Conversion credit distribution
Conversion credit distribution describes how an attribution model allocates the credit for a conversion across the interactions that preceded it. Every model — single-touch or multi-touch, rules-based or algorithmic — is fundamentally a different distribution rule. Understanding distribution as the shared concept clarifies why models disagree even on identical paths.
What this means
A conversion path is a fixed sequence of interactions. What an attribution model adds is a rule for splitting one unit of credit (or one unit of value) across that sequence. Last-click puts all of it on the final touch; linear spreads it evenly; position-based front- and back-loads it; data-driven learns the split from data.
Seeing every model as a distribution rule makes their disagreements legible: they are not measuring different events, they are dividing the same event's credit differently.
Why this framing helps
Treating distribution as the unifying concept lets you reason about model choice deliberately. If you believe early discovery matters, choose a rule that distributes more credit upstream; if you only care about the closing touch, a last-touch distribution suffices. The choice encodes an assumption about how value is created along the path.
It also explains reconciliation: any valid distribution sums to the same total conversions, so totals match across models while per-channel splits diverge. Google's attribution documentation enumerates the standard distribution rules. The discipline is to pick a distribution that matches your causal beliefs and to test those beliefs with incrementality.
- Every model is a credit-distribution rule over a fixed path
- Totals reconcile across rules; per-channel splits differ
- Choosing a rule encodes a causal assumption to be tested
How it appears in analytics and logs
Different per-channel numbers for the same conversions reflect different distribution rules, not different underlying events — the path is fixed, the allocation changes.
Diagnostic use case
Frame any attribution model as a credit-distribution rule to compare models on the same path and predict how each will shift channel credit.
What WebmasterID can help detect
WebmasterID records the ordered touches in a first-party path, the input any credit-distribution rule applies to.
Common mistakes
- Thinking different models see different conversions, not different splits.
- Choosing a distribution rule without an explicit causal rationale.
- Never validating the assumed distribution with experiments.
Privacy and accuracy notes
Credit distribution operates on recorded interaction paths, not personal identity. This page is educational, not legal advice.
Related pages
- Fractional attribution
Fractional attribution assigns each touchpoint a fraction of a conversion rather than the whole credit, so a multi-touch path distributes one conversion across several channels. It is the mechanism behind linear, time-decay, position-based, and data-driven models, and it explains why per-channel conversion counts can be decimals that still sum to the real total.
- Custom attribution models: power and rope
A custom attribution model lets you define your own credit rules — adjusting weights, lookback, and channel treatment beyond the presets. The flexibility can fit a real, unusual journey, but it just as easily encodes the answer you wanted. A custom model is only as honest as the assumptions you can defend.
- Value-based attribution
Value-based attribution assigns the monetary value of a conversion — not just a count of one — across the touchpoints in the path. It matters because optimizing for conversion counts treats a low-value and a high-value sale identically; distributing value lets bidding and analysis favor the channels that bring more revenue, provided conversion values are passed accurately.
- Attribution analytics
See how credit distributes across touches.
Sources and verification notes
- Google Analytics Help — Attribution and attribution modelingEnumerates rules that distribute conversion credit across touches.
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.