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Attribution models

Modeled vs observed conversions

Observed conversions are directly recorded from events that the system actually saw. Modeled conversions are statistical estimates that fill gaps left by consent declines, cross-device journeys, or blocked tags. Modern reports blend both, so understanding which conversions are measured versus estimated is essential to reading a total honestly and not treating an estimate as a count.

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What this means

Observed conversions come from events the platform genuinely recorded and could tie to an interaction. But consent declines, tracking prevention, and cross-device hops leave gaps where conversions happened but could not be directly attributed. Modeled conversions estimate that missing volume using patterns from the observed data.

Reports increasingly present a blended total. The number is partly fact (observed) and partly estimate (modeled), and the two have very different epistemic status even though they appear in one figure.

Why the distinction matters

Treating a modeled total as if it were exact leads to false precision — comparing two periods whose modeled shares differ, or trusting a small segment that is mostly estimated. The modeled portion has uncertainty and depends on having enough observed data to train on; thin segments model poorly.

Google documents conversion modeling and when modeled conversions are included. Good practice is to know your modeled share, lean on observed conversions for high-stakes decisions, and read modeled totals as directional. Keep an observed first-party baseline so you can see how much of any movement is real versus re-estimated.

How it appears in analytics and logs

A reported total that includes modeling is a best estimate for the unobserved portion, not a literal count; the modeled share carries uncertainty.

Diagnostic use case

Distinguish modeled from observed conversions in a report so you know how much of a total is directly measured versus statistically estimated.

What WebmasterID can help detect

WebmasterID emphasizes directly-observed first-party events, giving a clear observed baseline to compare against modeled platform totals.

Common mistakes

Privacy and accuracy notes

Conversion modeling estimates aggregate outcomes precisely to avoid tracking individuals through gaps. This page is educational, not legal advice.

Related pages

Sources and verification notes

Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.