A Methodological Framework for Rigorous Meta Ads Experimentation

Direct-to-consumer (D2C) brands increasingly rely on A/B testing to optimizepaid social advertising, yet common execution errors—inconsistent attribution,underpowered samples, mid-test edits, and metric flexibility—undermine inferentialvalidity.[13] This paper develops a methodological framework for decision-gradeexperimentationonMeta(Facebook)Adsthatbalancesstatisticalrigorwithbusinessguardrails. We synthesize established practices in online experiments[3, 12, 13] andoperationalize them into a prioritized test sequence, integrating power analysis,Sample Ratio Mismatch (SRM) diagnostics,[21, 23] optional CUPED variancereduction,[6] and economic guardrails (e.g., ROAS thresholds, delivery balance,frequency parity).Evidence and scope. All analyses use a synthetic/simulated dataset calibratedto D2C benchmarks; no live-traffic data are analyzed. The simulation demonstratesthe analytical workflow and decision logic without making empirical claims aboutreal-world effectiveness. The contribution is methodological: a reproducibletemplate practitioners can adapt and validate in production.