If you can't trust your numbers, you can't trust your decisions. Marketing effectiveness is where strategy gains clarity: media mix modelling, incrementality testing, experimentation, and predictive modelling that connect marketing activity to revenue.
MMM tells you where contribution comes from. Incrementality experiments prove causation. CRO compounds the value of the traffic you already have. Predictive models turn the history into foresight. Together they form a measurement system you can take to a board.
Models are only useful if you can trust them. We validate modelled results with live experiments, report uncertainty honestly, and tie every recommendation to the revenue it is expected to move.
Attribution breaks a little more every year. Media mix modelling measures what your marketing actually contributes.
Learn more →Controlled experiments that isolate the true impact of your spend, so you stop paying for conversions you would get anyway.
Learn more →More traffic doesn’t always mean more sales. We make every visitor count by improving how users move, think, and act.
Learn more →Forecasting, churn, and lifetime value models that show you what’s coming, so you can act before your competitors.
Learn more →MMM, incrementality experiments, and attribution cross-checked against each other, so no single model is taken on faith.
Models built and validated by people who understand the maths, with confidence intervals rather than false certainty.
Results translated into the language of finance: contribution, ROI, and forecasts leadership can sign off on.
Marketing effectiveness is the discipline of measuring what marketing actually contributes to business outcomes, then using that evidence to improve it. It is broader than analytics, which collects and reports data, and broader than attribution, which assigns credit to touchpoints. Effectiveness work asks the harder question: what would have happened without this spend, and what should we do differently as a result.
Attribution assigns credit for a conversion to the touchpoints that preceded it, based on observed correlations. Incrementality testing runs a controlled experiment with a holdout group to prove what the marketing actually caused. Media mix modelling uses aggregated historical data and statistical regression to estimate each channel’s contribution, including offline and brand activity that attribution cannot see. The three answer different questions, and the reason we run them together is that each one checks the others.
Start with whichever answers the question you are actually stuck on. If you cannot defend budget allocation across channels, media mix modelling gives you the contribution picture. If you suspect one channel is taking credit it does not deserve, usually brand search or remarketing, an incrementality test is faster, cheaper, and more conclusive. In practice we often run a targeted experiment first, because its result becomes a calibration point for the model.
Media mix modelling needs at least 12 to 24 months of historical spend and outcome data, so the model can see a full seasonal cycle and enough variation in spend to learn from. Incrementality testing and CRO depend on conversion volume rather than history, so they are usually available earlier. If your data is not ready, exploratory data analysis or an analytics build is the honest first step, and we will tell you that rather than sell you a model.
Media mix modelling delivers the most value for organisations investing at least one million dollars a year in marketing, because below that level the gains available from reallocating budget may not justify the modelling effort. Incrementality testing and conversion rate optimisation have no such threshold and often pay back at much smaller budgets, since a single experiment that stops wasted spend on a non-incremental channel can cover its own cost outright.
Python for modelling, Meridian for media mix modelling, GrowthBook for experimentation, and your existing warehouse, usually BigQuery, for the data layer. We prefer open and inspectable methods over vendor black boxes, because you cannot trust a number whose assumptions you are not allowed to see. The models, code, and experiment configuration are built in your environment and belong to you.
We report in the language finance uses: contribution, incremental return, payback period, and forecast ranges with confidence intervals rather than false precision. Every recommendation carries the revenue it is expected to move and the uncertainty around that estimate. Measurement work only pays off if the person controlling the budget trusts and understands the output.
Let's discuss how marketing effectiveness can drive measurable results for your business.
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