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Attribution models

Default channel grouping: the buckets before the model

Channel grouping is the rule set that sorts raw source/medium values into named channels — Organic Search, Paid Social, Email, Direct. Every attribution model operates on these buckets, so a mis-grouped touch is mis-attributed no matter how good the model. Grouping is upstream of, and quietly governs, attribution.

Verified against primary sources

What this means

Raw data arrives as source/medium pairs like 'google / organic' or 'newsletter / email'. Channel grouping is the rule set that maps these into human-readable channels. GA4 ships a default grouping, and tools let you define custom groupings for your own conventions.

Why grouping precedes attribution

Attribution credits channels, so the channel a touch lands in is decided before any model runs. An untagged paid campaign that falls into Organic Search, or a referral that should be Email, mis-credits every model identically. Custom groupings that drift from your tagging conventions create silent, persistent errors.

Get tagging and grouping right first; only then does the choice between attribution models become meaningful.

How it appears in analytics and logs

If a channel's volume looks wrong, suspect grouping rules — untagged campaigns, ambiguous mediums, or custom channels — before suspecting the attribution model.

Diagnostic use case

Audit channel grouping before debating attribution models: a touch sorted into the wrong channel poisons every downstream credit split.

What WebmasterID can help detect

WebmasterID makes channel classification visible so mis-grouped traffic is caught at the source, before it distorts any attribution view.

Common mistakes

Privacy and accuracy notes

Grouping classifies source/medium strings, not people, and needs no personal identifiers. WebmasterID keeps channel classification first-party and transparent.

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.