The more financial institutions know about their customers, the better their ability to know and predict customers' needs.
Financial service organizations that take the time to collect and analyze this information can be proactive and get out in front of the competition. They are also in a better position to react when customers show unanticipated interest in a new service.
The information being captured in banks' data warehouses is essentially every piece of information they can garner from every transaction and every contact with a customer.
Banks have always captured this information but generally have never been able to gather it in one place and make use of it.
For the last 10 or 15 years bank marketers have begun to take pieces of this information (the name and address of the customer, the type of account, the balance, the length of time the account has been open, etc.), and tie them together with other accounts in the household in the form of a marketing customer information file.
More recently banks have been expanding the use of analytical and predictive information to identify the profitability of customer households in order to develop even greater sensitivity to key household members.
Even without the most sophisticated technology, smaller and small-town banks have long used data bases and marketing strategies.
They really knew their customers. They just did it all in their heads.
Unfortunately, when the bank employees and executives went elsewhere, they took with them what they knew.
Even at its best, this old system was put to use in a reactive mode. Bank officials could deliver good service only to a customer who was coming to them with a need.
These employees never could use their "internal data bases" effectively to put something in front of customers before they experienced a need.
As banks became more centralized and highly computerized, the information available at the branch level started to diminish. This took a toll on customer satisfaction.
Today banks are working to rebuild the loyalty they enjoyed years ago, through more sophisticated data base management systems.
These let bankers capture enough information to gain a solid grasp of how a customer household relationship is doing (is it improving or getting worse, and are there things that the bank can do about it?).
Banks' data warehouses are expanding the volume of information that is captured and putting it into usable form.
The information is also being refreshed, or updated, far more frequently.
Most warehousing systems being built these days-very few are in their final form-are refreshed daily.
At the end of the day's posting of transactions, all the most current customer information is updated.
Older customer information systems, many of which are still in use, are refreshed monthly, at best, and at some big banks even less frequently because of expense and logistics.
Banks may have been among the first institutions to see the light when it comes to data warehousing, but now an ever-increasing number of companies is acknowledging the importance of using these knowledge-based systems for marketing.
However, marketing is only one application of data warehousing, the one getting the most attention.
A variety of other customer relationship management issues can be addressed by banks and other companies with the help of data warehouses.
Baseline customer information can be enhanced by other types of statistical information (most notably demographic) to get a complete picture of what is going on in customer relationships.
Of course, data warehouses are expensive, and they take years to build. This can create problems when securities analysts are reviewing a company's quarterly return on investments.
Because financial institutions are graded on how well they perform financially, long-term investments may hurt a bank's standing on Wall Street.
Even so, a number of financial institutions have committed themselves to building multimillion-dollar data warehousing systems. Costs of $10 million or $20 million or more are not unheard of.
The complexity and cost of the undertaking can be overcome by relying on smaller "data marts."
These are essentially limited-purpose data warehouses, of which there are two types.
One is literally a data base that is separate from the data warehouse itself. It is fed by extraction of information from the warehouse, but it is separate for the sake of convenience and ease of use.
The other type is a virtual data mart, meaning that it is internal to the warehouse, which has been segmented to offer easy, direct access to information.
Data marts for marketing can be particularly useful while multipurpose warehouses are being built. One advantage is that an institution can build a warehouse in pieces by adding up a collection of data marts. They are easier to build, and the cost, even for a large organization, can be a few hundred thousand dollars, as opposed to millions.
Also, data marts can be completed and operational within three to four months, as opposed to three or four years for a data warehouse. Thus, financial institutions can begin to use information sooner. Sales forces can capitalize on newly identified opportunities, and results can be demonstrated to management and investors.
The only difference between data warehousing applications for financial service organizations and for other industries is the nature of the information that's being maintained. The ultimate objective is consistent across industry lines: to generate repeat business and build customer loyalty.
For the past three or four years, customer loyalty programs have been a hot topic at industry conferences. All executives, no matter what business they are in, want to know what they can do to identify their best customers and decide which prospects are the best to pursue. Then there are retention strategies: using information to make sure the best customers are happy and stay loyal.
Retention is in many ways the focus today, and to some extent this is changing the operating philosophy of many companies, not just banks.
Banks may have started data base marketing earlier than other industries because they were capturing so much information for so long.
Retailers, by contrast, historically did not collect a great deal of customer information.
At best, retailers had some data on their proprietary credit card customers, but they weren't capturing all they could and, more importantly, weren't using it effectively.
Retailers have begun using data base marketing more, and they are therefore changing from a product type of orientation to more of a "customer-centric" marketing approach.
Changing from a product management, balance-sheet-driven operating philosophy to customer-centricity is a fairly dramatic and traumatic transition for many organizations, banks included.
Ultimately, the difference comes down to how easy it is to capture the information the company needs.
The more layers between a company and the consumer, the more creative it needs to be in developing ways to capture the information it needs to plan well for long-term profitability.
Getting decision-makers at a company to cooperate requires a political and educational effort. It also takes a commitment of time and money.
Once a financial institution conquers these challenges, it is more likely to create pathways to greater profit.
As a financial institution data base marketing practitioner, the more information you know about your customers and their relationships with your institution, the better you can build a fruitful portfolio of services, now and in the future, to make all your customers "best customers." Mr. Leach is managing director of SIGMA Marketing Group, a data base marketing firm in Rochester, N.Y.