Budget allocation backed by the model.
Marketing mix modelling deployed for the clients where it actually earns its place — multi-channel, multi-year, with the data quality and scale that makes the model's outputs defensible.
From channels to insights.
MMM done honestly, or not at all.
Marketing mix modelling has had a revival over the past few years — some of it deserved, some of it not. The deserved part: attribution models built on last-click or multi-touch logic systematically under-credit upper-funnel spend, and MMM is the honest answer to that problem. The undeserved part: MMM deployed against twelve months of data across two channels produces outputs that look rigorous on a slide and collapse under real-world stress.
We deploy MMM where it genuinely works: multi-channel spend with meaningful upper-funnel investment, at least 18-24 months of historical data, and the data quality to support hierarchical modelling. Open-source modelling frameworks run against the warehouse — not black-box vendor services with opaque assumptions. Outputs stress-tested against holdout periods and structural-break scenarios. Budget-allocation recommendations explained with confidence intervals, not produced as single-number estimates.
Where MMM is not the right tool — short history, limited channel diversity, inadequate data quality — we say so. Pushing a model that does not hold up is worse than running none.
What makes the difference.
Open-Source Modelling
Models run on open-source frameworks against your warehouse. No black-box vendor service with unverifiable assumptions. You can audit the code, the parameters, and the sensitivity — and so can your finance team.
Data Quality Gate
We do not deploy MMM without the data history, channel coverage, and measurement quality that make the outputs meaningful. Most proposals that ask for MMM end with a recommendation to fix the data first.
Holdout Validation
Models validated against holdout periods and structural-break scenarios — not just against in-sample fit. The outputs are trustworthy because the validation is honest about what the model can and cannot predict.
Budget Allocation Outputs
The model's primary deliverable: channel-level budget allocation recommendations with confidence intervals. Finance and leadership get recommendations they can defend; marketing gets allocations they can execute against.
Integration With Last-Click
MMM as a complement to last-click and multi-touch attribution, not a replacement. Each model answers different questions — MMM for upper-funnel and cross-channel share, attribution for operational bid decisions.
Ongoing Retraining
Models retrained on a defined cadence — quarterly typically — with drift monitoring between retraining. MMM is not a one-time deliverable; it is a recurring discipline with its own operational lifecycle.
Running the model.
Suitability Assessment
Honest assessment of whether MMM is the right tool for your situation. Data history, channel diversity, and commercial context all matter. Many assessments end with a recommendation to fix data quality first.
Model Design
Model architecture, feature selection, and validation approach defined. Priors and assumptions documented explicitly. Stakeholder alignment on what the model will and will not answer.
Build & Validate
Model built on historical data. Holdout validation, sensitivity analysis, and structural-break testing performed. Results reviewed with finance and marketing before any operational use.
Deploy & Iterate
Budget allocation recommendations integrated into planning cycles. Model retrained on a defined cadence. Drift monitoring between retraining. The MMM practice becomes a standing capability, not a one-time study.
Common questions.
Ready to model the mix?
Let's talk about whether MMM fits your situation — and how an open-source, honestly-validated model would change your budget allocation decisions.
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