Causal Measurement
Evidence that distinguishes correlation from causation
Every marketing organization faces difficult questions.
Did the influencer campaign generate incremental sales, or did promotions drive the increase? Did retail media outperform social, or were both simply benefiting from seasonal demand? Why did sales remain flat after increasing investment? Are different teams claiming credit for the same outcome?
These questions cannot be answered reliably with descriptive reporting or attribution alone. They require methods designed to estimate causality.
Central Control helps organizations measure the true incremental impact of marketing using rigorous causal inference. We tailor our approach to the business question, the available data, and the practical constraints of each market.
Every measurement problem is different.
We help clients tackle challenges including:
Measuring incrementality when randomized experiments are impractical
Sparse or low-frequency sales data
Limited geographic variation in international markets
Overlapping effects from advertising, promotions, pricing, and seasonality
Conflicting signals from platform reporting, attribution, and internal analyses
Budget allocation across competing channels and investments
The right methodology depends on the question and the evidence
Our approach is driven by the business question, the available data, and the level of confidence required. Depending on the situation, we may employ:
Cluster Randomized Trials (Geo RCTs)
User-Level Randomized Experiments
Synthetic Control Methods
Bayesian Structural Time Series (BSTS / CausalImpact)
Difference-in-Differences
Interrupted Time Series
Matched-Market Designs
Composite Signal Modeling
Marketing Mix Modeling
Hybrid approaches combining multiple causal methods
Real-world data requires practical solutions
Some markets offer only monthly sales reporting, limited geographic splits, or weak signals from emerging channels such as influencer marketing. We combine evidence from multiple sources to build practical causal analyses that support better decisions while clearly communicating uncertainty.
Better evidence leads to better investment decisions
Our expertise extends beyond any single methodology. We recommend the approach best suited to each business question, balancing scientific rigor with practical constraints. Whether the solution is a randomized experiment, a quasi-experimental design, or a hybrid analytical framework, our objective is the same: credible evidence for better marketing decisions.