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The actionable behavioral rules suggest specific actions that may influence certain behavior in the stakeholders' best interest. In mining such rules, it was assumed previously that all attributes are categorical while the numerical attributes have been discretized in advance. However, this assumption significantly reduces the solution space, and thus hinders the potential of mining algorithms, especially when the numerical attributes are prevalent. As the numerical data are ubiquitous in business applications, there is a crucial need for new mining methodologies that can better leverage such data. To meet this need, in this paper, we define a new data mining problem, named behavior action mining, as a problem of continuous variable optimization of expected utility for action. We then develop three approaches to solving this new problem, which uses regression as a technical basis. The experimental results based on a marketing dataset demonstrate the validity and superiority of our proposed approaches.