Diagnose a failing campaign

Your vaccination-lift test produces no aggregate effect and a negative effect among seniors. Put together a triage plan covering: (a) exposure verification (deliverability, inbox placement, opens/clicks under mail privacy protections), (b) targeting leakage/crossover and contamination checks, (c) logging/ETL audits for timestamp, timezone, and dedup errors, (d) outcome validity (claims/EHR coverage gaps and lag from shot to claim), (e) lift suppressors (fatigue, frequency-capping bugs, creative rendering issues), (f) heterogeneity and calendar interactions, (g) re-test design (A/A, split-by-seed, staggered rollouts) plus rapid corrective experiments, and (h) decision criteria to pause, pivot, or proceed. List the example queries or plots you would run and the minimum additional data you would request.

Overview: The question assesses skill in experimental diagnostics, causal inference, data-quality auditing, and analytics-led troubleshooting for randomized CRM campaigns, with emphasis on exposure verification, contamination detection, logging/ETL integrity, outcome validity, heterogeneity analysis, and re-test design.