by Mark Chussil

A few weeks ago Netflix raised its prices enough for people to notice. (It also split its DVD-rental and streaming-video services into two companies, Qwikster and Netflix, but it abandoned that move.) They lost 800,000 subscribers, they gained outrage and uproar, and their stock price fell sharply.

Assessment of their pricing strategy: Wrong. Or not. Let’s take a look. Then we’ll find we’ve answered the wrong question, which will lead us to the right one.

Disclaimers. I subscribe to Netflix. I don’t have special knowledge of the company and I haven’t talked to its strategists. I haven’t formally analyzed their strategy. On the other hand, we’re merely going to speculate, so we can relax our standards. Moreover, I have analyzed many other strategies, and one learns a thing or two. End of disclaimers.

Netflix raised their prices. Bad move.

1. They suffered a non-trivial loss of revenue. It amounts to something on the order of $20 or $30 million per quarter. That could sap profits precisely when they need the money to invest in wave-of-the-future streaming.
2. They lost customers that could have migrated to streaming. Those customers now become happy hunting for salivating competitors.
3. They made themselves vulnerable in their bread-and-butter business. As of this writing, Blockbuster’s website launches with an unsubtle ad specifically targeting disgruntled Netflix rental customers. (For more on Blockbuster and mistakes, see It’s Working!)
4. It was an awfully big price boost. Don’t they know there’s a bad economy out there and their customers are hurting? Netflix looks cold and out of touch.
5. The negative publicity might dissuade new customers from giving Netflix a try. They lose not only the 800,000 but also some portion of the people who would otherwise have signed up in the future.
6. Loyalty is essential in subscription-based markets, especially when switching costs are low. It’s generally cheaper to keep a customer than to get a customer, and a customer who leaves in a huff may not soon return.
7. That Qwikster thing was not customer-friendly; people with both DVD and streaming services would have to manage two accounts. If Netflix was tone-deaf enough to propose that, did they really know what they were doing with their prices?

Upstanding strategists, a question

Those are some pretty strong arguments, if I did say so myself. It doesn’t look good for Netflix. But we, being upstanding strategists, know we must consider scenarios and decisions from all angles before we judge.

So, let’s ask a question that I have found ranks among the best an upstanding strategist can ask in his or her career of critical thinking and rigorous decision-making. Let’s ask why a smart person would have done what initially appears not-smart. After all, the folks at Netflix are smart and they want to succeed. They set out to do something smart, not something not-smart.

Netflix raised their prices. Good move.

1. Companies often boost prices when costs go up. Netflix’s costs are going up. Their streaming service requires lots of licensed “content” (movies and TV shows). Those licenses cost more and more as content owners see subscribers growing and realize the value of their content.
2. 800,000 customers sounds like a lot. (Well, it is.) Still, it’s only 3.25% of Netflix’s 24.6 million customers in the USA, as of Q2 2011, and each quarter Netflix adds more than twice as many as the 800,000 they lost. (Key data here came from Business Insider.)
3. Netflix was realistic and had done its homework. They expected to lose some subscribers when they raised their prices. They even took pains to explain their price move to subscribers; how many other companies do that?
4. Pretty much by definition, many or most customers who leave after a price increase are those most sensitive to price and most likely to buy the least-expensive subscription plan. The increase in profits from higher prices could outweigh what those lost customers would cost.
5. Netflix may want or need to amass cash for the major investments it faces. Its strategists know well that much of their market will switch to streaming, and they surely fear a visit from the ghost of slow-to-adapt Blockbuster.
6. Netflix made clear that it is committed to streaming and to the long-term health of its company. Those are important signals to competitors and to investors.
7. It is arguably more important to be a clear market leader in streaming video than it is in DVD rentals. Netflix may want to incent its DVD customers to move to streaming so that Netflix stays in front. (See also Room for One.)

The wrong question

Did you notice the wrongness of the question we implicitly asked? We debated as though Netflix’s strategy had been to lose 800,000 subscribers. But losing subscribers wasn’t their strategy; it was a consequence of their strategy. Perhaps an intended consequence, perhaps unintended; perhaps expected, perhaps a surprise; but not their strategy.

That leads us to the questions we should ask: what did Netflix’s leaders want to accomplish, and, given that objective, did they choose a good strategy?

The right answers

It seems that Netflix wants to be strong in streaming without exiting rentals. Both markets are viable, and it makes sense to me that Netflix would want to be in both. Why forgo the wave of the future? Why turn their back on a prosperous business that can thrive for years to come? I’m willing to accept be-strong-and-prosper as their objective.

My short answer to the second question, did Netflix choose a good strategy, is not just refreshingly brief; it is also admirably accurate. It is this: I don’t know. As I said, I haven’t been inside the company and I haven’t spoken with their strategists.

This is brief and accurate too: whether they chose a good strategy is knowable.

It is not knowable by citing their loss of 800,000 subscribers and a great deal of market valuation. Those are outcomes, and even good strategies get bad outcomes from time to time. Plus, many strategies require more than a few weeks to achieve their desired effects. (See also Who Did Best?)

Whether Netflix chose a good strategy is knowable because it is analyzable. I know it is because I’ve performed such analyses on other businesses, and because other people have too.

Such an analysis for Netflix would not look like gap analysis or financial analysis (too narrow) or like trend analysis or benchmarks (little or no relevant past). It would not rely on anecdotes or arbitrary performance targets. (See also All About Models.)

Rather, it would delve into exactly the points, all of them, raised by the fourteen reasons why Netflix was wrong, or not.

Judgment

Dear reader, if you are a pundit or guru who wants to pronounce lurid and/or definitive judgment on Netflix’s goodness and smartness, then you must be sorely disappointed in this essay. I’m afraid it’s not going to get any better for you. But if you are not such a pundit or guru then you will, I hope, join me in three conclusions.

First, we cannot, and therefore should not, gauge the rightness or wrongness of a strategy merely by noticing whether we feel joy at its outcomes at this very moment. We should gauge the quality of the strategists’ decision and decision-making.

Second, it is useful to debate the rightness or wrongness of a strategy. Debate surfaces and clarifies important issues to analyze.

And finally, whether Netflix has gone vile or smile (sorry, my best rhyme at this time), they deserve kudos for their efforts to avoid the pitfalls of those who came before. Think about this: Blockbuster could have been Netflix if only they’d strategized differently.

Update, February 21, 2012. “Comcast to start ‘Streampix’ Video Service,” in The New York Times.

Update, January 26, 2012. Netflix reported 4th quarter 2011 results. Profits beat “expectations,” despite dropping 13.5%. Its subscriber base grew and sales went up 47%. Its stock surged, but perhaps for reasons not related to its strategy.  See CNNMoney, “What’s behind Netflix’s 20% spike?” So, with the passage of a near eternity — oh, six months or so — did Netflix go vile?

Update, January 26, 2012. See also “Netflix Will Rebound Faster than You Think,” by Annika Olson and Eddie Yoon of The Cambridge Group, writing in the HBR Blog Network.

Update, December 25, 2011. Netflix CEO Reed Hastings, speaking about the Qwikster move, was quoted by CNN. “‘We berate ourselves tremendously for that lack of insight because it didn’t need to be that way,’” CEO Reed Hastings admitted recently at a conference. “But you know, in three or five years, we aren’t going to remember it. It’s going to be, `’Did we succeed at streaming?”‘” Worth noting too: CNN pronounced Netflix’s move “dumb” purely on the basis of the short-term loss of subscribers and value in the stock price. Hastings may yet turn out to be wrong, but he’s obviously playing for the long term. For more on judging strategies, which isn’t such an easy business, see Who Did Best?.

Update, November 22, 2011. For more on the financial and competitive maze Netflix has to navigate, see “Netflix warns of losses for all of 2012.”

by Mark Chussil

On May 10, 2011, thirty intrepid, curious, thoughtful strategists joined me in a three-hour workshop at the SCIP2011 Conference. Our subject was “Why Strategies Fail.” (See the end of this post for access to a 30-minute video of a similar speech.)

The workshop combined audience interaction, experiential learning, live simulations, performance art, and ah-ha surprises. This essay presents conceptual highlights from the workshop even if, alas, it falls short on interaction, experiences, simulation, performance, and surprise. As you read please imagine a lively, dynamic, engaging session, thanks to a great group of participants.

At first glance “Why Strategies Fail” is an odd subject. After all, don’t we really want to know what makes strategies succeed?

One thing we strategists must do to succeed is to not fail. And there’s sad evidence that we aren’t necessarily good at not-failing.

First, don’t fail

What do these companies have in common?

Blockbuster Video. Borders. Chrysler. Circuit City. Delta Air Lines. Enron. General Growth Properties. General Motors. Hollywood Video. Kmart. Lehman Brothers. Six Flags. Texaco. Texas Rangers. Trans World Airlines. United Airlines. Washington Mutual. Worldcom.

What they have in common is that they all went bankrupt.

Some of them have emerged from bankruptcy. Still, it’s safe to say that none of them wanted to enter bankruptcy in the first place.

It’s not immediately clear how they came to such unhappy ends.

• They didn’t enter bankruptcy only when times were bad. Some did in good times.
• They didn’t enter bankruptcy because their industries were imploding. They had competitors who survived and even prospered.
• They didn’t enter bankruptcy overnight. Some took decades to fail, meaning that generations of well-intentioned strategists didn’t prevent the fall.

Bankruptcy is only one form of failure. We also say a strategy failed when it misses its performance targets, loses ground to competitors, or costs its author his or her job.

Strategies fail when

When strategists choose bad strategies, strategies fail. That sounds obvious until we remember that no strategist purposely chooses a bad strategy. Strategists are smart, experienced, industry-savvy, data-rich, and highly motivated to succeed. They want to choose smart strategies. Yet smart strategists can and do choose bad strategies.

If you doubt that smart strategists choose bad strategies, look again at the list of companies above. Do you believe they employed, promoted, and trusted incompetent strategists? Do you also believe their senior management approved bad strategies due to incompetence of their own?

Because smart strategists choose bad strategies, we’re unlikely to prevent bad strategies merely by shuffling people around. That’s an expensive, haphazard way to solve the problem of strategies that fail.

Instead, let’s address why smart strategists can mistakenly believe that a bad strategy is a good strategy. That’s what we did in the workshop.

Seven habits of highly ineffective strategizing

I didn’t call this section “the” seven habits because there are more than seven. And even though you may read them in a few minutes, take a few seconds to consider why we spent a few hours on them in the workshop. It’s because it’s more effective, not to mention more fun, to learn through experience than through lectures. In the workshop we discovered each of these seven habits experientially.

Here, HHIS = habit of highly ineffective strategizing.

HHIS#1: Using wrong paradigms

Discovered in the workshop by deconstructing Marketing 101 with a simple pricing question.

We have strategy problems. How should we position our product? How should we defend against a new entrant? How should we price as we enter or exit a recession?

We say that if you have a hammer, you see problems as nails. There’s also the reverse to consider: if you have a nail, you need a hammer.

The tools we choose to solve strategy problems are often accounting-based spreadsheets, trend lines, anecdotes, and advice from confident-sounding people. Those tools rely on paradigms that may not fit strategy problems. For example, a trend line assumes that conditions from the past will persist into the future. If the past will persist, we don’t have a very hard problem; if it won’t, the trend line itself isn’t reliable.

We need to use thinking and tools based on relevant paradigms. That means if we have a strategy nail, we need a strategy hammer.

Further reading: Why Do War Games Work?

HHIS#2: Seeking pseudo-precision

Discovered in the workshop with an interactive exercise and a case study from a business war game.

When a strategy fails, we reasonably turn attention to the analysis and forecasts that led us to adopt the strategy. We figure that if we can make the analysis and forecasts more precise, we’ll be more likely to succeed in the future.

That may be true if lack of precision is the problem. In my experience, though, lack of precision is rarely, if ever, the problem.

A large telecommunications company faced a new competitive threat. Their strategists had been unable to choose whether to respond with Strategy A or Strategy B. Think of how they could have resolved their impasse: take a vote, have the boss rule, wait (for what?) and see, kick the decision up to top management, get a consultant to make a recommendation. Note that those options are merely means to make a choice. They decided instead to work it through in a business war game.

In their business war game we had them role-play their company and the new competitor, and we used a strategy simulator to estimate the outcomes. The choice between Strategy A and Strategy B came down to whether strategists would prefer to lose 20 points of market share or 40. To make a good decision, who cares if it’s 20 versus 40, or 19 versus 37, or 20.311 versus 38.726?

Quantifying helps. Precision, not so much.

Further reading: Predictable Competitors and Predicting Competitors.

HHIS#3: Relying on anecdotes and stories

Discovered in the workshop with a vigorous virtual debate between Steve Burd, CEO of Safeway, and Craig Heckert, CEO of Supervalu.

We humans love anecdotes and stories. We glow as we imagine ourselves the hero, which we call aspiration and inspiration. We shudder as we imagine ourselves the victim or villain, which we call fear or lessons learned.

Anecdotes and stories prove that something is possible even if, swept up in a good tale, we forget that possible doesn’t mean probable. But anecdotes and stories hardly provide solid ground to make complex decisions.

A question I find helpful is this: It works in practice, but does it work in theory? No, I didn’t scramble practice and theory when I wrote that.

Something may appear to work in practice. We infer that through a process that goes like this: I did X, then Y happened, and I like Y, so X works. But we all know that X-preceded-Y doesn’t mean X-caused-Y, especially in a field as turbulent, complex, and interconnected as competitive strategy.

Asking “does it work in theory” injects intellectual discipline where we otherwise would have only assertion, inflation, persuasion, and frustration. It asks whether we can draw plausible cause-and-effect links from X to Y before we risk our Y on that X.

Further reading: Numbers, Circular Reasoning, and Numbers and The Burden of Anecdote.

HHIS#4: Assuming our strategy will work

Discovered in the workshop in a miniature business war game on the automobile industry.

If you ask strategists “will your competitors do what you want them to do?,” of course they’ll answer “maybe, but probably not.” Yet tools commonly used in strategy development implicitly assume that your competitors will do what you want. When’s the last time you saw a spreadsheet take competitors’ reactions into account as it forecasted your business’ future profits or market share? And on the off chance that it did, how strong, sustained, or clever was the competitive response that was fed in?

In the workshop we ran a miniature business war game based on the automobile industry. Teams of workshop participants role-played various car-makers as they allocated production and forecasted results in three consumer segments.

What we saw mirrored what I’ve seen in hundreds of business war games I’ve conducted for companies and at conferences: teams made rational decisions that rammed head-on into competing teams’ rational decisions. Teams raced to expand in the segments they considered desirable. Teams assumed an orderly exit from shrinking segments. Every team expected to gain market share somewhere. Not a single team expected to lose market share anywhere. Net result: overproduction in every segment, and performance below expectations.

Of course strategists want to grow; people don’t like strategies that will shrink their businesses. And in a business war game, as in real life, some businesses will grow.

But hopes for at least some will be dashed, in real life as in war games. Does that reflect not-good-enough strategies or too-optimistic hopes? A full answer goes beyond this space and perhaps your patience. Let’s just say that we may inadvertently cause not-good-enough strategies, too-optimistic hopes, or both, when our strategy-development tools implicitly assume our strategy will work.

Further reading: Honey, We Shrunk the Industry and Honey, We Shrunk the Industry Again.

HHIS#5: Believing we can make it happen

Discovered in the workshop in case studies based on two business war games.

The phrase “make it happen” stirs the accountability and glory centers of our brains. It feels triumphant to proclaim that we will make it happen. It feels magisterial to demand that you must make it happen.

The thing is, it’s only not up to us/you whether it happens. Others are involved, such as competitors, whose brains are similarly stirred. Then there are customers, suppliers, distributors, regulators, shareholders, and financiers, not to mention constraints from budgets and technology.

When it happens, it’s not necessarily because we made it. And even if we made it happen, we didn’t necessarily do it in a way we prefer. Perhaps we made our profits happen by cutting costs when expected sales didn’t materialize.

We train telescopes on our goals and microscopes on our budgets, and what we need is a wide-angle lens to scan our scenarios. After all, if we fail to make it happen because of the things that swoop out of a metaphorical left field, perhaps we ought to pay more attention to left field.

Further reading: The How-Likely Case.

HHIS#6: Deciding while being human

Discovered in the workshop in several interactive exercises.

Imagine an unfair coin. A fair coin, when flipped, comes up heads 50% of the time and tails 50% of the time. The unfair coin you’re imagining comes up heads 60% of the time and tails 40%.

Say you repeatedly flip your unfair coin with humans and ask them to predict the result of the next flip. Humans observe previous outcomes closely. They perceive patterns and construct elaborate schemes and rules. They regard correct predictions as vindication and incorrect predictions as an imperative to refine their systems. They believe that with practice they can do better.

Say you flip your unfair coin with rats (or the rat equivalent of such a coin) and reward them with food for each correct prediction.

The rats out-perform the humans.

The rats learn that guessing the rat-equivalent of heads every time will maximize their food. They get fed 60% of the time. The humans, with their big brains and complicated systems, get it right less than 60% of the time.

I’m not against big brains. I’m not against complicated systems. I’m not for rats and I’m not against humans.

What I am against is forgetting that we’re human, and therefore subject to human biases and foibles, when we make decisions. Overconfidence; groupthink; innumeracy; confirmation bias (believing only the data with which we agree); much more. We can fight those biases and foibles if we learn and try. The point is, we have to learn and try.

Further reading: It’s Working! and Marvelous Techniques.

HHIS#7: Not figuring out what it’s about

Discovered in the workshop in a very cool team exercise.

In business schools, at conferences, and in news reports, everything arrives labeled. This is a marketing case, that’s a sales problem, this is competition, that’s product development. We reach for mindsets and tools with matching labels and get to work.

Challenges in real life don’t come labeled. (Notice, by the way, that “challenge” is itself a label.) We’re predisposed to think of finance, prices, and trend lines if we have spreadsheets just as we think of nails if we have hammers, but that says as much about our thinking as it does about the challenge at hand.

We included an unlabeled challenge in the “Why Strategies Fail” workshop. I’m not going to describe it here, partly because it would lose too much in the translation and partly because you may experience it someday with me or someone else and I don’t want to spoil it for you. The point of the unlabeled exercise is that it’s easy for us to fail if we don’t pay attention and figure out what it’s about. Doing so requires taking a few minutes to think, to question assumptions, and to be willing to be wrong.

Further reading: When I Was Wrong.

The bottom line

We strategists often think in terms of strategy fundamentals: understanding customers, anticipating competitors, seeking profitable markets, achieving market share, controlling costs, and so on. When we build a strategy we carefully deploy the fundamentals. When a strategy fails we investigate what went wrong with the fundamentals.

I suggest that there is also such a thing as strategist fundamentals. Strategist fundamentals drive and reflect how we think. If you review the seven habits of highly ineffective strategizing we’ve covered here, you’ll see they are about those thinking fundamentals.

We’ve come full circle. We began with why strategies fail and now we end with how strategies succeed. Good strategies come from good decisions. Good decisions come from good decision-making. Good decision-making comes from good strategist fundamentals; that is, from good thinking. And that’s the bottom line.

For more information

Please feel free to contact me if you’d like to know more about the “Why Strategies Fail” workshop or others that I’ve conducted for companies and conferences around the world. Or, visit How to Think Better on ACS’ website.

You can view and download The ACS Why Strategies Fail Bibliography. It lists essays by ACS, with links to full text, and lists thought-provoking books that have shaped my thinking.

You can view and download The ACS Business War-Gaming Bibliography. It lists essays by ACS on business war games, with summaries and links to full text.

Update. You can watch a video of Mark Chussil’s “Why Strategies Fail” speech for The IE Group (30 minutes; not the same as the 3-hour workshop for SCIP). It was delivered at the Chief Strategy Officer Summit in New York City on December 9, 2011.

Those who are exquisitely aware of the value they bring to decision-making wonder why they struggle to get decision-makers to listen. I have suffered from this. So have you. Pretty much all humans above the age of one know that frustration. Oh when will those decision-makers ever learn?

We will delicately pass over the question of why we don’t ever learn that they’re not going to learn.

A long-running thread in the Strategic and Competitive Intelligence Professionals (SCIP) group on LinkedIn asks “Why is it so difficult to sell competitive intelligence?” It’s a reasonable question, especially because, from a CI perspective, competing without CI is like picking a fight in the dark.

(This essay isn’t about CI per se. You can substitute anything else of exquisite value to decision-making. I’ll use “CI” for brevity but I’ll mean any such decision support.)

The question itself — “why is it so difficult to sell” — presumes that the customer, the decision-maker, should buy. Perhaps we should challenge that presumption and ask a different question: what has to happen for a rational decision-maker to decide to buy CI?

Here’s a flowchart that outlines what has to happen, from the decision-maker’s perspective. It is a flowchart, not the flowchart, and your version might be different. Mine might not be exactly right but it’s close enough for now.  

 We notice these things about the flowchart.

• Five paths lead to “stop” and only one to “go.”
• The steps that take us toward “go,” expressed from the customer’s perspective, may sound strange to a person already convinced of CI’s value.
• The steps that take us toward “stop” do not necessarily mean the decision-maker disapproves of CI.

So, why might a rational decision-maker not buy what the CI professional wants to sell? Thinking through which reasons might apply can help the CI pro figure out whether and how to move forward.

• Lack of information. The decision-maker may not know the threats or opportunities faced by the business. She or he may not know the ability of CI to identify and clarify those threats or opportunities.
• A belief that the right action is already being taken. If the decision-maker believes that nothing can be done, or that she or he has already set the right action in motion, then there’s no use in getting more data.
• Overconfidence, groupthink, etc. We humans suffer a variety of biases that affect our decisions. An overconfident person — are you sure it’s not you? — would think it irresponsible to spend time and money to find an answer when he or she already has the answer.
• Low value of information. For CI to have economic value, it must cost less than the benefit it will provide. The cost of CI is fairly clear. The benefit may not be: it depends on the odds that you’ll learn something new, the odds that the new knowledge will affect action, and the difference that action can make in the bottom line. Using his or her mental calculator, the decision-maker may sincerely come to a different conclusion than you. (See also All About Models.)
• Not enough. The decision-maker may be out of bandwidth, the company treasure chest might be dry. Asking for something that doesn’t exist, especially if it could lead to further demands for that same nonexistent something, might not fly.

Notice that perceptions, not “reality,” pervade the reasons why a decision-maker may not want to buy what you want to sell. I’m not saying their perceptions are right or wrong. I am saying 1) that they are perceptions and 2) that customers use their perceptions, not yours, to make their purchase decisions.

If you’re finding it difficult to sell CI, then your perceptions, which also are not “reality,” differ from those of the decision-maker. You must change her or his mind to make the sale, or you must change your mind to get past “difficult.” Here I will draw from a chapter called “How to Change Someone’s Mind” in my book Nice Start:

“When I play a game, I assume that I may win or lose. Why would I play if I don’t have an opportunity to win, and why would you play if you don’t? Changing minds is the same thing: I have to be open to the possibility that my mind may change if I want you to be open to the possibility that yours may change.”

The point isn’t to pick a fight with the decision-maker. The point is to dispel the dark. Add light to the conversation, and receive it too. Whoever wins, you both win.

No stunning message about strategy here. Just a brief good time. Hope you enjoy. — MC

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In politics we expect opposing viewpoints. That’s what happens when reasonable people constructively disagree as they forge sober solutions to the pressing issues of the day. Of course they use a special political language that just happens to sound like radiant ideological purity, informative ad hominem attacks, and thoughtful sound bites.

In business we expect decisions to come from careful analysis in which experts lay out the options, quantify the costs and benefits, and make the right choices. So it may seem perplexing when two senior executives, in symbiotically linked companies, publicly and decisively disagree on a key decision.

The issue at hand is whether to develop new, more-efficient engines for existing single-aisle jetliners; that is, to “re-engine” current aircraft. The executives represent Airbus, maker of the jetliners, and Rolls-Royce, the top engine supplier for those jets.

Today’s quiz

Match the opinion to the executive:

Opinion 1: “If you look at the benefits of re-engining and all the costs, there’s no net benefit. We can’t make the business case work.”

Opinion 2: “The business case is really very convincing [for the re-engining].”

Executive 1: Tom Enders, Chief Executive of Airbus.

Executive 2: Robert Nutall, Vice President of Strategic Marketing for Rolls-Royce.

Not knowing who said what makes it easier for us to focus on the core issue, the disagreement itself, so I’ll reveal the answer later.

The source of the quotations is an article in today’s Wall Street Journal. I’ll provide the citation later too, because the article title would spoil the suspense.

Delaying judgment

We might not expect the two executives to disagree. Each is smart, each has plenty of data and analysis, and above all each has ample incentive to get this decision right. Yet they disagree. And not just a little: they have staked out starkly opposite positions.

I don’t know enough about re-engining to have an opinion about it. I do know that adherence to an answer, whatever it is, increases the more its advocate is called upon to defend it and the more public the venue in which the disagreement is aired. (Think about the implications for all that dignified political discourse.) Call it “face,” call it commitment, call it defensiveness, call it the desire to win; once we humans have a story we tend to stick to it.

If sticking to a story is a problem, then we should avoid adopting stories too soon. You can do that by delaying judgment as long as possible.

By “delaying judgment” I don’t mean you should procrastinate or prolong the process. I mean you should resist the temptation to adopt a story while you ponder the problem. You can do that by splitting the problem in two. First part, decide how to structure your analysis. Second part, put in the numbers and apply the structure.

I have built many strategy simulation models that many strategists have used to assess their strategy options. These models answer the question “if we do this and they do that, what’s the net result?” Over the years I’ve been pleasantly surprised at how readily strategists have accepted the results of my simulators. I believe it is because my clients and I use that two-part process.

Agreeing first on the structure ensures that we’re taking into account everything that we should. Such a discussion tends to be highly constructive. It’s not only that everyone feels heard; it’s more than that. It’s that it synergistically combines the expertise and experience available within the client’s organization.

When the structure is done, then the numbers go in and the results come out. Of course there’s further discussion and what-if analysis. The point is that this process shifts the nature of the debate from advocacy to discovery. It’s the equivalent of teamwork in analysis.

Quiz answer

Opinion 1 goes with executive 2 and opinion 2 goes with executive 1. Airbus sees the business case for creating new engines for the Airbus aircraft; Rolls-Royce doesn’t. The Journal article is “Rolls-Royce Isn’t On Board With Airbus’s Engine Plan,” by Daniel Michaels and Peter Sanders.

I don’t know how Messrs. Enders and Nutall came to their differing conclusions. It’s even possible there’s no solution: they may agree on all the numbers yet have different judgments for acceptable ROIs or levels of risk.

If you’ve got irreconcilable differences, locate the disagreement by comparing the structures behind the relevant models. (Might be helpful for Airbus and Rolls-Royce, if they’re not already doing so.) To avoid irreconcilable differences in the first place, start your analysis by reaching agreement on how you’ll answer the question.

“I try not to think with my gut. Really, it’s okay to reserve judgment until the evidence is in.” — Carl Sagan

Further reading

All About Models. Discusses “mental models” and how our thinking affects our analysis.

House, MBA. Discusses the structure of problems, and discusses how business war games clarify problems and solutions.

“The Limits of Reason: Why evolution may favor irrationality,” by Sharon Begley in Newsweek, August 5, 2010. Discusses why irrational advocacy can be effective despite its irrationality.

We will end this essay with a valuable point about costs and prices. We will begin with an enigma about assembling products at home.

As I home-improve my home, I come across products whose directions warn me not to “overtighten” something. This confuses me. How am I supposed to know how much tightening is too much? If something shatters, cracks, or crumbles, I figure I’ve overtightened it, but that information is tardy and not helpful. Remember, I don’t want to undertighten it either. I don’t want it to fall apart and spill a Caution! Awfully Hot! liquid on me.

A pleasant alternative would be “go ahead and tighten until you reach the limits of your strength.” To do that, the item being tightened would have to be strong enough to withstand my formidable strength, and we all know that sturdy is out in these cost-conscious times (which apparently have been in effect for 30 years or so, based on how often we are warned of the delicacy of items about to be tightened).

I think the elegant beauty of “do not overtighten” can only be understood from the perspective of a lawyer. (Your Honors, I hereby state for the record that I am not, have never been, and expect never to be against lawyers. I almost became one myself. Ipso facto. Habeas corpus. De minimis non curat praetor.) Or understood from the perspective of folks in product support, who in turn are dealing with a situation created by cost accountants (about whom I also have nothing against). Let’s replay the scenario.

Customer: “I was putting the product together and it broke.”
Support: “When did it break?”
C: “Sunday.”
S: “No, at what point in the assembly process? Were you tightening something?”
C: “Yes.”
S: “You must have overtightened it. The instructions specifically state not to overtighten it. Bad customer. There is no warranty, express or implied, on overtightened items. We would be happy to sell you a replacement. Want my advice?”
C: “Yes, please.”
S: “Do not overtighten it.”

Businesses tend to overtighten. They do it because they’re led there by simple, persuasive logic, which we can boil down to this: it is cheaper to print “do not overtighten” on the instructions than it is to supply products that can withstand formidable strength. It is even virtuous to do so, because driving excess costs out permits lower prices, which makes customers happy. That’s true. Until we overtighten. Unfortunately, the dividing line between nice-and-tight and overtight is as hard to discern in business as it is in home improvement.

Overtightening in business leads to being unable to get an airline seat for days if your flight is canceled. (See “Police Called to Quell Unruly Passengers at J.F.K.” Notice that the airline blames the weather, not overtightening.) Overtightening leads to being put on hold for hours. Overtightening leads to being vulnerable to hiccups anywhere in a long supply chain. Overtightening leads to nutritional quality gradually being subtracted from food.

Nobody intentionally overtightens. Probably nobody thinks he or she does overtighten. We’re just listening to that eloquent cost analysis showing how much money we lose when there’s an empty seat on an airplane, when phone operators are sitting idle, when we don’t outsource to Mars, and when we compare natural proteins to polyglutamatinousbiphosphorescentcosamine.

You will notice that this essay is not entitled “In Defense of Wasteful Practices.” I am not defending waste; I am in favor of efficiency and productivity. All I’m saying is that cost analysis makes an implicit assumption: if saving $1 is good, then saving $2 is twice as good. It makes another implicit assumption: saving the next dollar is equally good as saving the previous dollar. In other words, the tools we use to gauge the goodness of tightening make it hard to tell the difference between nice-and-tight and overtight.

There’s even an opportunity to create (inadvertently?) a vested interest in overtightening. If a person is paid a percentage of a company’s profits or cost savings, for example, then he or she has an incentive to err on the side of tightening too much rather than too little. That’s especially true if the person doesn’t plan to stay long at the company. We might get the same effect if a person expects much compensation in the form of stock options, because the “benefits” of overtightening (profits are up!) are evident before the downsides kick in (sales and market share are down).

Some companies, notable for their rarity, explicitly reject overtightness. I had a Sears Craftsman ratchet wrench that I broke, perhaps by overtightening some poor thing. Sears offers a lifetime warranty on Craftsman tools. I took the wrench to Sears, found my way to the tool department, and presented the wrench to a friendly Searsperson. I said that I’d bought it a long time ago, far, far away, and it broke. Before the word “broke” had faded from my lips, I had a replacement wrench in my hand, along with a receipt documenting that there was no charge. Similarly, there’s Nordstrom, and LL Bean, and Chelsea Audio Video, which is even more guy-fun than the tools department at Sears.

So how can you avoid that sickening feeling that comes right after you’ve overtightened?

• Beware of the death of 1,000 tightenings. I believe it was Jay Russo and Paul Schoemaker, in one of their marvelous books about decision-making, who gave a terrific example of how we devolve from nice-and-tight into overtight. (If it wasn’t Profs. Russo and Schoemaker, please correct me.) Imagine that customer surveys show product A is statistically indistinguishable from product B, and product B is indistinguishable from product C, and C from D, and D from E. It does not follow that A is indistinguishable from E. If A through E involve successive minor cuts in quality, it may appear that customers won’t notice or care about each cut, but when you add them all up A is far from E. Think about airlines: an inch of legroom here, a baggage fee there, no more meal service, and pretty soon you just want anesthetics. Available for your convenience for a small extra charge. (Update: see “Airline fees: Where will they pop up next?“)
• Don’t filter out what you need to see. “Customers aren’t asking for something else,” you say? How do you know? Multiple-choice surveys make it hard for oddball ideas to get through. People with complaints get shuttled to the complaint department, which emphasizes placating; how do the complaints get to R&D? Comparisons with competitors — “we perform in accordance with accepted industry standards” — are meaningless if you and your competitors behave the same way. Which gives creative upstarts the opportunity to beat coasting incumbents. (See Further Reading, below.)
• Why, oh why? We all make assumptions; the trick is periodically to ask why oh why they work for your business. Here are some examples: We must sell out our capacity; it’s better to sell a perishable good at a low price than to have it go unsold; we should scale capacity to fit peak demand; we should maximize demand; customers are more sensitive to price than quality. I’m not saying those assumptions, and others, are right or wrong. I am saying they are assumptions (possibly supported by data; see Further Reading), and before we continue to risk our businesses on them we should ask why we continue to believe them. I’ve found in business war games I’ve conducted and strategy simulations I’ve built that a good test question is this: will our strategy work if our competitors do the same thing we plan to do?

Competitive strategy. Some assembly required. Do not overtighten.

Further Reading
About overtightening: Gross Galactic Product.

About upstarts versus incumbents: With All This Intelligence, Why Don’t We Have Better Strategies?

About assumptions: House, MBA, More Internet Users than People, and Doesn’t Make Sense.

About being misled by well-intentioned data: It’s Working! and Self-Fulfillment

About the strategy equivalent of the Tower of Babel: Motor Swilling Forbidden.

Update, February 16, 2010: For an interesting commentary on layoffs as an ineffective form of overtightening, see The Case Against Layoffs, by Jeffrey Pfeffer, a professor of organizational behavior at the Stanford Graduate School of Business, published by Newsweek.

Update, May 6, 2010: For a discussion of the effect of the Icelandic volcano on super-efficient transportation and manufacturing networks, see The Days The Earth Stood Still, by Daniel Gross, published by Newsweek. Take what he wrote and add in the time, effort, and money it took to return those networks to normal; for example, stranded passengers waiting additional days. Not to mention the resulting angst. For instance, see Iceland volcano: passengers still stranded vent fury at the airlines.

Update, August 26, 2010: See We’re Mad as Hell, by Daniel Gross, published by Newsweek. He says Bureau of Labor Statistics data suggest “companies may have pushed workers as far as they can go.” “When the BLS reported the second-quarter productivity numbers on Tuesday, August 10, the results were a little shocking. For the first time in several years, productivity actually fell — at a 0.9 percent annual rate. Workers put in more hours, but output didn’t keep up. They simply can’t run any faster.”

Update: About pricing assumptions: ”Yes, You Can Raise Prices in a Downturn,” an interview with Benson P. Shapiro (Malcolm P. McNair Professor of Marketing Emeritus at Harvard Business School), Frank V. Cespedes (senior lecturer in the Entrepreneurial Management unit at Harvard Business School), and Elliot Ross (a former McKinsey consultant and President of The MFL Group in Beachwood, Ohio).

Update, December 21,2010: Air travel is severely snarled in Europe due to snow. According to an article on CNN.com, European Commission Vice President Siim Kallas, the EU’s top travel official, “took issue with airport infrastructure which he said was the ‘weak link’ in the chain, and urged airports to ‘get serious’ about planning for severe weather conditions.” Overtightening, overconfidence, or both? By whom?

Update, January 6, 2010: Today the Wall Street Journal reported that American Airlines wants to save about $3 per flight by selling more of its tickets directly to consumers rather than through “global distribution systems” such as Sabre and online travel agents. The move could make it more difficult for customers to comparison-shop with other airlines. Also, CNN.com reported on possible airline fees that might appear in 2011: infant fees, in-person check-in fees, credit-card fee, baggage fees by weight and distance, and carry-on bag fees. Debatable whether it’s overtightening, regular old tightening, or a profound (mis?)understanding of pricing psychology.

Update, January 27, 2011: The Wall Street Journal wrote about Cookie Cutters: Girl Scouts Trim Their Lineup for Lean Times. Management of the Girl Scouts of America think it’s a good move to reduce cookie varieties. Bakers and many Girl Scouts disagree. The issue is perhaps less about the numbers and more about the way one views and interprets the numbers.

Updates, May 20, 2011: CNN.com posted two overtightening-related stories on May 16, 2011. One that hints at further overtightening in the airline industry and one that holds out some hope for “premium flyers.” The former is Fewer Hubs Mean Fewer Options for Fliers. The latter is Little Planes Get Big Seats.

Update, October 24, 2011: David Carr, in The New York Times, writes about an egregious form of overtightening in Why Not Occupy Newsrooms? He points out how media companies (Gannett, owner of USA Today, in particular) have overtightened their staff, costing tens of thousands of jobs, hurting their stock price, and not helping their customers, but enabling them to pay large bonuses.

In today’s Wall Street Journal (October 16, 2009), we find these contrary positions in “Safeway Shifts Tactics in Grocery Price War:”

“Last month, [Safeway CEO Steve] Burd conceded had the chain moved quicker to lower prices, it would be ‘doing a bit better than we are now.’”

“Increased use of promotions ‘destroyed our gross margin,’ Supervalu CEO Craig Herkert said last month at a Goldman Sachs conference. ‘More items really cheap don’t bring in more people.’”

There is truth in the notion that focus is valuable and in the notion that diversification is valuable. There is truth in leadership and in delegation. There is truth in caution and in action. There is truth in mass marketing and in segmentation. There is truth in price and in quality. Pick a business model, pick a buzzword, pick a fad; there’s probably truth in it, and in its opposite.

There’s also falsehood, or at least incompleteness, in each of those perspectives. Both Burd and Herkert expressed dissatisfaction with their results. Each CEO wished, if just a little, that he’d done what the other had done… and what the other was blaming for his dissatisfaction.

Burd and Herkert are experienced, high-powered professionals with plenty of data, analysis, and advice at their disposal, yet they come to opposite conclusions. I’m not criticizing them; quite the contrary, I’m sympathizing with them because they’re trying to cure sick systems with many moving parts.

Let’s consult with Gregory House, fictitious M.D., of the eponymous TV show. If you like House, he’s the witty, incisive doctor whose cool, unrelenting pursuit of the truth cures the patient. If you don’t like House, he’s the nasty, sarcastic doctor whose cold, unrelenting pursuit of his ego cures the patient. Cool or cold, he cures. If I were sick enough to need House, I’d want House.

Yes, House (the show) is fictitious and, I’m told, unrealistic. Still, the show beautifully illustrates ways to get insight into tough questions rigorously and effectively.

(Here I’m going to talk about Burd/Safeway and Herkert/Supervalu as though I know what I’m talking about when, in fact, I don’t. All I know is what I read in that Journal article. Plus, of course, what I’ve learned in 30+ years studying competitive strategy. I’m just using those CEOs and companies to illustrate what we can learn from House.)

House (the doctor) begins by writing symptoms in modest-sized letters on a white board. That’s significant for three reasons: he can add, he can erase, and he keeps everything in view. He writes those symptoms without unnecessary detail or precision; he simply writes “high blood pressure” and “headache.” Partly that’s because watching someone scrawl numbers and details isn’t exciting television. Partly it’s because it keeps him and his team focused on the big picture.

Strategy equivalent: I’ve seen, and I’m sure you have too, strategists spend time debating whether to adjust a price by 1.0% or 1.5%. There’s time for that later. Let’s figure out first if, say, anyone is suffering from malignant prices.

For Burd and Herkert, the symptoms are apparently pain in the profit: sales below expectations and margins below expectations. (It is unclear from the Journal article whether we should say expectations, desires, or targets. We’ll just say expectations.) Prices are not symptoms; their role in the symptoms has not yet been determined.

House writes those symptoms without interpretations. He may write “high blood pressure” and “headache;” he doesn’t write “headache from high blood pressure.” He and his staff are free to link high blood pressure and headache as they search for a diagnosis, but they don’t presume it. Something else might cause both (a side effect from medication, a thrillingly obscure disease). A symptom might even be irrelevant or temporary; the headache may go away after the morning’s 17 espressos wear off. He also collects relevant information about the patient.

Strategy equivalent: relevant business, competitive, and market information, without interpretation. Relevant: we must, for example, understand our competitors because our performance depends in part on what they do. It’s not good enough to study only our business. Without interpretations: notice the difference in assumptions and implied action between “our prices are too high” and “our prices are 10% above competitors’.” Note too that SWOT analysis builds in interpretations. Is it a strength or a weakness to price 10% above competitors?

Burd and Herkert would presumably know that the other is suffering too. They’d know also that they have similar big-retailer business models. They can readily measure prices in their markets.

Then House does something remarkably powerful: he starts fights. He doesn’t hold back his critiques as his staff suggests possible diagnoses. His staff doesn’t hold back their critiques as he suggests possible diagnoses. The fighting gets heated (perhaps inevitable when the boss’s favorite expression is “you idiot”) but everyone is clear that the objective is to diagnose and cure the patient. There are a few rules that guide the fighting: 1) treat causes, not symptoms; 2) find causes that fit all the symptoms; and 3) the way you do your job is to be part of the fight.

Strategy equivalent: resist jumping to advocacy and conclusions, resist anecdotal reasoning. Brainstorming, scenario planning, and what-if simulations help a lot. Business war games are particularly terrific for conducting civilized and highly constructive fights.

Still, in business there is tremendous pressure to advocate rather than debate and to act rather than diagnose. Moreover, strategists simply do not have the business equivalent of a body of medical literature based on centuries of scientific investigation. Without that deep knowledge it becomes especially important to question assumptions and to look for causal mechanisms. What has to happen for a price cut to work for Supervalu and for steady prices to work for Safeway? Hint: it’s not just one or two items. Think it through. Do the assumptions and causal mechanism make sense to you?

Burd and Herkert implemented different price moves, presumably after tracing the effects of price changes on their financials, taking into account fixed costs and variable costs. Both are unhappy. That suggests the price moves might not be the cause, or at least not the sole cause, for the same reason that we’d say 17 espressos might not cause a headache if identical twins both got headaches but only one had 17 espressos. What else could it be? Perhaps they have something else in common that could causally explain their shared unhappiness. (If they don’t, then we’d treat them as separate cases; there are multiple causes of headaches.) Similar business models that are unsuitable for the economic climate? New competition? Demographic changes? Inappropriate performance expectations? Notice that our statement of symptoms — performance below expectations — makes the latter possibility easy to see.

Finally, House adjudicates the fights. He’s willing to make a judgment call but that’s his last resort. He prefers the hypothesis/test method. If the 17 morning espressos are responsible for the headache but not the high blood pressure, then it must be true that the headache will fade as the espressos wear off but the blood pressure will stay high. That approach helps narrow the possibilities. Notice that it doesn’t completely confirm the role of the 17 espressos in the headache. If the patient had sipped the espressos at an outdoor café during rush hour, the headache might be the result of exhaust fumes, not espressos. House would need another test to prove that 17 espressos can cause a headache: administer a new batch of espressos where the air is clean.

Strategy equivalent: think like an experimenter. For example, think of Herkert’s statement as a hypothesis that low prices destroyed Supervalu’s gross margin and didn’t bring in more people. Think of Burd’s hypothesis that they could have done better if they’d cut prices sooner. How can we validate or dismiss those hypotheses? Look at comparable businesses who took those actions. Survey customers. Run simulation-based tests. Be careful how you measure: in a recession, “not bringing in more people” may be success, if other businesses are bringing in fewer people. And watch out for the exhaust-fume problem. What else was going on at the same time that Safeway held its price and Supervalu increased its promotions? The other-things-going-on might have been at Safeway and Supervalu, at competitors, in the economy, and so on, and might have nothing to do with price.

I haven’t conducted any of those experiments; all I’m doing is applying a TV show to a newspaper article so as to make a point and illustrate a technique. Let’s go with my imagination, though, for the sake of that point and technique.

• It wouldn’t be surprising to discover that comparable businesses are disappointed with their results. That would suggest that Burd and Heckert are not doing something uniquely wrong.
• It wouldn’t be surprising to find that customers are cutting back on purchases (buying less and/or buying cheaper) to deal with the economic crisis. That too would suggest nothing unique about Burd and Herkert.
• It wouldn’t be surprising to find that simulations show it’s possible to succeed with steady or lower prices, if the rest of the business adjusts appropriately. (An appropriate adjustment for steady prices might be to reduce capacity to fit a smaller number of customers. For lower prices, an adjustment might be to negotiate lower prices from suppliers or to buy lower-end goods.) We could check to see if Burd and Herkert have made such adjustments.
• We could measure competitors’ results and see if Safeway and Supervalu have gained, held, or lost market share. Their performance might have slid, but if they are outperforming the competition then they might actually be doing well. In other words, the diagnosis might be unrealistically high expectations, and the treatment is to change expectations. They can try to improve performance too, of course, but the role of price cuts is not clear.

There’s much more on this website on the subject of expectations and strategy decision-making, in both the Blog and the Library sections.

Update. There are options besides holding and cutting prices. See ”Yes, You Can Raise Prices in a Downturn,” an interview with Benson P. Shapiro (Malcolm P. McNair Professor of Marketing Emeritus at Harvard Business School), Frank V. Cespedes (senior lecturer in the Entrepreneurial Management unit at Harvard Business School), and Elliot Ross (a former McKinsey consultant and President of The MFL Group in Beachwood, Ohio). See also Predicting Competitors.

I presume you would like to predict your competitors’ moves better than you do now. Say, for instance, their prices. Let’s work on that, perhaps with a surprise, a jolt, or even a shock as we go along.

We structure today’s harangue around a pricing quiz involving simple math. It’s not a math test, and your enjoyment (or not) of this essay doesn’t depend on whether you enjoy math.

Question 1. Your competitor charges $900 for their product. In month 1, they raise their price by 10%. In month 2, they hold their price steady. In month 3, they cut their price by 10%. In month 4, they raise 10%; month 5, hold; month 6, cut 10%; and so on. What is their price at the end of month 30?

Many people say that the competitor’s price will be $900 at the end of month 30. They figure the 10% increases cancel out the 10% decreases. That’s wrong, as we can see at the end of the first three months. Month 1: $900 + 10% = $990. Month 2: $990 + 0% = $990. Month 3: $990 – 10% = $891. It didn’t cancel out.

Answer to question 1. The competitor’s price at the end of month 30 is $814.

Question 2. What will be the competitor’s price at the end of month 31?

We’ll get to the answer in a moment.

The nice thing about patterns is that we can describe them in numbers. The competitor’s price changes +10%, 0%, -10%, +10%, 0%, -10%. We feel validated because history fits the pattern so well. We feel confident because the statistical “explanatory power” is so high; it’s perfect, in this case. We feel we can predict what happens next, as we do with the great ebb and flow of the tides, the majestic rising and setting of the sun, and the news-cycle persistence of a juicy scandal affecting not me.

We shouldn’t feel validated or confident. At least not yet. And we may not yet be ready to predict the competitor’s price.

Answer to question 2. We can say that the competitor’s price will be 10% higher than $814, or $895. That’s what the pattern says, to n decimal places, where n is a number larger than we require. But what’s much more interesting, if we put down our calculators and put on our thinking caps, is to ask and answer question 3.

Question 3. Why has the competitor’s price been rising, holding, falling?

(Did you ask question 3 before you answered question 2? Give yourself a round of applause if you did. I’ve run this quiz with hundreds of managers in workshops I’ve delivered. Everyone turns right away to math. No one asks question 3, at least not out loud. What good is an unasked question?)

We humans do two things with patterns. First, we see patterns. It’s part of how we figure out how the world works. We test perceived patterns with science, we accept perceived patterns as experience, we even enshrine perceived patterns as superstition.

The second thing we do with patterns is assume they will persist. After all, part of what makes a pattern a pattern is that it (seems to) persist. The tides, the sun, the scandals.

Patterns persist when the underlying forces persist. If the moon were destroyed by a Death Star, Earth’s tides would change. Over time Earth’s rotation (and hence the number and duration of sunrises and sunsets) is changing because… well, it’s complicated, and not germane to your competitor’s price. The length of a scandal is projected to rise and fall with the level of a society’s blaminess. Change the moon, Earth’s rotation, or a society’s blaminess, and the tides, sunrises, and scandals will change.

So what’s controlling your competitor’s price? You wouldn’t ignore the world and set your prices according to +10%, 0%, -10%. Neither would your competitor, unless they have turned over control of their prices to a trend-line equation that will next announce $895 in an eerie mechanical monotone.

Now we’re getting somewhere. What’s controlling your competitor’s price is how they make decisions. A strong pattern in prices is likely to mean that your competitor (or you) have linked prices to something. If their suppliers change prices periodically, then your competitor might change its prices to maintain their margins. If your competitor heavily incents its salespeople to make the numbers each quarter, their salesforce might lobby mighty hard for quarter-end price cuts. Or perhaps that’s how you have priced, and your competitor is predicting and responding to changes they expect you to make!

So which is it? Suppliers? Salesforce compensation? Competitive dynamics? Something else? The point is that people, not calendars, control prices.

Notice that the pattern, whatever’s behind it, makes it hard to know what your competitor will do if you make a change. What would happen if you raise or cut your price when you’re “supposed” to hold? Would they follow? If neither you nor they have deviated from the pattern in a while, you have no data to suggest what they will do. You have to understand your competitor, you have to get inside their heads, you have to model their business, you have to figure out why they price as they price.

Sidebar. I’ve run many, many business war games around the world. They always produce surprises (that’s a good thing) for the management teams that participate. Why? Most of those teams have already analyzed their strengths, weaknesses, opportunities, and threats; what surprises are left? Here’s why I think they get surprised. SWOT analysis mentally places you inside your own company and asks you what you think your competitor will do. A business war game, on the other hand, asks you what you would do if you were your competitor. The latter question explicitly encourages you to look through your competitor’s eyes; the former doesn’t. With the latter you understand them better, you get inside their heads, you model their business, you figure out why they act as they act. End of sidebar.

Intellectually we all know that no one delegates pricing decisions to a calendar. That said, take a look around and see how often people (maybe you and me too) assume patterns will persist. You will see people (maybe you and me too) frame problems in terms of patterns rather than understanding. Listen to pundits, read newspapers, look at analyses.

If you understand your competitors better, you can make better decisions. You can understand better how they will respond if you do this or if you do that. (Do you think they will raise their price to $895 no matter what you do?) Which is, of course, the reason why we try to predict them in the first place. We want to make better decisions for the environment we think we’ll face.

Update: See Predicting Competitors. This essay is about illusions about predictability; that one is about delusions of predicting.

Further reading:
It’s Working!
Precision In, Garbage Out
The Rules
You’re Got the Data. Now What?

I commend to you Joel Lovell’s courageous and perspicacious essay “What Do They Know?” Subtitled “True Confessions of a Conflicted Money Guru,” Mr. Lovell muses about what to tell people about finances during the crisis and wonders why some public advisors seem confident despite being wrong. His essay is a fine companion to Sharon Begley’s “Why Pundits Get Things Wrong“.

As my readers know, I’ve wondered for a long time why smart people make bad decisions. Yes, there is the media-mogul preference for sound bites and the public’s clamor for tell-me-what-to-do-oh-oracle advice, but such responses don’t address the question. Public prognosticators don’t give bad recommendations because people want sound bites or oracular advice; the prognosticators would rather be right than wrong. The question remains, why do they get it wrong?

I think there are a few reasons why they (and the rest of us) get it wrong.

• They and we, being human, are prone to all the distortions and biases that we read about in books about social psych. (In addition to books I’ve previously cited, check out Sway: The Irresistible Lure of Irrational Behavior and The Drunkard’s Walk: How Randomness Rules Our Lives.)
• The tools we use to exercise due diligence and thorough analysis can systematically mislead us, giving us positive reinforcement for bad decisions. For example, forecasts that extrapolate the past into the future won’t work if the future is qualitatively different from the past. (See It’s Working! and With All This Intelligence, Why Don’t We Have Better Strategies?)
• The problems we face are more complex than we can handle in our heads. (See When I Was Wrong.)
• Perhaps more subtle: we believe our situations or businesses are different. In other words, we believe in the exceptionalism of our times and/or businesses. (See The Good, the Bad, and the Lucky.)

Note that those reasons suggest in what directions we should seek long-term improvement.

Accountability and oversight, not so hopeful for more than short-term symptomatic relief. The problem is, no matter how sternly we apply oversight and hold people accountable, we’ll continue to make bad decisions if we continue to rely on misleading decision-making techniques.

Regulatory change, more hopeful to the extent that it curbs decision-making that is known or can reasonably be expected to produce negative outcomes. (That’s as opposed to regulatory changes that prescribe specific behavior or technologies, about which I make no comment here.) Profits for today often override prudence for tomorrow (hence mortgages get written that people cannot pay back), so it can be beneficial to ensure that prudence is part of the equation. It’s worth remembering that we already accept such regulations in numerous areas of our lives, including licensing healthcare professionals, FAA safety standards, and insurance on bank deposits.

Just as we’d be better off preventing cancer than treating it, we’d be better off making good decisions than fixing bad ones. We need better-quality decisions, and better-quality decisions come from better-quality decision-making. As we assess proposed solutions to crises big and small, let’s start by asking how each proposal will help us make better decisions.

Executive compensation is top-headline news these days. It commands the attention of the President and Congress. It’s an attractive problem, full of righteous indignation, handy villains (“them”), and clear action plans. And although I’m not against “solving” it, I suggest that it’s not the important problem.

Let’s imagine that the executive-compensation problem has been solved to your satisfaction. Legislation has been enacted, paychecks have shriveled, remorseful CEOs have issued their mea culpas, defiant CEOs have left the building, resilient CEOs have gotten back to work. Or CEOs remain free to negotiate whatever deals they want with their boards of directors. Or something else. Whatever you believe is the perfect solution, so it shall be done.

Now that we’ve handled executive compensation, is the economy back in order? Are we all back to work? Are our retirement funds healthy again? I don’t think so.

Whatever we do with executive compensation, it is the equivalent of sticking a finger into a leaking dike. Giving executive compensation the finger may stop a disturbing (cash) flow, but the dike is still structurally unsound.

(We may want to cut compensation and super-sized bonuses on other grounds, such as the impropriety of using taxpayer money to make bailout beneficiaries rich. That’s a different issue. Let’s not think, though, that cutting compensation makes executives more competent, or less.)

The economy is not in trouble because executives got paid too much. The economy is in trouble because executives (and others in their companies, and people in government, and even we the consumers) made bad decisions. Not just a few executives and not just a few times.

Why did so many people make so many mistakes?

I have written eloquently about problems in corporate decision-making. (See, for example, “With All This Intelligence, Why Don’t We Have Better Strategies?,” “What You Pay For,” “It’s Working!,” and “Suffering was Optional.”) What those essays have in common is that they describe ways in which sober, dedicated business professionals are systematically misled by the tools of the trade. Spreadsheets, for instance, say nothing about competition. Trend lines extrapolated from the past are irrelevant if the future is unlike the past. Doing whatever it takes to make the numbers makes us look good and the performance targets look reasonable even if we hit them by eviscerating the company. (Update to this post: see “When Goal Setting Goes Bad,” an interview with Professor Max Bazerman of the Harvard Business School.)

As I suggested in “To Bail or to Bail Out,” it would be useful — more useful than capping executive compensation, though they are not mutually exclusive — to require the auto industry to use techniques such as business war-gaming as a condition of receiving public funds. War-gaming and other strategy simulations help companies create better strategies, and they would raise the odds that taxpayers will be repaid. I would advocate the same for other industries wanting public assistance. I believe in business war-gaming because I’ve seen it work in the 100 or so that I’ve conducted for Fortune 500 companies.

The point isn’t business war games, though, and if you prefer a different technique that’s fine with me. The point is that we need to get better at decision-making, not dike-plugging. More of the same, only better, won’t do. (See “Precision In, Garbage Out.”) Sticking a finger in the executive-compensation dike merely guarantees we’ll be the first to go underwater when the dike breaks.