1. A computer-implemented method comprising: receiving a list of keywords via a data network interface for processing by a processor of a computer system; ordering the list of keywords from high activity to low activity, the high activity corresponding to keywords with a statistically significant number of return on investment (ROI) events, the ROI events corresponding to revenue-generating events; partitioning the list into at least two sets, a head partition including keywords with an activity level above a predefined threshold, a tail partition including the remainder of the keywords in the list; modeling the keywords in the head partition based on a set of variables, the modeling including generating a revenue per click (RPC) value prediction for each of the keywords in the head partition, the RPC value prediction being based on the set of variables, past keyword revenue performance data, and historical bid density by category for each keyword; scoring the keywords in the head partition based on the modeling; and clustering head partition keywords with tail partition keywords having at least one common variable into at least one keyword cluster.