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: The system of the present invention is used to execute data consolidation to combine data from Google, MSN and Yahoo, at least. The user does not need anymore to combine by himself data in spreadsheets. The system allows creating consolidated campaigns that span across search providers, letting the user effortlessly build reports that previously took hours to create each month. The system of the present invention is particularly suited for agencies. A tool designed for agencies needs to allow for management at the client level as well as the agency level. The system client center gives each member of the team a summary view of the client accounts that they're responsible for, along with emerging trend indicators for every major metric.

Abstract: The system of the present invention is used to optimize the returns of advertising campaigns on the internet. The system allows to create portfolios of keywords and to optimize these portfolios. Moreover, the system allows to classify the keywords in the portfolios according to their past data and to agitate the keywords which held a stagnant position over a pre-set period of time. The system support different implementations of the keyword classification in portfolios, different implementations of keyword bids agitation and different implementations of optimization. The various implementations can meet the specific aspects of different industries and/or the distinctive optimization approach of different agencies. The system can leverage the cloud computing whereby shared data, process and information are provided to computers and other devices on-demand. The delivery model involves the provision of dynamically scalable and virtualized resources as a service over the Internet.

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.