A series of techniques, methods, systems, and computer program products for advertising portfolio management is disclosed herein. More specifically, the herein disclosed techniques enable receiving data comprising a plurality of marketing stimulations, and receiving data comprising a plurality of engagement metrics. The received data is analyzed to determine a set of engagement weights associated with the engagement metrics. The determined engagement weights are in turn used to calculate the effectiveness of particular marketing stimulations through a set of marketing channels. Additional data in the form of measured responses (e.g., sales figures, survey results, etc.) are used to form a learning model wherein the learning model comprises one or more of, a stimulus-response predictor, a stimulus-engagement predictor, and an engagement-response predictor. The predictors can be combined into a cascade of models for determining the effectiveness of marketing stimulations on consumer engagement, and for determining effectiveness of marketing stimulations on measured responses.
MEDIA SPEND OPTIMIZATION USING ENGAGEMENT METRICS IN A CROSS-CHANNEL PREDICTIVE MODEL