1. A computer-implemented method for determining one or more actions, comprising: receiving, using one or more processors, data related to one or more characteristics associated with an entity; receiving, using the one or more processors, data related to a plurality of segments that are associated with the one or more characteristics, wherein each segment is associated with action information; determining, using the one or more processors, assignments between the entity and the plurality of segments based upon a comparison of the one or more characteristics associated with the entity and the one or more characteristics associated with the plurality of segments, wherein an assignment includes membership probabilities indicating the extent to which the entity can be considered a member of two or more segments at any point in time, thereby avoiding misclassification caused by a hard-segmentation approach, and wherein determining the assignments includes: determining a first assignment between the entity and a first segment by calculating a first membership probability that includes a value indicating a certainty level of entity membership in the first segment, and determining a second assignment between the entity and a second segment by calculating a second membership probability that includes a value indicating a certainty level of entity membership in the second segment; building, using the one or more processors, an action-effect model corresponding to the entity, wherein building includes using soft-clustering and weighting the entity according to membership probabilities indicating the extent to which the entity can be considered a member of each segment in the plurality of segments; and using the action-effect model to determine, using the one or more processors, one or more actions for the entity based upon the membership probabilities associated with the entity and the action information associated with each assigned segment.