Simba documentation

PyMC-Marketing & Simba — How the Open-Source Project and Platform Relate

What is PyMC-Marketing?

PyMC-Marketing is the leading open-source Python library for Bayesian marketing analytics. It provides statistical models for:

• Marketing mix modeling (MMM)
• Customer lifetime value (CLV)
• Media saturation and adstock effects
• Bayesian inference for marketing measurement

PyMC-Marketing is built on top of PyMC, one of the most widely used probabilistic programming frameworks in the world.

How Simba and PyMC-Marketing Relate

Simba is an enterprise platform built on PyMC by the team behind PyMC-Marketing. Simba’s modeling engine is its own PyMC code; it does not import PyMC-Marketing. The two share the same statistical foundations and conventions (adstock, saturation, lift tests as likelihood observations), and the tutorial notebooks in this repository use PyMC-Marketing to teach them. Think of it this way:

Layer	What It Is
PyMC	The probabilistic programming engine (open-source)
PyMC-Marketing	The marketing-specific modeling library (open-source), by the same team
Simba	The enterprise platform, with its own PyMC engine, UI, data validation, scenario planning, and optimization

Simba writes the PyMC code so you don’t have to. The platform translates your UI configurations (priors, saturation, decay) into a PyMC model, runs Bayesian inference, and presents results through an intuitive interface.

What This Means for Users

Full Transparency

Because Simba is built on open-source foundations:

• The modeling methodology is publicly available and peer-reviewed
• You can inspect how priors, saturation, and adstock are implemented
• Results are reproducible — there’s no proprietary “secret sauce” hiding behind the numbers
• Academic and industry researchers continuously improve the underlying methods

No Vendor Lock-In

Your modeling logic is built on an open standard. If you ever wanted to:

• Audit the methodology independently
• Reproduce results in a Python environment
• Transition to a custom implementation

…you can, because the foundation is open-source.

Community-Driven Improvement

PyMC-Marketing benefits from contributions by statisticians, data scientists, and marketing analysts worldwide. Improvements to the open-source library flow into Simba as platform updates.

Scientific Rigor

PyMC and PyMC-Marketing are used in academic research, published in peer-reviewed journals, and maintained by professional statisticians. This gives Simba a level of methodological credibility that proprietary tools can’t match.

What Simba Adds on Top

While PyMC-Marketing provides the statistical models, Simba adds the enterprise platform layer:

Capability	PyMC-Marketing	Simba
Bayesian MMM models	Yes	Yes (its own PyMC engine)
No-code UI	No (Python required)	Yes
AI Data Validator	No	Yes
Scenario planning	Manual implementation	Built-in
Budget optimization	Manual implementation	Built-in, risk-adjusted
Multi-model management	Manual	Built-in
Portfolio modeling	Not supported	Built-in
Smart defaults	Not available	Auto-generated
Enterprise security	Self-managed	Encrypted at rest, TLS 1.2+, Cyber Essentials
Support	Community	Dedicated support + Managed tier

For Technical Users

If you’re familiar with PyMC-Marketing and want to understand how Simba maps to the library:

• Model configuration in Simba corresponds to defining a PyMC-Marketing MMM model with custom priors, saturation functions, and adstock transformations; the concepts map one to one even though the code is Simba’s own
• Model fit runs Bayesian inference using PyMC’s MCMC sampling
• Smart defaults are derived from your data and industry benchmarks; PyMC-Marketing’s documented parameter ranges are a useful cross-check
• Optimization uses the fitted posterior distributions to find optimal budget allocations

Learn More

• PyMC-Marketing: pymc-marketing.io
• PyMC: pymc.io
• Simba: getsimba.ai

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*See also: Bayesian Modeling

What is Simba?*

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