What this Skill helps with

Attribution Modeling Advisor compares attribution approaches against a specific business question, data setup, sales cycle, and decision cadence. It explains what each model can and cannot support rather than naming one model as universally correct.

Who this Skill is for

It is intended for marketing analysts, performance teams, finance partners, agencies, and founders deciding how channel credit should inform reporting or budget discussions.

What you provide

  • The business decision the model should support
  • Customer journey, conversion events, sales cycle, and channel mix
  • Available user-level, event-level, CRM, and spend data
  • Identity, consent, privacy, and tracking constraints
  • Current platform, analytics, and reporting models
  • Known offline conversions or data gaps

What it produces

  • A comparison of suitable attribution models
  • Assumptions and data requirements for each option
  • Expected biases and blind spots
  • A recommended primary view plus comparison views
  • A measurement and validation plan
  • Questions that must be resolved before implementation

How the Skill works

The workflow starts with the decision, then checks data availability and journey complexity. It compares rule-based, platform, data-driven, incrementality, and marketing-mix approaches where relevant, keeping model output separate from causal evidence.

Installation and first use

  1. Download and add the Skill to Claude.
  2. Describe the decision, channels, journey, and current reporting.
  3. Provide event definitions and available data sources.
  4. Ask for missing-data and bias checks before a recommendation.
  5. Review the proposal with analytics, finance, and privacy owners.

Example workflow and expected output

Describe a B2B SaaS journey with paid search, paid social, webinars, sales calls, and a 90-day cycle. Ask for a model comparison table, recommended reporting view, validation tests, and warnings about identity gaps.

Limits and review guidance

Attribution assigns credit under assumptions. It does not prove that a channel caused an outcome. Privacy restrictions, missing touchpoints, platform self-reporting, and small samples may limit the available choices.

Frequently asked questions

Which attribution model is best?

The answer depends on the decision, data, journey, and tolerance for bias. The Skill compares options for that context.

Is data-driven attribution causal?

Not by itself. Causal questions often require experiments or other incremental measurement.

Can it implement tracking?

No. It produces requirements and a measurement plan for technical review.