Analytics Reference
Attribution Models Reference
Every attribution model is wrong, and choosing one is really choosing which mistake you can live with. I use this table to set expectations before a reporting change, because the model swap will move every channel's numbers and someone will assume performance changed.
| Model | Credit assignment | Biases toward | Good for |
|---|---|---|---|
| Last click | All credit to the final touch | Brand search and retargeting | Simple, short sales cycles |
| First click | All credit to the first touch | Top of funnel channels | Understanding discovery |
| Linear | Equal credit to every touch | Channels with high touch volume | Long considered purchases |
| Time decay | More credit to recent touches | Closing channels | Long cycles with a clear close |
| Position based | 40 percent first, 40 percent last, 20 percent middle | Discovery and closing together | Balanced default |
| Data driven | Modelled from observed conversion paths | Whatever the data supports | Accounts with enough volume |
| Method | What it answers | Requirement |
|---|---|---|
| Incrementality test | Would this conversion have happened anyway | Holdout group and patience |
| Media mix modelling | How channels contribute at an aggregate level | Years of spend and outcome data |
| Self reported attribution | How did you hear about us, in the buyer's words | One form field |
| Modelled conversions | Estimates for unobservable users | Platform provided, consent dependent |
Notes
Adding a self reported attribution field to your lead form is the highest value measurement change available to most B2B teams, and it takes an afternoon. It captures podcast mentions, word of mouth and community referrals that no tracking system can see, and it consistently contradicts the platform reported picture in useful ways.
Changing your attribution model is not a performance change, but it will look like one in every dashboard. Announce it, restate a prior period under both models, and give stakeholders the comparison before they find the discrepancy themselves.
Data driven attribution needs enough conversion volume to model paths meaningfully. Below that threshold it behaves erratically and a position based model is more stable and easier to explain, which matters more than theoretical accuracy when the output is a budget decision.
Attribution capabilities, default models and consent requirements change regularly across Google, Meta and analytics platforms, including the retirement of several legacy models. Verify against the current platform documentation before rebuilding reporting.
Frequently asked questions
Which attribution model should I use?
Data driven if your account has the volume for it, position based if not. More important than the choice is applying one model consistently and never comparing numbers across models.
Why do my platform numbers add up to more than my actual sales?
Because each platform claims credit for conversions it touched, and touches overlap. Reconcile in one place, usually your CRM, and treat platform reported conversions as optimisation signals rather than accounting.
Is incrementality testing worth the effort?
For any channel taking a large share of budget, yes. A geographic or audience holdout answers the only question that matters, which is what would have happened without the spend.
Official sources
Platforms change specifications without notice. Check the primary documentation before a launch that depends on an exact value.
Read next
You might also like
Ranked by how closely each page overlaps with this one, using a similarity model over the whole library.
More Analytics Reference
UTM Parameter Reference
The five UTM parameters, what each one is for, naming conventions that survive scale, and common tagging mistakes.
GA4 Metrics Glossary
Plain language definitions of GA4 metrics and dimensions, including engaged sessions, engagement rate and key events.
Conversion Tracking Reference
Conversion tracking methods across GA4, Google Ads and Meta, including client side, server side and enhanced conversions, and when to use each.