How does attribution work in Google Analytics, and what are the different models?

Attribution decides which marketing touchpoint gets credit for a conversion. It matters because users rarely convert on first contact — they might arrive via organic search, return through a social post, then convert on a branded search.

The models:

• Last click — all credit to the final touchpoint. Simple, and systematically over-credits branded search and remarketing, which sit at the end of every journey.
• First click — all credit to the first. Over-credits discovery channels and ignores what closed the sale.
• Linear — credit split evenly across all touchpoints.
• Time decay — more credit to touchpoints closer to conversion.
• Position-based — typically 40% first, 40% last, 20% distributed among the middle.
• Data-driven — GA4's default. It uses machine learning on your own conversion paths, comparing those that converted with those that did not, to assign fractional credit. It is the most defensible model because it is derived from your data rather than an assumption.

Note: Two important caveats. First, GA4 removed the rule-based models from reporting in favour of data-driven and last click, so options are narrower than they were. Second — and this is what interviewers want — no attribution model is truth. They are different lenses on incomplete data, and cross-device, cookie restrictions, and offline touchpoints mean parts of the journey are invisible. The practical use of attribution is comparing channels consistently over time, not calculating exact contribution.