This paper shows how to learn probabilistic classifiers that model how sales prospects proceed through stages from first awareness to final success or failure. Specifically,we present two models, called DQM for direct qualification model and FFM for full funnel model, that can be used to rank initial leads based on their probability of conversion to a sales opportunity, probability of successful sale, and/or expected revenue. Training uses the large amount of historical data collected by customer relationship management or marketing automation software. The trained models can replace traditional lead scoring systems, which are hand-tuned and therefore error-prone and not probabilistic. DQM and FFM are designed to overcome the selection bias caused by available data being based on a traditional lead scoring system. Experimental results are shown on real sales data from two companies. Features in the training data include demographic and behavioral information about each lead. For both companies, both methods achieve high AUC scores. For one company, they result in a a 307% increase in number of successful sales, as well as a dramatic increase in total revenue. In addition, we describe the results of the DQM method in actual use. These results show that the method has additional benefits that include decreased time needed to qualify leads, and decreased number of calls placed to schedule a product demo. The proposed methods find high-quality leads earlier in the sales process because they focus on features that measure the fit of potential customers with the product being sold, in addition to their behavior.