AnalyticsAttribution, incrementality and testsIntermediateAnalyses your data

Meta attribution-window comparison (1-day vs 7-day click and view)

Interprets results exported with several attribution windows so you understand how much of reported performance depends on window and view-through credit.

For: Performance marketers, Marketing analysts, Growth marketers · Works with: Meta Ads, Any capable chat model (ChatGPT, Claude, Gemini, others)

Public beta. This prompt was drafted with AI assistance and checked by automated rules, but it has not been reviewed or tested by a person yet. Treat it as a starting point and check the output. It is hidden from search engines while in beta. Use the “Was this prompt useful?” box to tell us what works.

Your prompt

Act as a measurement analyst. I exported the same campaigns for September 2026 under different attribution settings. Data (campaign | spend | results under each window I list): Campaign | Spend | Results 1d click | Results 7d click | Results 7d click + 1d view Prospecting | 6000 | 80 | 150 | 260 Retargeting | 2500 | 140 | 190 | 230 Windows included: 1-day click; 7-day click; 7-day click plus 1-day view. Business context: considered purchase with a typical decision time of a few days. My backend (order system) result count for the period is 310 (all channels, deduplicated orders). Tasks: 1. State assumptions, including that windows are nested and that later windows include earlier ones (verify this against the data and tell me if the data contradicts it). 2. Compute, per campaign, the share of results coming from each window increment (for example 1-day click, extra from 7-day click, extra from view) and the cost per result under each window. 3. Explain what a large view-through share can and cannot tell me; say that it is not proof of incrementality. 4. Compare to my backend count only if comparable, and explain why they may not reconcile (dedup, other channels, delayed conversions, time zones). 5. Recommend which window to use for which decision (for example optimisation reporting vs efficiency vs budget decisions), as a reasoned suggestion, and what experiment would settle incrementality. 6. List missing data. Rules: do not invent numbers or benchmarks. Missing or inconsistent data must be flagged. Verify arithmetic. Do not claim causation from attribution windows.

Customize

The fields start with example values so you can see how the prompt works. Everything stays in your browser.

Period.

Paste your table. The default is small fictional sample data so the preview works; replace it with your own export.

The windows in the export.

Why windows matter.

From your own order system.

What the backend number includes.

Example of what to expect

Illustrative only. It describes the kind of result this prompt aims for; real output varies by tool, model and run.

Result shares by window increment, cost per result under each window, a view-through caveat, a backend comparison with reasons it may not reconcile, a window recommendation per decision type and an incrementality experiment idea. (Illustrative.)

Expected format: Assumptions, increment table, interpretation, recommendations.

How to use it

  1. Export consistent windows.
  2. Compare to backend data carefully.
  3. Use holdouts to test incrementality.

Limitations

  • Attribution windows do not measure causal impact.
  • Window definitions and availability change with platform updates.

Platform notes

Meta Ads

Official docs read · checked 2026-10-11

Meta results depend on the attribution window and breakdowns in your export; state them. We read only the Insights API overview, not the detail pages on attribution or data delays.

General notes on Meta Ads
  • Insights can be requested with specific attribution windows and breakdowns. Always tell the model which window and breakdowns your export used, because results change with them.

Any capable chat model (ChatGPT, Claude, Gemini, others)

Official docs read · checked 2026-10-11

Works in any capable chat model. Paste a small anonymised export and recompute the key numbers yourself: chat models can misread columns or miscalculate.

General notes on Any capable chat model (ChatGPT, Claude, Gemini, others)
  • These prompts are plain text and work in any chat assistant. For analytics prompts, paste a small, anonymised export; a chat model can misread columns or miscalculate, so recompute key numbers yourself.

Source, license and attribution

Origin
Original by MarketerTools
Publisher
MarketerTools
License
Original work by MarketerTools, free to copy and use. Informed by the linked documentation; no third-party text is reproduced.
Platform assumptions checked
2026-10-11

Documentation and references behind this prompt

These informed the structure and the platform notes. A reference is not a license, and no third-party prompt text is copied here.

Spotted an attribution error, or are you a source owner with a request? Use the chat button at the bottom right and quote prompt ID ana-018. We will correct or remove it.

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