Advanced measurement

Understand what is genuinely driving marketing performance using marketing mix modelling, incrementality testing and geo experimentation.

What it is

Advanced measurement uses statistical methods to answer marketing questions that attribution and platform reporting can’t answer on their own.

Marketing mix modelling (MMM) uses aggregated media, business and external data to estimate how different marketing channels contribute to outcomes while accounting for factors such as seasonality, pricing and market conditions.

Incrementality testing measures what happened because of marketing by comparing exposed and unexposed groups. Geo experimentation applies a similar approach across regions or markets where individual-level testing isn’t practical.

These methods work alongside attribution rather than replacing it. Attribution can support day-to-day campaign optimisation, while advanced measurement helps answer bigger questions about effectiveness, channel investment and budget allocation.

Why it matters

Privacy changes, browser restrictions and platform modelling have made customer journeys more difficult to observe from beginning to end.

At the same time, attribution tells only part of the story. A platform can report that it received credit for a conversion without answering whether that conversion would have happened anyway.

Advanced measurement provides a more independent view of marketing effectiveness. It helps organisations understand incremental impact, compare investment across channels and make bigger budget decisions with stronger evidence.

Used together, MMM, incrementality and attribution provide different views of performance, each suited to different decisions.

What Louder does

Marketing mix modelling - model design, data preparation, calibration against experimental results, and interpretation that a non-technical audience can act on.
Incrementality testing - conversion lift studies, holdout design, power analysis before the test rather than after.
Geo experimentation - market matching, test and control design, and analysis robust to the fact that markets aren’t identical.
Calibration - using experimental results to validate model outputs, which is what separates a model from a hypothesis.
Decision framework - a documented view of which method answers which question, so results don’t get applied to the wrong decision.

Common challenges

Tests underpowered from the start. A holdout runs for two weeks at a scale that could never have detected the effect size in question. The result is inconclusive, and inconclusive gets read as “no effect”.
Correlated spend across channels. When all channels scale together, no model can separate their contributions. Without deliberate variation the data simply doesn’t contain the answer, however sophisticated the method.
MMM used for tactical decisions. Aggregate models can’t tell you which creative or keyword to change. Applied at that level they produce confident recommendations with no support.
Attribution and MMM reconciled by averaging. The two answer different questions. Splitting the difference produces a number that answers neither, and it’s remarkably common in board packs.