Tag Archives: paul carroll

How We’re Wired to Make Bad Decisions

Business is a contact sport. Some companies win while others lose. That won’t change. There is no way to guarantee success. Make the best decisions you can, and then fight the battle in the marketplace.

Yet research into more than 2,500 large corporate failures that Paul Carroll and I did found that many big decisions are doomed as they come off the drawing board—before first contact with the competition. Why?

The short answer is that humans are far from rational in their planning and decision-making. Psychological and anthropological studies going back decades, including those of Solomon AschStanley MilgramIrving JanisDonald Brown and, more recently, Dan Ariely, consistently demonstrate that even the smartest among us face huge impediments when making complicated decisions, such as those involved in setting strategy.

In other words, humans are hard-wired to come up with bad decisions. Formulating good ones is very difficult because of five natural tendencies:

1. Fallacious assumptions: If “point of view is worth 80 IQ points,” as Alan Kay says, people often start out in a deep hole.

One problem is the anchoring bias, where we subconsciously tend to work from whatever spreadsheet, forecast or other formulation we’re presented. We tend to tinker rather than question whether the assumptions are right or whether the ideas are even worth considering. Even when we know a situation requires more sophisticated analysis, it’s hard for us to dislodge the anchors.

See also: Downsizing: Common Sense in Decision-Making May Lead to a Trap  

Another strike against expansive thinking is what psychologists call the survivorship bias: We remember what happened; we don’t remember what didn’t happen. We are encouraged to take risks in business, because we read about those who made “bet the company” decisions and reaped fortunes—and don’t read about those that never quite made the big time because they made “bet the company” decisions and lost.

2. Premature closure: People home in on an answer prematurely, long before we evaluate all information.

We get a first impression of an idea in much the same way we get a first impression of a person. Even when people are trained to withhold judgment, they find themselves evaluating information as they go along, forming a tentative conclusion early in the process. Premature conclusions, like first impressions, are hard to reverse.

A study of analysts in the intelligence community, for instance, found that, despite their extensive training, analysts tended to come to a conclusion very quickly and then “fit the facts” to that conclusion. A study of clinical psychologists found that they formed diagnoses relatively rapidly and that additional information didn’t improve those diagnoses.

3. Confirmation bias: Once people start moving toward an answer, they look to confirm that their answer is right, rather than hold open the possibility that they’re wrong.

Although science is supposed to be the most rational of endeavors, it constantly demonstrates confirmation bias. Ian Mitroff’s The Subjective Side of Science shows at great length how scientists who had formulated theories about the origins of the Moon refused to capitulate when the moon rocks brought back by Apollo 11 disproved their theories; the scientists merely tinkered with their theories to try to skirt the new evidence.

Max Planck, the eminent physicist, said scientists never do give up their biases, even when they are discredited. The scientists just slowly die off, making room for younger scientists, who didn’t grow up with the errant biases. Planck could just as easily been describing most business people.

4. Groupthink: People conform to the wishes of the group, especially if there is a strong person in the leadership role, rather than ask tough questions.

Our psyches lead us to go along with our peers and to conform, in particular, to the wishes of authority figures. Numerous psychological experiments show that humans will go along with the group to surprising degrees.

From a business standpoint, ample research, supported by numerous examples, suggest that even senior executives, as bright and decisive as they typically are, may value their standing with their peers and bosses so highly that they’ll bend to the group’s wishes—especially when the subject is complicated and the answers aren’t clear, as is always the case in strategy setting.

5. Failure to learn from past mistakes: People tend to explain away their mistakes rather than to acknowledge their errors, making it impossible to learn from them.

Experts are actually more likely to suffer from overconfidence than the rest of the world. After all, they’re experts. Studies have found that people across all cultures tend to think highly of themselves even if they shouldn’t. They also blame problems on bad luck rather than take responsibility and learn from failures. Our rivals may succeed through good luck, but not us. We earned our way to the top.

See also: How to Lead Like a Humble Gardener  

While it’s been widely found that some 70% of corporate takeovers hurt the stock-market value of the acquiring company, studies find that roughly three-quarters of executives report that takeovers they were involved in had been successes.

The really aware decision makers (the sort who read articles like this one) realize the limitations they face. So, they redouble their efforts, insisting on greater vigilance and deeper analysis.

The problem is that that isn’t enough. As the long history of corporate failures show, vigilant and analytical executives can still come up with demonstrably bad strategies.

The solution is not to just be more careful. Accept that the tendency toward decision-making errors is deeply ingrained and adopt devil’s advocates and other explicit mechanisms to counter those tendencies.

Who Wins? Goliath or David, Big or Fast?

Why did we call David an underdog? He was young and smaller, whereas Goliath was older and a giant of a man, “whose height was six cubits and a span”. Goliath was an experienced warrior, a veteran soldier, whereas David was a mere shepherd. Goliath was outfitted with modern weaponry and all David had were his shepherd’s tools.

This contrast and disparity is because there were three types of warriors in ancient times:  First, there were warriors who fought with slings and bows as lightly armored troops, forming bands of skirmishers. Second, there were the more heavily armored soldiers who formed the bulk of the infantry as foot soldiers, who fought in close-quarters combat with swords, axes, pikes and spears, such as Goliath. Lastly, there were warriors who fought on horseback as the cavalry.

Goliath was a foot soldier with sword and spear, David a skirmisher with a stave and sling.  As the story goes, Goliath and David started their duel with some distance between them, Goliath expecting David to draw near and engage in combat. He wanted to engage David in a hand-to-hand fight where his reach and strength would make him unbeatable. David, however, decided on a different strategy, which played to his strengths as a shepherd, using a sling to defend his flock against lions and wolves.  He rejected the heavy armour and focused on what he knew best — excelling at attacking from afar with great accuracy.

So here was David, the shepherd, experienced in the use of a devastating, precise weapon, up against a giant weighed down by a hundred pounds of armour and incredibly heavy weapons that are useful only in short-range combat. Goliath was a sitting duck. He didn’t realise it, but he had been outsmarted before the combat had even begun.

So how does this story relate to the insurance industry?

If you are a “large” and “established” insurance company (Goliath), the headlines regarding the disruption in insurance are provoking concern at the C-level.  Much of the material equates “large” to “lethargic and slow to react”, while “established” equates to “old and legacy”. In contrast, the material positions small, new and agile companies (David) as extremely innovative and disruptive.  The message is that these “disruptors,” with their new business models and digital capabilities, will make large, established companies irrelevant very soon.

Are larger, established insurers destined to be lethargic, slow off the mark, or can they become agile and innovative? It is definitely possible, if the larger, established insurers leverage their strengths and act like a new disruptor. In fact, Chunka Mui’s book, “The New Killer Apps:  How Large Companies Can Out-Innovate Start-ups”, co-authored with Paul Carroll, suggests such a scenario.

See also: InsurTech Start-Ups: Friends or Foes?  

So, how can established insurers disrupt these “disruptors”? Certainly not by fighting them on their chosen ground and with their weapons of choice. Insurers need to aggressively experiment and learn and accept that failures are part of the process of innovating. They need to leverage the strength of being big, with deep experience and expertise, and combine that with greater agility and innovation. It may even involve cooperative endeavours that could look more like Goliath and David working together than working as dire competitors. For now, however, we’re concerned about refashioning Goliath’s capabilities.

So what are the strengths that established insurers can use to forge ahead and disrupt them?

Your key strengths are precisely what are mentioned as your weaknesses – “legacy” – the legacy of reputation, the legacy of large customer bases and the legacy of experience and expertise. Those legacies are still highly valuable. It is the legacy mind-set, legacy business models and legacy technology that needs to be reconsidered.

Let us peel off a layer from your strengths.

The legacy of reputation

Whilst this should be a positive for most established insurers, sadly many reputations have been impacted with perceptions of not paying claims. No amount of statistics published by the industry will change the perception, because trust has been impacted. And increasingly, consumers are placing their trust in the voices of other consumers using an array of social media options.

Insurers can create a new business model with an underlying digital platform where consumers can easily rate your services openly, and anonymously, if they choose, with the assurance that they will be responded to and engaged with. They can also engage with other customers. This will go hand-in-hand with the creation of communities or interest groups. It will increase trust levels, bridge any trust deficits and help insurers build reputation. Even a slip in service is seen as acceptable if it is transparently acknowledged and acted upon. Doing that builds more trust and reputation. Do more of it and openly.

 The legacy of the customer base

The customers of today are fickle and loyal to nobody. They will change service providers for the slightest of reasons. How can you get them to be loyal to you? Engaged with you? The customers of today do not engage with “brands” as much as they engage with each other, often through social media.

Can you create communities from such customers? Certainly. Communities can revolve around any commonality or interest. Insurers can build communities revolving around areas of interest or even around the insurance type. For example, you could foster a community of insured musicians, passionate about their instruments. They could be part of such communities on the popular social media platforms. Insurers could take advantage of these social media platforms or create a simple one of their own focused on the special tasks involved in caring for these expensive musical treasures. The insurer’s proactive, preventive approach will also help them to keep a low claims ratio. Community-based groups are also less likely to make fraudulent claims because they are “known” within the community.

The legacy of experience and expertise.

There is a wealth of knowledge and experience in your company, knowledge about customers, about risk, about financial modelling of events, and about the business as a whole. This is likely not being leveraged to the extent it could be. By taking that knowledge and expertise from people’s minds into a system that can leverage it opens up possibilities for the business.  Insurers can automate and configure the business to rapidly adapt to change, using it to grow the business rather than hinder the business. A good example of this is the Majesco Transformation Framework, a path to modernizing without losing the essential aspects of an insurer’s foundation.

See also: Getting to 2020 — Defining the Unknown (Part 2)  

So, the end goal is to embrace your “Goliath” position while integrating and employing your “David” tools for better competitive strength. To accomplish this you need a robust platform that can support the core of the business with a digital front-end that engages the customer. Robust platforms with back office and front office components, rich in insurance content for products, processes and channels allow the traditional insurers to be big and agile. And as noted in The New Killer Apps, “Yes, small and agile beats big and slow, but big and agile beats anyone — and that combination is now possible.”

Rebuttal: Protection Gap Is Not a Myth

As with most articles I read at Insurance Thought Leadership, I enjoyed The Myth of the Protection Gap. I do agree with the author (Paul Carroll) that not everything that can produce a negative outcome or loss needs to be insured. In fact, we are now in an era where we can buy insurance for nearly any property we own with a swipe of an app on a smartphone. Assuming that these companies are not charities, this approach is counterproductive, simply because it forces users to waste time having to remember to insure the thousands of small dollar items we own, when we can just afford to replace them. So place me in the camp that says insurance is for instances where we could not otherwise reasonably expect to be made whole again.

But the protection gap itself is very real. I will use Paul’s hypothetical example to illustrate a counterpoint to his conclusion:

“To make the math simple, let’s pick a country at random and make up some numbers out of whole cloth. Let’s imagine we’re Gabon, and we, as a nation, incur $1.5 billion of losses a year, while only $500 million is covered by insurance. We’re told we have a protection gap of $1 billion. We should buy $1 billion of additional coverage.

It’ll only cost us $1.3 billion.

That’s because — again, in very rough numbers — the insurer has to tack on 20% on top of the losses to cover expenses and needs its 10% profit margin to keep shareholders happy.”

Let’s break this down: If the losses for Gabon are $1.5 billion per year, with $500 million covered, then how much insurance do they need to buy? The article is suggesting the answer would be an additional $1 billion.

But that is not the right answer. The right answer is that Gabon should not buy any insurance!

How is that possible? Well, if I know with certainty that my losses over time will be $1.5 billion, then instead of buying insurance I can set aside funds to pay those anticipated losses. To put it another way, if I were insuring an entity that will have $1.5 billion losses each year, then the premium I would charge MUST start at $1.5 billion (because I know for sure that those will be the losses ) and then tack on expenses for managing those claims, issuing paper and, of course, my profit margin.

Am I nitpicking? Yes, I am.

The hypothetical example likely meant that losses would average $1.5 billion per year and not BE $1.5 billion. But words matter, and, in this hypothetical example, the word “average” changes enough of the example to magically make the protection gap appear in full vengeance.

How?

Well, averaging $1.5 billion per year in losses can mean lots of things. It could mean $1.5 billion each year, every year, OR it could mean a $30 billion loss happening exactly once in the next 20 years (or an infinite set of other combinations).

Uh-oh.

It is this uncertainty in the losses that makes insurance such a valuable tool for risk management. Insurance is that tool that allows Gabon to manage its cash flows in such a way that it can function day after day and not have to worry about finding $30 billion at a moment’s notice. Insurance is not about paying for the average annual losses, it is about paying for the extreme losses and avoiding the cash flow crunch associated with that. The smoothing out of volatile cash flows IS the peace of mind that is often marketed to consumers of insurance.

90% of California homeowners lack earthquake insurance. The take-up for flood coverage is similar. These perils have caused hundreds of billions of dollars in property loss, the bulk of which were uninsured. Tens of thousands of families became homeless. We’ve seen it In Louisiana after Katrina and in the tri-state area after Sandy, and we will see it again. The protection gap is not a myth, it is very real, and these perils will continue to cause hundreds of billions of dollars in damage. These are losses that homeowners and businesses cannot fund themselves. They require insurance to protect them from these catastrophes.

This fact alone provides a wonderful opportunity for our entire industry to grow by solving huge and emerging problems faced by societies. This is why we exist; this is our irreplaceable contribution to society.

The Latest Charts on Internet Statistics

Mary Meeker gave her always-anticipated, annual presentation on the state of the Internet this week, and I thought I should share with you. Here is the link to her massive, 213-slide presentation: http://www.kpcb.com/internet-trends.

You will certainly see numbers or even whole slides proliferate in coming days and weeks, but I encourage you to at least skim through this. Meeker, a partner at venture capital firm Kleiner Perkins Caufield Byers, has become an institution in Silicon Valley because of this presentation, which serves as a reference point for many innovators.

You won’t find huge surprises — unless I’ve missed something — but I thought a few things were worth noting:

— The main one for me is maturing of voice recognition, which she covers starting on slide 112. Just when you thought you were starting to figure out how to move to mobile, Silicon Valley starts to move to another disruptive technology for you to cope with….

See also: Solution to Brain Drain in Insurance?

Meeker has a chart showing that voice recognition is now about 90% accurate even in a noisy environment with speakers who have a variety of accents. That is up from 70% just six years ago. She shows essentially straight-line improvement since 1970 and says that, once voice recognition hits 99% accuracy, the human interface with computers will quickly move to voice, with all kinds of implications.

Now, she hasn’t always been accurate. Back in the late 1990s, when she was a securities analyst at Morgan Stanley, she was one of the main characters pumping air into what turned out to be a bubble of valuations for Internet start-ups. Personally, I wouldn’t assume that her chart will continue to show straight-line growth for voice recognition. It’s a lot easier, typically, to get from 70% to 90% than it is get those last few percentage points of improvement for any technology.

I’m also a bit jaded because I’ve been hearing about voice recognition for a good 25 years, at least since I saw a demonstration at a conference I attended during my days as a technology reporter at the Wall Street Journal. A gentleman was supposedly chosen at random from the audience and, despite a heavy Russian accent, had his speech recognized almost perfectly when he spoke into a microphone. Yet here we are 25 years later, and uses of voice recognition are almost always part of phone trees where the choices of response are quite limited — and where the system doesn’t seem to hear you when you demand a live person, no matter how you loud or distinct you are when you say the word “representative.”

Still, anyone who has used an Amazon Echo or similar device knows how great it can be to be able to just call out a question about the weather or what the score is in a baseball game. And the change caused by voice recognition will be disruptive enough that any thinking about new user interfaces should at least contain some experimenting with voice recognition. You need to figure out how close it is now to being useful for your purposes and to stay on top of developments in coming years. I don’t think it’ll be 25 years before I write again about voice recognition, and, when I do, I’ll probably dictate to my computer.

— Starting on slide 137, she does a nice job laying out the latest stats on the connected car. Nothing startling, if you’ve been following along, but lots of good material.

— Beginning on slide 185, she offers some trend lines about the Internet that include some names you won’t know and might want to note — I certainly didn’t know some of them, and I follow this stuff pretty closely.  She singles out Slack, a communication system that is becoming popular in some circles, especially the younger types. She also mentions Looker, an interesting data platform, plus Mapbox, Datadog, Ionic Security and so on.

— The last 10 slides or so contain some good stats about cyber security.

See also: Best Practices in Cyber Security  

There is plenty of other good material, including about opportunities in China and India, but I wanted to single out the sections that touch most closely on the themes we’ve been hitting about innovation at Insurance Thought Leadership.

5 Ways to Flub a Big Decision

Business is a contact sport. Some companies win, while others lose. That won’t change. There is no way to guarantee success. Make the best decisions you can, then fight the battle in the marketplace.

Yet research into more than 2,500 large corporate failures that Paul Carroll and I did found that many big decisions are doomed as they come off the drawing board—before first contact with the competition. Why?

The short answer is that humans are far from rational in their planning and decision-making. Psychological and anthropological studies going back decades, including those of Solomon AschStanley MilgramIrving JanisDonald Brown and, more recently, Dan Ariely, consistently demonstrate that even the smartest among us face huge impediments when making complicated decisions, such as those involved in setting strategy.

In other words, humans are hard-wired to come up with bad decisions. Formulating good ones is very difficult because of five natural tendencies:

1. Fallacious assumptions: If “point of view is worth 80 IQ points,” as Alan Kay says, people often start out in a deep hole.

One problem is the anchoring bias, where we subconsciously tend to work from whatever spreadsheet, forecast or other formulation we’re presented. We tend to tinker rather than question whether the assumptions are right or the ideas are even worth considering. Even when we know a situation requires more sophisticated analysis, it’s hard for us to dislodge the anchors.

See Also: Better Way to Think About Leadership

Another strike against expansive thinking is what psychologists call the survivorship bias: We remember what happened; we don’t remember what didn’t happen. We are encouraged to take risks in business, because we read about those who made “bet the company” decisions and reaped fortunes—and don’t read about those who never quite made the big time because they made “bet the company” decisions and lost.

2. Premature closure: People home in on an answer prematurely, long before we evaluate all information.

We get a first impression of an idea in much the same way we get a first impression of a person. Even when people are trained to withhold judgment, they find themselves evaluating information as they go along, forming a tentative conclusion early in the process. Premature conclusions, like first impressions, are hard to reverse.

A study of analysts in the intelligence community, for instance, found that, despite their extensive training, analysts tended to come to a conclusion very quickly and then “fit the facts” to that conclusion. A study of clinical psychologists found that they formed diagnoses relatively rapidly and that additional information didn’t improve those diagnoses.

3. Confirmation bias: Once people start moving toward an answer, they look to confirm that their answer is right, rather than hold open the possibility that they’re wrong.

Although science is supposed to be the most rational of endeavors, it constantly demonstrates confirmation bias. Ian Mitroff’s The Subjective Side of Science shows at great length how scientists who had formulated theories about the origins of the Moon refused to capitulate when the moon rocks brought back by Apollo 11 disproved their theories; the scientists merely tinkered with their theories to try to skirt the new evidence.

Max Planck, the eminent physicist, said scientists never do give up their biases, even when they are discredited. The scientists just slowly die off, making room for younger scientists, who didn’t grow up with the errant biases. Planck could just as easily have been describing most business people.

4. Groupthink: People conform to the wishes of the group, especially if there is a strong person in the leadership role, rather than ask tough questions.

Our psyches lead us to go along with our peers and to conform, in particular, to the wishes of authority figures. Numerous psychological experiments show that humans will go along with the group to surprising degrees.

From a business standpoint, ample research, supported by numerous examples, suggest that even senior executives, as bright and decisive as they typically are, may value their standing with their peers and bosses so highly that they’ll bend to the group’s wishes—especially when the subject is complicated and the answers aren’t clear, as is always the case in strategy setting.

5. Failure to learn from past mistakes: People tend to explain away their mistakes rather than to acknowledge their errors, making it impossible to learn from them.

Experts are actually more likely to suffer from overconfidence than the rest of the world. After all, they’re experts. Studies have found that people across all cultures tend to think highly of themselves even if they shouldn’t. They also blame problems on bad luck rather than take responsibility and learn from failures. Our rivals may succeed through good luck, but not us. We earned our way to the top.

While it’s been widely found that some 70% of corporate takeovers hurt the stock-market value of the acquiring company, studies find that roughly three-quarters of executives report that takeovers they were involved in had been successes.

The really aware decision makers (the sort who read articles like this one) realize the limitations they face. So, they redouble their efforts, insisting on greater vigilance and deeper analysis.

The problem is that that isn’t enough. As the long history of corporate failures show, vigilant and analytical executives can still come up with demonstrably bad strategies.

The solution is not to just be more careful. Accept that the tendency toward decision-making errors is deeply ingrained and adopt devil’s advocates and other explicit mechanisms to counter those tendencies.