Database analysts often encounter data on individuals for whom a zero profit is recorded as a consequence of their nonresponse to a solicitation. However, among the individuals who do respond, there is a wide variation in the profit values. As response rates from direct mail or telemarketing solicitations are typically low, there are a lot of profit values about zero. The literature on the estimation of a regression model based on these “censored” data is extensive. It was Tobin who first discussed this problem in the “censored regression” context. Because Tobin used the probit analysis to build his model, Goldberger nicknamed the model tobit (Tobin’s probit). In this article, I present a genetic approach to building a database marketing censored regression model and call it the Genbit Model.