This is like giving marketers a smart creative assistant that knows marketing theory. It helps design and test AI-generated ads and campaigns in a structured way, then measures what actually works with real customers.
Marketing teams are experimenting with generative AI for content, but usually in an ad‑hoc way that ignores marketing theory and lacks rigorous measurement. This work proposes a design system that structures how generative AI is used in marketing and evaluates its impact empirically, so teams can move from random AI experiments to repeatable, evidence‑based campaign design.
A theory-driven design framework plus empirical results that can be embedded into a marketing organization’s processes (playbooks, templates, prompt libraries, and evaluation protocols) and improved over time with proprietary campaign data.
Hybrid
Vector Search
Medium (Integration logic)
Context Window Cost and the need for high-quality labeled campaign performance data to keep the system calibrated.
Early Adopters
Instead of being another generic ‘AI for marketing’ tool, this work formalizes how generative AI should be designed and evaluated in marketing using theory and controlled experiments, which is attractive to large advertisers and agencies that need methodological rigor rather than one-off AI tricks.