Abstract: In the present approach, a master computing system directs slave computing systems in the purchasing of advertising within one or more online advertising channels by building a set of models for predicting the outcome of advertising purchases based on data received from the advertising channels, evaluating those models to determine which one(s) to use, allocating an advertiser's budget across the slave systems and channels for a given time period, and adjusting those allocations during the time period based on performance results received from the slave systems. In turn, the slave systems attempt to purchase advertisements within their channels based on their allocation and adjustments and report performance results back to the master system.

Abstract: Historical data from keywords in a pay-per-click internet advertising campaign are used to predict performance of other keywords with the goal of optimizing a keyword portfolio to maximize returns from the advertising campaign. A computing system receives the keyword portfolio for the advertising campaign, and classifies the keywords based on whether or not sufficient historical data exist to generate acceptable predictions about the performance of the keywords in the advertising campaign. Historical data are then used to make performance predictions for keywords having sufficient data. For keywords without sufficient historical data, generic prediction functions are created based on a generic change rate obtained from keywords with sufficient historical data. These generic prediction functions are then used to predict keyword performance in the advertising campaign. Predictions for keywords with and without sufficient historical data are then used to optimize the advertising campaign.