Evaluate Attribution Models

Command: evaluate-attribution-models

Inputs

• campaigns – list of campaigns/programs to analyze.
• models – attribution models to compare (first, last, linear, position, time-decay, data-driven).
• audience – finance | marketing-lead | ops | exec.
• metrics – choose KPIs (pipeline, revenue, CAC, payback, LTV, ROAS).
• confidence – optional minimum data confidence threshold to highlight gaps.

Workflow

1. Data Preparation – pull campaign performance, cost, and pipeline/revenue outcomes.
2. Model Execution – run requested models, normalize windows, and apply weighting rules.
3. Sensitivity Analysis – compare outcomes vs benchmarks, highlight variance drivers.
4. Narrative Assembly – contextualize trade-offs, governance considerations, and risks.
5. Recommendation Engine – propose primary model, fallback, and rollout/QA checklist.

Outputs

• Attribution comparison deck/table with KPI deltas per model.
• Recommendation memo with decision, rationale, and risk mitigations.
• Rollout plan including QA steps, owner assignments, and monitoring hooks.

Agent/Skill Invocations

• attribution-architect – leads methodology comparison.
• marketing-intelligence-lead – ensures narrative + stakeholder alignment.
• attribution-playbook skill – documents rules + templates.
• exec-dashboard-blueprint skill – packages executive summary.