Abstract
We describe an architecture for calibrating marketing measurement from a brand’s own governed operating record, as an alternative to both multi-touch attribution (which depends on user-level tracking that is failing structurally, not just through cookie deprecation) and media-mix modelling calibrated by purchased incrementally experiments.
The core proposal: every recommendation a marketing system issues can carry a pre-registered expected outcome; adoption and non-adoption of that recommendation can both be measured against it; and declared campaign targets can function as pre-registered treatment groups. Together these turn ordinary operation into a continuous source of causal evidence, at no marginal cost, without tracking individual users.
This is an architecture paper, not an empirical one. It specifies the design and the evidence it would need to validate or falsify, states its own limits plainly (including a measured instance where the approach failed and why), and reports no results from a deployment. A companion empirical paper is intended to follow.
The paper gives particular attention to markets , India foremost among them where user-level journey reconstruction was never viable, due to messaging-led discovery, cash-on-delivery purchasing, and marketplace checkout, rather than only to recent privacy regulation.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Mishra, Abhishek, "Brand-Signal Attribution: Calibrating Marketing Measurement from a Governed Operating Record", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12944