Marketing Engineering

Core concepts in this department

• Multi-Touch Attribution

  Multi-touch attribution assigns credit for a conversion across all marketing touchpoints in the customer journey, using rules (linear, time-decay, U-shaped) or data-driven models. Most MTA implementations confuse correlation with causation and systematically overvalue bottom-funnel channels that intercept already-decided buyers.

• Marketing Mix Modeling

  Marketing mix modeling is a top-down econometric approach that estimates the causal contribution of each marketing channel to sales using aggregate historical data and controls for non-marketing drivers (price, distribution, seasonality, competition). Privacy-first MMM has returned as deterministic user-level tracking has eroded.

• Causal Inference

  Causal inference is the statistical machinery for estimating causal effects from data rather than just describing correlations. In marketing it involves directed acyclic graphs (DAGs) for identifying confounders, instrumental variables for unobserved confounding, and quasi-experimental methods like difference-in-differences and synthetic control.

• Incrementality Testing

  Incrementality testing measures the causal lift produced by a marketing intervention by comparing treated units to untreated control units, typically using geographic randomization (geo-lift), user holdouts, or ghost bids. It is the empirical gold standard for answering 'what did this spend actually cause?'

• Causal Discovery

  Causal discovery is the family of algorithms that infer directed causal structure from observational data alone, using conditional-independence tests (PC, FCI) or score-based search (GES). Applied to business data it produces testable DAGs that replace ad-hoc causal intuitions with falsifiable hypotheses.

• CausalImpact (Bayesian Structural Time Series)

  CausalImpact is Google's open-source implementation of Bayesian structural time series for measuring the causal effect of a known intervention on a time-series outcome. It constructs a counterfactual from correlated control series and reports the posterior distribution of the cumulative effect with calibrated uncertainty.

• Directed Acyclic Graph (Causal DAG)

  A Directed Acyclic Graph is the formal representation of a causal structure: nodes are variables, directed edges are direct causal effects, and no cycles are permitted. DAGs encode the identification assumptions needed to estimate causal effects from observational data via the back-door and front-door criteria.

• Unified Measurement Architecture

  A Unified Measurement Architecture combines Marketing Mix Modeling, Multi-Touch Attribution, and randomized incrementality experiments into a single decision stack, with each method calibrated against the others. MMM supplies top-down causal anchors, MTA supplies in-flight optimization, experiments supply the ground truth.

• Geo-Lift Testing

  Geo-lift testing is an experimental design that randomizes marketing treatment across geographic markets (DMAs, metros, countries) to measure incremental impact when user-level randomization is infeasible. Synthetic control and open-source libraries like GeoLift construct matched control markets from pre-period covariates.