01

What we are exploring

How measurement systems and experiments can work together so that everyday reporting stays honest. The questions are practical: which numbers can be trusted, how to check them, and how to act when they are uncertain.

02

Why experimentation matters

Attribution assigns credit; it does not measure what would have happened without the spend. Controlled experiments, such as holdouts and geo tests, are one of the few ways to observe that difference directly.

Illustration: reported attribution compared with an experiment estimateConceptual chart without real data. The platform-reported figure is a single precise-looking number; the experiment estimate is a range. The gap between them is what calibration measures.ILLUSTRATIVE, NO REAL DATAPlatform-reported effectExperiment estimateCalibrated everyday measurementinterval = plausible range of the incremental effect

03

Measurement and experiments

Experiments are occasional and expensive; reporting runs every day. Using the first to check and correct the second is the core idea: test when you can, and calibrate everyday measurement with what the tests show.

04

Cross-market learning

Can what was learned in one market, campaign or segment inform decisions elsewhere? Knowing when evidence transfers, and when it does not, avoids re-running every test everywhere.

05

Decision-making under uncertainty

Every estimate comes with a range. Budget decisions should reflect that range instead of treating a single reported number as exact.

Questions

The questions, in plain language

  • Did marketing cause the outcome, or merely receive credit for it?
  • Can what we learned from one market or campaign inform decisions elsewhere?
  • How should budget move when performance estimates are uncertain?
  • Can experiments check whether everyday measurement is telling the truth?
  • How can tests adapt as evidence arrives, without wasting spend?

Outputs

Publications and working papers

No publications are listed yet. Working papers, notes and presentations will appear here when they exist.

Research and client work stay separate.

Becoming a client never means your data is used for research. Any research use of client data would require separate, explicit consent, anonymization, appropriate contractual terms and ethics approval where required. Participation is never a condition of receiving consulting services.

Research collaborations

Researchers and companies interested in marketing measurement, experimentation or budget allocation under uncertainty are welcome to get in touch.