Measurement Incrementality

Determine whether marketing activity caused business impact, rather than merely receiving attribution credit. Separate delivery, engagement, attribution, correlation, and true incrementality across all channels.

How This Skill Orchestrates

1. Script execution (scripts/baseline_gap_calculator.py): Estimates baseline (what would have happened without marketing) using 4 methods: simple average, trend-adjusted, seasonal (YoY), and control-based. Calculates incremental gap, lift %, iROAS, iCPA. Identifies confounders
2. Script execution (scripts/test_control_validator.py): Power analysis for experiment design — validates sample size, calculates minimum detectable effect, estimates required duration, and revenue-at-risk in the control group
3. Script execution (scripts/incrementality_claim_checker.py): Classifies claims across the 5-level evidence hierarchy (delivery → engagement → attribution → correlation → incrementality). Flags overclaiming. Provides safe executive language per level
4. Reference lookup (references/experiment_design_guide.md): 8 measurement methods with selection criteria, experiment templates (conversion lift, holdout, geo-lift, MMM), confounder library, benchmark search protocol, incremental lift benchmarks by channel, the "attribution tax" table
5. Reference lookup (references/executive_claims_policy.md): Claim language templates by evidence level, CFO test, report language templates (strong/moderate/weak), what to NEVER claim
6. Web search (Claude — MANDATORY): Search for "[PLATFORM] conversion lift [INDUSTRY]", "marketing incrementality benchmarks [YEAR]", "Kantar [CATEGORY] [COUNTRY]" before interpreting results
7. Upstream context (Claude): Pull media allocation from media-routing-planner, campaign data from weekly-control-tower, margin data from margin-simulation
8. Visual output (Visualizer): Evidence hierarchy dashboard + baseline vs observed chart + experiment design card

Models used (all from Measurement layer):