PyMC-Marketing powered MMM
Enterprise marketing mix modeling built on PyMC-Marketing
PyMC-Marketing gives teams a transparent Bayesian modeling engine. SIMBA turns that engine into a practical platform for uploading data, fitting models, reviewing ROI, and optimizing budgets.
PyMC-Marketing pulse
A living, well-maintained open-source project
These numbers reflect public activity in the upstream PyMC-Marketing repository — not SIMBA customer data. We track it so you can see, at a glance, that the engine under SIMBA is moving forward every week.
snapshot Apr 26, 2026
Recent deployment activity
Every merged PR is effectively a deployment of the upstream library
Adding Consideration Sets Model to Mixed Logit
@NathanielF·7d ago·merged PR
feat(mmm): chainable mu effects
@williambdean·8d ago·merged PR
fix: cap jax below 0.10.0 due to breaking changes in new release
@isofer·12d ago·merged PR
Bump version to 0.19.3
@juanitorduz·12d ago·merged PR
fix: register xr.DataArray deserializer in SpecialPrior to fix round-trip (#2496)
@PabloRoque·12d ago·commit on main
fix: register xr.DataArray deserializer in SpecialPrior to fix save/load round-trip
@PabloRoque·12d ago·merged PR
Add DiagnosticsPlots class with 6 time-series diagnostic methods
@isofer·13d ago·merged PR
What's new
Releases & news
Stay current with upstream PyMC-Marketing releases and our own writing on PyMC-Marketing-powered MMM.
0.19.3
<!-- Release notes generated using configuration in .github/release.yml at main --> ## What's Changed ### New Features 🎉 * Update serialization process (#2379) by @isofer in https://github.com/pymc-labs/pymc-marketing/pull/2391 * Support fixed/user-defined scaling factors for…
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0.19.2
<!-- Release notes generated using configuration in .github/release.yml at main --> ## What's Changed ### Bugfixes 🐛 * Fix MaskedPrior with non-scalar Prior parameters by @juanitorduz in https://github.com/pymc-labs/pymc-marketing/pull/2464 * Fix: IndexError in MultiDimensiona…
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0.19.1
<!-- Release notes generated using configuration in .github/release.yml at main --> ## What's Changed ### Bugfixes 🐛 * Make MMM workflow idempotent by @ricardoV94 in https://github.com/pymc-labs/pymc-marketing/pull/2413 * Restore metric coordinate order after to_xarray by @cet…
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0.19.0
<!-- Release notes generated using configuration in .github/release.yml at main --> > [!WARNING] > We have migrated all our documentation to the more complete and modern class `multidimensional.MMM`. See the migration guide: https://www.pymc-marketing.io/en/latest/notebooks/mmm…
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0.18.2
<!-- Release notes generated using configuration in .github/release.yml at main --> ## What's Changed ### Documentation 📖 * mmm utils cleanup by @juanitorduz in https://github.com/pymc-labs/pymc-marketing/pull/2285 ### Maintenance 🔧 * Fix compatibility with latest pytensor by…
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Built on PyMC-Marketing
Open-source transparency, productionised
PyMC-Marketing gives you the modeling primitives. SIMBA wraps them in a workflow your whole team can use without leaving Bayesian rigour behind.
Configure Bayesian priors without writing PyMC code
PyMC-Marketing exposes priors for adstock, saturation, and every coefficient. SIMBA turns that into a typed UI: pick distributions, see live previews, and ship the same model PyMC-Marketing would fit from a notebook.
- Distribution previews update as you change parameters
- Smart defaults seeded from your historical spend
- Every prior is auditable and exportable
Prior builder
Each row maps 1:1 to a PyMC-Marketing prior — no translation layer.
Saturation & adstock
Saturation, adstock, and seasonality come built-in
PyMC-Marketing ships first-class transformations for diminishing returns and carry-over effects. SIMBA exposes them in your model spec so you never have to invent your own response curves.
- Tanh, logistic, and Hill saturation curves
- Geometric and Weibull adstock for memory decay
- Fourier seasonality and external regressors
DIY PyMC-Marketing vs SIMBA
| Area | DIY PyMC-Marketing | SIMBA |
|---|---|---|
| Modeling engine | Open-source PyMC-Marketing code | PyMC-Marketing powered models in a guided workflow |
| Users | Data scientists and Python users | Marketing science, analytics, and business teams |
| Outputs | Custom notebooks and charts | Reusable ROI, response curve, scenario, and optimization views |
Drive PyMC-Marketing through your AI assistant
The SIMBA MCP Server lets Claude, Cursor, and other MCP-compatible clients fit PyMC-Marketing models, inspect results, and run scenarios using natural language.
Frequently asked questions
Is SIMBA a replacement for PyMC-Marketing?
No. SIMBA is a platform built on PyMC-Marketing for teams that want the transparency of the open-source modeling approach with a product workflow around it.
Can technical teams still inspect assumptions?
Yes. The point of the SIMBA approach is to keep model assumptions visible while making the workflow easier for non-notebook users.
Which version of PyMC-Marketing does SIMBA run?
SIMBA tracks recent stable releases of PyMC-Marketing closely. The PyMC-Marketing pulse on this page shows the upstream version we are aligned with, and we expose the exact version used for any given model run inside the platform.
Is SIMBA a fork of PyMC-Marketing?
No. We use upstream PyMC-Marketing as a dependency — the same package you would install from PyPI. That means our model logic stays in lockstep with the open-source library and you can reproduce a SIMBA model in a Python notebook if you ever need to.
Does SIMBA contribute back to PyMC-Marketing?
Yes. We work closely with PyMC Labs and contribute fixes, examples, and documentation upstream. Building on a well-maintained open-source foundation only works if we help keep it healthy.
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