1. A computer-implemented method for improving automated real-time bidding for a particular campaign in an online network-based auction system by automatically identifying and prioritizing a group of high-performing online placements from a plurality of online placements, the method comprising: calculating a similarity measure for each of a plurality of other campaigns, indicating a measure of similarity between each other campaign and the particular campaign, based on a measure of historical performance for each respective campaign with respect to a frequency of target action performance in response to campaign-related impressions delivered via one or more selected online placements; selecting one or more of the other campaigns based on the calculated similarity measures of the respective campaigns; scoring, by a bidding system, each of a plurality of online placements by executing a scoring algorithm including: submitting a plurality of real-time bids for a particular online placement over time; wherein at least a portion of the submitted bids comprise winning bids for which an impression is delivered via the particular online placement to an internet-connected device of a respective user; monitoring user actions related to impressions delivered via the particular online placement, and generating observed performance data based on the monitored user actions, including determining whether an actual performance of the particular online placement meets a target performance threshold for online placement performance by: determining a number of the impressions delivered via the particular online placement; determining whether a particular user action is performed in association with each impression delivered via the particular online placement; determining an actual number of the user action performances in association with the impressions delivered via the particular online placement; accessing confidence level data defining, for the target performance threshold for online placement performance, a mapping between (a) a target number of user action performances and (b) a number of impressions delivered, wherein the target number of user action performances mapped to each reference number of impressions delivered indicates a number of user action performances required to ensure a defined probabilistic likelihood that the number of user action performances meets or exceeds the target performance threshold; determining, based on the confidence level data, the target number of user action performances mapped to the actual number of the impressions delivered via the particular online placement, the target number of user action performances indicating the number of user action performances required to provide the defined probabilistic likelihood of meeting or exceeding the target performance threshold; comparing the actual number of the user action performances with the target number of user action performances; and in response to determining that the actual number of the user action performances meets or exceeds the target number of user action performances, selecting the particular online placement for further use in the particular campaign; obtaining related-campaign performance data regarding the performance of the particular online placement for the selected one or more other campaigns; calculating a performance score for the particular online placement based at least on (a) the observed performance data regarding the performance of the particular online placement for the particular campaign and (b) the related-campaign performance data regarding performance of the particular online placement for the one or more other campaigns; receiving, at the bidding system, from a real-time online bidding exchange via a communications network, a series of bid requests, each identifying an online placement defined in digital content being loaded or rendered by an internet-connected device; for each received bid request, executing, by the bidding system, an automated real-time bidding algorithm in real-time during the loading or rendering of the respective digital content, the automated real-time bidding algorithm including: identifying, based on information contained in the bid request, the respective online placement; determining the calculated performance score for the respective online placement; determining whether to submit a real-time bid based at least on the calculated performance score for the respective online placement; and in response to determining to submit a bid, submitting the bid, including a determined bid price, to the real-time online bidding exchange.