Attribution models compared: rules, data-driven, and MMM
GA4 offers three models: data-driven, paid and organic last click, and Google paid channels last click. First click, linear, time decay and position-based are gone. A marketing mix model reads weekly totals instead of paths.
By Joris van Huët, Founder & CEOPublished 5 min read
GA4 now offers three attribution models, and the rule-based ones it used to offer are gone. Google removed first click, linear, time decay and position-based from Google Ads and GA4 in 2023, after reporting that less than 3% of Google Ads web conversions were attributed with them (Google's own data, not independently audited). What remains is data-driven credit, whose help pages print no accuracy measurement, plus two last-click variants. A marketing mix model is a different kind of tool: it reads weekly totals, not paths.
Which attribution models does GA4 offer?
GA4's help page says "there are 3 attribution models available in the Attribution reports": data-driven, paid and organic last click, and Google paid channels last click. Paid and organic last click gives 100% of a key event's value to the last channel clicked (or engaged with, for YouTube). Google paid channels last click does the same for Google Ads channels only, and falls back to paid and organic last click when the path has no Google Ads click. All three exclude direct visits from credit unless the path consists entirely of direct visits.
The model is only part of the setting. The key event lookback window sets how far back a touchpoint can earn credit. For most key events the default is 90 days, with 30 and 60 as alternatives. For acquisition key events, such as a first visit, it is 30 days, switchable to 7.
What happened to first click, linear, time decay and position-based?
Google's page says they "are no longer available as of November 2023". Its April 2023 announcement gave the reason: "less than 3% of Google Ads web conversions are attributed using first click, linear, time decay, or position-based models" (Google data, global, February to March 2023, vendor-published). It added that rules-based models "don't provide the flexibility needed to adapt to evolving consumer journeys", and said conversion actions still using them would switch to data-driven attribution starting in September 2023.
How does data-driven attribution work, according to Google?
The help page says the model evaluates "both converting and non-converting paths" and, "using a counterfactual approach", contrasts what happened with what could have occurred. It describes comparing users who saw an ad with "similar users in a holdback group", computed from "randomized controlled trials" of Google ad exposures. Its worked example: four ad exposures lead to a 3% probability of a key event; without the fourth, the probability drops to 2%, so the fourth "drives +50% key event probability". Conversions can be reattributed for up to 7 days after the conversion.
Two limits sit in the documentation. In Google Ads, the model looks at interactions on Google's own ad surfaces: Search including Shopping, YouTube, Display and Demand Gen. And volume matters: Google recommends "at least 200 conversions and 2,000 ad interactions in supported networks within a 30-day period", and says that depending on data availability, last click and data-driven "can have the same results in certain situations". The model is trained and reported by Google, so treat its ranking as a claim to test.
Where does a marketing mix model differ?
It takes different input and answers different questions. Meridian, Google's open-source MMM, "generally expects your marketing data to be aggregated" by time, for example by week, "and ideally, by geo". It is built to answer three questions: the historical ROI and contribution of each channel, the response curve of each channel, and how to allocate a future budget. It is "focused only at channel-level", and for more granular insight Google's own guidance is "data-driven multi-touch attribution for your digital channels". Meta's Robyn says an MMM "will need a minimum of two years of historical weekly data". Meridian calls MMM "an example of causal inference from observational data", so it still rests on assumptions.
How do you check which model to trust?
Compare two models on your own purchases in GA4:
- Open Advertising, then Attribution, then Attribution models, and select your purchase key event.
- Choose paid and organic last click in one column and data-driven in the other. The report's "% Change" columns show how credit moves between them.
- Rank your channels by key event credit under each model.
Pass: the ranking barely moves, so the model choice is not driving your budget call. Fail: a channel jumps or drops several places, so the model is deciding for you, and that channel is the one to test with a holdout before you act. If you compare with Google Ads numbers, use paid and organic last click: Google says GA4 uses last click for Google Ads conversions built on key events.
Sources, 30 September 2026: Get started with attribution (Google Analytics Help, 2026); Select attribution settings (Google Analytics Help, 2026); Key event attribution models report (Google Analytics Help, 2026); First click, linear, time decay, and position-based attribution models are going away (Google Ads announcement, April 2023); About data-driven attribution (Google Ads Help, 2026); An introduction to Meridian (Meridian documentation, Google, 2026); Amount of data needed (Meridian documentation, Google, 2026); About MMM as a causal inference methodology (Meridian documentation, Google, 2026); An Analyst's Guide to MMM (Robyn documentation, Meta, 2026).
Related answers
Frequently asked questions
Which attribution models does GA4 have?
Three: data-driven, paid and organic last click, and Google paid channels last click. Google says first click, linear, time decay and position-based were no longer available as of November 2023. All three exclude direct visits from credit unless the whole path is direct.How does Google's data-driven attribution work?
Google says it evaluates both converting and non-converting paths and, using a counterfactual approach, contrasts what happened with what could have occurred. It describes comparing exposed users with a holdback group. Google's help pages print no accuracy measurement, so check its channel ranking against a holdout.How is a marketing mix model different from multi-touch attribution?
A mix model reads aggregate weekly spend and sales by channel, not individual paths. Meta's Robyn documentation says it needs a minimum of two years of historical weekly data. Multi-touch credit assigns value along paths of converted buyers; a mix model estimates how sales respond to spend.
Go deeper: Incrementality testing, explained.
Sixty-second versions of these ideas: Causality Engine on YouTube Shorts.
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Terms in this article
- Attribution ModelAn Attribution Model defines how credit for conversions is assigned to marketing touchpoints. It dictates how marketing channels receive credit for sales.
- Attribution ReportAttribution Report shows which touchpoints or channels receive credit for a conversion. It identifies which campaigns drive desired actions.
- Causal InferenceCausal Inference determines the independent, actual effect of a phenomenon within a system, identifying true cause-and-effect relationships.
- CounterfactualCounterfactual is a hypothetical outcome that would have occurred if a subject had received a different treatment.
- Google AnalyticsGoogle Analytics is a web analytics service that tracks and reports website traffic.
- Marketing MixThe marketing mix is the set of actions a company uses to promote its brand or product. It traditionally includes product, price, place, and promotion.
- Marketing Mix ModelingMarketing Mix Modeling (MMM) is a statistical analysis that estimates the impact of marketing and advertising campaigns on sales. It quantifies each channel's contribution to sales.
- Multi-Touch AttributionMulti-Touch Attribution assigns credit to multiple marketing touchpoints across the customer journey. It provides a comprehensive view of channel impact on conversions.