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First-Click Attribution: What it is, How to Access it, & How to Combine it with Modern Measurement
First-click attribution remains important, but as it vanishes from everyday tools, how can we still access it and combine it with evolving measurement methods?
Marketing attribution has never been more critical, or more complicated.
Since Google deprecated its traditional attribution models, many marketers have been left without a clear way to understand which efforts drive leads and conversions in the initial stages.
Ad platforms haven’t helped, often claiming credit for touchpoints regardless of their actual role in the customer journey.
Still, in this environment, first-click attribution remains essential. It sheds light on brand awareness, highlights top-of-funnel performance, and maps the early interactions that introduce people to your business.
This guide covers what first-click attribution is, how recent platform changes have disrupted legacy measurement strategies, and what you can do to adapt.
If you’re looking to regain clarity on where your customer journeys really begin, this is the place to start.
We discuss:
💡 Pro Tip
Ruler continues to support first-click attribution along with every attribution model Google has removed. It tracks UTMs, click IDs, page views, and referrers across multiple touchpoints, connecting these to conversions like phone calls, live chats, and form fills. By syncing directly with your CRM or ecommerce platform, Ruler attributes offline events such as meetings and demos back to the original marketing source. With access to all single-touch and multi-touch models previously available in Google Analytics, you can select the attribution method that best fits your business needs, rather than being confined to platform defaults.
Book a demo and see how Ruler can simplify your measurement
First-click attribution is a single-touch model that gives 100% of the conversion credit to the first touchpoint in a customer’s journey.
When someone completes a conversion, whether that’s making a purchase, signing up for a service, or filling out a form, this model looks back to the initial interaction they had with your brand and assigns full credit to that source.
Unlike last-click attribution, which focuses on the final interaction, or multi-touch models that spread credit across several touchpoints, first-click attribution answers a specific question:
What originally brought this customer to us?
This approach is especially useful when you’re focused on building market awareness.
It helps you see which channels and campaigns are doing the work of introducing your brand to new audiences.
If you’re investing in demand generation or early-stage marketing, understanding where new customers first come from often matters more than knowing what nudged them across the line.
First-click attribution has been a staple in marketing measurement for years. But in recent times, it has become harder to access and apply.
The biggest shift came in September 2023, when Google removed four rule-based attribution models from both Google Analytics 4 and Google Ads, including first click.
Related: How to access, compare, & improve your Google Attribution
Google claimed these models were outdated and underused, citing that fewer than 3% of advertisers relied on them.
In their place, Google pushed its data-driven attribution (DDA) model, which uses machine learning to assign conversion credit.
While DDA may sound more advanced, it offers no transparency. Marketers can’t see how decisions are made or which touchpoints are getting credit, making it difficult to analyse or trust.
The removal of first-click attribution has created a significant gap. Marketers can no longer use Google Analytics to clearly identify the channels responsible for starting customer relationships.
Instead, they are left with last-click attribution or a machine-learning model they can’t interrogate.
At the same time, platform attribution continues to distort the picture.
Each ad platform, Google Ads, Meta, LinkedIn, TikTok, Amazon, runs its own attribution system, often defaulting to last-click with unique lookback windows.
Theses platform assigns conversion credit to the last interaction a user had with one of its ads – regardless of whether or not it was the last interaction in the journey.
And, these platforms report conversions independently, each claiming credit for the same customer.
As a result, marketers often see inflated conversion numbers that don’t match reality.
This makes it difficult to allocate budget fairly or understand which activities are truly driving new demand.
In short, with GA4 dropping first-click and ad platforms focused on last-touch attribution, marketers now have limited visibility into what’s generating awareness and pulling in new audiences.
Even though Google has moved away from first-click attribution, it still plays a vital role in understanding how customers discover your brand.
First-click helps answer one specific question: which channels are most effective at introducing new audiences to us?
If you’re running brand awareness or top-of-funnel campaigns, it’s one of the clearest ways to measure impact.
But while first-click is great for diagnosing discovery, it doesn’t tell the whole story.
Relying on it alone, especially for budget decisions, can lead to blind spots.
You risk over-investing in channels that attract attention but don’t convert.
On the other hand, relying only on last-click might favour channels that close deals but don’t bring in new demand. Neither model on its own gives a full picture.
That’s why combining first-click with multi-touch attribution is so important.
First-click shows how people found you. MTA shows how they moved through the journey.
Together, they give you both clarity and context.
You get the simplicity of first-click when that’s enough, and the depth of MTA when it’s not.
The challenge, of course, is that most tools no longer support this kind of analysis.
GA4 has dropped rule-based MTA models altogether, and even before that, its attribution was limited.
It couldn’t track offline outcomes, had a 90-day lookback window, and missed key interactions.
Ad platforms can’t be relied on either, they only report what happens within their own ecosystem, and each one claims more credit than it deserves.
To properly track and model full journeys, marketers now need third-party tools that go beyond what GA4 and ad platforms can offer.
These tools must be able to capture every click, session, and touchpoint, then link them to outcomes and costs to show the real impact of your marketing.
Ruler is one example. It tracks UTM parameters, click IDs, referrers, and page views across the entire customer journey.
It then connects these touchpoints to conversions like form fills, phone calls, and live chats.
All that data is synced with your CRM or ecommerce platform, tying marketing activity to pipeline stages or completed purchases.
When someone reaches a key milestone, like booking a demo or making a sale, Ruler sends that data back to the relevant ad platforms using the original click ID.
This creates a feedback loop that improves bidding, reporting, and decision-making.
All attribution activity is housed in Ruler’s dashboard, which includes every model previously available in GA4 – first click, last click, linear, time decay, and more.
You can switch between them depending on the question you need to answer.
Use first-click to see which ads, campaigns, and pages drive initial visits.
Then use MTA to understand what nurtured and closed the sale.
Importantly, Ruler doesn’t cap your lookback window at 90 days. It can track long, complex journeys over months or even years, ideal for businesses with longer sales cycles.
In short, combining first-click with multi-touch attribution gives you flexibility.
It helps you understand both where demand starts and how it converts, without relying on incomplete or biased data.
💡 Pro Tip
Tools such as Ruler address the biases present in ad platforms, giving you an accurate, unified view of marketing effectiveness while overcoming GA4 attribution challenges. This supports validation of your results and optimisation of algorithms to target real drivers of success. Curious to learn more? Book a demo and discover how Ruler can transform your measurement strategy.
Even when first-click attribution was available in tools like GA4 – or still is through third-party platforms – it has fundamental limitations that marketers need to be aware of.
As the name suggests, click attribution only captures interactions where someone actively clicks through to your website.
Anything outside of that is invisible to the model. This creates a major blind spot, especially for upper-funnel marketing.
Many of the touchpoints that build awareness don’t involve a click at all.
Display ads, YouTube pre-rolls, TikTok videos, and other brand-led campaigns are often designed to create familiarity, not drive immediate action.
A user might see your ad several times over the course of a few days, then later search for your brand directly and convert.
First-click attribution, in this case, would credit the direct visit or the organic search, completely overlooking the original ad impressions that sparked interest.
The same issue applies to offline channels.
Traditional attribution models are built to track digital clicks, not real-world influence.
TV spots, radio ads, billboards, and magazine placements can all lead to digital conversions, but they’re rarely captured in standard attribution tools.
The result is a skewed picture where offline efforts appear to have no measurable impact, even when they’re driving significant demand.
This disconnect highlights a broader problem: most attribution systems were never designed to reflect the full range of touchpoints in a modern marketing strategy.
They focus on what’s easy to track, clicks and digital channels, and ignore the broader context in which customers experience your brand.
The gap between what’s measured and what’s actually happening continues to grow, leaving marketers with incomplete data and misguided conclusions.
Attribution offers a detailed view of how individual customers move through their journey, but on its own, it can’t explain everything.
As mentioned, click-based models fall short when it comes to measuring the impact of offline channels, non-click interactions, and broader market trends.
Related: How to integrate offline data into your digital strategy & targeting
To build a complete picture of performance, marketers need to combine attribution with other measurement approaches, namely, marketing mix modelling and incrementality testing.
Marketing mix modelling uses statistical analysis to assess how changes in marketing inputs, such as spend, impressions, reach, seasonality, and price, affect business outcomes over time.
Unlike attribution, which tracks user-level paths, MMM works at an aggregate level.
It helps you understand the contribution of upper-funnel efforts like brand campaigns, video advertising, or out-of-home media, activities that rarely produce immediate clicks but shape long-term customer behaviour.
Attribution and MMM together provide more perspective than either method alone.
Attribution shows individual-level journeys. MMM identifies patterns in overall performance.
But both are correlation-based. They show relationships between inputs and outcomes, not causation.
This becomes a problem when you need to make high-stakes budget decisions or evaluate whether a new tactic truly moved the needle.
That’s where incrementality testing plays a key role.
It answers the essential question: What would have happened if we didn’t run this campaign?
Instead of relying on correlations, incrementality uses controlled experiments to isolate the true effect of marketing activity.
For example, geographic holdout tests compare results in regions exposed to a campaign against those that weren’t. The difference in performance shows the actual incremental lift, removing guesswork from cause-and-effect.
These experiments provide the causal evidence needed to validate insights from both attribution and MMM.
The result is a more complete measurement framework:
Each method has its place. Combined, they give marketers the ability to make decisions with greater confidence, balancing short-term performance with long-term brand investment.
💡 Pro Tip
When you combine attribution with incrementality testing and marketing mix modelling, you gain a clearer view and solid proof of what’s effective, guiding smarter budget choices. Our marketing measurement framework explains how to get started.
At Ruler, we’ve built a model that blends attribution with impression-level and aggregate insights to better reflect how customers actually move through the funnel.
Related: The future role of DDA and impression modelling
Our approach applies weightings derived from MMM to attribution data, helping close the gaps left by click-only tracking.
For example, upper-funnel channels like paid social or display advertising are often undervalued because they don’t produce conversions directly.
By combining data-driven attribution with impression modelling, we give these channels proper credit for generating the demand that lower-funnel campaigns later convert.
This avoids the common trap of over-investing in branded search and retargeting, channels that often claim conversions simply because they’re the last interaction before a purchase.
Instead, our model surfaces the full journey, showing how early-stage campaigns contribute to acquisition and growth over time.
The result is a more balanced understanding of performance that supports better budget decisions across the funnel, from awareness to conversion.
First-click attribution has become harder to access, but its value hasn’t changed.
It still plays a crucial role in helping marketers understand how customers first discover a brand.
That insight is vital when you’re investing in top-of-funnel activity or trying to assess brand awareness efforts.
But on its own, first-click attribution is incomplete, and today’s marketing landscape demands more than a single perspective.
To get a clearer picture of performance, marketers need to combine attribution with marketing mix modelling and incrementality testing.
Together, these methods offer a way to see both the individual paths customers take and the broader market forces that shape results.
With the right tools in place, you can track full customer journeys, account for non-click interactions, and link every touchpoint to real business outcomes.
At Ruler, we’ve built our platform to fill the gaps left by legacy tools like GA4.
We give you access to all major attribution models, including first-click, and combine them with impression modelling and CRM integration to deliver a more accurate, useful view of what’s driving performance.
If you’re ready to understand what’s really working across the funnel, book a demo with Ruler and see how we can help you rebuild your attribution strategy.
