AnalyticsMeta Ads analysisIntermediateAnalyses your data

Meta Ads breakdown analysis (placement, age, device) with aggregation cautions

Interprets breakdown reports without falling for mix-shift traps: checks volumes, compares like with like and flags where aggregates can mislead.

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 paid-social analyst. Analyse the breakdown data below for last 30 days. Breakdown dimension(s): placement. Objective: purchases. Attribution window: 7-day click. Data: Placement | Spend | Impressions | Purchases Facebook Feed | 5200 | 380000 | 128 Instagram Stories | 2100 | 210000 | 26 Instagram Reels | 1800 | 260000 | 31 Audience Network | 400 | 90000 | 1 Tasks: 1. State assumptions, including whether breakdown rows add up to the campaign total (check this and report any gap). 2. Compute spend share, result share and cost per result by segment from the raw columns. 3. Identify segments with higher or lower efficiency, but first flag segments with too little volume to judge. 4. Check for mix effects: explain whether a segment's overall result could be driven by shifts in how spend was distributed rather than by the segment itself, and which extra cut of the data would test that (a Simpson's-paradox-style check). 5. Warn that breakdowns often reflect where the delivery system chose to spend, not randomised comparisons, so differences are not causal evidence about audiences. 6. Recommend careful actions (for example exclusions only with enough data) and what to test. Rules: do not invent numbers or segments. Missing columns must be flagged. Verify sums and recompute shares. Never present a breakdown difference as proof that a segment "performs better" without caveats.

Customize

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

Period.

Dimension(s) in the export.

Campaign objective.

As exported.

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

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.

A sum check against the total, spend and result shares with computed cost per result, low-volume flags, a mix-effect check, a warning that delivery is not randomised, and cautious next steps. (Illustrative.)

Expected format: Assumptions, segment table, flags, mix-effect check, cautions, next steps.

How to use it

  1. Check that the breakdown sums to the total.
  2. Request an additional cut to test mix effects.
  3. Exclude segments only with sufficient evidence.

Limitations

  • Breakdown performance reflects delivery decisions and attribution settings.
  • Small segments produce noisy cost-per-result numbers.

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.

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