“Riddle-Thinking” Applied

Problem solving in the real world often cannot be done through simply analyzing information or facts as they are given to you. Companies often face this issue when trying to market their products to an international audience consisting of different cultures. Multinational chains like Starbucks, McDonalds, and KFC often fail to break into specific countries and expand as successfully as they have in the US or western Europe. The following example illustrates how “riddle-thinking” can help companies be more astute.

Imagine a CEO who invests a lot of money into marketing his product internationally, having found immense success locally. He finds the best translators, marketers, and distributors to get the product launched in several countries abroad. Out of four new markets, all do well except one—where the product flops spectacularly and he loses money. He has to figure out why and fix the issue soon or consider withdrawing his presence, as it’s simply costing too much.

The team at the company’s headquarters mull over the reasons it failed. They look at everything they can think of—the local economy, the price point, the market in general, even the political and cultural climate in the country in question. They find nothing to explain the dismal sales. They run through a list of questions much like the one in the previous section, and soon realize there’s something big they’re not seeing. It’s a real riddle!

The problem persisted until the CEO heads to the country himself to see what’s up. Within just a few moments of being in a store that sells his product, he spots the problem. The color of the package design closely matches another completely unrelated product in the store—a muddle that has rather embarrassing connotations for his own product. By being literally in the store that his potential customer is in (i.e. the context), he sees the problem—people are mistaking his product for something else. He goes home, completely changes the design of the product, and soon sees sales pick up in that country.

In this case, no amount of analyzing and mulling over potential strategies would have helped the CEO or his team realize what the problem was. Nor is it possible for them to account for each and every factor that influences the success or failure of a product in a given market.

Ultimately, it turned out that the problem was never with the product itself, but the way it was being perceived due to its similarity to another product. The CEO utilized some divergent thinking to try to account for more localized factors by visiting the country where his product had failed. Since his product succeeded in three of the four markets he had launched it in, he could probably infer that the product itself was not the issue. Instead, it had to be something restricted to the one place where it failed, and the CEO turned out to be right.

Steve Jobs used fairly similar tactics when he was first trying to promote the MacBook. Knowing how laptops are displayed in stores everywhere, placed in a line on display one beside the other, he was keenly aware that the MacBook would be perceived as just another laptop when placed alongside others. Sitting beside a Dell or an HP laptop would undermine the fact that it ran on a software that was completely different, among other novel features.

To solve the issue, he came up with the idea of having a separate section for Apple laptops to help it stand out from its competitors. This eventually resulted in Apple deciding to run their own stores in the US and, later, worldwide. Just like the CEO in the example, Jobs put himself in the shoes of his customers and simulated their experience while buying a product to enhance the way it is perceived in the market. If we go back to the steps laid out in chapter one, this enabled the CEO to implement step two, which was research more effectively since he now has the relevant information that he did not previously possess. This, in turn, helped him solve the problem.

Example 2

A certain much-loved cheese is usually made traditionally, but manufacturers are increasingly producing it in factories. A company decides to scan the country for

local artisans to find out more about their recipes and techniques, so they can replicate them in their own massive industrial kitchens. An engineer looks at a farm cheese industry and notices the many steps it takes to make the cheese. He also notices that the farmer spends considerable time running back and forth with trays of milk and curds at varying stages of the cheese-making process. The engineer makes a note of the process and technique and knows that he can build a more efficient factory setup that removes the need to run back and forward between sections of the kitchen or carry trays.

He designs a complex cheese factory mechanism that speeds up the process. The company excitedly awaits the day when they start producing this delicacy on a larger, more profitable scale. And then one day, the cheese is ready . . . and it’s terrible. Nobody can figure out what’s wrong. All the right ingredients have been used (even better ones!) and the techniques followed with machine-like accuracy. After spending all this money, the company soon realizes they didn’t grasp the totality of the cheese-making process as well as they thought. What did they miss? It’s a real riddle. What else goes into cheese making that they didn’t include in their ultra-modern and sophisticated factory process?

They keep asking this question and keep getting the same answer: nothing. They did everything correctly and ultra-efficiently. The engineer looks again at his

assumptions. Could the problem actually lie in the solution itself? He goes back to

the cheese farm and looks again and suddenly understands. By dawdling and

running back and forth with trays of curds and milk, the farmer actually gave the

cheese vital cooling time between steps of the process, resulting in a better, more

delicate flavor—something the factory design completely eliminated because the engineer labeled this part of the process “inefficient.”

Instead of looking to find what was wrong with the factory, he used riddle-like thinking and asked, could it be that the factory is too efficient? The engineer was able to see that his assumptions were unfounded. What he thought was simple inefficiency was in fact a vital part of the cheese-making process. He neglected this because his main goal was to speed up the process of producing cheese. Unable to account for any logical reason the farmer would spend so much time running back and forth with the trays, he simply concludes there isn’t one.

This is a mistake we are often prone to making in real life, but it is important to remember that the inability to rationalize something just requires a different approach.

Ultimately, after exhausting all explanations, he was forced to go back to the traditional method of making cheese to identify the factor he had missed. However, he might never have found the source of the problem unless he took the time to question a very fundamental premise, i.e. “the way we are doing this is obviously right.” When faced with a seemingly intractable issue, it isn’t always easy to admit that maybe our approach is wrong. However, recognizing mistakes is part of the process of learning, and the engineer only realized his mistake after admitting defeat.

All it took was challenging all the assumptions he had made in the process of designing his modernized factory.

Sherlock Holmes, the famous detective from Arthur Conan Doyle’s stories, frequently succeeds in solving the most complex mysteries due to this simple tactic. He rarely assumes anything, and keeps his mind open to all sorts of possibilities, ones that others unconsciously reject or do not even consider. He then uses these possibilities to form a chain of inferences that usually helps him perform the logical gymnastics his fans have come to love.

As the engineer was to discover, even one assumption can break the entire process. Technically, he did not make any errors in replicating the process itself, as he followed each step meticulously as the farmers did. Yet, his cheese came up short because it missed a key ingredient that the traditional method did not dispense with: time.

It is not clear that even the farmer is aware of this being a key ingredient of his cheese. He might not be carrying the trays back and forth repeatedly due to an awareness of its benefits, but simply the fact that the process ultimately produces great cheese. If the engineer had utilized his systems thinking more perceptively and tried to account for how each component of the cheese-making process contributes to its flavor, he might have saved himself much time and resources.

Example 3

Zookeepers have purchased a new and exotic pair of animals that they hope to breed with. The animals arrive, and everyone excitedly waits to see the results of their breeding program. But after years and years, the couple produce no offspring. The zookeepers research the animal in exhaustive depth. They do whatever they can to facilitate the process, and can’t understand what they’re doing wrong. Everything is perfect, and yet the pair won’t breed! In fact, the animals could often be observed fighting with one another despite the best efforts of everyone involved.

Eventually, a renowned animal expert comes to visit and sees the problem in a heartbeat—the zoo has mistakenly been trying to mate two females. In seeking ever more complicated solutions to get offspring from their breeding pair, the zookeepers have missed one crucial factor—they don’t have a breeding pair! This example is very similar to a real-life incident that occurred in northern Japan, wherein zookeepers spent years trying to breed two hyenas that ultimately turned out to be both male. They had been given the hyenas as a male and female couple, and did not think to question that until faced with their failure to breed them.

In both cases, the mistake was the very first step that the zookeepers took—forming the pairs of animals for breeding. Yet, when they tried to solve the problem, they never questioned this very foundational premise that they had long assumed to be true. It wasn’t the animals that were somehow failing to reproduce; they couldn’t possibly as a matter of biological fact.

Instead, the zookeepers had, going back to the steps in chapter one, failed to identify the specifics of their problem. As such, any future steps, like researching and identifying biases, were doomed to failure. Like in the previous example, the path to finding the right solution begins by accepting one's own mistakes. This is how the engineer and zookeepers both recognize their faulty assumptions, and are ultimately able to rectify them.

One important distinction between this example and the previous one is the fact that in the latter, the engineer himself makes a wrong assumption. However, here, the zookeepers are given information by someone in authority that they unconsciously take to be true. This makes it much harder to spot the problem, since it did not originate in the thinking of the zookeepers themselves. While the engineer doubted the farmer, the zookeepers trusted the animal supplier, yet both are problematic in their respective circumstances.

The next time you face some annoying, puzzling, seemingly impossible scenario in your own life, pause for a moment. Can you approach it like a game, a riddle, or a simple brain teaser? Some of the problems we encounter in life have enormously high stakes, but their underlying mechanisms may ultimately be no more complex than the “tricks” you’ve identified in the riddles above. Many businessmen, leaders, great thinkers, artists, and scientists have had extraordinary Eureka moments not by staying within their own conceptual frameworks, but in stepping momentarily outside them. The previous chapters outline the various ways of thinking they utilize to do so, and it is up to you to figure out which one suits your circumstances the best.

We can all learn different skills, different languages, and so on to succeed in life. But the most fundamental skills of all is the ability to think, to learn, to adapt, and to think about your own thinking. Do this, and any skill set is open to you. Better yet, you have the conceptual vocabulary to appraise your own processes, make adjustments, and improve.

As was mentioned before, we rarely think about the way we think. This process, called metacognition, is the key to avoid falling into logical traps, thinking lazily, or missing relevant information that is often staring us in the face. The best way to avoid this is to debate with yourself and challenge the conclusions you arrive at. This will help train your mind to consider all possibilities in any given situation, allowing you to see things more clearly and make correct inferences from the information available to you.