Method and system for optimising the selection of an advertisement for viewing in a digital media environment. When online, a user may access a webpage that includes a variety of advertisements and may click on an advertisement of interest in order to gain additional information about said good or service. Selection policies or sets of rules may be used in order to tailor advertisements to particular users or groups of users in order to increase the likelihood that the user will select the included advertisement and /or make a purchase. The method comprises steps to apply measurement techniques such as reinforcement learning and a concentration inequality to estimate performance measures of a current policy and a proposed alternative policy. Statistical confidence levels are provided for the estimated performance measures in order to select and implement the policy that will has the most likelihood of success. Measures of performance may include click rate, conversion rate of clicks to verified purchases and so forth. In this way, advertisement success and revenue generation is optimised.
Risk quantification for policy deployment