SOMIN: An Explainable AI and LLM Platform for Real-Time, Data-Driven Digital Marketing Strategy

摘要

In this industry demonstration, we present SOMIN - an Explainable AI and LLM Platform for Real-Time, Data-Driven Digital Marketing Strategy recommendation. This is achieved through two primary, interconnected subsystems: a predictive model for performance analysis and an LLM for semantic understanding. The system is built on SoWide-ViT, a ''wide and deep'' neural network for advertiser-side CTR prediction. It processes tabular features (campaign settings), text (ad copy, headlines), and visuals (images or keyframes) through TabTransformer, multilingual BERT, and a Vision Transformer (ViT). Replacing the previous ABN model with ViT improved performance, reaching an F1-score of 0.78. Explainability comes from ViT's self-attention, which produces heatmaps highlighting influential regions. High-performing ads showed relevant cues (e.g., gaming objects), while low-performing ones exposed distracting elements. Heatmaps show where the model focuses, while the LLM explains why and suggests improvements. A multimodal GPT-5 interprets creatives, finding flaws such as weak hierarchy, poor CTAs, or off-brand imagery. At scale, the Content Library and Perspective Studies classify competitor ads into 16 marketing concepts, clustering them into Personas (e.g., ''Chocolate Connoisseur'') and Tensions (e.g., ''Work Stress''). The SOINSPIRE module converts Personas and Tensions into insights and campaign propositions, applying marketing theories to generate repeatable, data-driven Expressions.