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How Attribution Models Have Changed: Last-Touch vs. Multi-Touch
The difficulty in measuring complex journeys keeps last-click and multi-touch attribution in debate, but can either model be enough alone?
The way consumers interact with brands has changed. It’s no longer a straight line from ad to sale.
People move across channels, devices and platforms over days, weeks or months before making a decision.
This shift has made it harder for marketers to track what’s working and what isn’t. As a result, many are rethinking the value of traditional attribution models.
One debate that often resurfaces is whether last-touch or multi-touch attribution gives a clearer picture.
But with journeys this complex, can either model truly stand on its own? In this post, we take a closer look.
We discuss:
💡 Pro Tip
Ruler removes the guesswork from the last-click versus multi-touch debate by capturing every step of your customer’s journey with first-party tracking. It collects UTMs, click IDs, page views, and referrer data across multiple touchpoints, then links these interactions to conversions like phone calls, live chats, and form submissions. By integrating seamlessly with your CRM or ecommerce system, Ruler lets you accurately attribute offline events, such as meetings and demos, back to their original marketing source. Plus, with access to all the single-touch and multi-touch models once offered by Google Analytics, you’re free to choose the attribution model that best answers your specific business questions, rather than being limited by platform defaults.
Book a demo and see how Ruler can bring clarity to your measurement
Before looking at how attribution has evolved, it’s important to understand what it is.
Marketing attribution is the method marketers use to assign credit to the various touchpoints that contribute to a conversion. It’s how we decide which parts of a campaign are pulling their weight.
A touchpoint is any interaction a customer has with your brand – a paid ad, an organic search result, an email, a blog post.
Attribution models define how much influence each of these interactions had in the lead-up to a conversion.
Related: What is attribution modelling, why does it matter and how to get started
The aim is to understand which channels, messages or moments are making the biggest difference, so future activity can be planned with more precision.
For years, single-touch attribution was the industry standard, with last-touch attribution being the most commonly used model.
It works on a simple premise: the final marketing interaction before a conversion gets 100% of the credit.
If a customer sees a display ad, reads a blog post, clicks through an email, and finally converts after a branded search, only that last step, the search, is counted.
Last-touch attribution gained traction because it was easy to implement, simple to explain, and offered clear metrics that could be used to adjust campaigns quickly.
It also fits neatly into the way most ad platforms operate.
Google, Meta and others each apply their own form of last-touch attribution, reporting conversions in isolation to take credit for the outcome.
Since marketers spend much of their time inside these tools, it became the default.
Not necessarily because it was accurate, but because it was accessible.
First-touch attribution takes the opposite approach, giving 100% of the credit to the first interaction.
It can be useful in measuring what drives awareness, but like last-touch, it ignores the broader journey and the supporting touchpoints in between.
As digital channels evolved and buyer behaviour grew more complex, these simplified models began to show their limitations.
A typical B2B software buyer might go through multiple steps: discovering your product via a LinkedIn ad, reading blog posts, signing up for a whitepaper through an email campaign, attending a webinar, and then finally converting after a Google search or a direct visit.
In a last-touch model, all of that activity is ignored except for the final step, often a low-effort follow-up email or paid search ad.
That final nudge gets all the credit, even though it played a small role in comparison to the steps that built trust and understanding over time.
This skews performance reporting in favour of bottom-funnel channels and makes top-of-funnel tactics, like social ads, SEO, and content marketing, look ineffective.
As a result, marketers often reduce investment in areas that are in fact critical to long-term growth.
To make matters worse, platform-level reporting leads to overlapping and inflated numbers.
Google Ads may claim credit for a conversion. So might Facebook. Meanwhile, your CRM or ecommerce platform tells a different story.
The inconsistency has left many marketers questioning the accuracy of their attribution altogether, and searching for models that better reflect how people actually buy.
Multi-touch attribution emerged as a direct response to the shortcomings of single-touch models.
Rather than giving all credit to a single interaction, MTA distributes conversion credit across multiple touchpoints in the customer journey.
This provides a more realistic view of how different channels and campaigns work together to drive results.
There are several types of MTA models, each using a different logic to assign credit:
While the concept of MTA seems simple, implementing it is not.
It requires integrating data from multiple platforms, tracking users across channels and sessions, and applying algorithms that can interpret the journey.
It also means moving away from platform-level attribution and using a system that sees the full picture.
Initially, many marketers turned to Google Analytics to meet this need.
Earlier versions of Google Analytics allowed users to choose from various MTA models, offering flexibility depending on the question at hand.
But in 2023, Google removed these models, citing low adoption rates. In reality, growing concerns around data privacy and fragmented user journeys had already undermined their reliability.
Related: How to access, compare, & improve your Google Attribution
This left marketers with two options: last-click attribution (a step backward) or data-driven attribution.
In GA4, DDA uses Google’s own machine learning to assign credit across touchpoints.
But it’s a black box. Marketers can’t see exactly how decisions are made, which touchpoints are favoured, or why.
Worse, it’s based only on the data GA4 can access: no offline interactions, no CRM pipeline activity, and only a 90-day lookback window.
Related: 8 Limitations of GA4 and how to overcome them
In short, it stripped marketers of control and relied on incomplete data.
To fill the gap, many turned to third-party attribution platforms like Ruler Analytics.
Ruler uses deterministic matching to track touchpoints across sessions and channels using identifiers such as cookies, UTMs, fbclid, gclid, and device IDs.
When a user converts, via a form, phone call, or live chat, Ruler maps that conversion to their full journey and pushes the data into your CRM or ecommerce platform.
Unlike GA4, Ruler doesn’t stop at lead generation.
When deals progress in your CRM, say from lead to opportunity to closed, or if customers make additional purchases, those events can be sent back to ad platforms using the same identifiers.
This closes the loop, allowing platforms to optimise for deeper metrics like qualified leads or revenue, rather than superficial actions like clicks or form fills.
Crucially, Ruler includes the attribution models that GA4 removed. Marketers can choose models based on the question they want to answer, and examine journeys click by click to understand how credit was assigned.
Ruler also supports long and complex journeys, whether one week or six months, and includes offline channels such as print ads, in-person sales, or CRM updates.
This gives a complete view of marketing performance, allowing revenue to be modelled accurately, not just shallow conversions.
💡 Pro Tip
Ruler cuts through the bias often seen in ad platforms, offering a complete and accurate view of performance while overcoming the challenges of GA4 attribution. This lets you confirm your data and optimise your targeting towards what genuinely impacts outcomes. Interested in a demo? Schedule one today and explore how Ruler can enhance your measurement approach.
Book a demo of Ruler and get better clarity
While multi-touch attribution has gained momentum across the industry, last-touch attribution still holds value, in the right context.
For businesses with short sales cycles and straightforward campaigns, the simplicity of last-touch often outweighs the need for more advanced modelling.
Take, for example, an e-commerce brand running focused paid search campaigns targeting high-intent keywords.
If a customer searches, clicks an ad, and completes a purchase within minutes or hours, last-touch attribution can offer clear, reliable insights.
In cases like this, where journeys are short and the marketing mix is narrow, the added complexity of multi-touch models may not deliver enough extra value to justify the implementation cost.
However, for businesses with longer and more layered buying journeys, multi-touch attribution becomes essential.
B2B companies selling software with six-month sales cycles, for instance, need visibility into how awareness is built, how prospects move through the funnel, and which touchpoints actually influence the final decision.
Tools like Ruler can capture that journey in full, no matter how long the journey takes.
Retailers running both brand and performance campaigns, or service businesses that rely on nurturing relationships over time, also benefit from a broader view.
Multi-touch models help surface the real contributors to pipeline and revenue, not just the final interaction.
The key isn’t deciding whether first-click or last-click is inherently “better.” Attribution should be about selecting the right model for the question you’re trying to answer.
Here are a few scenarios to illustrate:
Attribution isn’t one-size-fits-all. The model you choose should reflect your goals, sales cycle, and marketing structure, not just what’s easiest to report on.
Even as more organisations adopt multi-touch attribution and advanced data-driven models, a fundamental catch remains.
Both last-touch and multi-touch approaches rely primarily on deterministic tracking, that is, capturing clear, traceable interactions such as clicks on ads or visits through tagged links.
This method is essential for building accurate user journeys, but it only tells part of the story.
Consider a typical example.
A potential customer sees your display ad while reading an article but doesn’t click.
Hours later, they search your brand name and convert via an organic result. Deterministic tracking assigns full credit to organic search, ignoring the display ad that planted the seed.
The outcome is a skewed view, one that undervalues upper-funnel activity.
This isn’t limited to display.
Social media campaigns, video content, podcast sponsorships, and offline efforts like print ads or events often influence decisions without prompting a measurable click.
When someone converts later through a direct visit or branded search, most attribution models won’t link that outcome back to the earlier exposure.
For brands investing in awareness campaigns, this gap poses a challenge.
CMOs using sophisticated multi-touch tools may still find their upper-funnel efforts underrepresented in performance reports.
The result can be reduced investment in channels that are, in reality, playing a key role in shaping consideration and intent.
Deterministic tracking remains crucial, it gives structure to attribution and enables detailed analysis.
But its limitations highlight the need for context.
Marketing impact doesn’t always come with a click, and a complete understanding of performance means recognising what can’t always be measured directly.
No single measurement method captures the full complexity of modern marketing.
While deterministic attribution, whether last-touch or multi-touch, offers valuable insights into user journeys, it falls short when it comes to understanding the impact of non-click-based interactions.
The answer lies in blending attribution data with broader methods, drawing from data-driven attribution, impression modelling, and techniques from marketing mix modelling.
This blended approach shifts focus from the individual to the aggregate.
It uses statistical analysis of macro-level performance data to understand how different channels, including those with no direct interactions, influence outcomes like sales, leads, or revenue.
Instead, it examines how variations in marketing spend, across time and across regions or segments, correlate with changes in business performance.
But while this blended model offers a more complete view, they still rely on correlation, not causation.
That’s where incrementality testing becomes essential. Incrementality answers a simple but powerful question: what would have happened if we hadn’t run this campaign?
These tests often use controlled experiments to establish causality. A common method is the geographic holdout test, where a campaign runs in one market but not in another.
If the campaign drives a measurable uplift compared to the control market, the difference represents the true incremental effect.
Without this testing, marketers risk overestimating the value of channels that merely capture existing demand.
When combined, these three methods form a complete measurement framework:
Organisations that adopt this triangulated approach often uncover a very different picture of marketing effectiveness.
Upper-funnel channels that attribution models once ignored may emerge as key drivers of growth.
Meanwhile, some lower-funnel tactics that appeared highly efficient are revealed to be capturing conversions that would have happened anyway. By combining methods, marketers can make smarter, more defensible decisions, both in the short term and at the strategic level.
💡 Pro Tip
Combining attribution with incrementality testing and marketing mix modelling gives you a more complete view and stronger data to prove what’s working and guide budget decisions. Our marketing measurement framework outlines how to get started.
Attribution has come a long way from the days of last-touch simplicity.
As the customer journey has grown more complex, so too has the need for models that reflect reality, not just what’s easy to measure.
Multi-touch attribution offers a more accurate view, but it’s not the whole answer.
Without accounting for view-through interactions, offline activity, and broader market forces, even the best models leave gaps.
The real solution lies in combining attribution with complementary methods: using deterministic tracking for user-level insights, blending it with aggregate modelling for broader trends, and validating with incrementality testing to separate real impact from noise.
This holistic approach helps marketers see what’s really working, and what isn’t, so they can invest budgets with confidence, not guesswork.
If you’re ready to move beyond isolated platform reports and start measuring marketing with the clarity it deserves, Ruler can help.
From deterministic multi-touch tracking across forms, calls and chats, to full CRM integration and attribution modelling that reflects the entire funnel, online and offline, Ruler gives you the tools to connect the dots and optimise for outcomes that matter.
Book a demo today to see how Ruler can bring clarity to your marketing performance.
