A quality checklist for marketing reports and AI summaries
Catch scope, currency, attribution and aggregation mistakes before sharing a marketing conclusion.
A quality review asks whether another person could reproduce the report and interpret it correctly. Most serious mistakes come from scope and definitions rather than arithmetic. Review those foundations before polishing the narrative or adding recommendations.
Work through it, step by step.
Check account IDs, dates, timezones and filters. Confirm that the latest period is complete enough for the comparison being made.
Inspect metric units and aggregation. Keep currencies separate, recompute compatible ratios from totals and avoid adding overlapping unique counts.
Read warnings, pagination and missing-data notes. Verify that the conclusion distinguishes observations, assumptions and causal claims.
Make it concrete.
An attractive summary averages campaign ROAS values even though spend differs greatly. Recalculating from compatible total value and spend can change the conclusion without changing any source data.
What to keep in mind.
Passing a checklist does not prove the underlying events are correctly implemented. Measurement validation remains a separate responsibility.
Your next useful step.
Attach a compact methods note and retain links to the underlying evidence. If a missing definition could reverse the recommendation, resolve it before sharing the conclusion.
Working with this in Liftaven
Choose a single business question before requesting data. Write down the accounts, dates and definitions that make the answer reproducible. Liftaven provides the reporting workspace and authenticated MCP access; decisions and changes remain with you.
Explore the AI workflowSources & context
This note combines original practical guidance with the reporting references below. Follow provider documentation for current definitions and availability; suggested investigations are not guarantees of a particular result.