Your attribution model is perfectly measuring the wrong journey

Best-in-class B2B teams have never been more sophisticated at measuring something that matters less and less

Disclaimer: The views here are my own and do not represent my employer or anyone else.

Marketing attribution has always been a mess, but it was a manageable mess. You had a finite number of channels, a rough methodology, and an unspoken agreement across the organization not to look too hard at the math 🤫. Multi-touch models distributed credit across touchpoints, position-based models weighted weighted the first and last interactions more heavily, time-decay models favored recency, and the most sophisticated teams experimented with causal and incrementality frameworks to get closer to the truth. Everyone had a preferred model, and everyone moved on. Budgets got allocated, campaigns got greenlit, and the imperfection was tolerable because at least it was consistent.

AI has broken that truce, and it has done it in several directions at once.

Let’s start with a buyer journey you and I can relate to.

A VP at a mid-market company starts researching your category. She opens ChatGpt and asks it to summarize the competitive landscape. Your brand appears in the output, framed in language you didn’t write and can’t control yet, and she forms an initial impression before ever touching anything your team can measure. That interaction never appears in your attribution stack; it happened, it mattered, and to every platform in your revenue stack it simply doesn’t exist.

A week later she visits your website after a colleague mentions your company in a slack channel. She lands on a page dynamically generated by your new AI content engine based on her firmographic profile. She spends four minutes on it, reads the case study section, and scrolls to pricing. Your CMS logs the session but has no way of knowing the experience she had was entirely different from what your last visitor saw. The personalization worked, but the signal is lost.

Your AI SDR then sends her a sequence two days later, triggered by an intent signal from a third-party provider. She doesn’t reply, but she reads it carefully and forwards it to a colleague with a note saying “this is exactly what we need.” That forward, arguably the most valuable signal in the entire journey, generates no data whatsoever. Three weeks later, after attending an AI-summarized recap of a virtual panel she never actually watched live because the agenda felt too boring, she books a discovery call. Your W-shaped or time-decay attribution model assigns the majority of credit to the content syndication touchpoint that happened to fire the day before she converted.

That is not a hypothetical anymore. That is tuesday.

The most sophisticated revenue teams today have obviously moved well beyond single-touch models. Not promoting any of these, but several platforms such as Hockeystack, Dreamdata, and Segmentstream etc. have done some really good work around account-level journey mapping, connecting CRM data to multi-touch influence across buying committees rather than individual contacts. The shift from contact-level to account-level attribution was the right move, and the teams that made it early are measurably better at understanding pipeline than those still working at the lead level.

But pretty much all major platforms are running into a structural wall that better tooling alone cannot solve. They can track what happens inside the observable infrastructure (your website, your Crm, your ad platforms, your marketing automation etc.). What they cannot see is the rapidly expanding layer of AI-mediated research that happens before a buyer ever touches anything you own. Quotes from research firms have been varying, but the median range suggests B2B buyers complete somewhere around 60-70, in some cases up to 75 percent of their evaluation before engaging with a vendor directly. AI tools are accelerating that shift, and the gap between where influence actually happens and where your attribution platform looks for it is widening every quarter.

The deeper problem is a closed loop that is going to compound over time. AI-powered attribution platforms are being used to measure journeys that are increasingly shaped by AI-generated touchpoints. The model tells you what’s working, you invest more in it, the model gets trained on that investment pattern, and the cycle reinforces itself regardless of whether the underlying causal logic holds. An account-level journey platform might correctly identify that accounts engaging with your thought leadership content convert at a higher rate. What it cannot tell you is whether your thought leadership is influencing those accounts, or whether accounts that were already inclined to buy are simply more likely to consume content along the way. The correlation is real, but the causality is still pretty much assumed.

The reframe i think matters more

The instinct when measurement breaks down is to find a better measurement tool; that instinct is understandable but increasingly insufficient. The more durable shift is a different philosophy entirely - one that accepts more uncertainty at the individual touchpoint level while deliberately getting sharper at the account and revenue level.

What does that actually mean in practice? It means treating account-level engagement velocity as a more reliable signal than any individual touchpoint. When multiple stakeholders at a target account are consuming content, responding to outreach, and engaging with your SDR motion within the same thirty-day window, that cluster of signals tells you something meaningful that no single attributed touchpoint can. The question worth asking isn’t “which channel sourced this opportunity” but “what combination of signals, across which roles, over what time horizon, correlates with accounts that close and then expand.”

It also means reorienting around outcomes you can measure with higher confidence rather than influence you can only approximate. Demand lift from ICP, pipeline velocity, the rate at which accounts move from first engagement to opportunity to close, is more honest than channel attribution because it reflects the aggregate effect of everything marketing did rather than a model’s best guess at decomposing it. Accounts that experience coordinated, multi-threaded engagement across marketing, SDR, and content tend to move faster. That observation is actionable even though as of today you can’t fully attribute why.

It also means figuring out innovative ways to invest in qualitative feedback loops that no platform can replace. win-loss interviews conducted within two weeks of a decision remain one of the richest sources of influence data available to any marketing team - what buyers say shaped their decision, which competitors they seriously evaluated, where they first heard about you etc. Most teams treat these as a nice-to-have, but the best ones are building systematic programs around them in close partnership with sales.

Unfortunately, i know very few teams doing genuinely good work here - and the ones that are have stopped arguing about which metrics to pick for annual planning, or how to combine MMM, MTA and other frameworks to do channel allocation in isolation.

The budget meeting nobody is ready for

Q1 is almost about to end and before we know it, we will be Q3. At that point, most marketing teams will start thinking about FY2027 planning with attribution data that is confidently directional but structurally incomplete in ways they cannot detect. The channels that receive the most credit will be the ones easiest to observe, not necessarily the ones doing the most work. There will be an ask from CMO to cut down the budget, and when it comes to annual budget reductions, no one would care about the attribution model anyway - the investments most at risk of being cut would be the ones operating furthest from conversion, building the brand presence and thought leadership that shapes AI summaries, peer conversations, and dark funnel research months before a buyer ever raises their hand.

The measurement gap is real and it will not be closed by the next generation of attribution tooling alone, at least not in the near term.

I am getting back to the world of marketing strategy and analytics after a bit of focus on Growth and Innovation. And, i am very excited to build novel approaches to think about where should $$ go and be tracked and validate some of the new world hypotheses i have, along with my marketing, sales and broader GTM friends.

Will share more as i learn more.