadcast, or traditional films, which may be why the media industry is such a dysfunctional place,” Peretti has said. “Executives make huge bets based on gut, it’s hugely expensive to take risks, and most projects fail.”
But what if you could automate your gut decisions? What if machine intuition is better than human intuition? Trends like the pervasiveness of mobile, the near-infinite storage and computer power of the cloud, and new methods of analyzing masses of data are not just important in themselves. They’re important because they’re allowing businesses to develop new business models that better serve customers. Models that revolve around capturing information and putting it to work. In their simplest form, these businesses rely on the information that’s already there to provide automated intuition. In their most complex form, they flip age-old processes upside down to put a new type of information at the center of the business.
Consider entertainment. Buy a DVD, and the creator of the content learns practically nothing about you. That’s why film producers have no choice but to go with their gut. Netflix, on the other hand, sees every button you push, every movie you like, every TV series you finish — and every one you don’t. From its 40 million subscribers, it’s built an incredible understanding of entertainment preferences. This database has given rise to hit show after hit show, from House of Cards to Orange Is the New Black to Marco Polo (which was popular with audiences, if not with critics) According to Netflix’s own calculations, this data-driven original content is much more valuable for them than the content it licenses, because more people spend more time watching it — despite the high production costs of a series like Marco Polo.
Increasingly, we observe that the companies harnessing machine insight to augment or replace human intuition tend to be native to the internet. From Amazon with its pricing algorithms to Nest with its learning thermostats and smart smoke detectors, these companies have been born in an era where it’s natural to digitally track every interaction with a customer.
But companies from the industrial era are also figuring out how to capture digital insights and feed them back into their business to build advantage. General Electric has made machine-generated insight a priority. Over the last few years, the industrial giant has built an enormous software group dedicated to leveraging all the data they can extract from their sensor-laden hardware. The company realized that it would be much cheaper, for instance, to improve the uptime of turbines by sensing upcoming failures and rescheduling maintenance appointments than by investing in ever-more expensive parts that might break less frequently. Because GE is delivering machine uptime via 1’s and 0’s, instead of via improvements in metallurgy, it’s able to deliver that uptime at a far lower cost than its competitors.
Similarly, 30-year-old software company Intuit has invested extensively in putting its online Mint product at the center of its users’ lives. Many people only think of Intuit once a year, when they use its TurboTax software. But Mint, its product for personal budgeting and bill paying, is becoming the beating heart of its users’ daily financial lives. Mint collects users’ financial information in one place, learns their patterns, provides recommendations for better ways to save, offers investment options, and integrates with TurboTax. Mint has allowed Intuit, a tax software company, to slowly add more and more services otherwise provided by banks. The data that it already uses to help streamline your tax submission is invaluable for the algorithms that will ultimately replace the intuition and experience of financial advisors.
Every business can benefit from using digital intuition to compete. The key is determining how to catch the wave. We believe that three questions can help you position your business for success in the era of automated insights:
- What’s your customer’s job-to-be-done?
- In a perfect world, what information would help you complete that job?
- If you had this information, what inside your business would need to change?
What’s your customer’s job-to-be-done?
Customers don’t buy products just for the hell of it. Customers buy products because a job arises in their lives for which they need a solution. The job of “I need to get this document from here to there with perfect certainty” is one that has existed for millennia. In Ancient Rome, the job required Caesar to hire his best charioteer to ride to the front lines of battle. Fifteen years ago, when that job arose, the name of FedEx popped into peoples’ minds. Today, we hire encrypted email services. But the job is the same. Knowing the job-to-be-done that your product is being hired to complete is the only way to be sure that the improvements you’re making are going to deliver the experiences your customers desire.
The key to General Electric’s evolution was the realization that it was no longer a provider of industrial machinery. It was part of an ecosystem that delivered productivity for their customers. Its products were hired along with a plethora of systems integrators, aftermarket services vendors, and other industrial machinery companies, to help businesses do things like fly planes, generate power, and pull oil out of the ground. And any innovation in service of helping their customers operate more efficiently would be welcomed.
This might seem obvious, but it’s often missed. Managers typically focus on how to improve their products and services the way they’ve always been improved. They invest in annual R&D cycles aimed at continuously improving features as opposed to exploring how the ever growing sea of information at their fingertips can be used to help them fulfill the job that arises in their customers lives.
In a perfect world, what information would help you complete that job?
The next question you have to ask is which information would help you complete that job better. What information could provide you with all the insight you need to make the best decisions on behalf of your customers?
In the case of Uber, the company knew from day one that its job was to make it as convenient as possible for customers to get from point A to point B. The key information required was where people were located when they needed a ride. For cab companies everywhere, that’s always been the holy grail of information; unfortunately, none of them had done a particularly good job of capturing it. Instead, cab drivers were forced to rely on intuition and an understanding of the city to head to areas most likely to support fares. But the fact that taxis were already on the street gave them an advantage over car services that need to come to customers from central garages only after they’ve been summoned. Uber used information about both where the cars were and where the customers were to blend both models into one: allowing customers to simply push a button and be connected with an available, nearby, driver.
Many of us already have access to the information we need. It’s stored in our systems, or our customers are willing to offer it to us. Some of us will need to go out and get it — whether through a partnership, public databases, or the development of new offerings. But one way or another, the key is recognizing the job we’ve been hired to do and figuring out what’s the ideal information that would help us complete that job best.
If you had this information, what inside your business would need to change?
The final question is the hardest to answer. Even when we have the ideal information that would let us generate insight and displace our reliance on human intuition, we still need to identify what we would change as a result to capitalize on it.
Typically, this question forces us to think about developing new business models. Certainly, Intuit, Netflix, and General Electric all had to build models that relied less on human experts than they did on algorithms. Such a model typically reduces the need for employees and reduces prices to customers. Often, this is an unpalatable option for executives. But the reality is that the more we can use machine intuition to improve our existing services and lower prices, the more attractive our offerings will be to more customers — in turn further improving our machine-generated insights. Find yourself in a virtuous cycle like this, and the more likely it is that your businesses will flourish. And when businesses flourish and grow, it’s more likely we can support employment and profitability over the long term.
The reality is that we’re in a time of transition. Machine-based intuition is changing the way companies work. The question is: who in your industry will figure it out first?
