Measuring the true incremental impact of media spend remains one of the toughest problems in marketing, especially in an era where privacy limits user-level tracking. This talk examines how geo-lift incrementality testing can be utilized to accurately measure the true causal impact of marketing and media channels. Attendees will learn what design decisions matter, how to analyze results, and common pitfalls to avoid when running marketing incrementality tests. The goal is to bring causal inference theory into real-world measurement, enabling practitioners to make informed, data-driven decisions with confidence.

Your Marketing Mix Model is only as good as the library you build it on. But how do you choose between PyMC-Marketing and Google Meridian when the feature lists look so similar? You need hard evidence, not marketing claims. Which library is actually faster on multi-geo data? Do their different statistical approaches (splines vs. Fourier series) lead to different budget decisions?

This talk delivers that evidence. We present a rigorous, open-source benchmark that stress-tests both libraries on the metrics that matter in production. Using a synthetic dataset that replicates real-world ad spend patterns, we measure:

• Speed: Effective sample size per second (ESS/s) across different data scales.
• Accuracy: How well each model recovers both sales figures and true channel contributions.
• Reliability: A deep dive into convergence diagnostics and residual analysis.
• Resources: The real memory cost of fitting these models.

You'll walk away from this session with a clear, data-driven verdict, ready to choose the right tool and defend that choice to your team.