Creativity-Driven Growth: Unlocking the Value of Generative AI in Multichannel Marketing

Abstract

Generative AI offers new potential for creativity in marketing, yet existing systems often lack behavioral grounding and multichannel sensitivity, limiting their effectiveness in real-world business scenarios. Addressing this gap, the authors propose CAMA-GPT, a modular architecture that combines content generation with temporal user behavior modeling, lead scoring, and causal attribution analysis. The framework comprises four integrated units: a prompt-tuned creative generator, a temporal graph neural network for multichannel path modeling, a lead qualification engine, and a SHAP-based analyzer for content-value impact. Through eight comprehensive experiments using datasets such as Criteo Click Logs, a customer segmentation corpus, and a conversational recommendation dataset, CAMA-GPT demonstrates consistent improvements. It achieves an 18.2% gain in creativity score, +13.5% AUC in behavioral modeling, 4.2× top-decile lift in lead scoring, and a peak $0.63 revenue per impression in email simulations—each outperforming corresponding baselines.