Yes,
we help you build customized MMM’s that
can be audited, stress-tested, and understood.
No black boxes.
No unverifiable uplift claims.
Every assumption, transformation, and coefficient is exposed, so you can independently assess the model’s validity.
Common Industry Practices in
Marketing Mix Modeling
Poor validation
Many “good fit” models aren’t tested outside their in-sample data, so they may simply be over-fit artifacts.
Misleading assumptions
Ignoring diminishing returns, lag effects, and time shifts leads to bad allocation guidance.
Control Overload Bias
Excessive controls often reduce the model’s ability to detect true marketing effects, undermining causality.
“Black box” pipelines
Models with hidden transformations or priors you cannot inspect, produce results you cannot challenge or reproduce.
The Gaussner Ethos
Transparent,
Open-Source,
Standardized MMM
We believe the only way to build trust in MMM is to dismantle the “black box” paradigm:
- Open pipelines: every data transformation and modeling assumption is visible
- Open code & synthetic models: inspect or run everything yourself
- Open scrutiny: models are designed to be challenged and tested
- Open discussion: we publish critical analyses of MMM research, limitations, and edge cases
Open MMM. Open for scrutiny.
Community Knowledge
0€ forever, no sign-up
- High-level technical tutorials on MMM methodology
- Code examples and labs in Python’s PYMC and Google Meridian
- Synthetic datasets and models you can analyze and extend
- Summaries of latest academic and industry research in digestible form
Hire Us
Quote on demand
- End-to-end transparency — every transformation, assumption, and parameter
- Verified reliability — out-of-sample tests, stability checks, and stress testing
- Explicit uncertainty —confidence intervals and sensitivity, not point estimate
- Clear causal logic — documented assumptions and limits, not implied causation