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Google Attribution: How to Access, Compare, & Improve your Attribution
Get the details on Google Attribution, tips for comparing models, and reasons it might be giving you an incomplete picture.
Attribution is at the heart of measurement.
It gives marketers a clearer view of how different channels and touchpoints work together across the customer journey, rather than just focusing on single interactions.
While GA4 is usually thought of as a tool for tracking website behavior and trends, it actually has some attribution capabilities built in.
It can track key events like form fills and apply attribution models like data-driven attribution and last click, helping you see not just what happened, but how it happened.
In this post, we’ll walk through where you can find these attribution models in GA4, how to compare them, and the common limitations you’ll want to watch out for.
Plus, we’ll cover some practical ways to improve your setup and get even more value out of the data
We’ll cover:
💡 Pro Tip
Ruler gives you a nuanced understanding of your marketing efforts with its multi-touch attribution comparison feature. Ruler not only offers the legacy models previously available in GA4 and Ads, but it expands beyond conversions, allowing you to compare the impact on opportunities, sales, deals, and even closed-won revenue. This comprehensive analysis empowers you to optimise your marketing strategy for maximum ROI.
Learn more about Ruler’s comparison feature or book a demo to see it in action.
In digital marketing, attribution is all about understanding which touchpoints in a customer’s journey deserve credit for a conversion.
A Google Analytics attribution model is the set of rules that determines how conversion credit is assigned across various channels, campaigns, or interactions that led up to a conversion event, like a purchase, form submission, or sign-up.
Think of it as a framework for answering the question: “Which parts of the user journey influenced this outcome, and how much credit should each get?”
Google Analytics tracks data from multiple sources, search ads, social media, email, direct traffic, and more.
The attribution model decides how to divvy up credit across those interactions. This matters because it shapes how you evaluate performance, allocate budget, and optimise marketing strategies.
Technically, an attribution model can rely on rules, algorithms, or even machine learning to assign value to different touchpoints.
It pulls from user behavior data stored in Google Analytics, like session source, campaign UTM parameters, and timestamped events, and applies a logic layer to that data to determine contribution.
Understanding which model you’re using – and its underlying assumptions – is crucial. It doesn’t just change how results look in reports; it fundamentally influences how you interpret the effectiveness of your marketing efforts.
In GA4, attribution modeling has been streamlined. Unlike Universal Analytics, which offered a variety of rules-based models, GA4 focuses on just two core attribution models:
Data-Driven Attribution and Last Click Attribution. This shift reflects Google’s emphasis on machine learning and simplifying how marketers interpret cross-channel performance.
GA4 uses Data-Driven Attribution as its default model for most conversion reporting. Unlike rule-based models, DDA uses machine learning to evaluate how different marketing touchpoints contribute to conversions based on actual user behavior within your property.
It analyses the full conversion path, looking at variables like:
DDA assigns fractional credit to each touchpoint proportionally, based on how likely that step was to drive a conversion. The model is dynamic. It updates over time as more data flows in, and it’s designed to reflect the real, often non-linear journey users take across channels.
While DDA is the default, GA4 allows you to switch to Last Click Attribution for comparison and analysis purposes. However, GA4 actually provides two distinct variants of this model, depending on your reporting context:
Paid last-click: This model gives 100% of the credit to the last paid touchpoint before the conversion. If the final interaction before the conversion is unpaid (e.g., organic search or direct), the model will look back and assign credit to the last paid channel in the path, if one exists.
Paid and organic last-click: This version assigns 100% of the conversion credit to the last non-direct touchpoint, regardless of whether it was paid or unpaid. “Direct” traffic is ignored unless it’s the only interaction, in which case it gets the credit.
Which version you’re looking at depends on the report type:
💡 Pro Tip
Attribution plays a key role, but it needs to be combined with other approaches as privacy regulations evolve, tracking becomes more challenging, and customer journeys grow increasingly complex. As platforms like Instagram and TikTok are treated more like offline media, focusing on views and impressions instead of clicks, a comprehensive measurement strategy is essential. Our framework helps you blend first-party data tracking, marketing mix modeling, and incrementality testing to give you a complete understanding of what truly drives your results.
Check out the framework for a more accurate approach to measurement
Google Analytics 4 gives you the flexibility to choose how you assign credit for conversions, whether you want to stick with the default data-driven Attribution or switch to last click attribution.
Here’s how you can change your attribution settings and compare different models to better understand your marketing performance.
1. In your GA4 property, click on “Admin” in the bottom-left corner of the interface.
2. Under “Data display”, select “Attribution Settings.” This is where you control how conversions are attributed across channels.
3. Under the section labeled “Reporting attribution model,” you’ll find a dropdown with the available models. By default, GA4 recommends and uses data-driven attribution, but you can switch to last click attribution if you prefer a more traditional, rule-based approach.
4. Below that, you’ll see an option to define which channels are eligible to receive credit for web conversions shared with Google Ads.
When you link GA4 to your Google Ads account, conversions tracked in GA4 can be imported into Google Ads for reporting and bidding. This setting determines which types of traffic – paid, organic, referral, etc. – are eligible to receive conversion credit within Google Ads.
If you restrict this to paid channels, for example, organic touchpoints won’t influence Smart Bidding decisions in Ads.
5. Finally, you have the choice to adjust your look back window. The lookback window controls how far back GA4 looks when assigning credit to a touchpoint. You can choose different windows for acquisition events and all other key events.
The minimum is 30-days and the maximum is 90-days. Shortening or lengthening the window can affect which channels are credited for long-consideration conversions.
Changing your default model is just one piece of the puzzle. GA4 also allows you to compare how different models attribute credit so you can see the real impact of your marketing efforts from multiple perspectives.
To compare models, navigate to Advertising > Attribution > Model Comparison
Here, you’ll find side-by-side breakdowns showing how each model distributes credit for conversions across channels and campaigns.
Use the dropdowns to toggle between Data-Driven Attribution and Last Click Attribution to see how the numbers shift. This is especially useful if you want to understand whether earlier touchpoints (like an initial organic visit) are being undervalued by Last Click.
Also under the Advertising section, you have Attribution Paths report (formerly known as Conversion Paths).
This visualises the sequence of interactions a user has with your marketing channels before completing a conversion event.
It shows:
This report helps you uncover high-impact assist channels that may not be getting full credit in simple models.
For example, you might find that social media regularly appears early in the journey, even if it rarely drives last-click conversions.
By using the Model Comparison and Attribution Path reports together, you get a much clearer picture of how different marketing channels contribute throughout the customer journey, not just at the final click.
While GA4 offers more flexibility and intelligence in attribution than its predecessor, it’s far from perfect.
In fact, for many businesses, especially those with long sales cycles or complex customer journeys, Google’s attribution model has some significant blind spots.
Let’s break down the key limitations.
As we mentioned earlier, GA4 attribution is capped at a 90-day lookback window.
That means Google Analytics will only consider touchpoints that happened up to 90 days before a conversion when assigning credit.
That might be fine for e-commerce sites with fast purchase decisions. But if you’re selling high-value services or products, think B2B software, real estate, or enterprise consulting, your sales cycle likely stretches well beyond three months.
In these cases, any initial touchpoints that occurred before that 90-day window are excluded entirely from attribution.
For example, imagine a potential customer clicks on a LinkedIn ad in January, signs up for a webinar in February, and finally becomes a qualified lead in May after speaking with your sales team.
The January ad click, arguably the trigger for the entire journey, won’t receive any credit in GA4. From the platform’s perspective, it never existed.
This time constraint can severely distort your perception of channel performance, especially when you’re trying to measure the effectiveness of upper-funnel or long-term brand awareness efforts.
To make matters trickier, GA4’s default DDA model, while more advanced, adds another layer of complexity.
It uses machine learning to assign fractional credit across touchpoints based on observed behavior. The goal is to distribute credit more accurately based on what’s truly influencing conversions.
But, it’s a black box. You see the output, a percentage of credit assigned to each channel, but not the logic behind it.
There’s no transparency about why one channel got 30% and another got 10%. You can’t audit the model or trace how decisions were made, which makes performance analysis difficult and strategic planning riskier.
One of GA4’s most significant gaps is its inability to track offline interactions or conversions natively. It does a solid job of capturing digital behavior, like form submissions or whitepaper downloads, but once a lead leaves the browser, GA4 loses visibility.
This is a major issue for B2B journeys. Picture this:
GA4 might capture the first step, but the most important milestones, like the demo and the phone call, are invisible to it. These are often the touchpoints that actually close the deal, but GA4 won’t attribute any credit to them.
Yes, GA4 is event-based, so you can track events like a phone number click.
But that’s limited to mobile devices and doesn’t distinguish between valuable leads and spam calls. There’s no context around what happened after the interaction.
First-party tracking tools solve this conundrum. One example is Ruler.
It uses click IDs, UTM parameters, and first-party cookies to track users across sessions and channels, even over long timeframes.
So when a visitor eventually converts, their entire pre-conversion journey is still intact. No limited lookback windows.
Let’s say someone clicks a paid ad, returns three weeks later via organic search to fill in a form, and converts via a phone call tracked in your CRM.
With Ruler, you can still link that conversion back to the original ad click.
You can even sync marketing source data into your CRM to follow the lead as it moves through your pipeline.
And once that lead becomes a closed deal, you can attribute the revenue back to the original campaign (using all the models previously available in UA), calculate ROI, and push those insights back into ad platforms for smarter optimisation.
💡 Pro Tip
Want to see how we track marketing source data with first-party tracking and tie it to business outcomes, spend, ROI, and ROAS? Book a demo and we’ll show you how it works.
GA4 only tracks what happens after someone clicks. That means if someone sees your display ad or social post, maybe multiple times, but never clicks, GA4 doesn’t register it at all.
This is especially problematic for upper-funnel campaigns designed to build awareness rather than drive immediate action.
Meta and other platforms offer some view-through attribution, but it’s extremely limited (often just 24 hours) and relies on third-party cookies, which are becoming increasingly unreliable due to privacy regulations.
And let’s face it, most users don’t see an ad and convert within a day.
As a result, those valuable upper-funnel impressions go uncredited in GA4. Instead, the last click, often from direct or branded search, gets all the glory, even though the heavy lifting happened earlier.
Some companies try to compensate by adding a “How did you hear about us?” field on forms.
It’s a nice idea in theory, but in practice, responses tend to be vague (“Google” or “the internet”), and most users don’t remember their actual journey.
A more scalable, data-driven solution is impression modeling.
Tools like Ruler’s DDA + Impression Model combine deterministic click data with probabilistic impression data to estimate the impact of upper-funnel campaigns.
It borrows concepts from marketing mix modelling to more accurately weight touchpoints, even if they didn’t generate a click.
This means impressions from display, video, or social campaigns aren’t ignored.
They’re factored into the model, which redistributes credit across the full journey, instead of just the final step.
It’s a game-changer for brands looking to measure true influence rather than just last-touch results.
Understanding how Google Analytics 4 handles attribution is crucial if you want to make smarter, data-informed marketing decisions.
While GA4 brings more advanced models like data-driven attribution and new ways to customise and compare, it also comes with important limitations – especially for businesses with long sales cycles, offline conversions, or heavy top-of-funnel investment.
Attribution isn’t just about giving credit. It’s about understanding what’s really driving growth.
And while GA4 offers a solid foundation, relying on it alone can leave gaps in your insight.
Whether it’s due to the 90-day lookback window, missing offline interactions, or untracked impressions, GA4 doesn’t always tell the whole story.
To truly connect marketing with revenue, you may need to supplement GA4 with additional tools and tracking strategies, especially those that bring in first-party data and cover the full customer journey.
Curious how Ruler can provide better attribution across all your channels? Book a demo with us today and take the uncertainty out of your marketing measurement.
