Budget allocation, backed by the model.
We apply Marketing Mix Modelling where it really pays off: many channels and many years, with data that makes the results reliable.
From channels to insights.
MMM with data, or not at all.
Marketing mix modelling has made a strong comeback in recent years, partly for good reason. Attribution models built on last-click or multi-touch logic systematically undervalue spend at the top of the funnel, and MMM is the honest answer to that. But with 12 months of data and two channels, it gives results that look fine on a slide and fall apart in practice.
We use it where it works: many channels with real investment at the top of the funnel, at least 18–24 months of history and data of enough quality. We use open-source frameworks on your own warehouse, not third-party black boxes. Results are tested on periods held out of the model and on structural-break scenarios, and budget recommendations come with confidence intervals, not a single number.
When MMM doesn't fit, because of limited history, few channels or poor data, we say so plainly. A model that doesn't hold up is worse than none.
What changes for you.
Open-source models
Models run on open-source frameworks on your warehouse. No black boxes: you and your finance team can audit the code, the parameters and the sensitivity.
Data check
We don't build MMM without the history, channel coverage and measurement quality that make results meaningful. Our first recommendation is often to fix the data first.
Validation
The model is tested on held-out periods and structural-break scenarios, not just the data it was trained on. So you know what it can and can't predict.
Budget allocation
The main deliverable: budget allocation recommendations per channel with confidence intervals. Leadership gets recommendations it can defend and marketing gets allocations it can carry out.
Alongside last-click
MMM complements last-click and multi-touch attribution, it doesn't replace them. Each answers a different question: MMM for the top of the funnel and channels together, attribution for daily bidding decisions.
Regular retraining
The model is retrained at a set rhythm, usually quarterly, with drift monitoring in between. MMM is not a one-off deliverable but a practice that repeats.
How the model runs.
Suitability assessment
An honest view on whether MMM is the right tool for you. History, channel variety and commercial context all matter. Many assessments end with fixing data quality first.
Model design
We define the architecture, the choice of variables and the way it will be verified. Assumptions are documented openly and everyone agrees what the model will and won't answer.
Build and validation
We build the model on the history, with held-out period checks, sensitivity analysis and structural-break tests. Results are reviewed with finance and marketing before use.
Use and evolution
Budget recommendations feed planning cycles, the model is retrained regularly and drift is monitored. MMM becomes a permanent capability, not a single study.
Common questions.
Model the mix.
Let's talk about whether MMM fits your case and how an open-source, honestly validated model would change your budget decisions.
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