Marketing attribution was supposed to be marketing’s proof of life.

Finally, a way to show what was working. A way to defend budget. To clarify value. To quiet critics with clean dashboards and hard data.

Marketing attribution (and the new technologies that made it possible) would solve John Wanamaker’s old quote … “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” Modern marketing meant we could give attribution to that mystery half! The only thing was, we really couldn't. At least not in a consistent and trustworthy way. 

Attributed MQLs and the quest for pipeline credit damaged marketing’s relationships with sales and the rest of the C-Suite when our creative attribution models (often backed by untrustworthy data or vanity metrics) made claims that were hard to swallow. In what Ardath Albee recently described as how the "...quest for growth at all costs pitted marketing against sales in the arms race for credit for revenue," marketing lost credibility. 

Somewhere along the way, attribution became less of a decision-making tool and more of a battleground. Instead of aligning teams, it drove them apart. Instead of improving marketing’s standing, it invited scrutiny. And instead of helping us make better decisions, it led to even more confusion about what’s actually moving the needle.

The Rise (and Rise) of Attribution

It didn’t start out broken.

Attribution wasn't just for optimization — it became a way to justify marketing's existence. Attribution has to answer the board's questions about performance, finance's questions about ROI, and sales' questions about lead quality. The weight became too much.

Early attribution models were designed to answer two reasonable questions: What marketing activities contributed to a conversion? And, Which conversions led to revenue? As B2B marketers moved beyond brand awareness to lead generation and revenue impact, attribution became the measurement mechanism of choice. Demand generation took over the lion’s share of the budget while the awareness budget dwindled … especially in the startup world. 

The promise was seductive: run a campaign, track every click, and know exactly what worked. Tools like Google Analytics, marketing automation platforms, and CRM integrations gave marketers the ability to show their work in ways that once seemed impossible.

And in the beginning, it worked. Campaigns could be optimized, budgets adjusted, and sales enabled. Attribution brought a level of discipline to marketing that made it feel more like a science. It also gave CMOs a way to talk about marketing in the language of finance.

But over time, the expectations grew. Attribution wasn’t just for optimization – it became a way to justify marketing’s existence. Attribution had to answer the board's questions about performance, finance's questions about ROI, and sales' questions about lead quality. The weight became too much. And then to make things worse? Buying committees grew larger and buyer behavior changed. 

Buyers, once at the mercy of the sellers as gatekeepers who only sold information in exchange for contact information (i.e. conversions which were attributable touchpoints), successfully searched for ways to do their research without interference and a seemingly endless stream of emails and phone calls from vendors.

That’s where things started to go sideways.

What Broke Attribution?

Fragmented Tech Stacks and Disconnected Data

B2B marketers operate in sprawling ecosystems. CRM, MAP, web analytics, ABM platforms, ad networks, data enrichment tools, intent platforms – each with its own data definitions and limitations. Add privacy regulations and cookie loss, and visibility into the full buyer journey becomes incomplete at best. Zero-click search and AI as a search engine are only the latest straws on this camel’s back.

"When leadership asks for pipeline data, they get three different answers." The Problem With Attribution, Saima Rashid, March 19. 2025.

Efforts to reconcile disparate systems with CDPs and custom integrations often fall short. Many companies still rely on spreadsheets to align campaign data with revenue outcomes. This isn’t just a technical debt problem. It’s a visibility problem that undermines confidence in the numbers.

Without a reliable way to stitch buyer behavior across systems, attribution turns into a game of best guesses. But decisions are still made based on those guesses. And when different teams pull different numbers from different tools, the result is predictable: no one agrees on what’s true.

Teams Working From Different Playbooks

Sales, marketing, and finance each have their own approach to measurement. Sales leans on CRM and forecasts. Finance models revenue and margin. Marketing reports on influenced pipeline and engagement trends. All valid. None identical.

And attribution? It sits in the middle. Often unsupported, sometimes mistrusted, and rarely consistent across functions. 

Attribution data becomes a source of debate, not clarity. And the longer those debates go unresolved, the more fractured the trust across the go-to-market organization becomes.

Attribution, Incentives, and the Battle for Credit

Attribution doesn’t just reflect activity. It influences behavior. When attribution becomes the scorecard, people start playing to the scoreboard.

That might mean marketing only runs lead gen plays that can be easily tracked. Or sales ignores inbound leads because they don’t show up in their model. Or finance pushes back on campaign funding because the ROI looks soft against last-touch metrics.

Measurement drives behavior. And when different attribution models reward different behaviors, alignment suffers. Internal teams compete for credit instead of collaborating for outcomes.

In organizations with a strong growth mandate and limited budget, the battle for "who gets credit" becomes especially toxic. What should be a discussion about what's working turns into a defensive argument about who's to blame.

This battle for the win is a symptom of something deeper: a lack of shared metrics across the go-to-market organization. Attribution is often the spark. But the real fire comes from the disconnect in how value is defined and rewarded.

The Myth of Precision

The final crack in the attribution story is the false promise of accuracy.

No model is 100% correct. Most aren’t even directionally close. But the charts look good in the board deck. And over time, the illusion of certainty becomes part of the problem.

Too often, attribution is used to validate budgets, not to improve strategy. It gives marketers a defensive posture rather than a performance mindset. When the model becomes the story, reality gets blurry.

It’s common for teams to argue over fractional percentages in a model that is based on incomplete data to begin with. Entire budget decisions get made on shaky foundations. And when those decisions don’t yield results, everyone starts pointing fingers—at each other and at the model.