Unified Measurement Framework
Triangulate truth from MTA, MMM, and incrementality
The Insight
No single measurement method gives the full picture. MTA is granular but misses non-click impact. MMM captures upper-funnel effects but lacks campaign-level precision. Incrementality is ground truth but can't run continuously. Each method has blind spots — used alone, they mislead.
The Opportunity
KLAR's unified measurement engine — combining MTA, MMM, and incrementality — is their single strongest differentiator. No competitor unifies all three into one dynamic model. Visualizing how these three methods converge on a 'triangulated truth' for each channel would be the ultimate product vision showcase.
The Proposal
- 1Build a per-channel measurement dashboard showing three independent estimates (MTA, MMM, incrementality) with confidence intervals
- 2Calculate a 'Unified Estimate' that weights each method based on data quality and recency — showing higher confidence than any single method alone
- 3Add a 'Measurement Health' indicator showing which methods have fresh data and which need recalibration
- 4Create a methodology explainer that educates brands on *why* triangulation produces better decisions than any single method
Skill Mapping
- Built measurement frameworks combining multiple attribution methodologies at Google (SKAN, MTA, MMPs)
- Pioneered end-to-end performance insights platform synthesizing data from multiple measurement sources
- Expert in translating complex measurement concepts into actionable business insights for C-suite audiences
Working MVP Prototype
Unified Measurement Framework
See how MTA, MMM, and incrementality converge on a triangulated truth — adjust method weights to explore sensitivity
€162k
€148k
€138k
€146k
Confidence Interval Overlap — Meta Ads
Recommendation: Validate
Moderate confidence in Meta Ads. Methods show some divergence — recommend running an incrementality test to calibrate MTA and MMM models for this channel.
Total Portfolio (All Channels)
€443k
± €11kUnified attributed revenue across 4 channels (weighted by current slider settings)
Cross-Channel Overview
Synthetic measurement data. In production, KLAR's unified engine dynamically weights each method based on data recency, statistical power, and channel characteristics — producing a single trusted estimate.