Camilo Andrés Cáceres Flórez

Camilo Andrés Cáceres Flórez

Staff ML Engineer (Applied AI), Data Science Expert, and AI Architect leading GenAI, data science, and MLOps at scale. Ph.D. in Mechanical Engineering (Mechatronics) from UNICAMP and EMBA in Strategic Leadership, with experience driving end-to-end ML platforms, computer vision, and data science initiatives.

Geo experiments for ads lift without slowing delivery

1 minute read

Published:

Geo experiments remain the most practical lift tool when auctions and budgets complicate randomization. The key is to design them so teams can run and ship without blocking roadmaps.

Design checklist

  • Power and overlap: simulate power under realistic spend patterns; pick geos that limit spillover.
  • Covariate adjustment: use CUPED or synthetic controls to shrink variance; validate uplift estimates with backtests.
  • Operational hygiene: pre-register metrics, failure modes, and rollback plans; add consent-friendly analytics only after approval.

Execution playbook

  1. Lock assignment and pre-period windows; freeze targeting changes during the test.
  2. Monitor overlap, pacing, and budget spend; stop conditions should be paired with rollback and communications.
  3. Publish dashboards with confidence intervals and next-step recommendations (scale, refine, or stop).

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