Why Most Marketing Attribution is Broken

Most marketing attribution is fundamentally broken. The attribution model you're using right now is likely giving you false confidence about what's actually driving your growth.

The Attribution Problem

A customer sees your LinkedIn ad, reads a blog post, signs up for your newsletter, receives three emails, searches for your brand on Google, clicks a PPC ad, and converts.

Which channel gets credit?

Traditional models force you to pick:

First-touch - LinkedIn gets 100% credit
Last-touch - Google PPC gets 100% credit
Linear - Every touchpoint gets equal credit

The problem? None of these represent reality.

Why Traditional Models Fail

1. They Oversimplify Complex Journeys

Modern customers research across devices and channels over days or weeks. Static attribution weights miss this complexity entirely.

2. They Ignore Dark Social

Up to 84% of social sharing happens through private channels invisible to your attribution model. You're making decisions based on incomplete data.

3. They Can't Track Cross-Device

Your customer researches on mobile, compares on laptop, converts on desktop. Most systems treat these as three different people.

4. They Create Perverse Incentives

Optimizing for last-touch makes you invest in bottom-funnel tactics that capture demand rather than create it. Brand-building gets starved of budget.

The False Confidence Problem

Attribution models give you the illusion of understanding. Precise numbers feel trustworthy, but precision isn't accuracy.

When attribution is flawed, you:

Double down on channels getting credit for others' work
Cut budget from channels actually driving growth
Optimize for short-term conversions over long-term brand building

A Better Approach

1. Accept Uncertainty

Perfect attribution is impossible. You can't track everything. That's okay.

2. Use Multiple Models

Look at first-touch, last-touch, linear, and data-driven models side by side. Patterns across models tell more than any single one.

3. Combine Quantitative and Qualitative

Supplement data with:

Customer surveys ("How did you hear about us?")
Sales team feedback
Win/loss analysis

4. Focus on Incrementality

Ask "what would have happened without this campaign?" Use holdout tests, geo experiments, and before/after analysis.

5. Measure Brand Impact

Track brand awareness, share of voice, organic search volume, and direct traffic trends.

6. Think in Systems

Stop thinking about isolated campaigns. How do your channels work together?

What Good Attribution Looks Like

Data Collection

Multi-touch tracking across devices
CRM integration
Survey data for untracked channels

Analysis

Multiple attribution models compared
Incrementality testing
Customer journey analysis

Action

Budget allocation based on full-funnel impact
Investment in brand and performance
Continuous testing and learning

The Messy Truth

We can't perfectly measure everything. But we can be more sophisticated:

Use multiple imperfect signals
Combine quantitative and qualitative data
Test incrementality, not just correlation
Make decisions with humility and iterate

Moving Forward

Question your attribution model:

What am I missing?
What false confidence am I getting?
How might this lead to bad decisions?

Better attribution isn't about finding the perfect model. It's about building a more complete picture and making better decisions despite imperfect data.