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Public Ad Libraries

Practical agency guides to Meta and Google public ad sources, reproducible search methods, evidence quality, and important limitations.

Direct answer

What this topic helps an agency decide

Public ad libraries are transparency and discovery sources that show selected information about ads a platform makes publicly available. They are useful for verifying advertiser identity, visible creative, copy, formats, and public activity context. Coverage and fields differ by platform. These libraries do not provide a competitor’s complete targeting, spend, conversion rate, attribution, incrementality, or profit.

Original decision aid

The public-source reliability check

Run this check before using a public ad record in a client recommendation or research report.

Provenance
Identify the official library, advertiser entity, country, filters, and access date.
A traceable source record
Coverage
Note which platforms, formats, dates, and fields the source actually exposes.
A disclosed collection boundary
Observation
Preserve the visible copy, creative, activity context, and source link.
Evidence another reviewer can inspect
Limitation
List the private performance fields the source cannot provide.
A claim boundary for the final brief
Curated reading path

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Common questions

Questions agencies ask about public ad libraries

Which public ad libraries does Advertisng currently support?

Advertisng currently supports research across public Meta and Google ad sources. It does not currently claim TikTok, LinkedIn, Pinterest, Snapchat, or private competitor performance-data coverage.

Can public ad libraries show a competitor’s ad spend or conversions?

Not for ordinary commercial competitor ads in a way that proves campaign performance. Available transparency fields vary, but a visible ad record does not establish spend, conversions, profit, targeting, attribution, or causal lift.

How should agencies compare records from Meta and Google?

Record the source and date, compare only fields that both sources actually expose, disclose coverage differences, and avoid combining unlike activity fields into one unsupported performance score.

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