Sir Andrew Likierman

An unusually practical article appears in the October issue of Harvard Business Review on the topic of performance measurement. I regret that I can’t share a link with you, because HBR content is not available online, except to subscribers of the magazine (perhaps the folks at Harvard haven’t yet read about the idea of Free). No matter. Though I can’t share the article itself with you, at least I can summarize it here.

Entitled The Five Traps of Performance Measurement, Andrew Likierman’s article is concise and valuable. Sir Andrew Likierman is no less than the Dean of the London Business School, a non-executive director of Barclay’s Bank, and Chairman of the UK’s National Audit Office. He knows of what he writes.

As strategists and change agents, we’re all part of the process of performance measurement and management in our organizations. The task of measurement is neither easy, nor especially satisfying, at least in the short run. And those charged with measurement easily fall prey to pitfalls and traps. Sir Andrew highlights five that are most common.

1. Measuring Against Yourself. While it is important to understand change in performance measures from one period to the next, it is vital to understand how the organization is doing relative to its competitors. While competitive benchmarks may be difficult and potentially costly to obtain, there are ways to understand how one is doing relative to the competition. Likierman offers the example of Enterprise Rent-A-Car’s Quality Index, which captures customers’ plans for future rentals. From a random telephone survey of recent customers, Enterprise is able to project future increases or decreases in market share.  
2. Looking Backward. Comparisons with last year’s numbers aren’t useful if you’re not also looking at leading indicators that help make better decisions now to predict future performance. Likierman moves from this assertion to a discussion of the quality of management decisions as a leading indicator of success. He cites one European investment bank that tracks the eventual outcome of deals the bank chose not to do; if they turn out to be bad deals, the no-go decision is rated as a success. It is as important to focus on what the organization chooses not to do as it is to track what is done.
3. Putting Your Faith in Numbers. Likierman asserts that numbers-driven managers tend to produce high volumes of low-quality data. There is a tendency to use popular measures (ones that may be fashionable in an industry), rather than choosing the right ones for the firm’s unique strategy. An example given is that of the Net Promoter Score, the likelihood that a customer will recommend a product or service to others. But the NPS is useful only if customers are likely to make purchase decisions on the basis of a recommendation. Another symptom of this trap is trying to fashion links to financial performance when no tangible links exist; the apparent ROI of such service functions as HR or especially IT is far more circumstantial than real. Most of the numbers that I’ve seen used in IT ROI calculations are somewhere between rough estimates and wishful thinking – certainly not reliable enough for making decisions.
4. Gaming Your Metrics. It comes as no surprise that people whose performance is being measured will attempt to influence those numbers. This happens at an institutional level, not just with individuals. As Likierman reports, Royal Dutch Shell has paid $470 million since 2004 to settle lawsuits contending that Shell overstated its reserves. Morgan Stanley was reported to have deliberately lost a €20 million deal in order to improve its position in a global ranking. And who hasn’t seen sales behavior change at the end of the fiscal year, either trying to book business in the current year, or delay booking until the following year? All of these are examples of deliberate decisions to manipulate measurements. Likierman accepts the reality that gaming will happen, but prescribes a diversity of indicators that would be harder to manipulate; law firm Cliffor Chance changed from simply measuring billable hours to a portfolio of seven criteria on which to base bonuses; including measures of work quality and integrity.
5. Sticking to Your Numbers Too Long. Once instituted, measurement systems become part of enterprise culture, and evolve more slowly than the organization itself. By deliberately stating the purpose of a measure (and connecting to a strategic intent, as in the balanced scorecard process), leaders are more likely to question an indicator that has outlived its usefulness. To me, the sign of a healthy strategic management process is one in which measures are easily and frequently challenged, and the portfolio of measures is frequently revised.

Likierman concludes by reminding us that most business managers are not experts in performance measurement, and that line managers suffer from hopeless conflicts of interest in the measures design process. Measures design must include checks and balances, and (I believe) is best enabled by an outside, objective facilitator.

Are these pitfalls present in your organization? What are the other traps you’ve seen? Please comment below.

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The ‘balance’ in balanced scorecard refers to the ideal of providing leaders with a balanced portfolio of lagging and leading performance indicators. Leading indicators are valuable because they help managers form an expectation of what will happen, and enable testing of the cause-and-effect hypotheses that are at the core of the strategic planning process. But identifying candidate leading indicators and selecting from among them requires careful consideration and a healthy skepticism of apparently easy answers.

But this careful consideration requires familiarity with the concept of causation. Having just pulled down my trusty copy of The Practice of Social Research – a textbook from an undergrad class I took too many years ago, I was daunted by the prospect of condensing a lengthy entire chapter on the nature of causation to offer a brief understanding here. Wikipedia’s definition is far more concise:

Causation: The belief that events occur in predictable ways and that one event leads to another. If the relationship between the variables is non-spurious (there is not a third variable causing the effect), the temporal order is in line (cause before effect), and the study is longitudinal, it may be deduced that it is a causal relationship.

When working with executives, I find real-world examples to be especially useful. One of my favorites offers a measurement of housing starts (a statistic produced monthly by the U.S. Census Bureau) as a leading indicator of the sales of so-called “white goods” (a wonderful old term meaning major appliances, like refrigerators, washing machines, stoves, etc.). An uptick in housing starts is an extremely reliable leading indicator of a nearly identical uptick in sales of white goods several months later. As those new housing units near completion they are furnished with new appliances. So if you’re in the business of making refrigerators, you’re going to be very interested in tracking housing starts.

Unfortunately, causation isn’t usually that obvious. One client of mine (some details have been changed) operated an inbound call center for taking resort hotel reservations. As part of a broad strategy, this firm undertook an initiative to increase revenue by cross-selling reservations for nearby dining and attractions, along with the core hotel reservations. Call-center agents whose performance had previously been measured as calls handled per hour were now expected to engage callers in more personal conversations about their vacation plans, in order to find opportunities to cross-sell the associated reservations. The new performance measures became average call duration (the hypothetical leading indicator that was expected to increase) and average revenue per call (the lagging indicator). The strategic hypothesis: keep customers on the phone longer, and they’ll buy (reserve) more.

It didn’t work out that way. Even though the call-center agents had been trained extensively in cross-selling, the majority of customers calling in were not easily engaged in the longer, more personal conversations. These customers called expecting a brief transaction (the hotel reservation) and were impatient with the questions. But a subset of customers calling in were quite happy to chat with the friendly reservations agents. Unfortunately, these chatty customers turned out to be especially unlikely to make dining and attraction reservations, and ultimately reduced the productivity of the agents.

A continuing theme in my posts has been that of strategy as a set of hypotheses to be tested. A tested hypothesis proven to be false is just as valuable as one proven true. Measure such as those in the example above enabled managers to quickly identify flaws in the hypothesis, and revise the strategy. The value of a measure is in its ability to enable valuable decisions.

Do you have a favorite example of a pair of leading / lagging indicators that you can share? Please comment below.

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A classic example of the Flaw of Averages involves the Statistician who drowned crossing a river that was on average 3 ft. deep.

Desperately casting around for a topic to write about today, I was grateful to see a link to an interview in the San Jose Mercury News with Stanford professor Sam L. Savage about his book, The Flaw of Averages (great title!). I’ve not read the book yet, but the review has certainly piqued my interest:

How does General Motors, Sam L. Savage wonders, explain the pathetic performance of its crystal ball? When Americans started driving hybrids, GM was still pushing Hummers. Executives at the giant carmaker — fully aware of union contracts, presumably prepared for rising gasoline prices and economic uncertainty — drove straight into the ditch of bankruptcy.

“Probability management” is often mismanaged by business leaders, says Savage, a consulting professor of management science and engineering at Stanford University and a fellow at the Judge Business School at the University of Cambridge. Savage, who has performed probability studies for Royal Dutch Shell, set out to right statistical wrongs in his book “The Flaw of Averages.”

The Information Age has transformed statistics into a vital field of study, yet Savage says many habits and practices have been slow to change from the “steam era statistics” of the Industrial Age.

Written for a business audience, “The Flaw of Averages” leavens the math with levity, even the occasional cartoon.

Well alright then. Working with business executives and their measures for so many years, I continue to be amazed at how easily decisions are made on the basis of numbers with little consideration for the risks and consequences of those decisions. I’ve been meaning to write at some length about the need for the discipline of risk management in change programs, but before doing so, we all need to take a deep breath and consider the magnitude of our collective innumeracy.

The topic has been covered before. I just pulled Innumeracy: Mathematical Illiteracy and its Consequencesby John Allen Paulos from my bookshelf, and thumbing through it, I remember how much I appreciated the book, but that is wasn’t the easiest read. From the back cover description:

Why do even well-educated people understand so little about mathematics? And what are the costs of our innumeracy? John Allen Paulos, in his celebrated bestseller first published in 1988, argues that our inability to deal rationally with very large numbers and the probabilities associated with them results in misinformed governmental policies, confused personal decisions, and an increased susceptibility to pseudoscience of all kinds. Innumeracy lets us know what we’re missing, and how we can do something about it.

Sprinkling his discussion of numbers and probabilities with quirky stories and anecdotes, Paulos ranges freely over many aspects of modern life, from contested elections to sports stats, from stock scams and newspaper psychics to diet and medical claims, sex discrimination, insurance, lotteries, and drug testing. Readers of Innumeracy will be rewarded with scores of astonishing facts, a fistful of powerful ideas, and, most important, a clearer, more quantitative way of looking at their world.

SinceI found that Innumeracy was not especially accessible, I haven’t yet found occasion to use examples from it. Perhaps The Flaw of Averages will be better. It looks promising. From the interview with Savage:

Q. What are the most common ways people foolishly apply the law of averages? Is it the faith placed in “average returns” on retirement portfolio?

A. Plenty of people have been caught off base by the Flaw of Averages in investing, but here is an example that is closer to home. Imagine that both you and your wife are right on time for appointments, on average.

When you go somewhere together, however, you will be late, on average. Why? If we model being early or late for each of you by flipping a coin (heads is early, tails is late), then the only way you will not be late as a couple, is if neither of you is late. This is like flipping two heads in a row, or one chance in four. Now expand this to a big industrial project with thousands of tasks, and you can imagine the implications.

We don’t have to imagine the implications – we live with them every day. More to come (soon, I hope), on the topics of innumeracy and strategic risk management.

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This series of posts (in three parts) is adapted from an article of the same name that appeared in Harvard Business Publishing’s Balanced Scorecard Report in 2006.

In Part I, I asserted that perception matters very much to the strategy of an organization. Perception of external stakeholders, of customers, and of employees. Often, the change program requires measurements of customer and employee perceptions. How organizations go about gathering these perceptions is a key factor in the success of the change program. In Part II, we examined the challenges of survey design, and its impact on the effectiveness of the strategy-driven perception research. Here, we conclude with consideration of alternatives to surveys, and an examination of how to use perception data in the context of the change program.

Consider Focus Groups or Interviews

While most perception measures come from surveys, focus groups and interviews are also valuable tools. Focus groups can be a component of a survey (answering the complex question, “why are employees unhappy?”), or can simply serve as a way of capturing the perceptions of a small group when surveys would not be effective or practical. A focus group can reveal complex root causes for perceptions that may not be anticipated in a set of multiple choice responses.

Focus groups typically involve eight to twelve subjects and one or two facilitators, with generally no more than five questions. Experienced facilitators employ several techniques to elicit and structure responses from participants, which may be captured through flip charts, note taking, or audio and video recording. Because facilitators affect results, it is vital that they be experienced, neutral, and knowledgeable about the topic. Specially designed focus-group facilities feature one-way mirrors to enable skilled observers to capture participants’ body language and group dynamics without influencing them. Internet and other technologies now enable on-line focus groups, from simple discussion boards and blogs to real-time sessions assembling people from different locations to interact with text, audio, and even video.

One-on-one interviews are another valuable technique. By separately asking members of a group with similar characteristics a standard set of questions—for example, asking salespeople the same questions about follow-up calls to customers after a store visit—you get the benefits of a focus group (more detailed, qualified responses and more flexible dialogue), without the burdens of scheduling and travel.

Put Your Findings in Context

With survey data in hand, how do you best present it? It’s easy to present survey data in graph form, but graphs by themselves don’t tell a very useful story. Including all the detail in a change program progress report is usually not necessary, though the detail should be available to the leadership team so they can drill down if they want. What is necessary, however, is context. The performance advocate for the overlying strategic objective should work closely with the research designer to analyze the survey findings, considering them in the context of data from prior surveys and other measures in the report—and presenting that context in the reporting.

Focus group feedback, including carefully selected participant quotes (paraphrased when necessary to ensure anonymity), should be summarized through the facilitators’ written analysis. The research designer should attend the management discussion of the findings to answer technical questions and help shape subsequent research requests.

Because of the very real danger of simply replacing the old “comfortable fictions” with new ones, leaders should balance their reasoned judgment with a healthy skepticism when making decisions resulting from their enriched understanding of stakeholder perception.

How is perception research done in your organization? Do leaders shun surveys because of what they might learn? Do employees suffer from ‘survey fatigue?’ Please offer your comments and insights below.

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This series of posts (in three parts) is adapted from an article of the same name that appeared in Harvard Business Publishing’s Balanced Scorecard Report in 2006.

In Part I, I asserted that perception matters very much to the strategy of an organization. Perception of external stakeholders, of customers, and of employees. Often, the change program requires measurements of customer and employee perceptions. Here, we consider how organizations go about gathering these perceptions, which is a key factor in the success of the change program.

Ensuring Survey Success: Skillful Research Design is Vital

A survey program is the best way to regularly monitor stakeholder perceptions. E-mail and Web-based survey tools enable faster design, execution, and analysis, and have reduced the cost considerably. Many enterprises already have e-mail address lists from the Web sites and customer databases they maintain for direct communication and marketing purposes. Wireless telephony and text messaging enable nearly real-time data collection and analysis. Technology, however, is no substitute for good research design, and in amateur hands, such tools amplify the risk of getting unactionable results or even causing adverse consequences.

Regrettably, to save money, many firms opt for do-it-yourself research design, even when they lack the expertise to do so effectively. The principles of research design are beyond the scope of this article; suffice it to say that unless you have a professional market research staff in house, do-it-yourself research design is risky, unlikely to yield much useful information, and can actually do harm. The “Hawthorne Effect” demonstrated that the very act of studying a group of subjects changes members’ behavior. A poorly designed survey can antagonize the subjects, or, if they fear repercussions from any negative responses, discourage candor. Either way, it can taint results.

The research designer must be both unbiased about the research topic and familiar with the study environment. Unwittingly or not, do-it-yourselfers often allow their leaders’ biases to creep into the questions. But subtle changes in the way a question is phrased—even the order of the questions—can dramatically affect how people respond. Allow leaders to contribute their opinions when establishing hypotheses, but keep them a safe distance from the actual design.

Strategy-driven research begins with the cause-and- effect relationships among strategic objectives on a strategy map. Strategic objectives for customers and employees require perception measures to give a full picture of performance. While financial measures like sales volume or market share reflect the results of customer behavior, leading measures of customer perception can help executives anticipate changes in these ultimate outcome measures.

Changing people’s perceptions is often central to strategy. Well-designed research might provide leaders with insights about which perceptions need to change or help test hypotheses about how a change in perception will actually occur. A customer perception measure can validate the effect of an internal process initiative.

For example, as part of its strategy to develop more profitable relationships with its customers, a bank undertakes an initiative to have customer service agents spend more time on the telephone with customers calling for support. By equipping agents with better customer information, the bank believes they will be able to probe beyond the original scope of each call and promote additional products appropriate to the caller. A sound customer feedback program can enable the bank to validate the assumptions underlying its strategy, or to uncover negative perceptions (e.g., that calls are taking too long or agents’ questions are intrusive). Only by measuring agents’ new behavior (for example, through average call duration), along with agent and customer perceptions, can increased sales reasonably attributed to the new strategy.

While an inexperienced research designer would simply begin by writing questions, a seasoned designer would start by studying the environment and its population groups, as well as the organization’s strategy, to decide the best method (e.g., surveys, focus groups, or data mining) and frequency, and how to segment the research population. Drawing on her academic and practical understanding of human behavior, statistical analysis, and survey design, she would then develop a comprehensive research plan and plan follow-on research.

Conquer Survey Fatigue

Leaders seeking “silver bullets” for intractable problems don’t often have the patience to execute surveys repeatedly. But few surveys provide full value in one execution. Periodic sampling of a population reveals trends that are impossible to see in a single snapshot. Often the absolute value of a perception measure is meaningless; the insight comes from tracking the direction and magnitude of change in the measure over time.

“Survey fatigue” is a common reason why organizations resist using surveys. Employees in firms with Internet survey tools may be bombarded with narrow, one-off surveys from multiple sources whose timing is not coordinated and that, over time, do little more than discourage response. Such surveys may be sent to every member of a large population when a relatively small, carefully chosen representative sample would be valid—thereby reducing survey frequency and minimizing fatigue. All research requests should be funneled through a single coordinator.

Rather than surveying the entire employee population annually, the organization could send the same basic survey to a controlled random sample every quarter (25% of the employee population), polling different subgroups each time. The sample size is determined mathematically, according to population size and acceptable margin of error. A basic set of questions could be asked each time, and carefully targeted new questions could be included when needed to understand new challenges. Under this model, no employee in the target group would receive more than one survey per year. By generating quarterly findings (rather than the usual annual ones), this model is especially appropriate for strategy reporting.

Next: Alternatives to Surveys

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This series of posts (in three parts) is adapted from an article of the same name that appeared in Harvard Business Publishing’s Balanced Scorecard Report in 2006.

When helping organizations design measures for their change programs, the moment comes when I float the idea of surveying employees or customers. Invariably, there is an uncomfortable silence, followed by protests that surveys are expensive, that they don’t tell them anything new, and that a steady diet of them annoys people and thus defeat their purpose. An unspoken source of resistance is leaders’ fear that survey results will challenge the comfortable fictions they may be sustaining to support their decisions.

True, surveys can be expensive, especially for professional research design and administration. But in the past decade, technology has gone a long way in offsetting their cost and complexity.

Customer and employee expectations and perceptions all matter to a company. Understanding them is vital to predicting the behaviors of these stakeholders: whether they’ll invest in your shares or buy your products or work hard. When done properly, surveys are essential for developing a balanced portfolio of leading and lagging indicators. And when survey results inform key management decisions that involve making large investments in pursuit of even larger revenues, the value can be substantial. But in the absence of clear-cut results, surveys may raise more questions than they answer.

Perception Matters

Understanding, shaping, and fulfilling the expectations of stakeholders is central to successful strategy execution. In for-profit organizations, strategy is rooted in the need to satisfy shareholders’ expectation of a return on their investment. The decision to invest (and, by implication, the stock price) is driven by investors’ collective expectations of the firm’s future performance. Customers’ perception of the value proposition predicts his or her behavior toward the firm, namely, whether he or she buys its products. Value, like beauty, is in the eye of the beholder. While executives may envision what the value proposition should be, it is customer perception that determines what the value proposition actually is. The concept of the customer value proposition applies to the firm’s internal customers as well. In my work with IT organizations, I’ve learned that expectations and perceptions shape behaviors that influence the quality of these internal service provider-customer partnerships—and ultimately, how efficiently the resources that drive enterprise performance and strategy execution are used.

Of course, human capital is a key intangible asset necessary for creating value. A company can influence, but not control employees’ expectations and perceptions of the firm. These factors largely drive their behavior: how hard they work, how well their actions support the firm’s interests, and ultimately whether they’ll continue working for the firm.

Perception drives behavior; that’s why the behavior of these stakeholders is indeed the firm’s reality. Woe unto the firm that doesn’t understand what its investors, customers, and employees are thinking.

Next: Ensuring Survey Success

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To my chagrin, the term ‘scorecard’ is widely used in both the disciplines of performance management and strategy execution, and without further qualification, has an imprecise variety of meanings. To some, it may mean a large collection of indicators of operational performance. To others, it is an ambiguous shorthand for ‘balanced scorecard,’ which is a well-developed set of related ideas and practices around strategy management and execution. Ambiguity comes from the fact that to some, the term ‘balanced scorecard’ means simply a collection of measures that has been balanced according to some real or imagine scheme. On far too many occasions, I’ve been approached by a conference attendee with a request to review and comment on his so-called ‘balanced scorecard,’ only to find that the proud offering is a only collection of operational measures with no connection to strategy. This is the basis of my Scorecard Blues. So let me be blunt: if a set of measures has been selected without the prior development of a strategy map, it cannot be properly called a balanced scorecard.

Even without the qualifier of ‘balanced,’ a ‘scorecard’ is seen as a group of measures, and / or the visual representation of those measures, and / or the tool for managing measurement data. Many software tools called ‘scorecards’ have been developed to facilitate the collection, analysis, and presentation of scorecards, both for operational and strategic use. Because the term ‘scorecard’ has so many diverse meanings and uses, it simply cannot be used alone without further explanation. But there is one trait that attaches to nearly every individual’s own definition of the term ‘scorecard.’

The lowest common denominator of nearly all ‘scorecards’ is the ubiquitous red – yellow (amber in Europe) – green summary indicator scheme (hence RYG). I am not aware of any research into the origin of the RYG scheme for summarizing measure data. Of course, the scheme is universally used in traffic control, but the meanings are simple: red simply means stop, and green simply means go. I suspect that the first use of colors to summarize data was in aviation and later in aerospace, where green means that an indicator is within its normal operational range, with yellow and red varying degrees of abnormal results. This origin suggests the basis for a similarly ambiguous term, ‘dashboard.’

It is easy to see the appeal of using this scheme to summarize a large portfolio of operational measures. At a glance, one can observe the RYG summaries of dozens or even hundreds of indicators and quickly know which ones are in need of immediate attention. The ability to translate and display a measure as an RYG indicator is a basic capability in every scorecard software tool I’ve seen.

‘Scorecards’ predate ‘balanced scorecard,’ and as the BSC discipline has evolved and matured, so has the sophistication of BSC reporting. Adaptation of ‘scorecard’ tools to BSC reporting has been rampant. But the inevitable application of RYG to BSC reporting has been problematic. Most organizations that I’ve worked with have struggled with RYG indicators.

The BSC development process identifies strategic objectives as the basis for subsequent selection of measures. During the first several months of the life of a BSC, an organization may have difficulty in selecting effective measures and obtaining measure data; strategic measures are often very different than operational measures, and require that new information sources be developed. New measures do not always lend themselves as easily to target setting; measuring an objective to ‘Manage turnover of key employees’ not only requires data on turnover, but definitions of ‘key employees,’ and what ‘management’ actually means. Setting a target for the concept of turnover is far from intuitive.

Even when a target can be set for a particular measure, the target is probably is only useful to delineate between the green and yellow indicators. I’ve had to preside over many spirited (e.g., angry) and wasteful discussions of the right distinction between yellow and red for a particular measure. One of my colleagues has even advocated using only red and green indicators to his clients, but with limited success.

Because effective BSC programs require transparent and frequent communication of strategic performance among selected stakeholders (e.g. employees), members of leadership teams can become quite defensive about the colors. In the language of scorecard color, red means ‘bad’ for operational indicators, but not necessarily so for strategic objectives, where ‘red’ may be interpreted as ‘objective needing attention.’ Cultural attachments to the meaning of ‘red’ often causes contortions to avoid putting a red indicator on a strategy map.

In practice, I’ve arrived at a couple of conclusions and approached to using the RYG scheme on BSCs:

1. Leadership teams insist on having RYG indicators, but need to be guided to a shared set of definitions of their meanings.
2. Strict numerical targets don’t work very well for setting the colors.
3. Without guidance, a leadership team will become obsessed by color setting and distracted from productive strategic management efforts.
4. A healthy set of strategic objectives will have a good mixture of the colors. A strategy map with all green objectives isn’t believable. A strategy map with all red objectives is too demoralizing. Either extreme is dysfunctional.
5. Because there is subjectivity in target setting, it is better to have leadership teams come to a subjective agreement on the RYG setting for each objective on a strategy map, each time measures are updated. The discussion that leads to a consensus decision often helps the leaders uncover and understand root causes of deficient performance far better than any automatically set indicator will.
6. Over time, the RYG indicators of strategic performance become far less important than the rich discussions of strategic objectives, cause and effect, and initiatives in the strategic management process.

Is your organization using RYG indicators on its scorecards? Does everyone agree on the meanings of each color? Do RYG indicators help or hinder strategic management? Please comment below.

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Strategy review meetings are the necessary venue in which leaders periodically evaluate the progress of the change program. They are typically held quarterly, and include a review of current data for performance measures that track progress toward established strategic objectives. In these meetings, leaders are also charged with identifying new initiatives, and managing the existing portfolio of initiatives. Leaders may establish new performance measures and discard those that aren’t revelatory. While this process is designed to implicitly test the hypotheses that comprise the strategy, at least once per year this meeting requires an explicit review of the entire strategy, with the expectation that modest changes will be made.

Clearly understood roles and responsibilities are vital to the success of these meetings, and should be explicitly stated, especially since they may be unfamiliar and not intuitive to those participating. The most important of these roles is that of Performance Advocate (PA).

The leadership team is typically comprised of the formal leader of the organization, along with his or her direct reports; but variants are possible, especially when the scope of the strategy being managed spans organizational or even enterprise boundaries. Leadership teams work best when they’re neither too small nor too large; most teams fall in the range of 8-12 persons. When teams are much larger than about 15 people, there is a substantially increased risk that the meeting becomes an arena in which politics and posturing take center stage. Smaller teams tend to be dominated by the formal leader of the organization, and have an increased risk of ‘groupthink.’

Each member of the leadership team, including the organization’s leader, take on the role of Performance Advocate. There is a named PA for each of the 15 to 25 (typically) objectives on the strategy map. Although I strongly recommend that there only be one PA for each objective, some organizations insist on sharing the responsibility for some or all objectives among two PAs. The result is often less productive since the division of labor is hard to manage.

Since there are almost always more strategic objectives than members of the leadership team, each team member will serve as PA for more than one objective. No member of the leadership team should be excused from the PA responsibility, and the allocation of these roles should be as even as possible. PAs usually volunteer or are appointed at the conclusion of the strategy map design process. 

The use of the term ‘Performance Advocate’ is deliberate – you should resist the inclination to say that each objective has an ‘owner,’ since the team as a whole is collectively responsible for the execution of the strategy. While an ‘Owner’ may be personally blamed for inadequate performance toward an objective, the PA helps the leadership team as a whole accept responsibility for strategic performance.

The primary responsibility of each PA is to ensure that the measurement and reporting data are complete before each strategy review meeting, and to lead focused discussions about their objective(s) at review meetings. Between meetings, PAs follow up on performance issues, drive the completion of action items, collaborate to resolve issues, and prepare for the next meeting.

In our article, Jay and I also identified supporting roles: Measure Coordinators who assist the PAs in preparing and analyzing measurement data; the Editor, who is responsible for pulling together and publishing the quarterly report that is the basis for the strategy review meeting; and an outside Facilitator, whose objectivity and expertise in the strategic management process is vital to keeping the meeting on-track and productive. Each of these roles, along with the evolution of the Strategy Review meeting in organizations new to BSC management, will be elaborated in future posts. 

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