Marketing Analytics & Measurement: Concepts and Frameworks

Measurement Strategy

Choosing few, decision-linked metrics and presenting them so trends and anomalies are unmissable.

Amplitude's framework pairing one value metric with the driver metrics teams can directly move.

Distinguishing numbers that flatter (impressions, followers) from numbers that inform decisions.

Comparing groups that started together to expose changes in behavior that averages conceal.

Bayesian/econometric models on aggregate data, privacy-proof, channel-agnostic, now open-sourced by Google (Meridian) and Meta (Robyn).

Estimating what would have happened without the marketing by withholding it from a comparable group, the causal logic beneath all lift measurement.

Using lift-test results as priors to anchor mix models in causal ground truth.

The trustworthy-experimentation discipline: OECs, guardrail metrics, and institutional skepticism of outliers.

Raab's category: packaged software building a persistent, unified customer database other systems can use.

Composing and governing the tool landscape, mapped since 2011 by Brinker's ever-growing supergraphic.

Collecting consented, owned customer data as third-party cookies and device IDs fade.

The regulatory frame (EU data protection law and Apple's tracking prompts) that now bounds all measurement.