Last Updated: October 13, 2025
So you've realized that last-click attribution is lying to you. Excellent. That's the first step.
Now comes the tricky bit: choosing an attribution model that actually reflects how your customers buy.
Because here's the thing—there's no perfect attribution model. They're all wrong. Some are just less wrong than others. The question is: which one is least wrong for your business?
Let's break down the six main attribution models, when to use each one, and how to choose without overthinking it.
Your attribution model determines which marketing channels get credit for sales. Get it wrong, and you'll:
Get it right, and you'll know exactly where to invest for profitable growth.
Rather important, that.
What it does: Gives 100% credit to the first touchpoint that introduced the customer to your brand.
Example: Customer sees your TikTok ad, clicks through, then returns via Google Search three days later and buys. TikTok gets 100% credit.
Best for:
The catch: Completely ignores everything that happened after the first interaction. It's like judging a book by its cover—you get some information, but you're missing the plot.
When to use it: When you're testing new awareness channels and want to measure their ability to introduce your brand to new audiences. Pair it with other models for a complete picture.
Real-world impact: First-touch will make your TikTok and YouTube campaigns look brilliant while making your Google Search and retargeting look terrible. Which is useful information, just not the whole story.
What it does: Gives 100% credit to the last touchpoint before purchase.
Example: Customer sees TikTok ad, Meta retargeting, then Googles your brand and clicks an ad. Google gets 100% credit.
Best for:
The catch: This is what Meta Ads and Google Ads use by default. Which is why they over-report ROAS (return on ad spend) by 40-60%. It's the participation trophy of attribution—everyone gets credit for showing up last.
When to use it: When you're comparing campaigns within the same platform. That's it. Don't use it for cross-channel decisions.
Real-world impact: Last-touch makes conversion channels (Google Search, retargeting) look like heroes while awareness channels (TikTok, YouTube) look like money pits. This is why brands cut TikTok, then wonder why their Google Search performance tanks three months later.
What it does: Distributes credit equally across all touchpoints in the customer journey.
Example: Customer has 5 touchpoints (TikTok ad, Meta ad, email, Google Search, retargeting). Each gets 20% credit.
Best for:
The catch: Treats all touchpoints as equally important. Spoiler: they're not. The TikTok ad that introduced your brand probably matters more than the third retargeting ad they ignored.
When to use it: When you're just starting with multi-touch attribution and want a simple, unbiased view of the customer journey. It's a good baseline before you get fancy.
Real-world impact: Linear attribution gives you a more balanced view than first or last-touch, but it still doesn't reflect reality. Use it to understand journey complexity, not to make budget decisions.
What it does: Gives more credit to touchpoints closer to the conversion. The closer to purchase, the more credit.
Example: Customer journey over 14 days with 5 touchpoints. The last touchpoint gets 40% credit, second-to-last gets 30%, third gets 20%, and so on.
Best for:
The catch: Still undervalues early awareness touchpoints. The TikTok ad that started the journey gets minimal credit, even though without it, nothing else would have happened.
When to use it: When you have a long consideration cycle and want to prioritize channels that close deals. But be careful—you might accidentally starve your top-of-funnel.
Real-world impact: Time-decay is better than last-touch, but it still favors bottom-of-funnel channels. You'll see Google Search and retargeting perform well, while awareness channels look mediocre.
What it does: Gives 40% credit to the first touchpoint, 40% to the last touchpoint, and distributes the remaining 20% among middle touchpoints.
Example: Customer journey with 5 touchpoints. First gets 40%, last gets 40%, middle three share 20% (6.7% each).
Best for:
The catch: The 40/40/20 split is arbitrary. Your actual funnel might work differently. But it's a reasonable assumption for most businesses.
When to use it: When you're moving beyond single-touch attribution and want a balanced view. It's the Goldilocks model—not too focused on awareness, not too focused on conversion. Just right for most brands.
Real-world impact: Position-based attribution gives credit to both your TikTok ads (that introduce customers) and your Google Search ads (that close them). This usually leads to better budget allocation than last-touch.
What it does: Uses machine learning to analyze your actual conversion data and assign credit based on which touchpoints statistically increase conversion probability.
Example: The algorithm notices that customers who see a TikTok ad + Meta retargeting convert at 4.2%, while those who only see Meta retargeting convert at 2.1%. TikTok gets more credit because it demonstrably increases conversion rate and attribution accuracys.
Best for:
The catch: Requires serious data volume to train accurate models. Below 1,000 conversions/month, the model won't have enough data and will give you nonsense. Also requires technical implementation and ongoing maintenance.
When to use it: Once you're spending €50K+/month on ads and have 1,000+ conversions/month. Before that, stick with position-based.
Real-world impact: Data-driven attribution is the most accurate model, but only if you have enough data. When implemented correctly, it typically shows that awareness channels deserve more credit than last-touch suggests, but less than first-touch claims.
ModelBest ForData RequiredComplexityAccuracyFirst-TouchAwareness measurementLowSimpleLowLast-TouchPlatform comparison onlyLowSimpleVery LowLinearJourney understandingLowSimpleMediumTime-DecayLong sales cyclesMediumMediumMediumPosition-BasedMost e-commerce brandsMediumMediumMedium-HighData-DrivenHigh-volume brandsHighComplexHigh
Right. Theory is lovely, but you need to make a decision. Here's the framework:
Use: Position-Based (U-Shaped)
You don't have enough data for data-driven attribution. Position-based gives you a balanced view without overthinking it.
Use: Position-Based, Test Time-Decay
Start with position-based. If your sales cycle is long (30+ days), test time-decay to see if it gives you better insights.
Use: Data-Driven Attribution
You have the data volume to make data-driven work. Implement it properly and you'll get the most accurate attribution possible.
If you're testing new awareness channels (TikTok, YouTube): Use first-touch attribution in addition to your main model to measure their ability to introduce new customers.
If you have a very short sales cycle (same-day purchases): Last-touch might actually be reasonable. But verify with incrementality testing.
If you're B2B or high-ticket (€1,000+ AOV): Use time-decay or data-driven to account for longer consideration.
Here's the model that matters more than any of the above:
Incremental Attribution
This isn't technically an attribution model—it's a measurement methodology. But it's the only way to know if your marketing actually works.
How it works:
Why it matters: All the attribution models above measure correlation. Incrementality measures causation. It's the difference between "this ad was clicked before purchase" and "this ad caused the purchase."
Real example: A beauty brand was celebrating 5.2x ROAS on branded search. Ran an incrementality test. Discovered 85% of those conversions would have happened anyway. True incremental ROAS? 0.8x. They were losing money.
Read more about causal inference and incrementality testing.
The problem: Switching attribution models every month makes it impossible to compare performance over time.
The fix: Pick one model and stick with it for at least 3 months. You need consistency to spot trends.
The problem: Using last-touch for Google Ads and first-touch for TikTok, then comparing them directly.
The fix: Use the same model across all channels. Otherwise you're comparing apples to oranges.
The problem: Meta uses last-touch, Google uses last-touch, TikTok uses last-touch. They all over-report.
The fix: Use third-party attribution software that applies your chosen model consistently across all channels. Or at least calculate blended ROAS to reality-check platform data.
The problem: Optimizing only for bottom-of-funnel metrics because that's what your attribution model favors.
The fix: Use multiple models. Look at first-touch to understand awareness, position-based for budget allocation, and incrementality for truth.
Pros: Free, built-in, supports multiple attribution models
Cons: Limited to web data, doesn't integrate with all ad platforms, less accurate post-iOS 14
How to set it up:
Pros: Built into Shopify, easy to use, shows customer journey
Cons: Limited to Shopify data, basic attribution only, no custom models
How to use it:
Pros: Most accurate, custom models, cross-channel, incrementality testing
Cons: Costs €500-5,000+/month depending on volume
Top options:
You should consider switching when:
Launched a subscription product? Move from first-purchase ROAS to LTV-based attribution.
Crossed 1,000 conversions/month? Time to implement data-driven attribution.
If your attribution model suggests cutting a channel, but when you cut it, overall revenue drops—your model is wrong. Switch to something that better reflects reality.
Adding TikTok or YouTube? Consider using first-touch attribution alongside your main model to measure awareness impact.
Attribution is evolving. Here's what's coming:
With cookie deprecation and privacy regulations, attribution is moving toward:
The next evolution combines attribution models with causal inference—statistical methods to determine actual cause-and-effect.
Instead of just tracking which touchpoints happened before purchase, causal attribution asks: "Would this purchase have happened without this touchpoint?"
This is where we're headed. And it's rather exciting.
Right. Stop overthinking and take action:
Remember: All models are wrong. Some are useful. The goal isn't perfection—it's making better decisions than you're making now.
Which is entirely achievable.
Data-driven attribution is most accurate, but only if you have 1,000+ conversions/month. Below that, position-based (U-shaped) is your best bet. But the MOST accurate? Incrementality testing. That's the only way to measure true causation.
Google Ads uses last-click attribution by default, but you can change it to data-driven, first-click, linear, time-decay, or position-based in your conversion settings.
Meta uses 7-day click, 1-day view attribution by default (last-touch within that window). You can't change the model, but you can adjust the attribution window.
Yes. Using different models for different channels makes comparison impossible. Pick one model and apply it consistently.
Rarely. Stick with one model for at least 3 months to build comparable data. Only change when your business model changes significantly or you hit new data volume thresholds.
Yes, and you should. Use position-based for budget allocation, first-touch to measure awareness, and incrementality testing for truth. Just don't mix them when comparing channels.
Attribution models determine HOW credit is distributed (first-touch, last-touch, etc.). Attribution windows determine the TIME PERIOD for giving credit (7-day click, 30-day click, etc.). Both matter.
Not necessarily. Google Analytics 4 supports multiple attribution models for free. But dedicated software (€500-5,000/month) provides more accurate cross-channel attribution and incrementality testing.
Ready to stop guessing and start knowing? When Meta says one thing, Google says another, and Shopify shows different numbers entirely, it's time for accurate attribution. Discover how to get clarity on your true ROAS.
Ready to implement attribution that actually reflects reality? Causality Engine uses causal inference to show you which touchpoints drive real, incremental revenue—not just which ones happened to be nearby.
