Data base marketing, touted as a sure-fire method of fattening banking's anemic share of its customers' wallets in the early to mid-'90s, is now viewed in some quarters as a failed initiative. A number of banks have spent lavishly to build a data-mining capability without, however, achieving improved cross-sales performance or a much-needed reduction in costs. True, these institutions have learned a good deal about customer characteristics. But they have been unable to translate this knowledge into higher profits. The knowledge is descriptive, not yet prescriptive.

But there is still hope of converting customer information into effective sales and cost-reduction programs. Those banks that would make a success of data base marketing must institutionalize a process that goes far beyond simple data-gathering. The process can be characterized as a continuous learning cycle. It encompasses the following eight steps: (1) capturing relevant data; (2) creating valid measures of customer value; (3) brainstorming value-enhancing strategies; (4) designing, via fractional factorial analysis, appropriate value propositions; (5) fielding or implementing these value propositions; (6) collecting data on the results of this field activity; (7) modeling the changes in expected customer value attributable to each value proposition; and (8) marrying every new customer to his or her optimal value proposition.

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