The New Measurement Trinity: MMM, Incrementality, and Human Insight

There is no single source of truth in marketing anymore. Only systems of evidence.

From One Truth to Triangulated Truth

For years, marketing teams have searched for a single source of truth. That pursuit has failed. No single measurement model can capture the full picture of how advertising works, especially in a world of privacy restrictions, cross-device journeys and declining click reliability. The most effective modern approach is not to find one perfect number, but to triangulate between several credible perspectives.

This is what I call the New Measurement Trinity: Marketing Mix Modelling (MMM) for long-term causality, incrementality testing for short-term validation, and human insight for contextual understanding. Together, they form a system of evidence that is both rigorous and resilient.

1. MMM: The Long View of Causality

Marketing Mix Modelling has re-emerged as the cornerstone of modern effectiveness measurement. Tools such as Google’s LightweightMMM and Meta’s Robyn have democratised access to econometric modelling, allowing brands of all sizes to quantify how different channels drive business outcomes.

MMM operates on aggregated data, making it privacy safe and ideal for understanding upper funnel media such as TV, radio and outdoor. It isolates the incremental contribution of each channel by correlating changes in spend or impressions with changes in business results.

A media client of ours recently used MMM to measure the impact of linear TV post iOS14. With digital signals weakened, the model revealed that television was responsible for nearly 30 per cent of total incremental sales during the campaign period, a contribution invisible in click based analytics. MMM gave them back visibility into the unseen.

2. Incrementality: Proving Short-Term Impact

If MMM is the satellite view, incrementality testing is the ground truth. Through geo experiments, holdouts or matched market designs, incrementality allows teams to measure the causal impact of a campaign over days or weeks.

Meta’s GeoLift and Google’s regional lift frameworks have made this method accessible and statistically sound. When structured correctly, incrementality shows how additional spend drives additional conversions, not what the platforms claim drove them.

Incrementality also acts as a calibration tool for MMM. By comparing near term lift tests with long term models, marketers can refine their assumptions and validate that their econometric trends are real. A good incrementality culture turns testing into habit, not exception.

3. Human Insight: The Missing Context

Quantitative models can explain correlation and causality, but not why people respond the way they do. This is where human insight, survey data, brand lift studies or prompted recall, completes the trinity.

We ran a brand lift study alongside an MMM analysis for a streaming client. The econometric model found a 12 per cent uplift in sign ups during their national campaign. The survey revealed that the same campaign drove a 15 point increase in spontaneous awareness, with message recall strongest among new category buyers. In this case, human insight and statistical modelling told the same story from different angles.

When survey data, incrementality results and MMM outputs align, confidence rises dramatically. This triangulated evidence allows teams to communicate effectiveness credibly, not just internally but to finance and leadership stakeholders who need proof, not probability.

From Determinism to Evidence Triangulation

Digital first teams grew up with deterministic click models that promised precision but delivered narrow visibility. Every conversion was traceable, every ROI measurable, until cookies disappeared and signal loss began. The solution is not to recreate old precision but to build new confidence through multiple independent measures.

Triangulation turns measurement into decision support, not just reporting. It accepts uncertainty but manages it scientifically. Rather than chasing an impossible perfect attribution model, the goal is to balance statistical truth, experimental evidence and human understanding.

In short, the future of measurement is not about knowing everything; it is about trusting enough.