The B2B SaaS attribution benchmark 2026

Every B2B SaaS marketing team eventually has the same meeting. Pipeline is up or down, someone asks which channels are working, and a dashboard answers with confidence. The answer usually comes from one rule: give all the credit to the last thing a buyer did before they converted. It is simple, it is the default in most tools, and it is wrong in ways that are easy to miss.

We wanted to measure how wrong. Not in theory, and not with a single customer story, but across a large enough set of real companies that the pattern would hold up. So we took six months of anonymized journey data from 1,200 B2B SaaS workspaces and credited the same pipeline twice: once with last-click, and once with a position-based multi-touch model. Then we compared the two.

The gap between them is what we call credit drift. When a channel gets more credit under last-click than under multi-touch, it is over-credited by the default report. When it gets less, it is doing work that the default report does not show. Credit drift is not a judgment about which model is "true". No model is. It is a measure of how much a team’s view of its own marketing depends on a reporting choice it may never have made on purpose.

This report is written for the people who have to act on that view: heads of marketing deciding next quarter’s budget, demand generation managers defending a channel, and RevOps leads who own the reporting stack. Each chapter ends with a short note on what the finding means in practice. If you only have five minutes, the takeaway at the end of each chapter and the recommendations in chapter 8 will give you most of the value.

The sample covers 1,200 B2B SaaS companies that track their website and product with Usermaven and have connected a CRM or billing source, so that pipeline can be tied back to the journeys that produced it. Every company in the sample created at least 40 opportunities between January 1 and June 30, 2026. All data was aggregated and anonymized before analysis, and no single company accounts for more than 0.5% of the opportunities studied.

Across those companies we analyzed 3.1 million tracked journeys and 186,000 opportunities. A journey starts at the first identifiable touchpoint, such as a visit from a campaign, a referral, or an organic search, and ends when an opportunity is created. We count a touchpoint as a session with a distinct source, a tracked email click, an ad click, or a logged sales activity. Repeat page views inside one session count once.

For the multi-touch view we used a position-based model: 40% of the credit to the first touch, 40% to the last touch, and the remaining 20% spread evenly across the touches in between. We chose it because it is the most common multi-touch model among companies in the sample that use one, and because it is easy to explain. The full method, the definitions we used, and the limits of the data are in the appendix at the end of this report.

186,000opportunities traced back through 3.1 million journeys at 1,200 companies.

Start with the headline comparison. When we credit the same 186,000 opportunities with last-click and then with a position-based model, the total is identical: the pipeline does not change, only who gets credit for it. But the split between channels changes a great deal, and it changes in a consistent direction.

Two channels gain credit under last-click: paid search and paid social. Three lose it: organic search, email, and referral. Organic search shows the largest absolute swing. It produces 31% of pipeline under multi-touch but only 18% under last-click, a gap of 13 percentage points. Paid search moves almost as far in the other direction, from 16% to 34%.

The pattern makes sense once you look at where each channel tends to sit in a journey. Organic search and referral are overwhelmingly early touches. They are how a buyer first finds a company, often months before they are ready to talk to sales. Email works in the middle, bringing buyers back when they have gone quiet. Paid search, and to a lesser extent paid social retargeting, sit at the end. They catch buyers who already know the company and are searching for it by name, or who are being reminded to come back and book a demo.

Last-click rewards whatever happens last. So it systematically favors the channels that close journeys and ignores the ones that start them. None of this means paid search is not working. It means that much of the credit it receives under last-click belongs, at least partly, to the channels that created the demand it captured.

How big is the drift for a typical company?

Averages can hide a lot, so we also measured drift company by company. For each company we added up the absolute differences between its last-click and multi-touch channel shares and halved the result. This gives a single number, total credit drift, that answers a simple question: what share of this company’s pipeline would be credited to a different channel if it changed models?

24%of pipeline moves to a different channel at the median company when it switches from last-click to multi-touch.

At the median company, 24% of pipeline changes hands between channels. At one in five companies the figure is above 35%. Put differently, for a typical B2B SaaS team, roughly one opportunity in four is being credited to a channel that multi-touch would not choose. That is large enough to change which channel looks best in a quarterly review, and large enough to change where the next dollar of budget goes.

Drift is highest at companies with longer journeys and more channels in play. Companies whose median journey has more than 30 touchpoints have a median drift of 31%. Companies with fewer than 10 touchpoints have a median drift of 12%. This is intuitive: when a buyer converts after a handful of interactions, the last one is a reasonable summary of the journey. When they convert after dozens across several months, it is not.

Drift is stable from month to month

One reasonable worry is that drift is noise: that the gap between models jumps around from month to month and averages out over time. It does not. When we measured each company’s drift separately for each of the six months, the month-to-month correlation was high, and the direction of drift for each channel stayed the same at 87% of companies across all six months.

That stability is what makes drift worth acting on. If paid search is over-credited in January, it is very likely to be over-credited in June. A team that looks only at last-click is not seeing a random error that will correct itself. It is seeing a consistent tilt that compounds every time a budget decision is made on top of it.

The months where drift did change most were the months with large campaigns. A product launch, a major webinar, or a large event pushed more first touches into the period and raised drift temporarily, because last-click credited the follow-up visits rather than the event that caused them.

Of all the channels we measured, paid search shows the largest gap between what last-click says and what the full journey suggests. To make the comparison fair across channels of different sizes, we expressed drift as a ratio: last-click credit divided by multi-touch credit. A ratio above 1 means the channel gets more credit from last-click. A ratio below 1 means it gets less.

Paid search has a ratio of 2.1. Under last-click it is credited with 34% of pipeline; under multi-touch, 16%. Email sits at the other end, with a ratio of 0.5: it gets roughly half the credit from last-click that a multi-touch model would give it.

Branded search does most of the capturing

Paid search is not one channel. It is at least two: campaigns that bid on a company’s own brand name, and campaigns that bid on category or problem terms. When we split them, the picture becomes much clearer. Branded campaigns account for 61% of last-click paid search credit in the sample but only 38% of paid search clicks earlier in journeys. Non-branded campaigns are the opposite: they appear far more often as a first or middle touch.

A buyer who searches for a company by name has, almost by definition, already heard of it. Something else, such as a blog post, a podcast mention, a colleague, or a review site, did the work of introducing the company. The branded ad is the last door the buyer walks through. Last-click gives that door the credit for the whole house.

61%of last-click paid search credit goes to branded campaigns, where the buyer already knew the company’s name.

What this does to budget decisions

Over-crediting matters because budgets follow credit. In the companion survey we ran alongside this benchmark, teams that report primarily on last-click said they had increased paid search spend over the last 12 months at nearly twice the rate of teams reporting on multi-touch. That is not proof that either group made the wrong call. It is a sign that the reporting model shapes the decision, whether or not anyone chose it deliberately.

The risk is a slow loop. Last-click makes branded search look like the best-performing channel. Budget moves toward it and away from the channels that create demand. Over time, fewer new buyers discover the company, so fewer of them search for it by name, and branded search volume flattens. By the time that shows up in the numbers, the cause is two or three quarters in the past.

None of this is an argument for cutting branded search. Protecting your brand terms from competitors is usually worth doing, and branded campaigns are often cheap. The argument is for evaluating them honestly: as a way to capture demand that already exists, not as the source of that demand.

Non-branded search does real work

The other half of the paid search story is easy to miss. Non-branded campaigns, the ones that bid on problems and categories rather than a company’s own name, look weaker under last-click than they really are. Their ratio in the sample is 1.1, close to parity, because their clicks are spread across the journey rather than concentrated at the end.

In journeys where a non-branded search ad was the first touch, the buyer went on to search for the company by name before converting 44% of the time. Under last-click, the branded campaign gets the credit for those journeys. Under the position-based model, the non-branded campaign that started them gets 40%. Teams that cut non-branded spend because it looks expensive on a last-click basis may be cutting the campaigns that feed their branded search volume.

Credit drift exists because B2B journeys are long. If buyers converted after one or two interactions, every attribution model would give roughly the same answer. So it is worth looking at the journeys themselves: how many touchpoints they include, how many channels they cross, and how long they take.

Across all 186,000 opportunities, the average journey included 27 touchpoints before an opportunity was created. The median was 19, because a minority of very long enterprise journeys pulls the average up. The median journey crossed four distinct channels and took 41 days from first touch to opportunity.

Deal size is the strongest single predictor of journey length. Opportunities under $5,000 in annual contract value take a median of 11 touchpoints. Opportunities above $50,000 take 46, more than four times as many. The time to opportunity grows in the same way, from a median of 9 days at the low end to 58 days at the high end.

More people, not just more visits

Longer journeys are not only the same person visiting more often. As deal size grows, more people from the buying company get involved. For opportunities above $50,000, we identified a median of 4.2 people from the buying account in the journey before the opportunity was created, compared with 1.3 for deals under $5,000. Each of them arrives through their own mix of channels.

This matters for attribution because most last-click reports look at the person who converted, not the account. If a senior buyer first found the company through a podcast and then asked a team member to book a demo, last-click will credit the team member’s visit, often a branded search or a direct visit. The podcast never appears.

The middle of the journey is the least visible part

First touches get attention because they are where acquisition budgets go. Last touches get attention because they are where conversion happens. The middle is mostly invisible in standard reports, yet in the median journey it holds about 80% of the touchpoints. These are return visits from email, product pages read twice, comparison pages, pricing page visits, and documentation. They are where a buyer builds the confidence to talk to sales.

Content plays a large role here. In journeys that ended in an opportunity, the median buyer viewed five content pages in the middle of the journey, and comparison and pricing pages were the two most common page types in the final week before conversion.

Long gaps between touches

Journeys are not only long; they are uneven. The median gap between two consecutive touchpoints was under a day in the final two weeks before an opportunity, but more than nine days in the first half of the journey. Many buyers make contact, go quiet for weeks, and then come back with a burst of activity when a project gets budget or a trigger event occurs.

Those quiet periods are where re-engagement channels earn their place. In journeys with a gap of more than 30 days, the touchpoint that ended the gap was an email click 29% of the time, an organic search 24% of the time, and a paid social ad 18% of the time. Last-click rarely credits any of these, because the buyer usually takes several more steps before converting.

Given how much drift last-click introduces, how many teams actually rely on it? We looked at which attribution model each company used in its most-viewed pipeline report over the six-month period. This is a better signal than asking teams what they believe in, because it shows what they actually look at.

Last-click is the primary model at 46% of companies, more than three times as many as any other single model. Multi-touch models of any kind (linear, position-based, data-driven, and time-decay) together account for 45%. First-touch, which has the opposite bias to last-click, is the primary model at 9%.

Last-click is usually inherited, not chosen

When we asked teams in the companion survey why they used their current model, the most common answer from last-click users was that it was the default in their tools. Only 18% of last-click users said they had compared it with another model before settling on it. Among teams using a multi-touch model, 71% said they had.

This is the core of the problem. Last-click is not a bad model because it is simple. Simple models are easy to explain and easy to audit. It is a problem when it is used without anyone knowing what it leaves out.

Teams that compare models trust their reports more

Trust in attribution data is low across the board. In the companion survey of 600 marketing leaders, 61% said they did not fully trust their attribution reports. But trust varied sharply by how teams worked. Among teams that regularly compare at least two models side by side, 54% said they trusted their reports, compared with 29% of teams that look at a single model.

54% vs 29%of teams trust their attribution when they compare two models, versus when they rely on one.

This may sound backward. Seeing two models disagree should, if anything, make a team less sure of the numbers. In practice it seems to work the other way. When teams can see where models agree and where they diverge, they know which conclusions are solid and which depend on assumptions. A single model gives a precise-looking answer with no way of knowing how fragile it is.

What about data-driven models?

Data-driven models, which use the company’s own conversion data to estimate how much each touchpoint contributes, are often presented as the answer to the problems of rule-based models. In the sample they were the primary model at 10% of companies, almost all of them above $10 million in revenue.

They can be useful, but they are not free of assumptions. A data-driven model needs enough conversions to learn from, and most B2B SaaS companies create far fewer opportunities than an e-commerce store creates orders. Among companies in the sample with fewer than 200 opportunities in the period, data-driven models produced channel shares that changed by more than 10 percentage points from one month to the next for at least one major channel. That is a sign the model was fitting noise.

For most teams, a transparent rule-based model compared side by side with last-click will give more stable and more explainable answers than a data-driven model trained on too little data.

Every analytics tool has a bucket for visits it cannot explain. Usually it is called direct traffic. In theory it is people who typed the URL or used a bookmark. In practice it collects anything that arrives without a referrer: links shared in private messages, clicks from some apps and email clients, links in PDFs and slides, and visits from people who heard the company’s name on a podcast or in a community and typed it in.

To estimate how much real demand ends up in that bucket, we looked at 312 companies in the sample that ask new signups or demo requests a free-text question such as "How did you hear about us?" We matched each answer to the channel that analytics recorded for the same person’s first session.

Across all answers, 41% of self-reported sources were recorded as direct. The share was highest for the channels that rely on people talking to each other. Nearly three-quarters of buyers who said a colleague or friend told them about the company arrived as direct visitors. So did about two-thirds of those who named a podcast and more than half of those who named a Slack group or community.

This is not a flaw in any one tool. Most of these channels simply do not pass a referrer. But it does mean the channels that are hardest to buy, such as reputation, word of mouth, and community presence, are the ones that standard reports are least able to see.

Self-reported attribution fills the gap, partly

Asking buyers directly is the most reliable way we found to recover this demand. It has its own weaknesses: people forget, they name the most recent thing they remember, and many skip the question. In the sample, 34% of respondents left the field blank or wrote something unusable. But the answers that do come back are often the only record of how a buyer first heard about a company.

The companies that got the most out of self-reported data treated it as a separate lens, not a replacement. They kept their tracked attribution for the channels it measures well, such as paid campaigns, search, and email, and used self-reported answers to size the channels it cannot see.

How to ask the question

The way the question is asked changes the answers. Companies in the sample that used a free-text field got answers that mapped to a specific channel 71% of the time. Companies that used a dropdown got more complete answers, but they clustered heavily on the first two options and on "Search engine", which buyers often pick because they last found the company through search, even if they first heard of it somewhere else.

The companies with the most useful self-reported data followed a few simple rules:

• Ask a single open question, and make it optional so it does not lower form completion.
• Ask it at the moment of highest intent, on the demo request or signup form, not in a later survey.
• Review answers by hand or tag them in batches each month, rather than forcing them into predefined categories at the point of entry.
• Compare the tagged answers with tracked first-touch data for the same people, so the two sources can be reconciled.

A new source of traffic has grown quickly over the last year: referrals from AI assistants and answer engines such as ChatGPT, Perplexity, Gemini, and Copilot. Volumes are still small compared with traditional search, but they are growing fast, and where they appear in a journey matters for attribution.

In the first half of 2026, 7.8% of journeys that ended in an opportunity included at least one visit referred by an AI assistant. A year earlier, using the same method on the same companies, the figure was 2.3%. That is growth of 3.4 times in twelve months.

More interesting than the volume is the position. In journeys that include an AI-referred visit, that visit is the very first touchpoint 58% of the time. Only 9% of the time is it the last. Buyers are using AI assistants to build a shortlist, ask which tools solve a problem, or compare options, and then clicking through to learn more.

That makes AI search one of the most under-credited channels under last-click, even though its share of pipeline is still small. In the sample, AI referrals were credited with 0.6% of pipeline under last-click and 2.4% under the position-based model, a ratio of 0.25, lower than any of the channels in chapter 2.

Much of it is still hidden

Like word of mouth, AI search does not always identify itself. Some assistants pass a referrer and some do not, and many buyers read an answer, then type the company name into a browser or search engine. In the self-reported data from chapter 5, 4% of answers named an AI tool, but only about a third of those buyers arrived with an AI referrer. The rest appeared as direct traffic or branded search.

This links back to the paid search finding. Some of the growth in branded search is likely buyers who first heard of a company in an AI answer. Last-click will credit the branded search ad they clicked afterward.

Which assistants send the traffic

ChatGPT referred the largest share of AI-referred visits in the sample, followed by Perplexity, Gemini, and Copilot. Perplexity sent fewer visits overall but cited sources more consistently, so a higher share of its referrals carried a clear referrer. The mix is changing quickly, so the useful habit is less about any one assistant and more about grouping all of them into a single channel that can be tracked over time.

Visits referred by AI assistants also behaved differently from search visits once they arrived. They viewed more pages per session, spent longer on comparison and pricing pages, and were 1.6 times more likely to return within a week. That fits the pattern of a buyer who has already done some research inside the assistant and arrives with a short list of questions to answer.

The patterns in this report hold across company sizes, but their strength changes. We grouped companies by annual recurring revenue and compared four measures: how many use last-click as their primary report, how long their journeys are, how much credit drift they have, and how over-credited their paid search is.

Smaller companies are much more likely to report on last-click: 58% of companies under $1 million in revenue do, compared with 27% of those above $50 million. That is partly about tooling and partly about time. Early-stage teams often have one marketer, and the default report is the one that gets used.

Larger companies have longer journeys and, as a result, more credit drift: 32% at the median company above $50 million, compared with 17% below $1 million. So the companies most likely to use last-click are the ones where it does the least damage, and the companies where it would do the most damage have mostly moved away from it. That is a reassuring pattern, but the middle is where it matters most. Companies between $1 million and $50 million in revenue have substantial drift and are still split roughly in half on whether they rely on last-click.

Paid search over-crediting falls with size

The paid search ratio moves the other way. Smaller companies over-credit paid search the most, at 2.4 times. A likely reason is that smaller companies rely more heavily on branded and high-intent search to capture the demand their founders and early customers generate through networks and word of mouth. That demand rarely shows up in analytics, so the branded search click gets the credit.

Self-serve and sales-led companies differ

Company size is not the only dividing line. We also split the sample by how companies sell. Self-serve companies, where most customers sign up and pay without talking to sales, have shorter journeys and lower drift: a median of 16 touchpoints and 19% drift. Sales-led companies, where an opportunity is created by a demo request or a sales conversation, have a median of 29 touchpoints and 27% drift.

Hybrid companies, which run both motions, have the most complicated picture. Their self-serve and sales-led journeys often start in the same place, with the same content and the same campaigns, but end in different places. Reporting on them together under last-click blends two very different patterns into one average that describes neither well. Hybrid companies in the sample that split their attribution reports by motion said in the survey that they trusted them more than those that did not.

The goal of this research is not to replace one model with another. Every attribution model is a simplification, and multi-touch models carry their own assumptions about how credit should be split. The goal is to make reporting choices on purpose and to know which conclusions depend on them. Here is where we would start, in order.

1. Find out which model your main report uses. If nobody on the team chose it, it is probably last-click. Write it down at the top of the report so everyone who reads it knows.
2. Add one multi-touch view next to it. Linear or position-based models are easy to explain and a good place to start. Compare channel shares across the two every month.
3. Split paid search into branded and non-branded. Report them as separate channels. Evaluate branded search on cost and brand coverage, and non-branded search on its role across the full journey.
4. Look at journeys at the account level. If your deals involve more than one person, a contact-level view will miss much of the journey. Group touchpoints by company before assigning credit.
5. Ask buyers how they found you. Add a free-text question to your demo or signup form and review it next to your tracked data. Use it to size the demand your analytics cannot see.
6. Track AI referrals as their own channel. Watch first-touch credit and growth, not last-click conversions.
7. Move budget only where models agree. When two models point the same way, act. When they disagree, run a holdout test or a budget shift on a small scale before committing.

Most of these steps take an afternoon to set up. None of them requires changing tools or hiring a data team. The common thread is visibility: seeing more of the journey, and seeing how much your conclusions depend on the way you look at it.

Month 1: make the current model visible

Spend the first month on the first three steps. Label your main pipeline report with the model it uses, add a second multi-touch view, and split paid search into branded and non-branded campaigns. Do not change any budget yet. The point of this month is to build a baseline that everyone on the team can see.

Month 2: widen the view

In the second month, move to account-level journeys and add the self-reported question to your forms. Set up AI referrals as their own channel. By the end of the month you should be able to answer three questions for each major channel: how much credit it gets under each model, where it tends to sit in the journey, and how much of its demand your tracking may be missing.

Month 3: test before you shift

In the third month, look at where the two models agree and where they disagree. Where they agree, you can act with more confidence. Where they disagree, design a small test, such as pausing branded search in one region or increasing spend on a demand creation channel for a fixed period, and measure the effect on pipeline rather than on attributed credit.

Sample

The sample includes 1,200 B2B SaaS companies that used Usermaven between January 1 and June 30, 2026, had connected a CRM or billing source, and created at least 40 opportunities during the period. Companies in the sample range from under $1 million to over $50 million in annual recurring revenue. North America accounts for 54% of companies, Europe for 31%, and the rest of the world for 15%.

Definitions

• Touchpoint: a session with a distinct traffic source, a tracked email click, an ad click, or a logged sales activity. Page views within a single session count as one touchpoint.
• Journey: all touchpoints for the people at one buying account, from the first identifiable touchpoint to the creation of an opportunity.
• Last-click: 100% of credit to the final touchpoint before the opportunity was created, excluding direct visits when an earlier non-direct touchpoint exists.
• Position-based: 40% of credit to the first touchpoint, 40% to the last, and 20% split evenly across the touchpoints in between.
• Credit drift: half the sum of absolute differences between a company’s channel shares under the two models. It is the share of pipeline that would be credited to a different channel after switching models.
• Paid search ratio: last-click credit divided by position-based credit for paid search.

Companion survey

Survey figures in chapters 2 and 4 come from a separate survey of 600 marketing leaders at B2B SaaS companies, run in July 2026. Respondents held a director-level or more senior marketing role. The survey sample overlaps with, but is not identical to, the companies in the behavioral data.

Limitations

All companies in the sample use Usermaven, so they may track journeys more completely than the average B2B SaaS company. Journeys can only include touchpoints that were tracked: offline events, private messages, and visits on devices a buyer never identified on are not included unless they were logged in a CRM. The position-based model is used as a point of comparison, not as a measure of true channel impact. No attribution model measures causation; for that, controlled experiments such as geographic or audience holdouts are needed.