When it comes to assessing performance, business executives can be a lot like old-time baseball scouts. They've been around so long that they've developed a gut feel for which statistics matter most. But as Michael Lewis describes in Moneyball, the Oakland Athletics discovered that the metric the team's scouts used to choose players had nothing to do with whether those players would score runs. They had been measuring the wrong thing, and executives may be making the same mistake.
The statistics that companies use most often to track and communicate performance include financial measures such as sales and earnings per share growth. Yet these have only a flimsy connection to the objective of creating shareholder value. Executives cling to these metrics because they are overconfident in their intuition, they misattribute the causes of events, and they do not escape the pull of the status quo.
Useful statistics have two qualities. They are persistent, showing that the outcome of an action at one time will be similar to the outcome of the same action at a later time; and they are predictive, demonstrating a causal relationship between the action and the outcome being measured.
Choosing the right statistics — metrics that will allow you to understand, track, and manage the cause-and-effect relationships that determine the value of your company — is a four-step process. I'll illustrate the process in a simplified way using a fictional retail bank based on an analysis of 115 banks by Venky Nagar of the University of Michigan and Madhav Rajan of Stanford. Leave aside, for the moment, which metrics you currently use or which ones Wall Street analysts or bankers say you should. Start with a blank slate and work through these four steps in sequence.
Step 1: Define your governing objective. A clear objective is essential to business success because it guides the allocation of capital. Creating economic value is a logical governing objective for a company that operates in a free market system. Companies may choose a different objective, such as maximizing the firm's longevity. We will assume that the retail bank seeks to create economic value.
Step 2: Develop a theory of cause and effect to assess presumed drivers of the objective. The three commonly cited financial drivers of value creation are sales, costs, and investments. More-specific financial drivers vary among companies and can include earnings growth, cash flow growth, and return on invested capital.
Naturally, financial metrics can't capture all value-creating activities. You also need to assess nonfinancial measures such as customer loyalty, customer satisfaction, and product quality, and determine if they can be directly linked to the financial measures that ultimately deliver value. As we've discussed, the link between value creation and financial and nonfinancial measures like these is variable and must be evaluated on a case-by-case basis.
In our example, the bank starts with the theory that customer satisfaction drives the use of bank services and that usage is the main driver of value. This theory links a nonfinancial and a financial driver. The bank then measures the correlations statistically to see if the theory is correct and determines that satisfied customers indeed use more services, allowing the bank to generate cash earnings growth and attractive returns on assets, both indicators of value creation. Having determined that customer satisfaction is persistently and predictively linked to returns on assets, the bank must now figure out which employee activities drive satisfaction.
Step 3: Identify the specific activities that employees can do to help achieve the governing objective. The goal is to make the link between your objective and the measures that employees can control through the application of skill. The relationship between these activities and the objective must also be persistent and predictive.
In the previous step, the bank determined that customer satisfaction drives value (it is predictive). The bank now has to find reliable drivers of customer satisfaction. Statistical analysis shows that the rates consumers receive on their loans, the speed of loan processing, and low teller turnover all affect customer satisfaction. Because these are within the control of employees and management, they are persistent. The bank can use this information to, for example, make sure that its process for reviewing and approving loans is quick and efficient.
Step 4: Evaluate your statistics. Finally, you must regularly reevaluate the measures you are using to link employee activities with the governing objective. The drivers of value change over time, and so must your statistics. For example, the demographics of the retail bank's customer base are changing, so the bank needs to review the drivers of customer satisfaction. As the customer base becomes younger and more digitally savvy, teller turnover becomes less relevant and the bank's online interface and customer service become more so.
Companies have access to a growing torrent of statistics that could improve their performance, but executives still cling to old-fashioned and often flawed methods for choosing metrics. In the past, companies could get away with going on gut and ignoring the right statistics because that's what everyone else was doing. Today, using them is necessary to compete. More to the point, identifying and exploiting them before rivals do will be the key to seizing advantage.