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Spillover Estimator

Estimate whether one commerce channel is creating measurable spillover into another channel using simple exports, campaign timing, and directional evidence....

personAuthor: leooooooowhubclawhub
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Spillover Estimator

Estimate cross-channel spillover without pretending to prove perfect attribution.

Skill Card

  • Category: Measurement
  • Core problem: Did growth in one channel also lift another channel?
  • Best for: Operators comparing TikTok, Amazon, DTC, creator, paid, and marketplace channel effects
  • Expected input: Source channel data + downstream channel data + timing context
  • Expected output: Directional spillover estimate + confidence note + action recommendation
  • Creatop handoff: Feed findings into budget allocation and channel planning

Before you run

Ask the user to clarify:

  • source channel to evaluate
  • downstream channel(s) to check for spillover
  • date range
  • major campaign or promo dates
  • whether they have exports, screenshots, or CSV data

If structured data is missing, say the result will be directional, not causal proof.

Optional tools / APIs

Useful but not required:

  • Shopify / WooCommerce export
  • Amazon sales export
  • TikTok Shop export
  • ad platform export
  • Google Sheets / CSV

If the user does not have APIs connected, ask for manual exports first instead of blocking the workflow.

Workflow

  1. Confirm channel scope and time window.
  2. Collect source-channel change signals.
  3. Collect downstream-channel change signals.
  4. Align timing around campaigns, creator drops, content bursts, or promo windows.
  5. Judge whether the downstream lift looks:
    • likely related
    • weak / mixed
    • insufficient evidence
  6. Explain the estimate with honest caveats.

Output format

Return in this order:

  1. Executive summary
  2. Spillover estimate
  3. Evidence blocks
  4. Confidence and caveats
  5. Recommended next step

Fallback mode

If the user only has weekly snapshots, rough screenshots, or partial exports:

  • use simple directional comparison
  • do not claim causal attribution
  • clearly label missing data and confidence limits

Quality rules

  • Never overclaim causality from timing alone.
  • Prefer directional clarity over fake precision.
  • Separate channel correlation from verified lift.
  • Make the user’s next measurement step obvious.

License

Copyright (c) 2026 Razestar.

This skill is provided under CC BY-NC-SA 4.0 for non-commercial use. You may reuse and adapt it with attribution to Razestar, and share derivatives under the same license.

Commercial use requires a separate paid commercial license from Razestar. No trademark rights are granted.