SIMBA Blog — Marketing Mix Modeling https://getsimba.ai/blog/ Marketing mix modeling insights, Bayesian analytics tutorials, and PyMC-Marketing perspectives from the SIMBA team. en Wed, 23 Sep 2026 00:00:00 GMT Building Towards Conversational Simba https://getsimba.ai/blog/conversational-simba-voice-agent-evaluations/ https://getsimba.ai/blog/conversational-simba-voice-agent-evaluations/ Tue, 22 Sep 2026 00:00:00 GMT What our voice-agent evaluations taught us about speed, corrections and user control, and how those lessons inform the way we want people to work with Simba. Should You Build a Geo MMM When Media Is Bought Nationally? https://getsimba.ai/blog/geo-mmm-national-media-buying/ https://getsimba.ai/blog/geo-mmm-national-media-buying/ Fri, 28 Aug 2026 00:00:00 GMT Regional sales do not make national media regional. When a geo MMM adds causal information, why population-apportioned spend fails, and a quick diagnostic. Forecasting a Product Launch With No Sales History https://getsimba.ai/blog/bass-model-new-product-launch-forecast/ https://getsimba.ai/blog/bass-model-new-product-launch-forecast/ Fri, 28 Aug 2026 00:00:00 GMT A Bass model needs p, q and M before a new product has sold anything. Learn them from previous launches, then update as the first weeks of sales arrive. When Should Your Budget Optimiser Use LTV? https://getsimba.ai/blog/maximise-conversions-or-profit-ltv/ https://getsimba.ai/blog/maximise-conversions-or-profit-ltv/ Fri, 28 Aug 2026 00:00:00 GMT Budget optimisation has two sensible modes. Without a trusted value per acquisition, hold spend fixed and maximise acquisitions. With one, let the budget move. Who Decided R-hat Had to Be 1.01? https://getsimba.ai/blog/rhat-1-01-convergence-threshold/ https://getsimba.ai/blog/rhat-1-01-convergence-threshold/ Thu, 27 Aug 2026 00:00:00 GMT R-hat 1.01 came from a March 2019 paper, reached PyMC in a single 2022 pull request, and never became a universal law. Here is the actual timeline. When Your MMM Won't Mix: Predict the Better Prior Before You Refit It https://getsimba.ai/blog/mmm-prior-sweep-pymc-marketing/ https://getsimba.ai/blog/mmm-prior-sweep-pymc-marketing/ Thu, 20 Aug 2026 00:00:00 GMT Compress a reference posterior into Gaussian likelihood messages, solve candidate priors analytically, and predict NUTS ESS before paying for the refit. Half-Life Is Still the Parameter: Extending the Idea to Delayed Adstock https://getsimba.ai/blog/half-life-delayed-adstock/ https://getsimba.ai/blog/half-life-delayed-adstock/ Tue, 18 Aug 2026 00:00:00 GMT Delayed adstock trades pure decay for build, peak and decay. Keep the half-life parameterisation, and the two questions it lets your team argue about. Who Gets the Credit? Decomposing Multiplicative Marketing Mix Models https://getsimba.ai/blog/multiplicative-mmm-attribution/ https://getsimba.ai/blog/multiplicative-mmm-attribution/ Thu, 13 Aug 2026 00:00:00 GMT Why channel removal effects overlap in a multiplicative MMM, how Shapley and Aumann-Shapley allocate that overlap, and what benchmark tests tell us about the trade-offs. Brand ROI Isn't Zero: Long-Term Marketing Measurement with PyMC-Marketing's VARX Workflow https://getsimba.ai/blog/long-term-marketing-effects-pymc-marketing/ https://getsimba.ai/blog/long-term-marketing-effects-pymc-marketing/ Sun, 09 Aug 2026 00:00:00 GMT Standard MMMs score brand ROI near zero. A two-stage workflow, time-varying baseline plus Bayesian VARX, now ships in PyMC-Marketing 1.0. Here's how it works. PyMC-Marketing 1.0 Is Here: What the Stable API Means for Your MMM https://getsimba.ai/blog/pymc-marketing-1-0/ https://getsimba.ai/blog/pymc-marketing-1-0/ Fri, 07 Aug 2026 00:00:00 GMT PyMC-Marketing 1.0 ships a stable API, multidimensional MMM by default, and roughly 1.5x faster sampling. What changed, what breaks, and how to upgrade. Half-Life Is the Parameter: A Two-Line Fix for Geometric Adstock https://getsimba.ai/blog/half-life-geometric-adstock/ https://getsimba.ai/blog/half-life-geometric-adstock/ Fri, 07 Aug 2026 00:00:00 GMT The standard geometric adstock rate means nothing in a marketing meeting. Reparameterize it in half-life units with a two-line change everyone understands. PyMC-Marketing vs Google Meridian: Which MMM Framework Fits Your Team? https://getsimba.ai/blog/pymc-marketing-vs-google-meridian/ https://getsimba.ai/blog/pymc-marketing-vs-google-meridian/ Sun, 26 Apr 2026 00:00:00 GMT A practical comparison of PyMC-Marketing and Google Meridian for teams choosing a Bayesian marketing mix modeling workflow. PyMC-Marketing UI: When to Use SIMBA Instead of Python Notebooks https://getsimba.ai/blog/pymc-marketing-ui/ https://getsimba.ai/blog/pymc-marketing-ui/ Sun, 26 Apr 2026 00:00:00 GMT When PyMC-Marketing notebooks are enough, when they become hard to operationalize, and where a UI like SIMBA helps business teams use Bayesian MMM. Bayesian MMM Explained: Adstock, Saturation, Priors and Calibration https://getsimba.ai/blog/bayesian-mmm-explained/ https://getsimba.ai/blog/bayesian-mmm-explained/ Sun, 26 Apr 2026 00:00:00 GMT A plain-English guide to the Bayesian MMM concepts behind channel ROI, response curves, uncertainty, adstock, saturation, priors, and lift-test calibration. How to Prepare Data for Marketing Mix Modeling https://getsimba.ai/blog/prepare-data-for-marketing-mix-modeling/ https://getsimba.ai/blog/prepare-data-for-marketing-mix-modeling/ Sun, 26 Apr 2026 00:00:00 GMT A practical checklist for building the weekly dataset your MMM needs: media spend, sales, controls, promotions, external factors, and clean naming. The Marketing Data Problem No One Talks About https://getsimba.ai/blog/the-marketing-data-problem/ https://getsimba.ai/blog/the-marketing-data-problem/ Mon, 06 Apr 2026 00:00:00 GMT MMM does not fail because of bad math. It fails because getting clean, consistent data from ten platforms into one place is still a manual nightmare. Introducing the SIMBA MCP Server: Marketing Mix Modeling Meets AI Assistants https://getsimba.ai/blog/introducing-simba-mcp-server/ https://getsimba.ai/blog/introducing-simba-mcp-server/ Sun, 05 Apr 2026 00:00:00 GMT Build Bayesian MMMs, measure channel ROI, optimize budgets, and run scenario forecasts through natural language in Claude, Cursor, or any MCP client.