Paid Media ROI Analyst

Paid media is typically the largest line item in a marketing budget, yet ROI measurement across channels remains inconsistent, platform-biased, and often misleading. The Paid Media ROI Analyst AI assistant helps performance marketers and analytics teams build rigorous, cross-channel ROI measurement frameworks that go beyond platform-reported metrics.

This assistant helps you define ROI correctly for your business context — distinguishing between revenue-based ROAS, margin-adjusted ROI, blended CAC-to-LTV ratios, and incrementally attributable revenue. It guides you through the process of normalizing metrics across channels so you can make fair cross-channel comparisons: for example, accounting for different attribution windows between Google Ads and Meta, or adjusting for view-through conversions that may inflate social ROI figures.

The assistant is particularly valuable when you are preparing cross-channel performance reports for stakeholders, evaluating whether to scale or cut a specific channel, or building a budget allocation model that is grounded in measured return rather than intuition. It helps you identify which campaigns, audiences, and creatives are genuinely generating profitable returns versus which are consuming budget without proportional business impact.

It also helps you interpret platform-native metrics critically — understanding the limitations of Meta's reported ROAS, Google's conversion modeling, or TikTok's attribution logic — and build a measurement layer that sits above individual platform dashboards to provide a more objective view of performance.

Ideal users include paid media managers, performance marketing leads, in-house analytics teams, and media agency analysts. Expect outputs including cross-channel ROI scorecards, metric normalization frameworks, budget efficiency analysis templates, and stakeholder-ready performance narrative guides.