Marketing attribution is a mess. There, I said it.
After 15+ years in marketing operations, I've seen countless teams waste time, money, and political capital chasing the perfect attribution model. The dirty secret? Perfect attribution doesn't exist. But that doesn't mean we should give up on it entirely.
Let's walk through what actually works when you're trying to connect marketing activities to revenue, especially in complex B2B environments with long sales cycles.
Last quarter, I worked with a firm that had spent six figures on a fancy attribution platform. Their marketing team was showing off beautiful dashboards with precise percentage allocations across twelve different touchpoints.
There was just one problem: it was mostly fiction.
The model couldn't account for dark social, offline conversations, or the VP of Sales who happened to golf with the prospect. It didn't capture how the CFO's previous experience with their brand influenced the purchase decision. And it certainly couldn't measure how the prospect forwarded our whitepaper to five colleagues.
Attribution tools promise certainty in an uncertain world. But tools alone can't deliver.
The first mistake most marketing teams make is starting with the attribution model instead of the business questions they need to answer.
Before you pick a model, ask yourself:
- What decisions will this data inform?
- Who needs to trust these numbers?
- What level of precision do we actually need?
- What's the cost of being wrong?
A B2B SaaS client recently scrapped their complex attribution setup and replaced it with a simpler approach focused on answering just three questions:
This clarity made their attribution efforts actually useful instead of theoretically perfect.
Attribution projects have a nasty habit of becoming black holes for marketing resources. I've seen teams spend months debating model parameters while their campaigns run on autopilot.
The opportunity cost is enormous:
- Engineer time integrating systems
- Analyst hours cleaning and reconciling data
- Marketing time spent in meetings about attribution instead of, you know, marketing
- Lost credibility when the numbers inevitably conflict with sales' perspective
One regulated industry client spent 8 months implementing a complex attribution model, only to have the executive team ignore it because it contradicted their intuition. The marketing team would have been better off running more experiments, and trusting "their gut".
Here's my two-cents approach for attribution that actually improves marketing performance:
Stop looking for one perfect model. Use different models for different purposes:
- First-touch for new market entry and awareness channels
- Last-touch for sales enablement and bottom-funnel activities
- Position-based for complex, high-consideration purchases
- Time-decay for fast-moving markets with short sales cycles
A healthcare technology company I worked with uses first-touch for their marketing team's KPIs and last-touch for their sales team's reporting. Everyone understands the limitations, but the approach gives useful directional data for their respective decisions.
The missing ingredient is an informed marketer - you've got to be able to weave the story together. And if you're the one being reported to, don't just ask for it in an email. Make the time to meet with your Marketing Head - context is everything.
Attribution models are hypotheses, not facts. Test them with controlled experiments:
- Pause specific channels in certain regions/segments
- A/B test budget allocation across similar audiences
- Compare influenced pipeline between exposed and unexposed accounts
One client was convinced their webinars were driving deals until we ran a controlled test comparing pipeline generation between accounts that attended webinars versus similar accounts that didn't. Turns out webinars were a result of interest, not a cause. It completely changed the model behind how they programmed their webinars, eliminated the friction the previous model had caused between expectations of lead gen vs actual, and it helped their sales team better understand the customer relationship journey.
The most important question isn't "who gets credit?" but "what would have happened without this marketing activity?"
Incrementality testing answers this by measuring the lift created by marketing, not just assigning credit for what would have happened anyway.
A B2B software company discovered their paid search campaigns were mostly capturing demand, not creating it. When they paused Google Ads in test regions, 70% of that traffic simply came through organic search instead. This insight saved them $400K annually.
Attribution will always be imperfect. Build trust by being transparent about the limitations:
- Document assumptions and blind spots
- Show confidence intervals, not just point estimates
- Reconcile differences between models
- Acknowledge what you can't measure
I've found that presenting attribution data alongside qualitative insights from sales conversations builds more credibility than pretending the numbers tell the whole story, or relying on them to do it when you're not in the room. We need to break the cycle of measuring what's easy to count, instead of what's most impactful to the organization.
If you're struggling with attribution (and who isn't), here's where to start:
Perfect attribution is a mirage. But practical, good-enough attribution that helps you make better decisions? That's achievable—and far more valuable than chasing an impossible ideal.
Just make sure you're not optimizing your attribution model while your campaigns are running on autopilot. The best attribution system in the world won't save bad marketing.