Google Meridian | Intro to Priors

— In marketing mix modeling, one of the hardest challenges is ensuring your model reflects the complex reality of your business. This is where Meridian's Bayesian framework shines. It allows us to use priors. Using priors is more than just a technical feature. It is fundamental to getting causal estimates that you can trust and act on. First, priors offer more stable and plausible results. Aggregated marketing data can be sparse or noisy. Priors provide a stabilizing effect, guiding the model towards plausible outcomes and preventing it from reaching incorrect conclusions due to limited data. Second, priors give you a result that's grounded in business reality. By incorporating knowledge from trusted sources like lift studies, the prior's stabilizing effect guides the model into alignment with this knowledge, increasing stakeholder confidence. You almost always have some intuition about your business. For example, it might be very rare to get an ROI greater than six in your industry. You can encode this intuition in your prior to guide the model toward more realistic results, even if you don't have hard data from an experiment. And lastly, Meridian offers intuitive model controls. Meridian offers intuitive ways to set a prior. It's like having a direct conversation with your model. Instead of tweaking abstract parameters, you're providing guidance in a language you understand. You can tell the model, "I have strong evidence that the ROI for my channel is around 1. 5. " Check out the documentation and our other videos on priors to learn more. Thanks for watching.