Traditional customer journey mapping relies heavily on assumptions and historical patterns. But what if we could let the data tell us what’s really driving customer behavior? Michele shares groundbreaking applications of Bayesian networks and neural architectures that reveal genuine cause-and-effect relationships in customer data. Learn how these techniques can identify hidden influences on purchasing decisions, predict the impact of marketing interventions, and optimize customer experiences – even with incomplete data. Oh – and you can download Microsoft’s Deep End-to-end Causal Inference (DECI) generative model from GitHub and use it for free.