Unified Marketing Measurement: Combining MTA, MMM, and Incrementality

User Journey:
Facebook Ad → Google Search → Email → Purchase
Attribution Models:
├── Last-click: 100% to Email
├── First-click: 100% to Facebook
├── Linear: 33% each
├── Time decay: More to Email, less to Facebook
└── Position-based: 40% Facebook, 40% Email, 20% Google

Sales = Baseline + β₁(Facebook) + β₂(Google) + β₃(TV) + β₄(Email) + Seasonality
Where β coefficients represent channel impact

Experimental Design:
├── Test Group: Exposed to marketing
├── Control Group: Not exposed
└── Measurement: Compare conversion rates
Incremental Lift = (Test Conversions-Control Conversions) / Control Conversions

LAYER 1: MTA (Tactical)
├── Purpose: Daily optimization
├── Cadence: Real-time
├── Decisions: Campaign/creative/audience adjustments
└── Trust level: Directional
LAYER 2: MMM (Strategic)
├── Purpose: Budget allocation
├── Cadence: Weekly/Monthly
├── Decisions: Channel mix, major budget shifts
└── Trust level: High for allocation
LAYER 3: Incrementality (Validation)
├── Purpose: Validate MTA and MMM
├── Cadence: Quarterly
├── Decisions: Calibrate other methods
└── Trust level: Highest for causation

MTA says: Facebook ROAS is 4.5x
MMM says: Facebook ROAS is 2.8x
Incrementality test: Facebook lift is 35%
Interpretation:
├── MTA is over-crediting (platform bias + attribution window)
├── MMM is closer to reality
├── Incrementality confirms Facebook drives real value
└── Action: Trust MMM for budget, use MTA for within-platform optimization

1. Run incrementality test on key channel
Compare result to MTA and MMM predictions
Calculate calibration factor
Apply factor to ongoing MTA/MMM
Example:
Incrementality shows Facebook is 30% less effective than MTA claims
Apply 0.7x multiplier to all Facebook MTA data
Update MMM priors based on incrementality findings

Causation Questions: Trust Incrementality > MMM > MTA
├── "Does Facebook actually drive sales?" → Incrementality
└── "Should I run this channel at all?" → Incrementality
Allocation Questions: Trust MMM > Incrementality > MTA
├── "How should I split budget?" → MMM
└── "Which channel is most efficient?" → MMM
Optimization Questions: Trust MTA (with calibration)
├── "Which creative is performing?" → MTA
└── "Which audience should I scale?" → MTA

Reality: MTA is over-crediting (retargeting captures, doesn't create demand)
Resolution: Use MMM ROAS for budget allocation, MTA for creative testing only

Reality: MMM may be under-crediting long-term brand effects
Resolution: Run longer incrementality test, update MMM model

Reality: Platforms over-credit (it's in their interest)
Resolution: Use first-party data as source of truth, apply discount to platform data

Technical Setup:
├── Server-side tracking (CAPI)
├── First-party data collection
├── Clean data pipeline
└── Unified dashboard
MTA Implementation:
├── Cross-platform attribution tool
├── First-party attribution
├── Platform data ingestion
└── Basic reporting

Incrementality Testing:
├── Design first geo test
├── Run Facebook/Meta holdout
├── Validate MTA findings
└── Document learnings
Survey Attribution:
├── Implement post-purchase surveys
├── Compare to digital attribution
└── Identify dark social contribution

MMM Implementation:
├── Collect 2+ years historical data
├── Build or buy MMM solution
├── Validate against incrementality
└── Create allocation recommendations
Integration:
├── Calibrate MTA with incrementality
├── Feed incrementality into MMM priors
├── Build unified reporting
└── Create decision framework

Quarterly:
├── Run one incrementality test
├── Refresh MMM model
├── Review calibration factors
Monthly:
├── Review MMM recommendations
├── Major budget decisions
Weekly:
├── MTA-informed optimization
├── Campaign adjustments
Daily:
├── Performance monitoring
├── Tactical decisions