What happened. What worked.
What to do next.
Lift tests, attribution and marketing mix modelling are how measurement gets done. They matter because they change budget decisions, not because a framework is complete.
Methods in service of decisions
What happened. What worked.
Understand
What drove growth?
MMM and contribution
+
Prove
What was incremental?
Experiments and calibration
+
Decide
Where next?
Scenarios and optimisation
=
Owned capability
Decisions you can stand behind
01 / Lift Tests
Prove Causation, Not Correlation
The gold standard of marketing measurement. Lift tests use controlled experiments to establish the true causal impact of your marketing activity, separating signal from noise.
How It Works
We design and execute rigorous holdout experiments where matched geographic regions or audience segments are randomly assigned to test and control groups. By comparing outcomes between groups, we isolate the true incremental effect of marketing activity.
Capabilities
Geo Experiments
Region-level holdout tests using matched markets with Bayesian causal impact analysis
Audience Tests
User-level randomised controlled trials across digital channels
Cross-Channel
Test incrementality across search, social, display, TV, and offline channels
Calibration
Use lift results to calibrate and validate your MMM and MTA models
Example Result
Illustrative lift, not a client result. Used to show how test versus control is read.
02 / Multi-Touch Attribution
See how journeys are credited, with the limits in view
Attribution can show which digital touchpoints receive credit on observed paths. It is not, on its own, a causal estimate of incrementality. We treat it as a tactical signal, then triangulate with experiments and MMM.
How It Fits
Algorithmic attribution assigns fractional credit across observed journeys. That is useful for diagnosing path structure and last-click distortion. It should not be read as proof that a channel caused the conversion, especially once identity is incomplete and offline activity is in the mix.
Capabilities
Algorithmic Models
Data-driven attribution models that learn credit allocation from your conversion data
Cross-Device
Stitch customer journeys across mobile, desktop, and tablet touchpoints
Not causal by default
Credit on a path is not the same as incremental impact. We say so, then calibrate with tests
Privacy-First
Built for a cookieless future with privacy-compliant data frameworks
Journey Attribution
Paid Social
18%
→
Organic Search
12%
→
Display
22%
→
Email
15%
→
Paid Search
33%
Illustrative journey credit, not a client result. Attribution credit is not a causal effect.
03 / Marketing Mix Modelling
Optimise the Full Marketing Portfolio
Bayesian econometric models that quantify the impact of every marketing channel on business outcomes, while accounting for seasonality, competitor activity, and macroeconomic factors.
How It Works
We build custom Bayesian regression models that decompose your KPIs into the contributions of each marketing channel, along with external factors. The models produce ROI estimates, saturation curves, and adstock effects that power budget optimisation and scenario planning.
Capabilities
Bayesian Models
Full posterior distributions for every parameter, giving you uncertainty estimates, not just point predictions
Scenario Planning
Simulate budget reallocation scenarios and forecast outcomes before committing spend
Brand & Performance
Separate short-term performance effects from long-term brand building impact
Saturation & Adstock
Model diminishing returns and carryover effects for each channel
Channel Contribution
Illustrative channel contributions, not a client result.
Methods only work if the organisation can run them
If you want these capabilities in-house, start with the operating model. If you already have a model, start by asking whether you can trust it.