Holistic Customer Understanding Through Multimodal Artificial Intelligence in Marketing Analytics

Abstract

This article examines how multimodal artificial intelligence (AI) contributes to holistic customer understanding in marketing analytics. It asks to what extent combining text, images, and other data modalities moves firms beyond narrow, touchpoint-specific predictions toward richer views of customer experience and behavior. Using a systematic literature review of peer-reviewed studies published between 2019 and 2023, the study identifies convergences and gaps in current applications of multimodal AI to sentiment analysis, recommendation, and behavioral prediction. Descriptive mapping summarizes dominant contexts, modality combinations, and model families, while thematic synthesis explores how studies conceptualize and operationalize “customer understanding”. The review finds that multimodal models consistently outperform unimodal baselines and capture nuanced affective and contextual cues, but remain concentrated on single tasks and public data sources, with limited integration into customer journey or CRM frameworks. Methodological limitations and reporting heterogeneity are highlighted to qualify the strength of existing evidence. Future research directions are outlined to embed multimodal analytics within longitudinal, cross-channel, and ethically governed approaches to customer insight.