The four pillars of marketing measurement

Marketing effectiveness is typically measured with four tool families:

  1. Attribution models (platform touchpoints)
  2. Marketing mix models (MMM) (aggregate or panel)
  3. Geo-experiments (randomised market-level tests)
  4. Panel causal designs (quasi-experimental methods)

Each has a distinct role. The key is understanding what each can—and cannot—identify.

Where panel methods fit

Panel methods sit between pure observation and full experimentation. They use natural variation in timing, location, or intensity of marketing actions to estimate causal effects, while making assumptions explicit and diagnosable. Unlike attribution, they model counterfactual outcomes. Unlike geo-experiments, they do not require randomisation, which makes them usable on historical data and long-run windows.

How MMM connects to panels

Classic MMM relies heavily on functional form choices (e.g., adstock and saturation) and can struggle with discrete interventions. Panel-based MMMs can be more credible when paired with design-first identification (e.g., staggered adoption, fixed effects, factor structures). In practice, MMM and panel designs often overlap—many workflows mix MMM-style transforms with causal panel identification.

Choosing the right tool

A practical selection rule:

  • If randomisation is possible, use geo-experiments first.
  • If repeated observations exist, use panel methods aligned to the treatment structure.
  • If only aggregate time series are available, MMM can still be useful but needs strong assumptions.
  • If you need real-time channel feedback, attribution helps for short-run optimisation but is not causal.

Treatment variation determines the method: discrete shocks often suit difference-in-differences or synthetic control; smooth variation can call for factor models or matrix completion; spillovers require explicit interference models; high-dimensional settings benefit from ML-assisted estimation.

Triangulation beats single-method certainty

The most credible measurement comes from triangulation: different tools, different assumptions, similar answers. Experiments validate panel estimates, panels extend experimental results, MMM guides strategic prioritisation, and attribution highlights segments worth deeper causal testing.

Takeaway

Panel methods are the connective tissue of the marketing measurement ecosystem. They complement experiments, strengthen MMM, and move attribution beyond correlation—provided the design assumptions are clear and the diagnostics are taken seriously.

References

  • Shaw, C. (2025). Causal Inference in Marketing: Panel Data and Machine Learning Methods (Community Review Edition), Section 1.3.
  • Abadie, A., & Gardeazabal, J. (2003). The economic costs of conflict: A case study of the Basque Country. American Economic Review.
  • Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics.