---
title: "Budget allocation, backed by the model."
url: "https://www.omnicliq.com/en/labs/mmm/"
description: "We apply Marketing Mix Modelling where it really pays off: many channels and many years, with data that makes the results reliable."
category: "labs"
locale: "en"
---

# 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.

## 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.

1. **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.
2. **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.
3. **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.
4. **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.
## FAQ

### When is MMM the right tool?
When you have real spend across many channels, enough history (usually at least 18–24 months) and a significant part of the budget in channels where last-click undervalues the contribution: brand, video, programmatic, out-of-home. For smaller accounts, simpler attribution is usually the right answer.

### How is open-source different from a vendor service?
Transparency. You can audit the code, verify the assumptions and understand the confidence intervals. Vendor services are black boxes that give results on faith. When finance asks "how do we know this is right?", open-source has an answer.

### How often does it need retraining?
Usually quarterly. More often in categories that change fast, less often in stable ones. The rhythm is set from the start of the engagement, because MMM is a repeating practice, not a one-off deliverable.

### What happens when MMM disagrees with last-click?
It usually does, and that is the point: MMM picks up top-of-funnel and cross-channel effects that last-click misses. We use each where it fits: MMM for strategic budget decisions, last-click and multi-touch for daily bidding decisions.

### How does it fit our wider measurement?
MMM runs on the same warehouse data that feeds BI, and its results go straight into planning. The attribution models used for bidding stay independent but are informed by the MMM view. All of it lives in one coherent measurement practice.