Attribution assigns credit to touchpoints that were present during a conversion journey. Incrementality tests whether those touchpoints actually caused the conversion. Attribution answers which channels were involved. Incrementality answers which channels actually moved the outcome. For budget allocation decisions, incrementality is the more reliable methodology.
Starting with data quality. Every engagement begins with a full audit of tracking, conversion events, and cross-platform discrepancies. Then an independent measurement layer is built outside of ad platforms. Then channel contribution gets validated with incrementality testing. Then everything gets connected to business outcomes through marketing mix modeling on a regular cadence.
A data warehouse is a centralized repository that pulls data from every marketing source into one place, reconciled and fresh daily. Without one, data lives in silos and teams spend more time reconciling spreadsheets than making decisions. Thrive builds marketing data warehouses on BigQuery using ThriveStack.
Marketing mix modeling is a statistical analysis that breaks down total revenue into contributions from each marketing channel, including channels that don't show up in attribution reports like linear TV and offline. Use it when you need to make strategic budget allocation decisions across the full program.
The foundational data engineering work typically takes two to four weeks. Attribution framework and incrementality testing setup follows. Full marketing mix modeling requires three to six months of clean historical data before the first model produces reliable output. Thrive builds toward the full system from day one.
