Advanced Data Mining

CIO — Have you ever seen one of those posters that at first glance looks like a jumble of colored dots? Stare at it, and a three-dimensional picture will jump out from the pointillistic background. Now, think of those dots as the bits of information about your customers contained in your company's databases. If you look at the dots of information in the right light and at the right angle, they will reveal patterns that yield insight into customer behavior.

The banking industry has stared hard at its customer data "dots" to analyze customer behavior, and it has learned valuable lessons for other industries that use data mining. Although banks have employed statistical analysis tools with some success for several years, previously unseen patterns of customer behavior are now coming into clear focus with the aid of new data mining tools.

Data mining is the automated analysis of large data sets to find patterns and trends that might otherwise go undiscovered. By studying these patterns and trends, banking executives can predict with increasing precision how customers will react to interest rate adjustments, which customers will be most receptive to new product offers, which customers present the highest risk for defaulting on a loan and how to make each customer relationship more profitable.

"We're trying to understand the needs, preferences and behaviors of customers," says Russel Herz, senior vice president in charge of liability product management at The Chase Manhattan Bank in New York. Data mining has become an indispensable tool in that pursuit. "It permeates everything we do—pricing, promotion and product development," he says. Data mining led Chase to take the unusual step of reducing required minimum balances in customers' checking accounts for two consecutive years because the bank learned that customers who have difficulty maintaining a minimum balance may take their business to competitors with lower minimum balance requirements. Executives at the bank reasoned that customers who defected to another bank because of dissatisfaction with Chase's checking account terms might desert the bank for their other banking needs as well.

So while it was clear that for a certain segment of Chase's customer base, the minimum checking account balance was a key factor in their choice of banks, Herz still wanted to know if those customers were profitable for Chase. On the one hand, if Chase was losing profitable customers because its minimum balance requirement was too high, then lowering it would make sense. On the other hand, if the defecting customers weren't profitable, then the smart decision would have been to leave the minimum balance alone.

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