Circuit City: Paying for Stupidity

Christian Sarkar — Wed, 02 May 2007 13:01:31 +0000

At the beginning of April StrategyWorld pointed out Circuit City’s foolish strategy to fire experienced employees to cut costs.

Now it’s time to fire the decisionmakers who came up with this bright idea.

According to the Washington Post:

“…the Richmond electronics retailer says it expects to post a first-quarter loss next month, and analysts are blaming the job cuts.

The company, which on Monday also revised its outlook for the first half of its fiscal year ending Feb. 29, 2008, cited poor sales of large flat-panel and projection televisions. Analysts said Circuit City had cast off some of its most experienced and successful people and was losing business to competitors who have better-trained employees.”

Will they never learn?

Perhaps they need to attend our FAST strategy webinar… although I doubt anything can help this kind of blindness.

Speaking of ethics, how about Duke’s cheating scandal?

Listen to this:

A survey released last year by Rutgers University professor Don McCabe showed 56 percent of MBA students acknowledged cheating in 2005. In other fields, 47 percent of graduate students said they cheated.

Note: these were graduate students, not undergrads.

John Hagel: FAST Strategy

John Hagel — Sat, 28 Apr 2007 00:02:34 +0000

When I work with senior executives, they express a real conflict: they know strategy is important (strategy, like IT, does matter) but they have a growing sense that traditional approaches to strategy are simply not up to the challenge of coping with increasingly uncertain business environments. What to do?

EDITOR’s NOTE: see our webinar with John Hagel on May 9th: “FAST Strategy: How to Get Results in Disruptive Markets”

My first piece of advice: throw out two basic tenets of business strategy. First, reject the notion that strategy is sequential – that detailed strategic blueprints need to be laid out before a company can proceed with operational implementation. Secondly, abandon the traditional strategy view that the relevant time frame for strategic planning is a five-year horizon.

My second piece of advice: implement a FAST approach to strategy. FAST in this case is an acronym for Focus, Accelerate, Strengthen and Tie it all together. This approach urges executives to move along parallel paths, operating on two very different time horizons: one horizon takes a five to ten year view of the business and the second horizon zooms in to a much more tactical six to twelve month view of the business. The one to five year horizon that is so loved by traditional business strategists actually receives very little attention in the FAST approach.

Focus is the key activity on the five to ten year horizon. This requires senior management to develop a common view on two key questions for their business. Five to ten years from now, what will the markets that we participate in look like? Then, what kind of business we will need to have in order to continue to create value in these markets? Unlike strategic approaches of the past, Focus does not require management to develop a detailed view of the future, but it does require management to develop a clear, high level view specific enough to help the company make important near-term choices.

To illustrate the high level nature of the view of the future, look at Microsoft. Back in the late 1970’s Bill Gates defined a Focus for Microsoft that could be summed up in two sentences. First, computing power is inexorably moving from centralized mainframes to desktop computers. Second, to be successful in the future, a computer company will need to “own” the desktop. Simple and succinct, yet specific enough that it helped Microsoft to answer the unexpected call it got from IBM to help IBM develop an operating system for a new desktop computer it was developing, rather than the twenty other calls it undoubtedly received on that same day. This Focus helped to guide the company for two decades. It only began to need retooling in the late 1990’s as the advent of the Internet set into motion fundamentally new forces.

On the six to twelve month horizon, Accelerate and Strengthen are the key requirements. By Accelerate, I mean identifying a few key operating initiatives that have the potential to significantly accelerate the movement of the company towards the long-term Focus. Once these initiatives are identified and agreed upon by senior management, the question is what can be done to help these initiatives increase their impact over the next six to twelve months? Management needs to set aggressive and measurable operating performance objectives for these initiatives over the next six to twelve months.

On the same time horizon, Strengthen also comes into play. Here, management needs to ask, what are the major organizational obstacles that are preventing us from moving even faster to achieve our operational objectives? Then the question becomes, what can be done over the next six to twelve months to “de-bottleneck” the organization and strengthen our organizational capabilities so that we can move even faster in the next six to twelve month cycle?

Tie it all together integrates these three streams of activities. The key to success with the FAST strategy is to frequently iterate back and forth across these two time horizons and refine efforts on all streams based on the results of efforts to date. The near-term operating initiatives will provide management with much more information regarding both the market place and the capabilities of the company. This should help to refine the longer-term Focus view. In turn, this refined Focus view will be helpful in selecting and shaping the next wave of near-term operating and organizational initiatives. Senior management must actively monitor progress on all three streams and play an active role in shaping initiatives and setting objectives.

The FAST strategy approach respects the need for both near-term performance and longer-term direction, learning and adaptation. It favors incrementalism but recognizes that, without direction, incrementalism will inevitably sub-optimize relative to longer-term opportunities. Properly focused, incrementalism provides significant advantages relative to more tempting “big bang” transformational initiatives:

Provides clear, near-term operational performance metrics to assess progress
Focuses management on delivery of significant near-term operating results consistent with longer-term direction
Enhances ability to fund major strategic thrusts by emphasizing the need for tangible returns early – initiatives potentially become self-funding
Helps to build organizational support for longer-term direction by demonstrating tangible returns quickly while at the same time helping to neutralize opposition
Accelerates organizational learning by providing clear metrics and creating rapid performance feedback loops
Strengthens ability to adapt based on new information gained from near-term operational and organizational initiatives

Few companies today have adopted anything like a FAST strategy approach. You can use five questions to determine whether a company is pursuing this approach:

Does senior management have a common view of what their markets will look like five to ten years from now and what the implications are for the kind of company they will need to develop? Some of the common issues companies confront here are:

Management avoids the issue entirely, claiming that the future is too uncertain
Management aims too low, under-estimating the amount of change that will occur and the size of the opportunities (and challenges) created by this change
Management fails to align around a common view – if interviewed individually, senior executives would offer very different views of the long-term requirements for success

Has this view of the future been adequately communicated throughout the organization? Some of the common issues at this level are:

Management under-invests in communication of the long-term view
Management fails to make this long-term view tangible to the organization by not offering specific examples of how near-term choices will be affected
Management communicates through the ranks rather than directly to the rank and file, leading to divergent and confused views as the message becomes distorted with each level of communication

Is there sufficient focus in the near-term on a few (3-5) high-impact operating initiatives that, over the next six to twelve months, can materially accelerate the company’s movement towards the long-term destination? Common challenges here are:

Management supports too many near-term operational initiatives, in part as “insurance” against uncertainty, with the result that initiatives are under-resourced and rarely achieve the impact anticipated
Near-term operational initiatives are not clearly tied to the long-term destination
Explicit six to twelve month operating performance objectives are not established

Is senior management identifying and addressing major organizational obstacles that prevent even more rapid movement towards the long-term destination? Some of the issues here are:

Organizational changes rarely tied to high impact operating initiatives
Explicit six to twelve month performance objectives are not established – how will management know whether the organizational changes are having the desired impact?

Are there systematic processes to assess near-term operating performance relative to the requirements defined by the longer-term destination? One of the most frequent issues here is:

Operating performance rarely assessed systematically in terms of implications for long-term direction

The FAST strategy approach provides a robust framework for incremental innovation. It offers a useful context for understanding how to create strategic advantage through sustained innovation in business practices enabled by IT capabilities. It also helps us to understand the deep business significance of the emergence of a much more flexible distributed service architecture. This new IT architecture will help businesses to accelerate their near-term operational initiatives even further. In doing so, it will provide a solid foundation for radical incrementalism.

EDITOR’s NOTE: For more on FAST Strategy, see our webinar with John Hagel on May 9th: “FAST Strategy: How to Get Results in Disruptive Markets”

John Hagel: FAST STRATEGY webinar

Christian Sarkar — Sun, 22 Apr 2007 15:44:57 +0000

“What if everything you learned about business strategy is WRONG?”

Why, then it’s time to sign-up for John Hagel’s webinar on FAST Strategy presented by StrategyWorld.org.

In this webinar John reveals why the basic principles of traditional strategy - the principles still taught at most business schools and company executive education programs - are wrong:

· WRONG: Develop a detailed strategy before moving to operational implementation
· WRONG: Focus on a one to five year time horizon to develop robust strategies.
· WRONG: Pursue a portfolio approach to business initiatives to cope with growing uncertainty.
· WRONG: Strategy is a specialized discipline that needs to be pursued by experts.

The alternative? FAST Strategy.

John shows us how some of the most successful companies in the world maintain their advantages, and we can begin to synthesize a new approach to strategy, an approach called FAST strategy.

This approach integrates four elements in an innovative way:

· Focus - building alignment around a long term, five to ten year destination.
· Accelerate - ensuring that the highest impact operational initiatives over the next six to twelve months receive a critical mass of resources
· Strengthen - designing organizational initiatives that can be implemented over a six to twelve month period to address key bottlenecks preventing the firm from moving even more rapidly.
· Tie it together - effectively integrating the first three elements in ways that speed up learning and enhance performance.

Here’s our pitch:

Some of the world’s most successful companies use John Hagel’s thinking to guide them as they navigate the turbulent waters of business competition. Don’t you think you owe it to yourself and your company to find out how they do it? For the price of a plane ticket, you can. And you don’t have to leave the comfort of your office. Mark your calendar, hold your calls, and join our webinar.

PS- space is limited, so please sign up and reserve your seat.

Susan Webber: Management’s Great Obsession

Susan Webber — Sat, 21 Apr 2007 04:55:58 +0000

Corporate America is obsessed with numbers.

Analyst meetings focus on earnings expectations, revenue growth and margins rather than business fundamentals. PowerPoint presentations look naked if they lack charts and graphs to buttress their message. Lofty corporate mission statements are often trumped by pressure to “hit the targets.” Job applicants are advised to stress tangible achievements, and above all, to quantify them. And the ultimate sign of a trend past its sell-by date: a January 2006 Business Week cover story, “Math Will Rock Your World”.

This love affair with figures increasingly looks like an addiction. Numbers serve to analyze, justify, and communicate. But numbers are abstractions. When they begin to assume a reality of their own, independent of the reality they are meant to represent, it is time to take warning, as some are. When McKinsey surveyed over 1000 directors of public companies in 2005, the majority made it clear they wanted to hear less about financial results and more about things not so readily quantified, like strategy, risks, leadership development, organizational issues, and markets.

We believe it is time for business managers to recognize the limitations of metrics. Mind you, we are not saying that metrics and measurement are bad things. A well structured performance measurement system is essential to the well being of large enterprises.

Nevertheless, quantitative measures can be and frequently are used naively. It is all too easy to abdicate judgment to the output of a model or scorecard. And even when measurements are recognized to be incomplete, the reflex is often to make the model more elaborate, when other approaches would yield more insight.

Metrics presuppose that situations are orderly, predictable, and rational. When that tenet collides with realities that are chaotic, non-linear, and subject to the force of personalities, that faith – the belief in the sanctity of numbers –often trumps seemingly irrefutable facts.

In an ideal world, we’d be able to offer a roadmap for the proper use of metrics –what should be measured and when, what are the best ways to measure, how should they be interpreted? But it isn’t simply space constraints that get in the way of this objective. Whether quantitative measures are beneficial or not depends less on the measurement themselves and more on how they are integrated into decision processes. Quick and dirty calculations can be helpful if users understand their limitations, and very precise models can be dangerous if given undue credence.

We’d like to give a broad overview of the problem, with the hope of making you better able to recognize when your metrics might be giving an incomplete or misleading picture, and offer some remedies.

Why We Love Numbers
Math-based techniques have led to important advances in business. Indeed, modern commerce would be impossible without them. But history demonstrates that a good thing can be taken to excess.

Frederick Taylor, whose 1911 “Principles of Scientific Management” inaugurated time and motion studies and job descriptions, accelerated the development of large scale manufacturing enterprises. Henry Ford not only created process engineering, and with it, the assembly line out of his search for efficiency, but his drive to achieve mass production and economies of scale in turn fostered administrative innovations like logistics planning, standard operating procedures, and functional administration design.

But Taylorism also unwittingly devalued workers, in effect treating them as machines. Some scholars believe that this dehumanization of the workplace strengthened and radicalized the union movement, a development that plagued industry for the next fifty years. Similarly, some companies took the Fordist pursuit of scale economies to the point where they lost strategic flexibility. Coca Cola, for example, built up a sizeable inventory of its distinctive 6 oz bottles in the 1930s, and was unable to respond when a struggling, twice bankrupt Pepsi was able to sell a 12 oz drink for a nickel by using recycled beer bottles.

Other techniques we simply applied too broadly. For example, during the heyday of econometrics in the 1970s, most large companies had an in-house econometrist and had a penchant for modeling problems, when simple back of the envelope calculations would often suffice.

We have a cultural bias in favor of science and mathematics. We see numbers as “hard” outputs: objective, reliable, repeatable, verifiable. But most management data are not as firm as, say, your company’s stock price at the close of trading. Even if we understand those limitations intellectually, we somehow lose that perspective when we wrestle with figures.

In fact, we have not only a romanticized view of management information but also of science itself. We attribute to science a rigor and degree of accuracy that would give scientists pause, and this cognitive bias has been repeatedly discusses in scientific literature. For example, in 1961, Michael Scriven called “inaccuracy” the key attribute of physical laws because, “its almost universal presence is a kind of unadmitted shocking fact like the Emperor’s nakedness and needs to be pointed out if we are to get a true picture of the role of laws.” Mind you, this article refers to fields we regard as scientifically mature, such as Newtonian physics. His arguments have been taken further in Professor Nancy Cartwright’s “How the Laws of Physics Lie”.

Matters that laymen would think are well settled, like what constitutes the nature of proof, are open questions. In addition, statistical inferences, which are the type of analysis commonly used in business, are not conclusive. First, even under the best of circumstances, measurements are not perfectly accurate. Second, the sample chosen for study may not truly represent the population as a whole. Third, correlation is not causation: there may be other factors at work, and the ones we have focused on may be secondary or even incidental (recall how ulcers were once believed to be caused by diet and stress?)

Since scientific findings are less solid than many of us would like to believe, it is prudent to regard management “findings” with a healthy dose of skepticism.

Common Mistakes
Since metrics provide a window for viewing our world, how can we recognize when the glass is clouded?

While this list is illustrative rather than exhaustive, watch for:

Focusing on numbers rather than behaviors. In the management information game, “how much” is easy to capture, when “how” can be more illuminating. Too often, companies unwittingly mimic the drunk looking for his lost keys under the streetlight, because that is where he can see well, rather than where he lost them.

Take R&D spending. It is an article of faith that companies should increase their R&D budgets if they want more new products. Yet a 2006 survey by Booz Allen of the top 1000 corporations, measured by their R&D spending, found “no discernible statistical relationship between R&D spending levels and nearly all measures of business success, including sales growth, gross profit, operating margin, enterprise profit, market capitalization, or total shareholder return.” Michael Schrage, a researcher at MIT and an expert on innovation and modeling, argues in a recent Financial Times article that R&D spending is similarly unrelated to innovation. He cites examples such as Reckitt Benckiser, the Anglo-Dutch cleaning products company, and Illinois Tool Works, both of which spend only 1% of sales on R&D (versus a US average of 4.5% and a European figure of 3.3%) when each is considered a highly innovative organization. Schrage also cites Apple Computer, whose R&D spending of 5.9% of revenues, considerably lags the industry norm of 7.6%. Conversely, GM has spend more on R&D over the last 25 years than any company on the earth, and its flirtation with bankruptcy shows how little this outlay has produced.

Framing the problem incorrectly. Sometimes mistakes are glaringly obvious, once they are pointed out. A financial services company regularly surveyed its network partners on their customer satisfaction, measured by ratings on various attributes of the service, and then would try to improve the low scores. However, no one had bothered to ascertain which aspects of service were important to these partners. As a result, considerable effort had been spent improving low ratings that didn’t bother the partners.

Or, since businessmen often compare competitive struggles to combat, consider a military example, the Vietnam War. Two key measures used to measure progress were the “body count”, meaning enemy deaths, and “hamlets under GVN (government of Viet Nam) control”.

The US saw the problem in conventional warfare terms, of gaining territory and thinning the enemy’s ranks. But this viewpoint turned out to be woefully misguided.

First, the body count totals were often exaggerated, sometimes by a considerable margin. Second, we didn’t know who we were killing. Were they really VC, or local sympathizers, or just civilians caught in the crossfire? The more we killed non-combatants, the more we alienated the population and facilitated VC recruitment. Third, and probably most important, we entirely misunderstood the nature of the war. The North Vietnamese saw it as a war of liberation, to eject yet another colonial power. The Vietnamese will had been hugely underestimated, and indicators of it were ignored. For instance, Rand experts who dealt with prisoner interrogation material from World War II, Korea, and Eastern Europe, had never seen interviews like the ones of VC, and concluded that unlike other opponents, they could not be coerced.

Thus, the body counts were relevant only to measure the progress towards exterminating the entire population.

If possible, the “hamlets under control” stats were even more dubious, These figures were reported by the GVN, which obviously wanted to maintain US sponsorship. Yet the US government took these reports at face value, ignoring objections of experienced US operatives.

These measures allowed the command structure to assert that the North Vietnamese effort was on the verge of collapse, until the Tet Offensive of 1968 dramatically demonstrated otherwise, shattering the belief that the US could win the war.

Overlooking perverse incentives and feedback loops. In 1975, Steven Kerr, former president of the Academy of Management, and most recently the Chief Learning Officer of Goldman Sachs, wrote the classic, “On the Folly of Rewarding A, While Hoping for B”. The article describes a range of “fouled up” incentive systems in athletics, academia, medicine, the military, and business.

Kerr lists the causes of these misguided incentives, two of which are particularly germane. The first is “fascination with an ‘objective’ criterion”. While managers like simple, quantifiable standards, they tend to work only in areas where the activities are highly predictable, and break down elsewhere. Second is “overemphasis on highly visible behaviors”, which tends to encourage individual action at the expense of activities like creativity and team building, which are hard to observe.

For instance, there has been a great deal of tooth-gnashing about the corporate fixation with quarterly earnings targets, which are objective, to the detriment of long-term competitiveness, which is harder to assess.

A 2005 McKinsey article, “Building the Healthy Corporation”, describes how some companies have responded by developing scorecards for “performance and health”. It recommends general measures in five areas, with particular emphasis on metrics.

Although the article sets forth a coherent, wide ranging program, it nevertheless runs the risk of being inadequate to the task. In simple terms, a problem that may have started with metrics is not necessarily solvable through metrics, or even metrics plus exhortation.

The article gives CEOs the hope that if they retool their systems and encourage siloized managers to play together, they can redirect their managers’ actions. And normally, that would be a good assumption. But in this case, the “health” program has two major hurdles to overcome. First, most of the health measures will be seen as soft, even if they are quantified (what does it mean to raise customer satisfaction from 3.2 to 3.5? What is that worth?), and as we discussed earlier, “soft” measures are generally taken less seriously than “hard” ones, like costs. Second, placating Wall Street has become an overarching objective in corporate America, reinforced daily in the business press. Even thought the article advocates educating analysts to the payoff to be gained by paying attention to these metrics, that sort of persuasion is an uphill battle. Similarly, the McKinsey piece also recommends cultivating new investors, ones more long-term oriented. Short of going private, it is hard to see how to put that into effect.

No middle level manager is going to do things differently unless he gets an unambiguous signal from the top, like Costco’s rejection of analyst calls to extract more short-term profit. Until top management demonstrates that it will not slavishly bow to the dictates of the financial community, its efforts to persuade the ranks otherwise are likely to fail.

Another danger is feedback loops. Measurement systems are often self-referential, and participants can, sometimes innocently, game the system. The seeming unending rise of CEO pay shows how this process operates.

You know the drill: a compensation survey determines what “comparable” CEOs earn, and then the CEO’s package is set in reference to this universe. But several perverse factors are at work. First, just as the children at Lake Woebegone are all above average, so too are boards reluctant to have their CEO paid in the bottom quartile or half. So there is a mechanism in place, that has clearly been operating quite effectively, to keep moving the averages upward, despite the fact that the evidence is weak to non-existent of any correlation between CEO pay and performance.

Why does this persist? One culprit is the social dynamic among the directors and the compensation consultants. But a contributing factor is that CEO pay is “market” pay, and “market” data is seen as objective, even virtuous. But this notion quickly breaks down. In a real market, such a price rise would fuel a search for substitutes, which in this case would be dark horse candidates, like business unit managers that might have delivered great performance but be wanting in polish (Liz Claiborne’s CEO Paul Charron, who had neither CEO nor apparel experience before he took the helm is the exception that proves the rule) . Similarly, few boards are willing to consider whether their CEO could really get a similar package anywhere else.

Misreading the data. Even when you have the right metrics, you may not interpret them correctly. And since this happens to the best and the brightest, it can certainly happen to you.

You may recall that LTCM debacle in 1998: a high-flying bond trading firm with the best analytical talent in the industry, including two Nobel laureates, had to be bailed out by a consortium of banks to prevent wide-scale disruption of the financial markets. Although there are different views of why the firm collapsed (the big reason is that it began to trade in markets in which it had limited experience), some argue that it was a “perfect storm” that perhaps no one could have anticipated. A more jaundiced and persuasive view is that their models assumed a normal distribution of events (a bell curve), when in fact markets often exhibit both an asymmetrical distribution and “fat tails” (meaning events “far” from the mean in a statistical sense have a greater likelihood of happening than assumed by a normal distribution). Given the huge bets they were taking in unfamiliar waters, it would have seemed a reasonable precaution to have stress tested their models using other distributions of events.

A 2006 article by Malcolm Gladwell discusses how various organizational and social issues have remained unsolved because the remedies assumed a normal distribution, when in fact the problem had a “power law” distribution (think of it as 80/20 on steroids). For example, the LAPD studied complaints of the use of excessive force. They had expected to see the complaints broadly distributed across the entire force, which would suggest that more training and better procedures were the answer. Much to its surprise, it found instead that the complaints were concentrated among a very few officers. The solution was to fire them, or at the very least, get them off the street.

Another factor is cognitive bias. The field of behavioral finance has analyzed the many ways that people fail to deal rationally with numbers. One phenomenon is anchoring. Individuals’ estimates are influenced by random suggestion. In one famous experiment, a roulette wheel generated an illustrative, and clearly arbitrary, value when participants were asked to estimate the percentage of countries in the United Nations that are in Africa. High numbers on the wheel elicited considerably higher guesses.

Or consider a more relevant illustration: acquisitions. Inevitably, the discussion of the pending deal revolves around the projections. The financial model assumes a reality of its own. Anything that isn’t incorporated in the model is implicitly assumed away.

Buyers continue to use this approach despite evidence that it produces bad outcomes. Depending on which study you choose, every analysis of mergers says that most fail (typical estimates are in the 60 to 75% range), and the buyer overpaying is the most frequent cause. Yet the “anchor” of the forecasts is powerful, and woefully difficult to dislodge.

Some Suggestions
The most important change a senior executive can make is a shift in mindset, to regard figures as a useful input rather than gospel. It helps to recognize when these quantitative measures are most valid (e.g., when applied to discrete processes that can be measured objectively, such as transaction processing) and when they are more tenuous. To reinforce a healthy skepticism:

Perform retrospective reviews. While post mortems are standard practice in sport and in medicine, they are virtually non-existent in business. A noteworthy exception: a major financial institution is analyzing the results of its bonus and promotion process to see if they in fact reward the behaviors that senor management believes they are rewarding.

Most companies would learn a great deal if, for instance, they looked at all their capital investment decisions from, say, 1999 to 2002 (both the projects approved and the ones turned down) to see which decisions were good, which were not so astute, and what if anything could have been done to improve the decision process.

Question the logic. Too often, managers are reluctant to ask how certain analyses were derived for fear of appearing ignorant. Yet it is important to inquire, particularly for one-off studies, what analytic methods were used, and what assumptions were implicit in the use of that methodology. For example, a regression analysis assumes a linear relationship between the variables. What if the relationship is actually a step function? Trying to fit a regression to the data would produce misleading results. Similarly, “real options” have become popular as a way of valuing investment opportunities. But option valuation is a tricky business. According to Black-Scholes, some variables, such as the length of the option and the implied volatility, have a significant impact on the option price. It is critical to understand the rationale for the use of the chosen values and run sensitivity analyses around those assumptions.

If you don’t’ have the appetite for this line of inquiry, engage someone with strong advanced math skills, such as a doctoral candidate in applied mathematics, to serve as a house skeptic.

Probe the data. As noted before, all data are not created equal. Generally speaking, the most reliable are ones of physical activity. Financial and accounting data are less solid, and metrics on consumer behavior are even more tricky. It is notoriously difficult to ascertain whether and why consumers will buy a product, since their responses are very much influenced by the test environment. Anyone who has done survey research, for example, will confirm that results can be skewed significantly by how the question is phrased. Similarly, researchers have found that when consumers are given a taste test and asked to rate, say, which salsa they like best, their answers are completely different if they are asked to rate the various attributes (spiciness, texture, etc.) and then say which they prefer. In a bizarre analogy to the Heisenberg uncertainty principle, the act of making the consumer explain why he likes a product shifts his choice.

And probably the most slippery data of all is personnel assessments, where most large companies force subjective, and despite their efforts, not very comparable information into tidy grids and rankings. (Doubters should read Patrick D. Larkey’s, and Jonathan P. Caulkiins’ 1992 paper, “All Above Average, and Other Unintended Consequences of Performance Appraisal Systems”, a provocative and well documented indictment).

The remedy, particularly for important decisions, is to understand the factual underpinnings and be willing to invest in additional research. For example, New Coke was considered a shoe in because it scored so well on sip tests compared to other colas. But consumers don’t buy soda in sips, they buy entire cans, and sip tests favor sweeter drinks. New Coke bombed because many target customers found it to be cloying. Had Coca Cola used multiple approaches to vet New Coke’s consumer appeal, they may well have surfaced its shortcomings.

Be alert to new information. A persistent mistake is attachment to an old perception of a situation (another manifestation of anchoring). Although it is fashionable to blame lumbering corporate reporting systems that filter out bad news, this tendency to dismiss new data is a well documented individual behavior. Recall Thomas Kuhn’s The Structure of Scientific Revolutions: scientists who grew up with an old paradigm simply could not accept a new model. A whole generation had to die out before a new theory, no matter how well proven, became widely accepted.

How can you overcome this cognitive inertia? By demonstrating keen interest in new developments, and fostering that attitude in others. The tried and true approach of talking directly to customers is invaluable. It also helps to ask front line staff often about trends and developments they see, and encourage them to pass them along. You will get a lot of noise along with some choice nuggets, but in this case, the discipline of cultivating awareness and mental flexibility is as important as any bits of intelligence you glean.

Consult your gut. A recent study published in the journal Science found that for complex decisions (defined in this study as involving 12 variables, versus 4 for the “simple” decision) unconscious decision processes yielded much better results than trying to reason it out. Our rational mind can comprehend a limited amount of data, while our unconscious processes, honed over thousands of years of evolution, are better at dealing with complicated situations. In these cases, studying the data and sleeping on it produces demonstrable better choices.

* * *

An overreliance on metrics can lead to “knowing the price of everything and the value of nothing.” Take heed: that is how Oscar Wilde defined a cynic, and cynicism is not viewed favorably in most organizations.

Yet American corporations have for some time been engaged in what can well be described as cynical behavior: taking aggressive accounting measures, engaging in short-term expediencies to improve results, too often displaying little concern for the impact of their actions on employees and communities. Now it is no doubt a stretch to blame these actions on the use of numbers. But the two do seem to go hand in hand.

Management is the art of making decisions in the face of uncertainty. Statistics and analysis can help us understand the nature of that uncertainty and dimension the risks we are taking. But they can also provide false comfort and engender undue confidence. Perhaps the biggest obstacle to giving up the metrics habit is that it will require executives to acknowledge their limitations.

Dumbing Down: A Strategy for Circuit City?

Christian Sarkar — Thu, 05 Apr 2007 04:11:38 +0000

Here’s an interesting article from Knowledge@Wharton:

Short-Circuited: Cutting Jobs as Corporate Strategy

Apparently, Circuit City announced on March 28 that it cut 3,400 jobs, or 7% of its workforce, effective that day, because the salespeople were paid “well above the market-based salary range for their role.”

The folks at Wharton are not impressed:

“That’s the most cynical thing I’ve heard about in a long time,” says Peter Cappelli, management professor and director of the Center for Human Resources at Wharton. “I like to think I’m cynical, but sometimes it’s hard to keep up.”

Another risk is that a downsizing company can get rid of people whose knowledge and experience are vital. Wharton management professor Daniel A. Levinthal points out that Circuit City’s decision to cut 3,400 veteran sales people “sounds like a massive de-skilling” of the company. Since the people who will be hired to replace the laid-off workers probably will not know the merchandise as well as the workers who were dismissed, customers who want to know how to set up a high-definition TV or why one music player is better than another might not receive the best advice.

If this is the case, Circuit City might have a hard time differentiating itself from its competitors. “These new people will be order takers and have less knowledge [about the merchandise],” says Levinthal. “Circuit City would now be competing against e-commerce because it’s become similar to e-commerce and lost its differentiation as a bricks and mortar store.”

Say goodbye to Circuit City. They are stuck between Wal-Mart and Amazon.com, and this latest move will accelerate their demise.

At the core, this is a failure of imagination. But it may also represent the end of a whole slew of similar companies, in the retail industry (and not necessarily just in high tech).

So what might they have done? Suggestions?

Perhaps they should check to see if their leadership is paid “well above the market-based salary range for their role.”

The Competitive Advantage Assessment: Can Your Company Compete?

Christian Sarkar — Mon, 05 Mar 2007 15:34:16 +0000

In 4 Sources of Advantage, authors Peter S. Cohan and Barry Unger tell us that technology leaders create success cycles by the way they perform four critical business processes – which they call “the four sources of advantage.”

The four processes are:

- Entrepreneurial leadership
- Open technology
- Boundaryless product development
- Disciplined resource allocation

So where does your company stand? If you’re seeking to accelerate your company’s, take this self-assessment and find out: [hint - A is good, B is bad]

Entrepreneurial leadership

1. Hiring
A. Hires engineers with strong technical skills and keen business sense; or
B. Hires engineers with strong technical skills and limited business sense.

2. Self-driven research
A. Gives engineers, say 10 per cent to 20 percent, of their time to work on projects they choose; or
B. Requires engineers to work exclusively on manager-directed projects.

3. Publishing
A. Allows engineers to publish current research in peer reviewed publications after appropriate patent disclosures have been made; or
B. Requires engineers to keep their research company confidential.

4. Peer recognition
A. Holds annual celebrations for engineers who develop innovative products; or
B. “Rewards” engineers who innovate by letting them keep their jobs.

5. Culture
A. Uses culture and supporting measurement and reward systems to emphasise how society benefits from its products; or
B. Uses culture and supporting measurement and reward systems to focus on enhancing shareholder value.

Open technology

6. Speed to market
A. Acquires companies or licenses technology to obtain rapid access to products its customers want to buy; or
B. Uses only internally developed technology to develop new products.

7. Customer perspective
A. Builds technologies that create customer value; or
B. Builds technologies that satisfy executive requirements.

Boundaryless product development

8. Cross-functional teams
A. Uses cross-functional teams of, say, engineering, manufacturing, sales, marketing, finance, and early
adopter customers, to design new products; or
B. Uses its engineering department to design new products.

9. Prototypes
A. Uses cross-functional team input to build new product prototypes; or
B. Manufactures product based solely on engineering blueprints.

10. Fast feedback
A. Redesigns prototypes using feedback from early adopter customers, manufacturing, and other functions; or
B. Redesigns products only after they’re out in the market.

Disciplined resource allocation

11. Timing discipline
A. Creates strong incentives to meet project milestones; or
B. Lets product development deadlines slide.

12. Expected value discipline
A. Validates development projects’ expected value (EV) via continuously updated market research and kills them if their EV goes negative; or
B. Once their budget has been set, sticks with development projects.

13. Learning discipline
A. Allocates resources and shares learning through control systems that measure competitive performance; or
B. Rewards those who tell the CEO what the CEO wants to hear and fires those who contradict the CEO.

14. Renewal discipline
A. Develops a deep bench of management talent; or
B. Dismisses ambitious managers to protect the CEO.

The Quick Fix Postal Solution: Fix Long Waits by Removing Clocks

Christian Sarkar — Mon, 05 Mar 2007 14:53:00 +0000

The US Postal Service has taken customer service to the next level of incompetence.

Instead of fixing the problem - the long wait in line - it has decided to work on perception instead: 37,000 post offices across the country have removed their wall clocks from retail areas.

This as part of a “retail standardization program” launched last year.

A spokesman for the U.S. Postal Service says: “We want people to focus on postal service and not the clock.”

The USPS seems to have lost track of what it means to build a positive customer experience.

USPS Customers know that the USPS is not as fast as FedEx, for example, and they don’t expect to be. They’re not using the USPS for efficiency but for cost.

So what’s the big deal? My research tells me that the clock is a fundamental part of the checkout experience - whether you’re in a grocery store or the post office. And not having a clock actually makes the perception of service quality go down, not up.

Says service guru Leonard Berry, “It’s silly, I guess they think people don’t have watches.”

StrategyWorld.org

Seeing Your Customers

5 Burning Questions