Attribution Models Overview
AnalyticsAlso: Marketing Attribution Models · Attribution Modelling
Quick definition
Attribution models are the rules that decide how much credit each marketing touchpoint receives when a customer converts. Different models assign credit differently: one might give everything to the last ad clicked, another splits it evenly across every touchpoint the customer saw.
A customer touches all four, then converts.
Credit distributed
Last-click hands all credit to the final touch. The default in most platforms. Cheap to run, blind to everything that warmed the customer up.
How it varies across Australia
Last-click remains the default model for most Australian businesses running paid media. Data-driven attribution is gaining ground among businesses with enough conversion volume to make the model statistically stable. Marketing Mix Modelling adoption sits well below the mid-market and is largely confined to larger advertisers.
See data and tracking scores across Australian industries →The five main attribution models
Gives 100% of credit to the final touchpoint before conversion. Simple, widely used, blind to upper-funnel work.
Gives 100% of credit to the first touchpoint. Good for measuring what introduces customers. Ignores the closing.
Splits credit evenly across every touchpoint. Feels fair but treats a chance impression like a deliberate click.
Weights recent touchpoints more heavily than older ones. Honest about how influence fades over time.
Uses machine learning to assign credit based on which touchpoints actually correlate with conversions. Requires high conversion volume to be stable.
What it actually means
Attribution models are the rules your analytics tools use to answer one question: when a customer converts, which marketing efforts get the credit?
Most customers don't see one ad and buy. They might click a search ad, see a retargeting display ad, open an email, come back via direct, and then convert. Every one of those touchpoints had some role. Attribution models are the system you use to divide up the credit between them.
The problem is that every model is an opinion about customer behaviour, not a measurement of it. Last-click attribution, still the default in most platforms, hands all the credit to whichever channel closed the loop. That makes search and retargeting look like geniuses and makes brand campaigns and social look invisible. Switch to first-click and the story flips. Switch to linear and everyone looks equally valuable.
None of these models is correct. Some are more useful than others for specific decisions. Choosing a model is choosing what story you want your data to tell, which means the choice has real consequences for how you allocate budget, evaluate channels and report to stakeholders.
The discipline most businesses lack isn't picking the right model. It's picking any model and holding it constant long enough to trust the trends it produces. Switching models mid-campaign to chase a better number is how marketing reporting becomes fiction.
The model you pick doesn't reveal the truth. It reveals which channel looks good under that particular set of assumptions.
How it shows up
Attribution models show up wherever channel performance is reported. The conversion numbers in your ad platforms each use their own model by default. Google Ads uses data-driven attribution for most accounts. Meta uses a mixed model with a specific conversion window. Google Analytics 4 (GA4) defaults to data-driven attribution for reporting but lets you compare models in the Attribution Comparison tool.
The gap between your GA4 numbers and your platform numbers is usually the attribution model doing different things with the same events. The conversion window also matters: a 7-day click window and a 28-day click window can produce dramatically different credited conversions from the same campaign.
If three people in your business give three different answers to 'how many conversions did we get last month?' you have an attribution model problem, not a data problem.
The Australian context
Australia's smaller media market means customer journeys are often shorter and less fragmented than US equivalents, which makes simpler attribution models like time-decay more reliable here than they would be in a market with broader media exposure. The Privacy Act amendments and the phase-out of third-party cookies are pushing Australian advertisers toward modelled attribution faster than many realise. Server-side tagging and first-party data strategies are becoming prerequisites for any attribution approach that depends on individual user tracking.
Where people get this wrong
Related terms
Common questions
Which attribution model should my business use?
Match the model to the decision you're making. For direct-response paid media with short sales cycles, time-decay or data-driven works well. For evaluating brand or upper-funnel channels, first-click or linear gives a fairer picture. The worst choice is whichever model you switch to when the numbers disappoint.
Why do my Google Ads and GA4 conversion numbers never match?
They use different attribution models, different conversion windows and different deduplication logic. Google Ads credits itself generously. GA4 applies a cross-channel model. Pick one as your planning source of truth and treat the other as directional.
Is data-driven attribution always better than rule-based models?
Not for smaller accounts. Data-driven attribution needs a meaningful volume of conversions to produce stable credit assignments. Below that threshold, the model is extrapolating from thin data and can be less reliable than a simple rule like time-decay.
How do attribution models relate to incrementality testing?
Attribution models divide credit across touchpoints that already happened. Incrementality testing asks whether the marketing actually caused the conversion, or whether it would have happened anyway. They answer different questions. Attribution tells you who gets the credit. Incrementality tells you if the credit is real.
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About New Rebellion
New Rebellion is a marketing intelligence consultancy. We build tools, score Australian businesses on how their marketing actually performs, and publish Debrief every day. This dictionary is part of how we work in the open.
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