By Mark Chussil

“Even a Giant Can Learn to Run,” reported The New York Times on January 2, 2012. IBM is that running giant, and its CEO, Samuel Palmisano, is departing. He leaves behind a remarkable legacy of performance, which he attributes (to the extent one can sum up a decade running IBM in a brief newspaper article) to a guiding framework containing four questions. They are:

1. “Why would someone spend their money with you — so what is unique about you?”
2. “Why would somebody work for you?”
3. “Why would society allow you to operate in their defined geography — their country?”
4. “Why would somebody invest their money with you?”

Those who read my essays know that I am not often impressed by the mere accumulation of happy numbers, since happy numbers alone do not prove good strategic thinking. Mr. Palmisano asks good questions, but, with all due respect, those questions are not unique.

What I infer made them unique in Mr. Palmisano’s hands is that he evidently saw to it that they were answered honestly and thoughtfully, and that action was taken consistent with those answers.

In a business culture that venerates growth and size above all else, Mr. Palmisano’s IBM chose margins. He allowed IBM to fall to be the world’s second-largest information-technology company, after HP. He even sold solid businesses that no longer fit with IBM’s core, such as IBM’s personal computer operation, a business with nearly $20 billion in annual revenue, reasoning that it would get the best price when it’s performing well . This is MBA 101 in theory, but how often do you see that theory in practice? The thing is, it’s a good theory.

And that’s why I congratulate Mr. Palmisano and IBM. There are as many ways to accumulate happy numbers as there are ways to be lucky, which is to say there are a lot of ways. But applying the diligence and discipline not only to ask good questions but also to answer them well, and to act on the answers, makes those happy numbers well-deserved.

By Mark Chussil

I get around a bit. I conduct business war games for companies in different industries and different countries; I speak at conferences; I teach classes. I’ve noticed a few things. This isn’t the grand unifying theory of strategy that we all crave. It’s just a few observations as we lurch, slide, dive, bound, stagger, drag, and hop(e) from 2011 to 2012.

Budgets versus investments

A classic difference between an established company and an upstart is that the former sees budgets and the latter sees investment. That seems true of countries too. Of course there’s nothing that forces established companies or countries to see budgets instead of investments; it’s a decision, an attitude, and some do focus on investments. The point is that upstarts always see investments, and when budgets confront investments, investments win. (See also Do Not Overtighten and Answering Four Questions Well.) I suspect there’s a link between a budgets culture and lack of agility. It’s unclear to me which is cause and which is effect.

Take my risks, please

I spoke a few weeks ago in New York City at a conference of chief strategy officers sponsored by The IE Group. Many speakers spoke of the need to encourage risk-taking, yet outside such conferences we see little patience or tolerance for the hiccups that come with risks. Take Netflix. People have written scorching articles about CEO Reed Hastings, based on a couple of ambiguous short-term metrics, when he’s clearly making a long-term shift to avoid copying Blockbuster’s implosion. I’m not saying Hastings is right, nor am I saying he’s wrong. What I am saying is that it’s wrong to judge the quality of a long-term strategy on the basis of ambiguous short-term metrics. (See also Netflix Gone Vile: Fourteen Reasons Why Netflix Was Wrong, Or Not.) And I am also saying that, in an intolerant you-should-have-known culture that demands eternal and infallible accountability, how can we expect even a successful CEO to accept necessary risks?

I’m still confused

The last observation for now is that much of the public discourse around debt and austerity confuses me. I don’t particularly mind confusion, especially because I’m used to it. I envy people who glow with the comfort of certainty even if I don’t admire them. Anyway, it seems to me that when supply exceeds demand there is no reason to increase supply, so until demand grows we’re not likely to hire or build much. The movie Field of Dreams had a catchy line but it’s backward. It’s not “if you build it, they will come.” It’s “if they come, you will build it.” We all — companies, countries, citizens — need to demand demand.

Thank you, and best wishes for 2012

Thank you for being here with me. Best wishes for you and your family to enjoy a safe, peaceful, joyous, and prosperous 2012.

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 oth-erwise 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, 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

I remember where I was when JFK was assassinated, when Neil Armstrong walked on the moon, when the Challenger exploded, and on 9/11.

Today, when Steve Jobs died, I was on vacation in Napa Valley. Even in a winery’s tasting room, someone thought the news important enough to burst into the room and make an announcement.

Steve Jobs was a complex man, a thinking man. http://bit.ly/qvAJsl He pushed others hard and he pushed himself hard. We find that motivating. No one is driven to achieve easy things; we are driven to achieve hard things.

When’s the last time we mourned the passing of a business leader? What other business leader would we mourn?

What do the answers to those questions mean?

I’m thinking about those questions and I want to take my answers to heart. I know that no one will burst into a room to announce my demise. Still, when my time’s up I want to be able to look back and honestly say I am satisfied with the decisions I made. Not in some revisionist or perfectionist way, but rather that I made thoughtful decisions with what I knew and believed at the time I made them. That applies to my personal life and my business life, because in truth there is no separation; there is only my life.

Farewell, Steve Jobs. Thank you for what you did for us, for your dedication to excellence, and for living consciously.

“I didn’t say it would be easy. I said it would be worth it.”
Heard from Dan Gibbons, one of my teachers.

“How we spend our days is, of course, how we spend our lives.”
Annie Dillard

“Don’t get called out on strikes.”
Leo Durocher (?)

“Do not pray for easy lives. Pray to be stronger men.”
John F. Kennedy

“May you live all the days of your life.”
Jonathan Swift

by Mark Chussil

Who did best? I’m not talking about the Democrats and Republicans. Not the New York Yankees and the Boston Red Sox. Not even the Beatles and the Rolling Stones.

I mean something BIG.

I mean Safeway and Supervalu, the huge supermarket chains.

If you’re still here you must be one of my faithful readers, and my faithful readers may remember an essay called House, MBA. In that essay, written with the American economy reeling, we saw that Safeway CEO Steve Burd wished he’d done what Supervalu CEO Craig Herkert did on prices, while Supervalu CEO Craig Herkert wished he’d done what Safeway CEO Steve Burd did on prices. (As they must say in an old proverb, the produce is greener in the other store.) We further explored what nasty, brilliant, truth-seeking, fictitious Dr. Gregory House would do to resolve the dispute.

Here’s how Safeway and Supervalu were doing at the end of 2008.

The Numbers are In!

A couple of years have passed since the House essay, since Mr. Burd and Mr. Herkert ranked #50 and #51 on the Fortune 500 list, and since they took their respective actions.

Think about where they started. Think about what they said in The Wall Street Journal of October 16, 2009, in “Safeway Shifts Tactics in Grocery Price War.” Safeway’s Burd said, “Had the chain moved quicker to lower prices, it would be ‘doing a bit better than we are now.’” Supervalu’s Herkert said, “Increased use of promotions ‘destroyed our gross margin… More items really cheap don’t bring in more people.’”

What would you expect to happen? What actually happened? Whose strategy worked? Here are their 2010 numbers, from the 2011 Fortune 500:

And the Winner is…

Pretty clear who did best, right? Not so much. Actually, not at all.

If you value, say, 10-year total return to shareholders, then Supervalu is doing better. If you value, say, return on equity, then Safeway is doing better. There is not a definitive, objective reality of one outperforming the other until you subjectively define success. You pick the metric, you pick the winner.

Plus, we don’t know what metrics Burd and Herkert wanted. We don’t know if they think they succeeded.

It gets less clear. Many things were in motion at the same time as their price moves. Changes in the economy, decisions about everything from advertising to sourcing to employment, Supervalu’s adjustment to a new CEO (Jeffrey Noddle left in 2009), and so on. Not to mention that companies’ cultures and capabilities differ. (See What You Pay For.) Not to mention randomness, uncertainty, and luck. (See Marvelous Techniques.)

It gets still less clear. Even if we knew exactly what each had done on price, how could we reasonably single out price as the driving force, the “reason” for one or the other to win? It just doesn’t make sense to do so.

What I think we’ve seen is that “who did best” is a difficult, complex question. It is not objective; it requires that we make value judgments. It is not clear who or what is responsible for “best” performance.

So what should we believe? How do we know who did best?

What Works Best

It seems to me that “who did best” is of limited value as a question and of even more limited value as a way to figure out which or whose strategy “worked.” It tells us what happened. It doesn’t tell us why it happened.

What I think is of far greater value is to change the question from “who did best” to “what works best.”

Treat that inquiry as a scientist would, looking for cause and effect. Treat it as a systems thinker or engineer would, balancing multiple moving parts and knowing everything is connected. Treat it as a chess master or military commander would, recognizing that it’s not only your moves that matter. Treat it as a statistician would, checking what’s random and what’s real. Treat it as a psychologist would, aware of human creativity, biases, and determination to succeed. It’s possible,* and it’s worth it.

* I like what Barney Pell said about a problem that needs only for people to think big and muster the will to take it on: “It’s only rocket science.”

by Mark Chussil

Albert Einstein said, “Things should be as simple as possible, but not simpler.”

No one wants to spend three months studying whether we ought to offer our new product in 182 colors or 183. No one wants to pluck decisions from the air when billions of dollars and thousands of livelihoods are at stake.

I didn’t say we don’t do those things. I just said no one wants to do those things. At least I hope no one does.

As simple as possible, but not simpler. So when it comes to business decision-making, what’s too simple, what’s not simple enough, and what’s just right?

Too Simple

I’ve been interviewed a few times recently (here, here, and here) about the value of daily-deal coupons from Groupon and LivingSocial. The deals are generally terrific for consumers. They’ve been good for Groupon and LivingSocial too. Groupon turned down a $6 billion takeover offer from Google early in 2011, and may be valuing itself in the $20 billion range. Not bad for a company that didn’t exist 5 years ago.

But are daily-deal coupons a good deal for the restaurateurs, merchants, and others who buy into the system and offer those deals to potential customers? How would you decide?

If you look around, such as in those interviews, you’ll find plenty of answers to that question. Some of those answers are learned, many of them are passionate, almost all of them are anecdotal. They’re not too simple because they’re wrong; on the contrary, they make good points. Rather, they’re wrong because they’re too simple.

They’re too simple because they isolate narrow pieces of the problem, like predicting life success on the basis of a high-school trigonometry test. (I hope that technique doesn’t work.) Yes, the coupons might cheapen a brand and that’s dangerous. Yes, on the other hand, the coupons might bring in new customers and that’s progress. But to make a good business decision about the coupons, we must account for both factors, and more.

In other words, the answer is it depends. Or, equivalently, there is no single answer that’s right for every business. Which is emphatically not the same as saying that there is no answer at all or that every business is different.

Before my interviews I spent a few hours creating a simple what-if simulator for restaurants that might use daily-deal coupons. It’s not pretty. Still, it handles multiple factors, such as how many tables the restaurant typically fills, how much the restaurant relies on repeat customers, and whether the restaurant has high fixed costs (e.g., fancy décor) or high variable costs (e.g., expensive ingredients). Based on those factors and others, you could get a reasonable idea whether the coupons would pay off for your restaurant. It depends. For some restaurants the coupons would pay off, for others they would hurt.

The point is that getting decision-making insight doesn’t have to be too complicated and that sound-bite answers are too simple.

Further reading: The Burden of Anecdote.

Not Simple Enough

With a friend and colleague I co-founded a company called Benefitics, which specializes in quantifying the social ROI of non-profit organizations. That means we estimate the monetary value to society of an organization’s activities and divide it by the cost of conducting those activities. We did it for Friends of the Children, on whose Board of Directors I am honored to serve, and for others.

We sloshed through a lot of academic research as we got our quantitative feet wet on our first project. We found deep thinkers whose studies shed rigorous light on genuinely tough social-science questions. The problem from our perspective was that their exacting methods were prohibitively expensive, in time as well as in money. Some studies had taken many years. If we modeled our analysis on those studies we would have to wait about 65 years for our results, which we felt was too long.

A great answer in 65 years would be useless (to us). Not simple enough. We didn’t need pinpoint precision or encyclopedic breadth. We needed something simpler: a good answer, soon. After all, an estimated benefit/cost ratio of 5.0 tells us what we really need to know even if the “right” number is 4.7 or 5.2.

So we developed a good answer. We used census data and government statistics rather than compile our own with new primary research. We made reasonable assumptions about what influenced what, and we tested those assumptions with smart people who know the field. Whenever we faced a choice about how to do something, we chose the analytically conservative path; if we introduced any bias, we would underestimate, not inflate, the results.

As thorough as a gold-standard scientific study? Our study was no slouch, but no, it was silver-standard. The good news: by avoiding not-simple-enough we saved 65 years.

Further reading: Precision In, Garbage Out.

Just Right

I ran a business war game recently in which the client and I wanted to use a simulation model so we could see which strategy ideas were promising and which were not.

We were budget- and time-constrained. I had one month to, among other things, put together a simulator. It had to be custom-calibrated for their industry. It had to handle multiple market segments, new competitors entering those segments, parallel universes, and the key “levers” each business could pull, to any degree, in any combination, and in real time. Impressed? I know I was, when it worked.

In the middle of the business war game strategists role-playing one of the client’s competitors took several actions including a price increase. The simulation model projected that the price-raising company would enjoy an increase in market share of several percentage points.

“Wait a minute!” cried someone from a competing team. “They raised their price and the model said their sales volume would go up? That doesn’t make sense.”

Was it a bug in a quickly developed model? Did the model fail to capture the way their industry worked? Did their industry have a perverse affection for higher prices?

No, no, and no. It wasn’t a bug; it didn’t misrepresent their industry; their industry, like most others, preferred lower prices.

Here’s what was going on. The price increase did not cause the gain in market share. It did not even contribute to it. The gain in market share came in spite of the price increase.

At the same time that they’d upped their price, they’d also spent more on marketing. Meanwhile, their competitors had moved, but less aggressively and less effectively. The gain in market share was due to the net interaction of everyone’s moves, aggressive or not, effective or not.

To demonstrate, we removed the price increase, left in all the other moves, and re-simulated. As you, I, and they would expect, the team would have gained more share if they had not increased their price.

And that’s what made the simulator just right on the simplicity scale. Sure, the model wasn’t “accurate.” (NB: no one can calculate “accurately,” or even discern accuracy, in any such analysis. But that’s another subject.) Even so, it moved in a sensible direction however the teams tuned their strategies, and the more forceful the action, the more the model would move. It gave sensible feedback. Just right for what we needed.

Further reading: What The Model Says.

Getting Your Analysis Just Right

Here’s what I’ve learned over 35 years working with analytic models. Well, not everything I’ve learned about them, but important stuff. And here’s what to notice and implement from those three simple stories.

An analysis is too simple if people, especially you, keep saying “but what if” or “but does it take into account” or “but that’s not realistic.” That’s the feeling you might have if you’re told Groupon is good, period, because it brings in new customers.

• It’s a bad sign when all the numbers in an analysis are about your business, when you’re never surprised by the results, or when you see happy numbers come out no matter what numbers you put in. It’s a bad sign when all you hear is anecdotes or when all you see is tunnel vision.
• It’s worth asking, as a sanity check on the analysis, if an big, obvious move by some¬one else (e.g., a competitor) would affect the analysis. If it wouldn’t, watch out.
• What may help it be just-right is adding realism. That may mean changing the conceptual underpinning of the analysis (e.g., don’t extrapolate history if you need to anticipate competitive dynamics). It may mean taking more factors into account.

An analysis is not simple enough if it won’t be ready on time to affect a decision, like a program that takes two days to calculate tomorrow’s weather forecast. That’s the feeling you might have when you find out that the data you really want will take 65 years to gather.

• It’s a bad sign when people quarrel about minute differences that just aren’t big enough to affect a decision. If that’s where the debate is, ask open-ended, non-confrontational questions such as what would we do differently if the minute differences turned out one way or turned out the other way.
• It’s worth asking, if the problem is that the analysis will take too long, if the decision can be delayed.
• What may help the analysis be just-right is to look for approximations or proxies. Approximations give you a reasonable number quickly. For example, you can average market-analysts’ forecasts of Microsoft Office sales rather than construct your own forecast. Proxies give you a similar or related number quickly. For example, look at sales of Windows PCs to get an idea of sales of Microsoft Office.

An analysis is just right if you can comfortably answer questions about and with the analysis. The analysis makes sense, it’s directionally correct, it focuses on the problem at hand. The analysis fits the need.

• It’s a good sign when people stop asking about the analysis and start getting excited about the results. It’s also a good sign when they want to run what-if experiments or have a copy to use themselves. Extra credit if they say they want to “play with” the model.
• It’s worth asking if you can apply what you created to related problems. The key is to recognize the general principles common to those problems. Experience at doing that is one reason I could built that strategy simulator in a month. (See also The Good, the Bad, and the Lucky.)
• What will help the analysis stay just-right is to filter proposed enhancements with this question: if we had this enhancement, what would we be able to do that we cannot do now? Which brings us to…

The Bottom Line

The purpose of an analysis is not to tell us a number we like. It’s not to deliver a definitive, immutable last word. It’s not to guarantee anything.

The purpose of an analysis is to positively affect the quality of a decision. Keep that in mind, and your analysis will be as simple as possible, and not simpler.

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.

by Mark Chussil

Attorney: All your responses must be oral, okay? What school did you go to?
Witness: Oral.
(Source: One of those humor emails that circulates eternally)

You ask a silly question, you get a silly answer. That much we know. But how do you skip the silly and go for better?

Questions and business war games

Business war games, in my experience (see Appendix), provide outstanding insights that greatly impact bottom lines. But why? What is it about business war games that produces insights that evidently elude other approaches?

I think the answer lies in the questions business war games ask. You ask a better question, you get a better answer.

The first, worst question

“For a moment, nothing happened. Then, after a second or two, nothing continued to happen.” — Douglas Adams

Imagine that we expect a new competitor to enter our market, or we plan to launch a new product, or we want to see what would happen if we cut our price, or we need to set performance goals.

We pull out our trusty spreadsheet, full of estimated sales and costs and with quarterly profits lined up in the traditional hockey-stick formation.

What is the question that our trusty spreadsheet implicitly answers? If it’s like most that I’ve seen in corporate use, it is this:

What will happen to us with this strategy, assuming it works?

“What will happen to us with this strategy” is fine as a first question. What makes it the worst question is stopping after we answer it. Which strategists are prone to do, because spreadsheet analysis looks detailed, precise, and definitive, and thus makes us forget the second, often-unspoken “assuming it works” part.

Here’s why accounting-based spreadsheets assume our strategy will work:

• They do not take competitors’ actions or reactions into account. Equivalently, they assume competitors will behave as we wish.
• They further assume that costs can be cut without affecting sales. It is a rare spreadsheet that explicitly links costs to the top line as well as to the bottom.
• They assume sales will grow as we command, costs will slow as we demand, and profits will flow as we expand.

Those assumptions, of course, leave us open to surprises, mostly unpleasant.

Further reading: Do Not Overtighten and With All This Intelligence, Why Don’t We Have Better Strategies?

A better question

Rick Blaine, Humphrey Bogart’s character in “Casablanca,” on Captain Renault: “Oh, he’s just like any other man, only more so.”

Of course savvy strategists worry about stopping after the first question. Fortunately, it’s a cinch to find a second, better question. The better question is this:

What do you think they will do?

“They” usually refers to competitors, although it may be anyone whose actions can affect us. Here, we’ll stick to competitors.

The need to answer this better question is obvious enough that people have provided an assortment of tools to do so. The tools include competitive intelligence, trend analysis, and SWOT (strengths, weaknesses, opportunities, and threats) analysis.

“What do you think they will do” is a major step forward because it explicitly looks at relevant others, especially competitors. Where it falls short is not in theory but in practice.

When human beings answer “what do you think they will do,” they tend to start with past behavior. They figure that future behavior will be like past behavior, only more so or less so. (This is called anchoring. See further reading, below.)

• Done well — say, by noticing that a competitor has hired a person with an unusual background to run their R&D — competitive intelligence can help you anticipate even quantum shifts. That, and much more, is truly valuable. Done less well, you may get only raw facts and figures. And no, numbers do not speak for themselves.
• Trend analysis, by definition, extrapolates the past into the future, and thus, by definition, will not expose possible shifts in direction. That makes it least useful precisely when you need it most, which is when the future will not look like the past.
• SWOT analysis theoretically can assay what competitors might do. In practice, it may not work out that way, as we’ll see when we get to the next question. By the way, the very act of assigning labels — strengths, weaknesses, opportunities, threats — prejudges effects and biases our thinking about what we should do.

Think, though, about how you make decisions. You don’t aspire to be as you were yesterday, only more so or less so. You strive to get out of the box and do something innovative and magnificent. So do your competitors.

Further reading: Decision Traps, J. Edward Russo and Paul J.H. Schoemaker, covers anchoring. See also Anchors Weigh Down Competitive Thinking. On trend analysis, Predicting Competitors.

An even better question

“The guy who invented the first wheel was an idiot. The guy who invented the other three, he was a genius.” — Sid Caesar

The previous question unintentionally leads us to start with the past and make adjustments. But what if (and notice the mind-shift in “what if”) we don’t default to the past? We could ask this question instead:

What would you do if you were them?

Logically, the two questions are related. The new one even seems like a precursor to answering the prior. But in practice they work differently.

“Oh, if I were them, here’s what I would do!” The even-better question triggers an imaginative, even playful, reaction not at all like what emerged from the previous two questions. Now we’re brainstorming, we’re role-playing, we’re exploring scenarios.

People are so eager to answer “what would you do if you were them” it’s hard to shut them up. They have fun and indulge their creativity. They get to show off their imagination and cleverness. They build on their knowledge and even their fears.

We can beef up this question with the information we may have compiled for the other questions, especially competitive intelligence. CI helps us tour competitors’ heads. We learn they’re struggling to survive, and we imagine desperation. We learn they’re outsourcing, and we infer future price cuts or long-distance distribution chains. We learn they have a radical new product almost ready to launch, and we imagine what else they would be getting ready to do.

Plus, we can think about CI from their perspective, where we are the target. If they learn our customer satisfaction is dropping, they may consider launching a switch blitz. If they learn we’ve hired a hotshot senior executive from a different industry, they may infer we’re planning to make big changes. If they learn our relations with our foreign distributors are stormy, they may give those distributors a discreet call.

There’s one more question to go because we’re still missing three things. Voluntary exercise in creative thinking: can you figure out what they are?

Further reading: I Didn’t Know You Could Do That.

The last, best question

“The enemy is anybody who’s going to get you killed, no matter which side he’s on.” — Joseph Heller, Catch-22

With the previous question we get into competitors’ heads and imagine where they could go. With the last, best question we put on competitors’ shoes and see where they could go. The best question, at least until you or I come up with a better one, is this:

You are them. Go forth and win!
Now what happens?

I mentioned that the previous question, “what would you do if you were them,” was missing three things that this question supplies. Here’s what they are.

1. Dynamic interaction. It’s easy in the previous question to think only of one move; that is, action at one point in time. (You might avoid that if you use role-playing.) In this question, multiple actions and reactions are built in.
2. Calculation. Here, with the “what happens” part, we explicitly address consequences. We did that in our first, worst question too, but here we take competitors into account and, with proper design, avoid the other mistakes as well.
3. Emotion. We human beings feel emotion when our strategies succeed or fail, and we respond with emotion: fear, desperation, retaliation, embarrassment, overconfidence, hubris. Competition isn’t computer against computer, it’s human against human. To understand our markets and our future, we have to include emotion.

That’s why our last, best question answers my initial question of why business war games work better than conventional techniques. Unlike the other techniques with which I am familiar, business war games alone combine dynamic interaction, calculation, and emotion:

• Dynamic interaction through teams role-playing their business, its competitors, and relevant others, through multiple moves.
• Calculation through simulation models that use team decisions to estimate results.
• Emotion through war-game design and humans’ natural competitiveness.

Further reading: About dynamic interaction, Predictable Competitors. About calculation, All About Models, What The Model Says, and The Model Whisperer. About emotion, Feeling is Believing.

The bottom line

My point isn’t that business war games are the only way to gain better answers to tough strategy questions. Perhaps other techniques can be made to produce similar benefits.

My point also isn’t that the business war games that I conduct are the only business war games to achieve those benefits. Other fine firms conduct business war games too.

Rather, my point is that we don’t improve answers only via precision or speed. Often it’s best to improve the questions we ask. We’ve all felt the magic of a great question.

Appendix

I’ve worked with business war games a lot. I’ve conducted a couple hundred, for Fortune 500 companies and at conferences and universities, on six continents. I’ve conducted others, involving millions of scenarios, in the privacy of my computer. All in all, I’ve observed the behavior, decisions, and performance of thousands of real-life strategists.

For some war-game case studies, see this page on ACS’ website.

You can view and download a free bibliography of two dozen articles and essays by ACS on business war games, with summaries and links to full text online.

by Mark Chussil

If we ran a survey asking “are you biased?” we would find no one is. Bias is a characteristic of others, not of ourselves. It’s about you, not me. Which is ironic, and the point.

We see others’ biases, or at least what we perceive as their biases, and don’t see our own. Our biases don’t feel like biases. They feel like self-evident, virtuous truth. That’s why I find it so valuable to read lucid commentaries on bias. They help me recognize and combat my own. My biases, that is, not my commentaries.

And so, kudos to two well-known writers.

Paul Krugman, the Nobel Laureate and columnist at The New York Times, wrote Ideas Are Not The Same As Race. He describes how reasonable self-selection can incorrectly appear as bias.

John Allen Paulos, the Temple University mathematician, ABC commentator, and author of Innumeracy, wrote Who’s Counting: Testing and Hiring Disparities Need Not Imply Bias. He describes how innocent coincidences can incorrectly look like bias.

(See also Fire! Or Maybe Not, my take on the New Haven firefighter discrimination case that went up to the United States Supreme Court.)

Bias doesn’t affect only hiring. It can show up in strategy decision-making. There’s the concept of investing in fast-growing products, which is circular because investment feeds growth and biased because it is self-fulfilling. (See also “viral,” which produces a similar result through a different mechanism.) Another example: many online or text-message surveys use only self-selected respondents. That doesn’t guarantee the surveys will be wrong but it does guarantee they’ll be right only if they’re lucky.

So far we’ve been talking about logical, mathematical bias. There are cognitive biases too, such as overconfidence, confirmation bias (paying attention to evidence that supports a view and discounting evidence that goes against that view), availability bias (vivid anecdotes that stick in our memories more than cold, sober science), and so on. For more on those subjects, I recommend The Psychology of Judgment and Decision Making by Scott Plous (Wesleyan University) and Judgment in Managerial Decision Making by Max Bazerman (Harvard Business School).

Bias is hard to eliminate when we’re unaware of it but not that hard to control when we are aware of it. Here’s what I’ve found helps.

• Pause when you hear the words “obvious” or “because.” Is something really obvious or really because?
• Ask the question implicit in Dr. Krugman’s and Prof. Paulos’ commentaries: Is there something other than prejudice (or whatever) that could cause this outcome?
• Learn, even a little, about probability and randomness, or make friends with someone who has. Prof. Paulos’ books are terrific and non-technical. So is Leonard Mlodinow’s The Drunkard’s Walk.
• Follow Prof. Plous’ advice: “Stop to consider reasons why your judgment might be wrong.” That’s what stress-testing, such as business war gaming, is about.
• Apply Prof. Plous’ advice when working with others. I’ve conducted business war games around the world, and the stop-to-consider process that’s built into them has led to stunning, profitable insights.
• Think about how to answer a question before actually answering it. That might seem silly or obvious, but we often jump to methods even faster than we jump to conclusions. I’ve found this approach tremendously effective when I build strategy simulation models. (See also All About Models.)
• Think you’re not biased? Just for fun, and to support cold, sober science, try Harvard’s fascinating, confidential Project Implicit.

“Fortunately for serious minds, a bias recognized is a bias sterilized.”
Benjamin Haydon (1786-1846)

Dr. Krugman tweets as @NYTimeskrugman. Prof. Paulos tweets as @JohnAllenPaulos. I tweet as @BusinessWarGame and @NiceStart.

by Mark Chussil

Investors suffer from a surplus of checks. I don’t mean checks in the sense of “here, let me write you a check.” I mean checks in the sense of due diligence, verification, and oversight. Not that there’s anything wrong with that, except for when they merely make sure you don’t overpay for your berth on the Titanic.

Some time ago I attended a meeting of investors, of whom I was not one. They filled a large, partially elegant room and listened to pitches from passionate entrepreneurs, of whom I was also not one.

The entrepreneurs all sounded highly competent, knowledgeable, and professional. They clearly knew their markets, their products, and their potential.

The investors all sounded highly competent, knowledgeable, and professional. They clearly knew investing, finance, and prudence.

I listened to the thirty or so questions asked by the investors following the entrepreneurs’ presentations. The questions dealt with checks for due diligence, verification, and oversight. Hey, if I’d been an investor, I’d want to be sure everything was legitimate too.

But that’s what every question was about. Every question sought a reason to believe that giving you a great deal of my money would be a wise move. Not a single question dealt with what could go wrong. What if, for example, a company already in a market wants to squash the entrepreneurial intruder? What if an applied-for patent is denied?

We humans are basically optimistic and energetic. We imagine things, we want things, we make things happen. I’m that way too; I wouldn’t have started businesses otherwise. But being an optimist doesn’t mean looking only for reasons to believe. An optimist steps up to a challenge. A fool steps off a cliff.

When we blend reasons to believe with ambition we get advocacy. I’m right, this is why I’m right, this is why we should do things my way, this is why you should follow me with your money or your career. That’s how we get ahead. That’s how we inspire action.

Due diligence may not protect you from frenzies of advocacy. At its worst, it’s like signing an ill-advised legal document because the spell-check said it was okay. Think it doesn’t happen? Remember that no one invests in a strategy or business expecting it to fail, yet somehow smart people invest in strategies and businesses that fail.

To make due diligence perform at its best, we must ensure that it does more than pummel the proffered reasons to believe things will go well. We must ensure that it also looks vigorously for reasons why things will fall apart. What if a whole bunch of mortgages sour at the same time? What if the market implodes for a couple of years? What if our product unexpectedly and publicly fails? What if credit dries up?

Looking at the dark side does not make you a pessimist or a naysayer. It makes you truly duly diligent.

There are many practical ways to do so. Stress tests. Business war games. Strategy simulations. What-if analysis. Thinking through a wide variety of scenarios. Role-playing with the equivalent of sparring partners. Corporate contrarians. Those are best practices used by optimists who want things to go right and know something can go wrong. After all, if an unexpected threat (or opportunity) can come out of left field, it is good to look toward left field before you commit to a strategy or investment. There are no guarantees, of course, but you can greatly improve your odds. I’ve seen it; it’s the rule, not the exception.

True-due diligence does more than protect you. It also helps you move forward, because you put together a better strategy and you have a better story to tell.

One last point. If true-due diligence demonstrates that it a strategy will produce unhappy results, that’s not a good enough reason to reject the strategy. First, you must compare the unhappy results to the outcomes you’d get under the status quo. If you get better results with the new strategy than you’d get without it, then, unhappy as it is, you should switch to it. Or, look for a better strategy. You’ve been forewarned.

“I observe the physician with the same diligence as the disease.”
John Donne (1572-1631)

This essay is a new, improved version of Checks and Balances, which this site retains for historical reasons.