An honest second opinion on the model that steers your media budget, from specialists in marketing modelling and effectiveness. For brand teams, consultancies, and agencies who need a verdict they can trust.
New here? What is an MMM audit →
Marketing Mix Modelling (MMM) estimates how media and other drivers contribute to sales or business outcomes. It is the model many organisations use to decide where next year's budget goes. When that model is wrong, or quietly fragile, misallocation compounds.
One-click MMM, auto-tuned pipelines, and “AI-powered” dashboards make it easy to produce a model. They do not guarantee the model answers the right business question, survives scrutiny, or agrees with your experiments.
Speed is useful. An independent audit checks whether the story on the dashboard is one you can stake budget on.
Priors, transforms, and optimiser settings often stay at tooling defaults, and quietly drive the ROI ranking.
A polished contribution chart can still rest on a specification that cannot separate media from seasonality or promo.
Business context, data quirks, and experiment evidence still need human review. That is what an audit is for.
Open-source stacks like Meridian, Robyn, and PyMC-Marketing, and vendor platforms, have made sophisticated MMM accessible. The difference between a well-specified model and one with hidden weaknesses is rarely visible from the dashboard alone.
An MMM informs where significant budget goes. A small misspecification can compound into serious misallocation over time.
The team that built the model knows it intimately, and may not see where it's weak. Independence brings a fresh pair of eyes.
Choices in model assumptions, adstock specification, data cuts, and validation strategy all shape the story. An audit tests whether those choices are defensible.
A credibility-led partnership combining deep technical expertise in Bayesian and causal inference with in-house marketing effectiveness leadership, including two IPA Effectiveness Awards technical judges.
15+ years in marketing analytics across FMCG, automotive, retail, and e-commerce. Led large-scale measurement projects for Fortune 500 brands. Founder of Tyedal and IPA Effectiveness Awards technical judge (2026).
Bayesian data scientist & causal inference specialist. Director of Inference Works, lead developer of CausalPy, and contributor to PyMC-Marketing. Former faculty applying Bayesian methods to decision-making research.
A written report with a plain-English verdict on whether the model is trusted and decision-ready, issues ranked by severity, and specific fixes. We start with how outputs are used, not only whether the maths looks tidy.
A focused engagement designed to give you a clear verdict without disrupting your measurement programme.
Free 20-min fit call, then optional fixed-fee scoping (£750, credited to the audit).
Model code or spec, the data, and any experiment results, under NDA.
Independent review against the checklist; reproduction if scoped.
Written report and a call to talk through findings and fixes.
Standard turnaround three weeks. Two-week rush available where capacity allows, typically +25%.
Every audit comes with a prioritised list of issues and concrete fixes. We can help implement improvements, or define requirements if a rebuild is the right call.
Pick the shape of your modelling estate. Indicative from-prices by market; final quote after scoping.
A single national (or single-market) MMM that guides budget decisions.
Several markets, brands, or regional models with shared methods.
Hierarchical or nested structures: brand → region → channel, multi-brand suites.
Book a free 20-minute intro call, or jump to packages and the fixed-fee scope.
