Product updates, engineering deep dives, and marketing science perspectives from the SIMBA team.
A practical comparison of PyMC-Marketing and Google Meridian for teams choosing a Bayesian marketing mix modeling workflow.
When PyMC-Marketing notebooks are enough, when they become hard to operationalize, and where a UI like SIMBA helps business teams use Bayesian MMM.
A plain-English guide to the Bayesian MMM concepts behind channel ROI, response curves, uncertainty, adstock, saturation, priors, and lift-test calibration.
A practical checklist for building the weekly dataset your MMM needs: media spend, sales, controls, promotions, external factors, and clean naming.
Marketing mix modeling does not fail because of bad math. It fails because getting clean, consistent data from ten different platforms into one place is still a manual nightmare for most teams.
Build Bayesian marketing mix models, measure channel ROI, optimize budgets, and run scenario forecasts through natural language in Claude, Cursor, or any MCP-compatible AI assistant.