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Résumé : This chapter introduces uplift modeling approaches, which aim at estimating the net effect of a treatment, such as a marketing campaign, on customer behavior. Uplift models allow users to optimize the selection of customers to include in marketing campaigns as well as a further customization at the individual customer level of the campaign design, for example, in terms of the contacting channel and the characteristics of the incentive that is offered. Such customization may even further increase the effect and return of the campaign. For uplift modeling, it is necessary to actively gather the required data by means of well-designed experiments or, alternatively, to passively gather the required data by tracking information on marketing campaigns at the customer level. The chapter discusses two regression-based approaches: Lo's method is based on logistic regression, whereas Lai's method and the generalized Lai method reformulate the uplift modeling problem to allow standard approaches.