Marketing Mix Modelling

Marketing Mix Modelling (MMM) determines how each channel contributes to business outcomes, but traditional approaches suffer from bias when spending across channels is correlated. Our causal MMM solves this through advanced econometric techniques that properly account for confounding and simultaneity.

We applyCausal Machine Learningto solve complex business challenges.

Using methods including instrumental variable estimation, causal forests for heterogeneous effects, and Bayesian structural time-series models, we separate the true causal impact of each channel from selection bias. This means you get unbiased estimates of how incremental spending in paid search, display, social, email, and offline channels actually drives revenue and conversions. We explicitly model how historical spending decisions correlate with unobservables (brand strength, seasonality, competitive intensity) that also affect outcomes, then isolate the true treatment effect of each marketing lever. Our methodology is grounded in [post-selection inference](/research#post-selection-inference) and [debiased machine learning](/research#double-debiased-ml).

Consumer goods companies deploying our MMM solutions improve marketing efficiency through better budget allocation. Media companies identify which channel combinations drive sustainable ROI. Financial services firms model the long-term brand impact of advertising separate from short-term conversion effects, revealing why some channels appear undervalued in traditional analysis.

The result is a unified view of marketing effectiveness that survives audit and passes econometric rigor tests, giving you confidence to reallocate budgets confidently.