Last Updated on January 21, 2025
Attribution models.
If you’re in B2B, you’ve heard of them, used them, maybe even fought over them in a boardroom. You’re probably even tired of talking about them.
But here’s the thing: attribution models aren’t inherently the problem. It’s how we use them that leads us astray.
Ask anyone who works in CPG if, especially those with a marketing mix that includes dozens of both online and offline channels, if they use attribution (or versions like MMM or incrementality – which as an aside, the author has to admit his preference for incrementality in many cases. Very underrated). It’s not even a question – of course they do!
When you’re in charge of $100M+ in spend, you want to know, at least to some degree of accuracy, that you’re spending it wisely.
B2B brands operate in a different landscape however, especially those enterprise software brands who have a complex suite of products and a long sales cycle with many stakeholders in the buying committee. Channel concentrate is typically higher, with less overall digital spend, and many touchpoints being somewhat opaque (…dark social?).
So, we end up applying, naively, similarly complex multi-touch attribution (MTA) models to enterprises with a narrow TAM and opaque customer journey.
Let me explain with an analogy from Nassim Taleb:
“According to Wittgenstein’s ruler: Unless you have confidence in the ruler’s reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler. The less you trust the ruler’s reliability, the more information you are getting about the ruler and the less about the table.
Attribution models are our rulers. They often tell us more about the model than the reality of our marketing, measuring our own biases, which go on to fuel our decision-making (which is why you’re probably overspending on Google and Meta).
Worse, we then use these flawed insights to make business decisions, shuffle credit, and justify budgets, ignoring the bigger picture: is the business actually growing?
The Blame Game: Why Attribution Feels Broken
I’ve sat in too many meetings where leaders argue over attribution. One team wants to switch from W-shaped to linear. Many haven’t even figured out how organic search reasonably fits into their go-to-market, so they’ll pick a simple model like last non-direct click or first click and run with it.
When the model makes the results look bad, they switch the model. And everyone believes this change will magically fix the problem.
Spoiler: it won’t.
Attribution models often become credit assignment systems—political tools to justify departmental budgets. SDRs argue they sourced the lead. Marketing claims credit for nurturing it. Sales insists they closed it. And instead of asking why the business isn’t hitting its targets, we waste time debating who deserves the gold star (and more headcount, which is the currency of power in large organizations).
Meanwhile, win rates plummet. Pipelines dry up. And we’re left wondering why shifting attribution shapes didn’t save the day.
Here’s the truth: the problem isn’t the model; it’s how we’re using it.
Using Attribution to Solve Business Problems (Not Create Them)
In my recent podcast episode with Chris Walker (out this week, find it here), he clarified his position on attribution models, which is remarkably similar to how I view them. He proposed a thought experiment. Imagine your win rate drops from 16% to 6% over 12 quarters. Is the issue:
- A demoralized sales team?
- Poor lead quality from marketing?
- A broken handoff between SDRs and sales?
The real question isn’t “what shape is our attribution model?” It’s “how do we fix our pipeline?”
Attribution should answer that question—not distract from it. It’s not about assigning credit; it’s about uncovering insights to solve specific business problems.
When we narrow in on the domain of SEO and its contribution to pipeline and revenue, the problem becomes even clearer.
Here’s an example:
For enterprise software companies, focusing solely on last-touch attribution is a recipe for disaster. Why? Because not all leads are equal. One might close a $50k deal that falls through; another, a $1M contract. A nuanced model that prioritizes high-value pipeline insights can shape a more effective strategy than generic website-based lead attribution ever will.
We’ve written in the past about how we often default to a last click attribution model.
That’s because many brands we work with are product-led growth companies who, whether you choose a first, last, or linear model, are looking to drive a low friction free user into the top of the product funnel. There’s not a lot of room, speaking of unit economics and channel CAC, for a whole lot of nurturing and sales-led conversations to get them into this stage. Thus, we chose the most difficult one to force us into driving more business value.
However, we’ve since begun working with very complex enterprises (some of the logos are on our website, many are not due to NDAs). We’re not using last click models with them. We’re digging into HubSpot and Bizible to reverse engineer the touchpoints and pages that drove the most revenue and highest propensity pipeline.
Doesn’t just apply to large enterprises, but the startups that sell to them as well.
For example, I’m working with an early stage data quality startup with a narrow TAM and a very specific salve for their data operations problems. To fulfill a successful organic program, we drill down to the center of the bullseye and write about pain points of data leaders that relate to the product.
However, there are only like 10 of those exact match phrases. To get those to rank, as well as to drive some relevant demand on related topics, and then push them further down the funnel, educating them on data contracts, we need to expand our sphere of topics. Sometimes, this means we’ll hit audiences that don’t actually have the propensity to buy.
But we don’t know what we don’t know until we know it.
So we analyze, as leading indicators, relevant organic traffic and website conversions. But not all conversions are created equal. We’re discovering, to our surprise, that certain topics we thought weren’t as relevant (i.e. they target a slightly more junior ICP) have a higher hit rate when it comes to generating qualified pipeline.
Thus, our “attribution model,” while still at the GA4 layer, measures proximate first click touch points, always goes deeper when it comes to making decisions about where to alter our organic strategy, as we base that on the deeper funnel insights we get from the CRM.
Attribution should be a means to an end—a tool to guide decisions, not a scoreboard for internal politics.
The Bigger Picture: Aligning Teams Around Shared Goals
B2B organizations are silo factories, despite one million keynote presentations from 1994 until today on how to align sales and marketing. Sales. Marketing. Product. Each subset within those teams. Even the hybrid teams, like “Growth.”
Each has its own metrics, its own priorities, and its own version of success.
Attribution models often reinforce these silos, focusing investments on narrow slices of the buyer’s journey. For example, marketing might pour money into sourcing leads that are already in-market because that’s where attribution says the ROI is.
But what about the 99% of your TAM that’s not actively buying? Who’s warming them up? Who’s building trust and awareness for the long game?
We take our own medicine over here at Omniscient. Why do you think we’re investing in a physical magazine? In our podcast? In IRL events and all the other branded stuff we do?
It’s because we know that it’s not very often that people are in the market for an organic growth agency, but when they are, we’ll be top of mind and possibly ahead of the pack because we’ve already proven trustworthy and hopefully somewhat smart.
But we’re also measuring the f*ck out of our core lead generation channels, including SEO and LLMs (which we invest in significantly), LinkedIn, referral partnerships, this newsletter, paid ads, and outbound.
It’s a good old fashioned barbell, some of which we apply to our (last click) attribution model, and the rest we look at as an investment in the 99% of our TAM who will eventually hire us (but not today).
When attribution drives investments into the wrong areas, it’s like obsessing over the rudder while ignoring the iceberg dead ahead.
The Path Forward: Rethinking Attribution
It’s time to flip the script. Instead of using attribution to argue over credit, we should:
- Focus on Business KPIs: Prioritize metrics that reflect overall health, like pipeline growth and win rates.
- Ask Better Questions: Use attribution to explore why metrics are changing, not just who “owns” them.
- Tailor Models to Context: Different businesses need different attribution approaches. One-size-fits-all rarely works.
In short, attribution isn’t about the past (who did what, clap clap, good job). It’s about the future—how to make better decisions to drive unified outcomes.
Actually, let me rephrase the summary of this essay: stop midwitting this stuff.
Ask yourself, “should we measure some components of our marketing investments?” Yes, probably. “All of them?” Probably not, at least not under the same measurement model.
“How should we think about this channel’s contribution to our overall north star metric or incremental revenue?” Well, it feasibly would contribute by…..
Okay, now figure out a model by which you can analyze whether or not that is happening and use it to make better decisions and investment allocations, and then just start shipping good work and talking to your customers more.
Aye aye aye!
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