System and method for predicting marketing campaign performance using multimodal ai

A computer-implemented method, comprising:

• collecting, by a computer, multimodal data from one or more advertisement platforms and one or more external data sources;
• generating, by the computer, token-level embeddings from the multimodal data using a transformer model to represent one or more of sentiment data, tone data, and one or more keywords;
• generating, by the computer, patch-level feature vectors from visual data using a vision transformer model to represent one or more of composition data, branding data, and one or more style features;
• encoding, by the computer, behavioral data as behavioral embeddings using a machine learning algorithm to represent audience engagement signals and performance trends;
• projecting, by the computer, the token-level embeddings, patch-level feature vectors, and behavioral embeddings into a shared cross-modal embedding space via modality-specific learned linear projection layers;
• computing, by the computer, cross-modal attention weights across the projected embeddings using a multi-head cross-attention mechanism to produce a unified cross-modal representation;
• integrating, by the computer, the unified cross-modal representation into a transformer-based multimodal fusion model comprising stacked multi-head attention layers and feed-forward sublayers to predict marketing performance metrics;
• generating, by the computer, one or more strategic marketing recommendations based on the marketing performance metrics and one or more historical trends;
• deploying, by the computer, campaign variants generated by the transformer-based multimodal fusion model in one or more live campaigns through one or more advertisement platform APIs, wherein each campaign variant corresponds to a distinct predicted marketing performance metric output by the transformer-based multimodal fusion model;
• and
• monitoring, by the computer, real-time campaign performance signals;
• and
• updating, by the computer, the transformer-based multimodal fusion model by:
• (i) computing a modality contribution score for each of the text, visual, and behavioral modalities by measuring a performance signal delta between predicted and observed campaign performance metrics;
• (ii) selectively amplifying or suppressing modality-specific embedding projection parameters according to the computed modality contribution scores;
• and (iii) applying gradient-based optimization to the cross-modal attention weights using the real-time campaign performance signals as a supervisory signal, thereby recalibrating the shared cross-modal embedding space and differentially weighting modality contributions.

A computer program product for generating marketing performance predictions, the computer program product comprising:

• one or more computer-readable storage media;
• and
• program instructions stored on the one or more computer-readable storage media to perform operations comprising:
• collecting multimodal data from one or more advertisement platforms and one or more external data sources;
• generating token-level embeddings using a transformer model to represent one or more of sentiment data, tone data, and one or more keywords;
• generating patch-level feature vectors using a vision transformer model to represent one or more of composition data, branding data, and one or more style features;
• encoding behavioral data as behavioral embeddings to represent audience engagement signals;
• projecting the token-level embeddings, patch-level feature vectors, and behavioral embeddings into a shared cross-modal embedding space via modality-specific learned linear projection layers;
• computing cross-modal attention weights across the projected embeddings using a multi-head cross-attention mechanism to produce a unified cross-modal representation;
• integrating the unified cross-modal representation into a transformer-based multimodal fusion model comprising stacked multi-head attention layers and feed-forward sublayers to predict marketing performance metrics;
• generating one or more strategic marketing recommendations based on the marketing performance metrics and one or more historical trends;
• deploying campaign variants generated by the transformer-based multimodal fusion model in one or more live campaigns through one or more advertisement platform APIs, wherein each campaign variant corresponds to a distinct predicted marketing performance metric output by the transformer-based multimodal fusion model;
• and
• monitoring real-time campaign performance signals;
• and
• updating the transformer-based multimodal fusion model by:
• (i) computing a modality contribution score for each of the text, visual, and behavioral modalities by measuring a performance signal delta between predicted and observed campaign performance metrics;
• (ii) selectively amplifying or suppressing modality-specific embedding projection parameters according to the computed modality contribution scores;
• and (iii) applying gradient-based optimization to the cross-modal attention weights using the real-time campaign performance signals as a supervisory signal, thereby recalibrating the shared cross-modal embedding space and differentially weighting modality contributions.