Mining Gold from Data Bases

By Allen, Leilani D. | Mortgage Banking, May 1996 | Go to article overview

Mining Gold from Data Bases


Allen, Leilani D., Mortgage Banking


A truism in business says it is much easier to sell a product to an existing customer than to a new customer. Obtaining new customers is a time-consuming and expensive process. Depending on the product, it can take months or years before the profits from that customer outweigh the acquisition costs. No one knows this better than mortgage bankers with their high origination costs.

Another truism is that 80 percent of your profits come from 20 percent of your customers. This suggests a business is able to track profitability by customer, continue to provide new and valuable products to sell back into the customer base, and have frequent, positive customer service contacts. Unfortunately for mortgage banking, most of its customers buy only one "product" (mortgage) at a time; years usually go by before they purchase the next one; and overall market conditions such as interest rates play a dominant role in the purchase decision.

However, mortgage companies can sell other kinds of products to the same customer base, which is one of the motivations behind banks buying mortgage companies. The premise is that mortgage customers are also prime candidates for other types of financial instruments, from home equity loans to annuities to mutual funds. This type of cross-sell, to be successful, has to be targeted to the individual customer, which requires assembling many important bits of information about his or her history and situation. This is where information technology comes in.

For the last 30 years, American businesses have been using computing technology to accumulate data on the business habits of Americans - what we buy, when, how and under what terms. From a technology perspective, we built production systems to collect and store this data, display it for update and reformatted it into reports that were used to guide decision-making. As time went by, more and more sophisticated technology was developed to extract and display the information, then perform "what if" analysis to guide corporate strategy. These were termed decision support (DSS) or executive information systems (EIS).

However, this was always ex post facto analysis performed by individuals often far removed from the actual business transaction. This necessarily limited the ability of the business to capitalize on its storehouse of knowledge. The other major problem was that production systems were usually built around a particular business function. Thus, in a bank there would be a system to track checking accounts, another to track savings accounts, another for CD ownership and so on.

As we know all too well, each loan a customer had (such as student, home, equity and car) was tracked by a different system. It was virtually impossible to obtain a view of a customer having a variety of product relationships with the bank. More importantly, it was almost impossible to determine alternative or future products the customer might buy.

Even if the information were available, who would receive it? We could certainly hand it to the normal sales force, but consumers are increasingly resistant to these kind of overtures. Far better to have the pitch made as part of a customer-initiated contact. …

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Mining Gold from Data Bases
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