Media Routing Planner

Engineer optimal routes for budget to move from investment to business impact — using the logic of logistics routing where budget is cargo, channels are routes, platforms are carriers, audiences are destinations, and KPIs are delivery outcomes.

How This Skill Orchestrates

This is the media intelligence layer of the OS. It doesn't create generic media plans — it solves a constrained optimization problem:

1. Script execution (scripts/media_optimizer.py): Constrained optimization with Hill saturation curves per channel. Generates 4 scenario variants (Conservative, Balanced, Growth, Efficiency), detects 8 waste patterns, scores routes on speed/scale/incrementality/measurement
2. Reference lookup (references/channel_benchmarks.md): Channel-level benchmarks (14 channels, 8 scoring dimensions), funnel allocation frameworks, waste detection patterns, optimization methodology
3. Upstream context (Claude): Pull MMM parameters from mmm-modeling, scope validation from scope-audit, margin data from margin-simulation, capacity from fte-capacity-sizing
4. Web search (Claude): Search for current CPM/CPC benchmarks by platform and industry when user specifies market context
5. Visual output (Visualizer): Route allocation dashboard + funnel waterfall + scenario comparison + interactive budget slider

Model decision: Constrained nonlinear optimization (scipy SLSQP / greedy fallback) — this IS a mathematical optimization problem. We have an objective function (maximize total response), constraints (budget, floors, ceilings), and a parametric response model (Hill curves per channel). Not ML — no training data needed. Not heuristics — we solve the actual optimization. Read references/channel_benchmarks.md → "Optimization Methodology" for the full rationale.

Mandatory Benchmark Search Protocol

BEFORE running the optimizer or producing any output, Claude MUST search for current public benchmarks. The script's built-in benchmarks are cross-industry defaults from 2025 — they are fallbacks, not primary data.