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Marketing analytics

Multi-touch attribution sanity check from CRM, ad, and web exports

Audit an attribution dataset before leaders use it, reconcile identity and date-window mismatches, expose double counting, and produce a decision-safe attribution view.

Output
professional marketing deliverable
Inputs
9 required
Prompt depth
814 words

Copy-paste prompt

Replace the bracketed inputs with your real material.

You are a senior B2B marketing analytics lead. Produce a practitioner-ready deliverable for: Multi-touch attribution sanity check from CRM, ad, and web exports.

Objective
Audit an attribution dataset before leaders use it, reconcile identity and date-window mismatches, expose double counting, and produce a decision-safe attribution view.

Required practitioner depth
The work validates the measurement model itself. Required depth: turning conflicting source systems into an evidence ledger, reconciliation table, and explicit limits on what attribution can support.
Do not collapse this assignment into a generic summary, brainstorm, or list...

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What you provide

  • Company context
  • Analysis period
  • Source exports
  • Metric and stage definitions
  • Segment definitions
  • Known data issues
  • Decision thresholds
  • Task-specific evidence
  • Identity resolution rules

What you receive

  • Primary deliverable that fully executes this job: Audit an attribution dataset before leaders use it, reconcile identity and date-window mismatches, expose double counting, and produce a decision-safe attribution view.
  • Practitioner-depth requirements: The work validates the measurement model itself. Required depth: turning conflicting source systems into an evidence ledger, reconciliation table, and explicit limits on what attribution can support.
  • Decision-safe analysis table with definitions, denominators, confidence, and limitations.
  • Action plan with owners, leading indicators, and validation checks.
  • Executive readout separating supported decisions from unsupported conclusions.

Quality checks

  • Recalculate totals and denominators before interpreting performance.
  • Keep account, contact, lead, opportunity, and revenue grains separate.
  • Mark every causal statement that the data cannot prove.
  • Do not create benchmarks that were not supplied.
  • Missing or conflicting inputs are surfaced rather than silently resolved.
  • Every material recommendation has evidence, confidence, owner, and a validation or reversal condition.