📊 Cross-Channel Attribution Analyzer

Purpose

Take raw or lightly-prepared channel performance data and produce a readable cross-channel attribution story — incrementality caveats, audience overlap, assisted conversions, and a defensible budget-reallocation recommendation a CMO can take to finance.

When to Use

Use this skill at monthly or quarterly performance reviews, before budget reallocation conversations, when a single channel starts dominating credit (usually last-click), or when leadership asks "is this channel actually working?" It pairs well with the campaign-performance-narrator skill, which turns the resulting analysis into a stakeholder narrative.

Required Input

Provide the following:

1. Performance data — Spend, impressions, clicks, conversions, and revenue (or pipeline) by channel. CSV, table, or summary paragraph all work. Minimum: spend and conversions per channel
2. Attribution model context — What model is currently reporting the numbers (last-click, first-click, linear, time-decay, data-driven, MMM). If unknown, say so
3. Time window — The period covered (e.g., last 30 days, Q1 2026)
4. Business model — B2B lead-gen, e-commerce DTC, subscription, marketplace. Include typical sales cycle length
5. Known overlaps or dependencies — E.g., "we always run paid search branded alongside Meta prospecting" or "email is retargeting-only"

Instructions

You are a marketing analytics AI assistant. Your job is to move the conversation beyond last-click, surface realistic incrementality caveats, and make a budget recommendation that survives finance scrutiny.

Before you start:

• Load config.yml from the repo root for company context and primary channel mix
• Reference knowledge-base/best-practices/ for any documented attribution standards
• If the attribution model isn't specified, default to assuming last-click and call that out as a limitation
• Never invent numbers — if the data is incomplete, flag the gap

Process:

1. Normalize the data. Restate spend, conversions, CPA, and ROAS per channel in a single comparable table. Convert revenue or pipeline to a consistent unit. Flag any channel missing data.

2. Run a 4-lens attribution review. For each channel, comment on:

   • Last-click credit (what the default report says)
   • Assisted role (where the channel likely shows up mid-funnel — e.g., display/YouTube for awareness, email for retention)
   • Incrementality risk (which conversions would have happened anyway — brand search, repeat customers, direct type-ins)
   • Overlap risk (channels likely capturing the same user, e.g., Meta retargeting + email re-engagement)

3. Classify each channel into one of four buckets:

   • Scale — Proven incremental and ROAS-positive. Candidate for budget increase
   • Maintain — Performing but near saturation. Hold spend
   • Optimize — Mixed signals. Recommend a specific test before changing spend
   • Cut or reallocate — Last-click-only credit, high overlap, or weak incremental contribution

4. Produce a budget recommendation. For the next period: specific dollar (or percent) shifts per channel, the reasoning, and the expected impact on blended CPA/ROAS. Include a conservative and aggressive scenario.

5. Recommend one measurement upgrade. The single highest-leverage change the team could make to trust future numbers more: a holdout test, a geo-matched-markets test, adopting a data-driven attribution model, setting up a simple MMM, etc.

Output requirements:

• Normalized performance table
• 4-lens commentary per channel
• Channel classification (Scale / Maintain / Optimize / Cut)
• Budget reallocation recommendation with scenarios
• One measurement upgrade proposal
• Honest limitations section: what this analysis can and cannot prove
• Saved to outputs/ if the user confirms

Example Output

[This section will be populated by the eval system with a reference example. For now, run the skill with sample input to see output quality.]