Credit LA Cicero, 11/4/1996, color neg.
Kenneth J. Arrow, professor emeritus in economics, Nobel Laureate

Nobel Prize-winning economist Ken Arrow started his career as a weather officer in the U.S. Army Air Forces during World War II. He worked with a group responsible for preparing long-range weather forecasts for military leaders.

As a trained statistician, he began to wonder if the forecasts were accurate.

After examining the old forecasts, he determined that the one-month predictions were no better than random chance. Based on this conclusion, Ken sent a message to the commanding general asking to discontinue the practice. Here’s the response he received:

“The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes.”

Like the general, CEOs need forecasts, but many are under the delusion that their current methods are valid. Every CEO wants to be rational by using data to run the business and make decisions. The problem is that data is not always helpful in predicting what is likely to happen in the future.

The Data Challenge

By definition, data is historical. It is a backwards look at what has happened. I have seen many CEOs who obsess over the data but never develop a system to turn it into a forecast. Other CEOs who are flush with data may be overconfident in their ability to calculate the future of everything.

In addition, some CEOs are too rooted in the past. They only ask: “How well did we do?” not “How well did we perform against our predictions?” The best ones ask: “How good are our forecasts?”

Predictive Metrics Facilitate Good Forecasts

Setting up predictive metrics to measure against is a critical first step. Most metrics reflect past performance though. Revenue, for instance, shows how many deals the sales team closed or how well frontline staff pitched a new service – last quarter. It provides no information about what might happen next quarter.

The ability to predict future business performance is one characteristic of a good metric. For example, the best predictive metric for sales in my opinion is the accuracy of revenue forecasts. Most every sales team follows a certain methodology or process. Sales professionals who excel in understanding and using the process can maximize the revenue possible within the given market conditions.

Consequently, accurate revenue predictions measure how well the sales team understands the sales process. For instance, at one of my former companies our final sales numbers were almost always within five percent of forecast each quarter. This excellence in forecasting gave me a crystal ball of sorts to understand how to lead the company into the future – and led to 31 consecutive quarters of double-digit revenue growth.

Collect Predictive Metrics from All Employees

CEOs need to collect forward-looking, relevant information from every department – not just sales. Very few CEOs require this, but those who do can drive continuous improvement in their predictive capabilities and steer the company in the right direction. They continually compare actual performance to forecasted performance to ensure the organization is on track.

It is important to challenge and teach all employees to be good predictors in their areas of expertise. CEOs must implement systems where their employees can provide the useful input needed to help make forecasts. This should be an ongoing process instead of just once a quarter or year.

Training the organization to make good predictions can give CEOs a huge competitive advantage – literally one step ahead. So be like Ken Arrow and constantly test the validity of your forecasts. This will help make your business forecasts more accurate, with an above-average chance for growth.

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