1. A computer-implemented method of targeting comprising: receiving a plurality of granular events, wherein a granular event comprises a type that defines an on-line activity between a client and an entity; preprocessing the received granular events to determine an amount of informational content of the granular event for target prediction, wherein the amount of informational content comprises at least one of a page view, an advertisement click, a link selection, a search query, a form completion, a posting of text, and an execution of a transaction; generating, in a computer, preprocessed data to facilitate construction of a model based on the granular events by clustering the granular events into a number of clusters based on the informational content for target prediction, wherein said preprocessed data comprises input features; generating a predictive model from said preprocessed data, the predictive model for determining a likelihood of a hypothetical user action, wherein the predictive model includes: a weight for the hypothetical user action, model parameters comprising linear combinations of said input features; training the predictive model by tuning the weight to optimize performance of the predictive model; selecting a user from a plurality of users; applying the predictive model to the selected user; scoring the user by using the predictive model; and scoring the user by using a Poisson type model based on the ratio between a predicted number of ad clicks and an estimated number of ad views.