Nonfinancial Data Can Predict Future Profitability

A professor of accounting shows that no single customer relationship metric can be used to predict future earnings.

November 01, 2005

| by Marguerite Rigoglioso

Data on intangibles such as customer satisfaction can yield significant forecasts of earnings only when they are analyzed in conjunction with financial statistics, according to recent research. Accounting Professor Madhav Rajan used a collection of wide-ranging customer relationship data from 115 retail banks to develop the most powerful guidance yet for managers wanting to know how to use nonfinancial metrics effectively.

His key finding is that no single customer relationship metric can be used to predict future earnings. Rather, when considered in relationship to other measures, such metrics can lead to a financial profitability number 15 percent closer to the actual figure for the coming year than is otherwise possible. Such a percentage is gold for the soothsayers of finance.

In his study, Rajan, the Business School’s Gregor G. Peterson Professor of Accounting, examined the interactions of metrics such as customer satisfaction, employee turnover, the speed of loan processing, and the average number of products and services customers purchased against prices, costs, and other figures.

“Looking at a customer satisfaction number alone doesn’t do you much good unless you know the costs involved in achieving it,” says Rajan, who conducted his research with Venky Nagar, associate professor of accounting at the University of Michigan. “Customers might be satisfied because you’re giving them everything for free, but this doesn’t say much about what your future profitability will be.”

In other words, says Rajan, a bank must compare customer satisfaction to a cost such as the amount of interest it pays out. “We found that high customer satisfaction and low interest costs push profitability significantly,” he says. The bottom line: A bank can rely on the customer service metric as a predictor of profitability only when its interest or “deposit” costs are low.

Similarly, the number of additional goods and services cross-sold to customers becomes predictive depending on the bank’s business strategy. “The cross-sell figure is valuable only when banks are focused on innovation, that is, when their strategy is to emphasize new products and services,” Rajan says. “For banks with a different strategy, the number is a far less reliable future indicator.” Innovation-oriented banks, then, will want to keep an eye on their cross-sell metric, but other banks may be wasting their efforts trying to capture such a number.

Today intangibles such as customer relationships account for more than half of total assets of firms in the United States. The importance of nonfinancial measures has set off a search for ways to capture and use these metrics to predict future earnings. It’s critical information for both managers plotting a firm’s future and investors seeking to gauge just how rosy that future will be.

Knowing which measures are most important for its needs can save a bank a lot of time and money when it comes to applying the popular “Balanced Scorecard” tool that many businesses are now using to assess their performance across various categories. It also helps investors know which numbers they should be paying attention to if they want to gauge the ongoing health of a company. “If today’s earnings look good, but the bank’s deposit expense or customer volume measures look bad, then the future of that firm may not be as bright as one may think,” Rajan explains.

Rajan’s study also has applicability in industries beyond banking. “The drivers won’t be the same, but the analytical methods we use in this paper are universal,” he says. “Anyone can use them to figure out which measures best capture a firm’s relationship and spell future success.”

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