The Future of MMM is Here — Powered by Causal AI and Large Transformer-Based Models

Many brands rely on Marketing Mix Modeling (MMM) to drive marketing investment decisions, but they know MMM has challenges. It can't separate every marketing strategy from the others (multicollinearity). It doesn't handle new marketing channels well. It struggles with correlation vs. causation. It is descriptive — telling you what happened, but not what to do next. Worse, different parts of the business use different models that each tell a partial story with conflicting recommendations. Marketing measurement is separate from trade optimization, pricing strategy, demand forecasting, and sales and growth planning.

This webinar unveils how combining transformer-based large models with Judea Pearl's causal framework solves these challenges — moving MMM from a descriptive, fragmented exercise to a unified, causal, and actionable decision engine for the enterprise.

Join Bharath Gaddam, Founder & CEO of Data Poem, as he presents a Fortune 100 case study showing how POEM365 — Data Poem's Large Causal Model — goes beyond attribution accuracy to answer the question that matters most: what should we do next? Bharath shares how this marketer moved from fragmented, descriptive analytics to a unified, actionable view across the organization — achieving efficiency gains and enterprise-wide alignment for the first time.