Marketing has more data than ever. More platforms, more dashboards, more AI-generated insights arriving instantly and at scale. And yet, when we talk to marketing leaders on discovery calls, in customer interviews, in G2 reviews — one word keeps coming up, unprompted, over and over: trust.
Trust isn’t something concrete or tangible, yet it is the core of whether a system is used or shelved. It’s the human judgment about whether the numbers in front of you are safe to act on. And across the conversations we’ve had this year — plus new research from Scott Brinker and Frans Riemersma in The State of Marketing Attribution 2026 — the same conclusion keeps surfacing: the technology got better, but the trust problem didn’t go away. If anything, more data and more AI-generated dashboards have made it easier to be confidently wrong.
The problem isn’t new, but it’s still everywhere
Listen to enough calls with prospects and a pattern emerges immediately.
“People still don’t trust the data.”
“Our biggest concern is that it’s right that we trust today that we understand where it’s coming from, that it’s scalable and all of those things, that we’re not going to have to replace it with something else.”
And a G2 reviewer, a Lead BI Strategist at an enterprise company, described exactly how that trust erodes in practice — not through one dramatic failure, but through the slow drip of numbers that don’t match:
“The reports and queries we generated from both Marketo and Salesforce often produced significantly different results, which led to confusion among users and eroded trust in our data collection and visualization processes.”
Three different companies, three different stages of evaluation and maturity, one identical failure mode: when two systems disagree, nobody trusts either one.
Lack of trust comes at a cost
Nowhere was this clearer than in Superside’s own account of where they started. Before adopting a unified attribution layer, Cassandra Gill, VP of Marketing at Superside, didn’t mince words:
“There was a total mistrust in the data, and it resulted in team members sticking to the same strategies that they had been running for months, uncertain of where to further focus their efforts for growth.”
If you asked five people at Superside how many SQLs a channel generated last month, and you’d get five different answers. This situation is all too common and goes beyond an inconsistency to become a strategic cost. Teams that don’t trust their numbers can’t make confident decisions. They don’t kill underperforming campaigns. They keep doing what they did last quarter, because at least that’s a known quantity.
Superside was carrying close to ten years of data debt by the time Cassandra joined. Fixing it meant redefining what counted as an MQL, cleaning up years of inconsistent UTM conventions, and rebuilding attribution models from the ground up. The payoff, once trust was rebuilt: 30% more enterprise pipeline at 31% lower cost per opportunity.
But the more important line from their story isn’t the results. It’s how her colleague Michelle described the goal of the entire project:
“The goal wasn’t just better reporting. It was building trust in data so that every spend decision was backed by numbers we trusted.”
That’s the reframe. Trust isn’t a side effect of good attribution. It’s the actual deliverable.
What the 2026 research says: trust is a design choice, not a data problem
This lines up almost exactly with what The State of Marketing Attribution 2026 (Brinker & Riemersma) found across the broader market. Their research draws a sharp line between what they call Attribution 1.0 and Attribution 2.0 — and trust is the dividing line running through nearly every row of their comparison:
Attribution 1.0 | Attribution 2.0 | |
|---|---|---|
Company Truth | One answer | Agreed models per case |
Success Mode | False precision | Managed uncertainty |
Data Assumption | Complete, observable | Fragmented, directional |
Purpose | Scorekeeping | Decision support |
The report’s core insight is almost counterintuitive: attribution maturity did not correlate with having more data. It correlated with knowing which data was usable, being honest about the rest, and aligning teams around trusted data. As Scott Brinker and Frans Riemersma’s State of Marketing Attribution, concludes:
“The value of modern attribution lies not in precision, but in coordinated action under uncertainty.”
That’s a hard pill for a lot of marketing orgs to swallow, because the instinct when data feels untrustworthy is to chase more precision — a better model, a cleaner dashboard, one definitive number everyone can point to. That’s the wrong fix. Precision isn’t what earns trust; alignment on how to act despite imperfect data is. Which is exactly where the automation myth creeps in — the assumption that if a system just did more of the reconciling for us, humans could step back and trust would follow automatically. It’s the opposite. The companies who actually solve this treat attribution as orchestrated, not automated: humans interpreting, humans challenging, humans deciding what the numbers mean before anyone acts on them. Trust, in other words, isn’t something a model produces. It’s something a team has to keep earning.
How to actually fix it
Pulling together what we’ve heard directly from customers and what the broader research confirms, the path out of “nobody trusts the data” looks less like a tooling upgrade and more like a discipline:
1. Stop chasing one “true” model.
Superside’s breakthrough wasn’t picking the perfect attribution model — it was accepting that different questions need different models, and getting stakeholders aligned on which model answers which question. The 2026 report calls this the shift from “one model” to “decision-dependent models.”
2. Fix the plumbing before you fix the story.
Superside’s UTM fields were dynamically overwriting the original source of every lead — meaning every attribution report was quietly wrong at the source. No dashboard, however elegant, survives broken plumbing underneath it. Clean, governed UTM conventions and consistent campaign metadata are unglamorous, but they’re the actual foundation.
3. Make disagreements visible, not buried.
Trust doesn’t only erode if the data is bad — it can erode because two systems disagree silently, and users find out the hard way. Give teams one shared, governed view instead of five spreadsheets pulling from five sources.
4. Move from “who gets credit” to “what’s actually working.”
As the report puts it, high-performing teams treat attribution as a neutral layer for cross-team conversation, not a scoreboard for department bragging rights. This is easier to do with a multi-touch model. With a multi-touch model, the many factors affecting buying decisions are reflected fractionally instead of forcing all credit onto one arbitrary touch. When credit is more evenly and accurately distributed, attribution becomes less of a credit war.
5. Treat trust-building as the deliverable, not the byproduct.
Superside didn’t set out to build a better dashboard. They set out to build trust, and the dashboard was the mechanism. That distinction changes what you measure for success: not “did we ship the report,” but “did people change a spend decision because of it.”
The bottom line
Every company we’ve talked to in 2026 has more data, more channels, and more AI-generated insight than they did a few years ago. Almost none of them have more trust. That gap — between what the data can technically show and what people are actually able to act on — is attribution’s real unsolved problem this year.
Fixing it doesn’t require a bigger stack. It requires the same unglamorous discipline Superside applied: clean up the plumbing, agree on what “truth” means for each question, make disagreements visible instead of silent, and treat every dashboard as a trust-building exercise, not just a reporting one.
Get that right, and the payoff isn’t just cleaner reports. It’s a team that finally acts on what the numbers are telling them.
