7 decision errors in business

Over the holidays, I spent some time reflecting on decision-making. No, I am not talking about the good and bad decisions I made in life (there are plenty of both, I can assure you) but about how to make the right decisions. This dragged me down a rabbit hole in entrepreneurial science that I found incredibly fascinating. Researchers are very interested in finding out how business owners make decisions that grow their businesses and I think these lessons also hold true for people managing investment portfolios. Today, I want to focus on a super interesting classification system of decision errors developed by Mark Meckler and Kim Boal and what business leaders can learn from the mistakes others have made before them. Tomorrow, I will apply the same framework to provide tips for investors on how to make the right decisions in their portfolios.

I focus on decision errors rather than people making the right decisions because I believe that we can learn more from what went wrong than from what went right. Much of the outcome of our decision is due to chance or being at the right place at the right time, but when things go wrong, we typically can isolate the mistake much better and think about how we could have done better. By gradually reducing the mistakes we make, we are providing more room for luck to strike and become successful.

The chart below shows the seven types of decision mistakes Meckler and Boal have identified. In what follows, I will go through them one by one and show in examples, how businesses made these mistakes. We also provide suggested solutions to avoid or reduce these errors, though often, we need to refer to books where readers can learn more about the subject.

The seven types of decision errors in business

Source: Meckler and Boal (2017)

The first two errors are the most familiar errors for most of us. In statistics, they are called type I and type II errors.

Type I errors are errors where business leaders think there is a correlation or causation between two events when in truth there isn’t. We just had the FIFA World Cup and often companies expect a significant pick up in revenues from sponsoring major sports events or scheduling TV ads during these events. In the vast majority of cases, there is no sustained increase in revenues or profits based on such advertising or sponsoring campaigns. Most famously, there is the Super Bowl indicator in the US that says if a fast-growing young company starts to run ads during the Superbowl or even name a stadium, their business will falter shortly after. The most extreme case in point are the ads for the Superbowl in 2000, but the 2022 Superbowl featured ads from FTX, Coinbase, Crypto.com, Draftkings and others.

Type II errors are errors where business leaders think there is no correlation when indeed there is one. In 1985, Coca-Cola decided to change the recipe for its soda. They evaluated the New Coke flavour with non-customers and found that these people preferred the taste of New Coke over Pepsi. Unfortunately, they forgot to test if existing customers would be turned off by the new flavour profile, which was the case. When New Coke launched, it quickly became a nightmare for the company, and it had to reintroduce the old flavour profile as classic Coca-Cola.

How to deal with these errors? Luckily, in the 21st century, we can test small changes to a product with customers and see if the change improves sales, profitability, etc. Companies like Google, Facebook, etc. constantly make small changes to their websites and apps that are then rolled out to a selection of customers. This kind of constant A/B testing then allows the company to gradually improve the product over time. A good book to read on how constant experimentation can improve business outcomes and avoid type I and type II mistakes is Jim Manzi’s “Uncontrolled”.

Moving on to type III errors, errors where the wrong problem is solved. This often happens when a problem is examined too narrowly without input from a broad panel of experts. In 2016, Daimler Trucks of North America predicted that due to advances in technology and the electrification of vehicles, the demand for electric vans would rise exponentially. They then struck a deal with UPS and FedEx to provide them with electrified short-haul fleets and recharging hubs on the outskirts of major cities. It was just before the launch of the new fleet that a Department of Energy consultant alerted the company to the fact that most US cities have an electricity grid that was not equipped to deal with that extra demand and would collapse if the fleet was rolled out.

This is an example that shows how a lack of perspective can lead to costly business errors. To reduce these errors, increasing diversity is key. Diversity in terms of getting people to participate in a project with many different fields of expertise as well as the diversity of opinion and experience. And of course, it requires a business to foster a culture where dissenters can speak openly and frankly and where dissenting opinions are taken seriously and not dismissed. In other words, good corporate culture is key to avoiding these type III mistakes.

Type IV errors are errors where a problem is accurately identified but a suboptimal or ineffective solution is chosen. Think about companies that use clumsy blockchain technology for frequent payments or to accept cryptocurrencies as payment. Nobody has ever complained about traditional centralised payment systems like PayPal, Visa or others, yet blockchain technology was all the rage for a while even though it was far inferior in many respects to these traditional payment systems. Jumping on the blockchain bandwagon to solve a problem that could have been solved cheaper, faster and more reliably with older technology was a costly mistake made by many businesses in the last five years. In this context, Daniel Kahneman in his book “Thinking Fast and Slow” talks about the illusion of validity and how to overcome it.

Errors of type V and type VI are linked again. Type V errors describe situations where a business takes action even though no action should have been taken. Elon Musk fired half the staff of Twitter after he bought the company and then realised that this would endanger the app, thus forcing him to hire former employees back is an extreme case of such behaviour. But in general, the cost-cutting undertaken by many businesses during a recession to stabilise share prices and reduce losses is a classic example of taking action where doing nothing would be the better long-term solution. I will have more to say about this in a separate post next week.

Type VI errors, meanwhile, are errors of inaction when a company should have taken action but chose to do nothing. Kodak’s decision to not get into the digital photography market fast enough or Microsoft’s decision to ignore the rise of mobile operating systems for phones are examples of this type of error. Both these decisions meant that a company that dominated a market entered a long period of decline that in the case of Kodak eventually led to bankruptcy. Being lean and remaining flexible is key to avoiding such mistakes, though admittedly, this is hard to do for large businesses that have vested interests in certain cash cow businesses (e.g. fossil fuel companies missing the boat on the rise of renewable energy, banks missing the boat on the rise of Fintech, etc.). Eric Ries’ “The Lean Startup” is probably a good starting point to explore how a business can be built for agility and innovation.

And finally, there is type VII error, or the ‘irreversible iatrogenic cascade of errors’ as Meckler and Boal call it. Admittedly, I had to look up the word ‘iatrogenic’ as well, but it essentially means an inadvertently induced negative reaction to a procedure. In other words, type VII errors are errors that compound with each other involuntarily. Banks during the financial crisis of 2008 are an example of that kind of systemwide failure. Regulators had warned about the declining lending standards of banks as early as 2006. Yet, regulators believed Fannie Mae and other mortgage lenders when they said that existing regulation was sufficient to prevent a major catastrophe. This then led to a type I error on the side of regulators where they identified the correct problem but thought it would have no correlation with banking stability. At the same time, banks created risk management tools and models that were supposed to measure the value at risk due to a decline in house prices. But these models failed to anticipate a nationwide collapse of house prices and thus committed an error of type III that compounded the impact of the type I error when house prices started to decline. When more and more mortgages defaulted, banks got into trouble and started to fail. Bear Sterns was allowed to fail (no action was taken by the Fed to rescue it) and it was digested by the financial system relatively well. When Lehman Brothers collapsed, a decision was taken at first, not to take action, thus committing a type VI error that triggered a cascade that brought the entire financial system to the brink of collapse. It is such a cascade of errors together with a trigger that some may call a black swan that caused the global financial crisis. And for any normal business, such a type VII error is often existential and leads to the collapse of a firm. To guard against it is virtually impossible, though if you read the paper by Mackler and Boal you can get at least a few hints of how to address these issues.

This concludes this long post. Rest assured; we have done the groundwork for tomorrow’s post already where I will apply the same framework to investment decisions.