Biography
Maya runs marketing data science at Paid Lens, where she owns the methodology behind published benchmarks and the confidence scoring applied to every recommendation in the decision queue.
Before Paid Lens she spent nine years building measurement and incrementality programs for ecommerce and subscription businesses, including holdout testing frameworks used across eight-figure media budgets.
She writes about what marketing data can and cannot prove, and why a model that cannot be audited should not be trusted with budget.
Areas of expertise
- Marketing measurement and incrementality
- Benchmarking methodology
- Explainable AI and confidence scoring
- Statistical significance in paid media
Works on
Articles by Maya Oyelaran
2026 Paid Media Efficiency Benchmarks: CAC, ROAS and Payback by Channel
Median CAC, blended ROAS and payback periods across 412 anonymized advertiser accounts, split by channel, spend band and business model.
Read articleThe State of Marketing Decisions: What 1,100 Recommendations Reveal
We analyzed which recommended actions marketing teams approved, ignored or reversed — and what separated the decisions that produced measurable impact.
Read articleExplainable AI in Marketing: Why Confidence Scores Matter More Than Predictions
A prediction you cannot audit is a guess with better typography. How to evaluate AI marketing tools on evidence, confidence and reversibility.
Read article