attribution-ads-helper

# Attribution Helper

## Purpose
Core mission:
- Diagnose attribution discrepancies across channels.
- Compare attribution window assumptions and their budget impact.
- Build practical attribution decision framework for optimization.
- Produce actionable attribution-aligned allocation guidance.

## When To Trigger
Use this skill when the user asks for:
- attribution model comparison
- conflicting ROAS/CAC by channel
- budget decisions under attribution uncertainty
- tracking and model interpretation support

High-signal keywords:
- attribution, tracking, model, predict
- roas, cpa, revenue, allocation, budget
- meta, googleads, tiktokads, youtubeads, dsp

## Input Contract
Required:
- channel_metrics_by_window
- attribution_windows
- conversion_event_definitions
- decision_context

Optional:
- offline_conversion_data
- holdout_or_incrementality_data
- MMM_or_ltv_inputs
- confidence_threshold

## Output Contract
1. Attribution Mismatch Map
2. Window Sensitivity Analysis
3. Decision-safe KPI View
4. Budget Reallocation Recommendation
5. Validation Experiment Plan

## Workflow
1. Normalize event and conversion definitions.
2. Compare performance under each attribution window.
3. Quantify decision deltas from model differences.
4. Propose allocation with confidence labeling.
5. Output validation experiments for unresolved gaps.

## Decision Rules
- If attribution views diverge materially, use blended guardrail plan.
- If one channel is highly view-through sensitive, reduce reliance on last-touch only.
- If incremental evidence exists, prioritize it over proxy metrics.
- If uncertainty remains high, allocate budget in capped test tranches.

## Platform Notes
Primary scope:
- Meta (Facebook/Instagram), Google Ads, TikTok Ads, YouTube Ads, Amazon Ads, Shopify Ads, DSP/programmatic

Platform behavior guidance:
- Keep window comparisons explicit per channel.
- Separate platform-reported and unified-attribution decisions.

## Constraints And Guardrails
- Never mix inconsisten...