AI-powered marketing measurement platform
A platform that gives companies and agencies the marketing mix modelling and incrementality testing of a full analytics team, without hiring one. It separates genuine marketing impact from noise and turns it into better budget decisions.
Platform reporting tells you what happened inside the platform. It does not tell you what would have happened without the marketing, which is the question your budget decision actually needs answered.
Platform reported ROAS
4.2×
What the platform attributes
Every platform attributes conversions. Not all of those conversions would have been lost without the campaign.
Incremental marketing impact
1.8×
What the marketing actually drove
Incremental measurement isolates the effect that would disappear if you stopped the marketing.
Marketing mix modelling and incrementality testing are powerful, but they normally need a data science team that understands Bayesian modelling and causal inference. Most brands and agencies with serious media spend do not have one. RightMeasure is a platform that does that work for you, with an AI analyst guiding each step and every result open to challenge.
Upload your spend and outcome data. The AI analyst, powered by Google Gemini, reads your columns, flags gaps, outliers and structural problems, and explains what it found in plain English.
The platform builds a Bayesian marketing mix model on Google Meridian and runs the checks an experienced modeller would: prior sensitivity, baseline tests and holdout validation.
Every result comes with an honest confidence read. When the data cannot tell channels apart, the platform says so and recommends the experiment that would.
Get channel ROI, saturation, budget scenarios and a client-ready report, with the evidence behind each recommendation and the option to override it.
You spend enough on media that a wrong budget call is expensive, but not enough to justify hiring data scientists with MMM and incrementality skills.
Offer clients independent measurement and evidence-backed budget advice without building a modelling team or relying on platform-reported numbers.
No prior expertise needed. The platform guides each step, explains the reasoning, and designs the lift tests that make the model more trustworthy.
Most measurement doesn't answer all of them. Good measurement tries to.
What did marketing actually contribute?
Separate the marketing effect from everything that would have happened anyway: seasonal trends, price changes, broader category growth.
Which channels created incremental value?
Not which channels were present when sales happened, but which channels caused sales that would not have occurred without them.
How confident should we be in the result?
A number without its uncertainty range is not evidence. Knowing how wide the estimate is matters as much as knowing the estimate itself.
Where should the next budget go?
Which channels still have headroom? Which are close to saturation? What reallocation improves return without requiring extra budget?
What should we test next?
Measurement improves when it is validated. Experiments close the gap between modelled estimates and real-world evidence.
A model can fit the data extremely well and still lead to the wrong marketing decision. Before we make recommendations, we test whether the conclusions survive reasonable changes to the model. This is one of the most important things we do, and one of the least common.
Does the channel ROI conclusion stay the same when reasonable modelling assumptions change? If the answer shifts materially, the decision is not yet reliable.
Are we accurately separating underlying demand from the contribution of marketing? An overly flexible baseline can quietly absorb media effects.
Can the model genuinely distinguish which channel drove the outcome, or are correlated spend patterns making that difficult? Identifiability matters.
Does the model still perform on data it was not trained on? Out-of-sample accuracy tells you whether the model has captured real patterns or fitted noise.
Would another defensible model specification lead to the same budget recommendation? Robustness across specifications builds confidence in the decision.
If the data cannot support a reliable estimate for a channel, we say so. False precision is a more serious problem than acknowledged uncertainty.
Three defensible model specifications applied to the same dataset. All produce an excellent predictive fit. All produce materially different channel ROI estimates, and different budget recommendations.
Standard baseline
4.2×
Meta / Paid Social · Illustrative ROI
↑ Increase investment
Tighter priors
2.1×
Meta / Paid Social · Illustrative ROI
→ Hold investment
Flexible baseline
1.3×
Meta / Paid Social · Illustrative ROI
↓ Test first
High predictive fit does not automatically mean reliable attribution. RightMeasure tests whether the business decision, not just the model, is stable before recommending action.
↻ Each engagement is a loop. Experiments feed the next model.
Media, business drivers and commercial outcomes: understood, cleaned and ready.
Data quality, demand patterns and measurement risks, identified before any model runs.
Incremental contribution estimated using robust Marketing Mix Modelling and sound prior assumptions.
Assumptions tested, channels interrogated, alternative specifications compared. The step most providers skip.
Robust findings, not fragile ones, translated into investment scenarios and practical recommendations.
Experiments validate the model. Evidence feeds back into the next iteration. Measurement improves over time.
Three capabilities. One objective: understand what marketing actually drives, and where the next budget should go.
Understand the incremental contribution of marketing across channels: online, offline, brand and performance, beyond what platform attribution can tell you.
Use controlled experiments to determine whether marketing caused the outcome, and calibrate your MMM against real-world evidence rather than modelled assumptions alone.
Turn validated measurement into a practical plan: where to invest, where to hold, where to test, and how to make the next budget work harder.
Illustrative examples. Each engagement produces outputs specific to your data, channels and decisions.
Share of incremental outcome by channel, with the 90% range shown. The width of the estimate is as important as the number itself.
Where the next unit of budget sits on the response curve, and how close a channel is to saturation.
Compare reallocations before committing budget.
The same channel estimated across 28 model specifications. A single spec reads 0.9×. The distribution tells the full story, which is why we run more than one.
RightMeasure combines Google's Meridian Bayesian MMM framework and a Google Gemini AI analyst with systematic model validation, sensitivity testing and causal diagnostics. For those who want to look under the surface.
RightMeasure is a marketing measurement platform that makes rigorous MMM and incrementality available to teams without a data science function.
The platform combines marketing science, causal reasoning and modern statistical modelling with an AI analyst, so that businesses and agencies can get reliable answers without in-house MMM or incrementality expertise.
We are independent: we do not sell media, and we have no interest in a particular channel looking good. The platform is built around understanding when a result is genuinely reliable enough to act on, and saying so plainly when it is not.
The differentiation is straightforward: most tools fit a model and report the result. RightMeasure actively challenges the model before recommending that a business acts on it.
A leading UK kitchen retailer
Ad platform reporting showed strong ROAS across paid channels. The business had no in-house team to build or challenge a marketing mix model, so it could not tell how much of that attributed revenue was genuinely incremental.
Run through the RightMeasure platform, the model showed that a significant share of attributed conversions were not incremental. By separating genuine marketing contribution from baseline demand, it identified £1.5m in incremental revenue the business had not previously been able to account for, and pointed budget away from channels that were taking credit without driving growth.
If you are trying to understand what your marketing actually drove, or whether your existing measurement is reliable enough to act on, let's talk.