About a dozen years ago, when I was
working for a large financial services firm, one of the senior
executives asked me to take on a project to better understand the
company’s profitability. I was in the equity division, which generated
fees and commissions by catering to investment managers and sought to
maximize revenues by providing high-quality research, responsive
trading, and coveted initial public offerings. While we had hundreds of
clients, one mutual fund company was our largest. We shuttled our
researchers to visit with its analysts and portfolio managers, dedicated
capital to ensure that its trades were executed smoothly, and
recognized its importance in the allocation of IPOs. We were committed
to keeping the 800-pound gorilla happy.
Part
of my charge was to understand the division’s profitability by
customer. So we estimated the cost we incurred servicing each major
client. The results were striking and counterintuitive: Our largest
customer was among our least profitable. Indeed, customers in the middle
of the pack, which didn’t demand substantial resources, were more
profitable than the giant we fawned over.
What happened? We made a mistake that’s exceedingly common in business: We measured the wrong thing. The statistic we relied on to assess our performance—revenues—was disconnected from our overall objective of profitability. As a result, our strategic and resource allocation decisions didn’t support that goal. This article will reveal how this mistake permeates businesses—probably even yours—driving poor decisions and undermining performance. And it will show you how to choose the best statistics for your business goals.
Ignoring Moneyball’s Message Moneyball, the best seller by Michael Lewis, describes how the Oakland Athletics used carefully chosen statistics to build a winning baseball team on the cheap. The book was published nearly a decade ago, and its business implications have been thoroughly dissected. Still, the key lesson hasn’t sunk in. Businesses continue to use the wrong statistics.
Before the A’s adopted the methods Lewis describes, the team relied on the opinion of talent scouts, who assessed players primarily by looking at their ability to run, throw, field, hit, and hit with power. Most scouts had been around the game nearly all their lives and had developed an intuitive sense of a player’s potential and of which statistics mattered most. But their measures and intuition often failed to single out players who were effective but didn’t look the role. Looks might have nothing to do with the statistics that are actually important: those that reliably predict performance.
Baseball managers used to focus on a basic number—team batting average—when they talked about scoring runs. But after doing a proper statistical analysis, the A’s front office recognized that a player’s ability to get on base was a much better predictor of how many runs he would score.
Moreover, on-base percentage was underpriced relative to other abilities in the market for talent. So the A’s looked for players with high on-base percentages, paid less attention to batting averages, and discounted their gut sense. This allowed the team to recruit winning players without breaking the bank.
Many business executives seeking to create shareholder value also rely on intuition in selecting statistics. The metrics companies use most often to measure, manage, and communicate results—often called key performance indicators—include financial measures such as sales growth and earnings per share (EPS) growth in addition to nonfinancial measures such as loyalty and product quality. Yet, as we’ll see, these have only a loose connection to the objective of creating value. Most executives continue to lean heavily on poorly chosen statistics, the equivalent of using batting averages to predict runs. Like leather-skinned baseball scouts, they have a gut sense of what metrics are most relevant to their businesses, but they don’t realize that their intuition may be flawed and their decision making may be skewed by cognitive biases. Through my work, teaching, and research on these biases, I have identified three that seem particularly relevant in this context: the overconfidence bias, the availability heuristic, and the status quo bias.
Overconfidence. People’s deep confidence in their judgments and abilities is often at odds with reality. Most people, for example, regard themselves as better-than-average drivers. The tendency toward overconfidence readily extends to business. Consider this case from Stanford professors David Larcker and Brian Tayan: The managers of a fast-food chain, recognizing that customer satisfaction was important to profitability, believed that low employee turnover would keep customers happy. “We just know this is the key driver,” one executive explained. Confident in their intuition, the executives focused on reducing turnover as a way to improve customer satisfaction and, presumably, profitability.
Michael J. Mauboussin is the chief investment strategist at Legg Mason Capital Management and an adjunct professor of finance at Columbia Business School. He is the author of The Success Equation (Harvard Business Review Press, forthcoming), from which this article was developed.
What happened? We made a mistake that’s exceedingly common in business: We measured the wrong thing. The statistic we relied on to assess our performance—revenues—was disconnected from our overall objective of profitability. As a result, our strategic and resource allocation decisions didn’t support that goal. This article will reveal how this mistake permeates businesses—probably even yours—driving poor decisions and undermining performance. And it will show you how to choose the best statistics for your business goals.
Ignoring Moneyball’s Message Moneyball, the best seller by Michael Lewis, describes how the Oakland Athletics used carefully chosen statistics to build a winning baseball team on the cheap. The book was published nearly a decade ago, and its business implications have been thoroughly dissected. Still, the key lesson hasn’t sunk in. Businesses continue to use the wrong statistics.
Before the A’s adopted the methods Lewis describes, the team relied on the opinion of talent scouts, who assessed players primarily by looking at their ability to run, throw, field, hit, and hit with power. Most scouts had been around the game nearly all their lives and had developed an intuitive sense of a player’s potential and of which statistics mattered most. But their measures and intuition often failed to single out players who were effective but didn’t look the role. Looks might have nothing to do with the statistics that are actually important: those that reliably predict performance.
Baseball managers used to focus on a basic number—team batting average—when they talked about scoring runs. But after doing a proper statistical analysis, the A’s front office recognized that a player’s ability to get on base was a much better predictor of how many runs he would score.
Moreover, on-base percentage was underpriced relative to other abilities in the market for talent. So the A’s looked for players with high on-base percentages, paid less attention to batting averages, and discounted their gut sense. This allowed the team to recruit winning players without breaking the bank.
Many business executives seeking to create shareholder value also rely on intuition in selecting statistics. The metrics companies use most often to measure, manage, and communicate results—often called key performance indicators—include financial measures such as sales growth and earnings per share (EPS) growth in addition to nonfinancial measures such as loyalty and product quality. Yet, as we’ll see, these have only a loose connection to the objective of creating value. Most executives continue to lean heavily on poorly chosen statistics, the equivalent of using batting averages to predict runs. Like leather-skinned baseball scouts, they have a gut sense of what metrics are most relevant to their businesses, but they don’t realize that their intuition may be flawed and their decision making may be skewed by cognitive biases. Through my work, teaching, and research on these biases, I have identified three that seem particularly relevant in this context: the overconfidence bias, the availability heuristic, and the status quo bias.
Overconfidence. People’s deep confidence in their judgments and abilities is often at odds with reality. Most people, for example, regard themselves as better-than-average drivers. The tendency toward overconfidence readily extends to business. Consider this case from Stanford professors David Larcker and Brian Tayan: The managers of a fast-food chain, recognizing that customer satisfaction was important to profitability, believed that low employee turnover would keep customers happy. “We just know this is the key driver,” one executive explained. Confident in their intuition, the executives focused on reducing turnover as a way to improve customer satisfaction and, presumably, profitability.
Michael J. Mauboussin is the chief investment strategist at Legg Mason Capital Management and an adjunct professor of finance at Columbia Business School. He is the author of The Success Equation (Harvard Business Review Press, forthcoming), from which this article was developed.
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