In today’s competitive consumer markets, determining whether new product sales are incremental or cannibalistic is crucial for informed decision-making. However, traditional machine learning approaches focus on correlation, falling short in offering the causal insights required for real-world business interventions.

This talk introduces a novel approach to sales analytics by integrating causal modeling and Bayesian methods, moving beyond black-box predictions. Evan will showcase a real-world case study in developing advanced causal sales analytics tools. The solution aimed to quantify how new product launches affected both internal and competitor sales, helping the company make data-driven decisions to optimize its product portfolio.

You’ll explore the limitations of standard statistical models and why causal and counterfactual thinking is essential for accurate sales attribution. Attendees will learn how to build models that not only predict outcomes but also answer “what if” scenarios, offering valuable insights into the trade-offs between sales incrementality and cannibalization.

By the end of this session, you’ll have a clearer understanding of how causal models can better guide product strategies, and Evan will even throw in a few award-winning statistics jokes to keep it light! Whether you’re a data scientist, business leader, or machine learning practitioner, this talk will equip you with actionable tools to transform your sales analytics.