2012 -The True Measures Of Success Most companies use the wrong performance metrics. Don’t be one of them. by Michael J. Mauboussin
Michael Mauboussin is considered an expert in the field of behavioral finance and has some famous books on the topic including, Think Twice: Harnessing the Power of Counterintuition and More More Than You Know: Finding Financial Wisdom in Unconventional Places.
Michael Mauboussin: The True Measures Of Success
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.
Michael Mauboussin: 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, onbase 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.
