A hard-won lesson from rebuilding attribution from the ground up
When I inherited our HubSpot instance, I thought our attribution was working fine. After all, leads were coming in, MQLs were being scored, and SQLs were flowing to Salesforce. What could be wrong?
Everything, as it turned out.
The Attribution Nightmare Most Companies Are Living
Here’s the brutal reality I discovered: For years, businesses have relied on flawed attribution models that only capture part of the picture. Our setup looked sophisticated on the surface—LinkedIn ads, ABM through 6sense, Google search campaigns, all feeding into HubSpot with proper scoring. MQLs went to Salesforce, sales validated them as SQLs, and life went on.
But something felt fundamentally wrong.
When leadership asked, “What’s driving our best leads?” or “Should we increase our LinkedIn ad spend?” I realized I was essentially guessing. We had touchpoint data, but no real understanding of what was actually working.
The breaking point came when I audited what happened to rejected MQLs. I assumed our nurture sequences were capturing these leads and moving them through a sophisticated funnel.
I was dead wrong.
Those leads—sometimes 30-50% of our monthly volume—were just sitting there. Stagnant. No nurture, no follow-up, no second chances. We were hemorrhaging opportunity and had no idea.
The Fatal Flaw in Traditional Attribution Models
Most attribution models assume linear progression: first touch → middle touches → conversion. But that’s not how B2B buyers actually behave, especially in complex sales cycles.
Here’s what I learned analyzing our data: People downloading educational content (guides, how-tos, industry insights) are in a completely different mindset than those downloading solution sheets. Yet traditional attribution models treat them exactly the same.
*Educational content downloaders ≠ Solution sheet downloaders. Different mindsets require different attribution approaches. The attribution insight that changed everything: recognizing different buyer mindsets require different measurement approaches.
After months of rebuilding, here’s the framework I developed:
The “Highway Enhancement” Attribution Model
Core Components:
- First Touch Attribution (40%): The campaign that introduced the prospect
- Highway Progression Influence (30%): Content and activities that moved them through our “highway” (nurture track)
- Final Stage Attribution (20%): The campaign that triggered conversion
- ABM/Intent Intelligence (10%): Account-level signals from 6sense and other tools
Why This Works:
- Credits the full journey while weighting key moments appropriately
- Accounts for educational vs. solution content with different attribution weights
- Includes account intelligence that traditional models ignore
- Measures “highway lift” – the value of proper nurturing vs. direct conversion attempts
Example Attribution Chain:
- Dr. Johnson (provider decision-maker) discovers us through “Cost Containment Blog” (40% attribution)
- Downloads payer case study, attends value-based care webinar, engages with emails (30% attribution)
- Requests demo after 6sense shows high intent from his health system (20% final touch + 10% intent data)
This gives us a complete picture of what’s actually driving conversions—and more importantly, what activities are worth scaling.
My #1 Recommendation: Start With a Brutal Audit
Before you touch any attribution models, you need to understand where you’re bleeding leads and revenue.
*Start here: Map your lead flow → Find the black holes → Analyze content performance → Measure time-to-conversion The attribution audit framework: where to begin when rebuilding your measurement system.
The Attribution Audit Framework:
- Map your current lead flow from first touch to closed deal
- Identify every handoff point between systems (HubSpot to Salesforce, marketing to sales, etc.)
- Track lead volume at each stage for the past 6 months
- Find the black holes where leads disappear without nurturing
- Analyze content type vs. conversion behavior (educational vs. solution-focused)
- Measure time-to-conversion by source and content type
Use tools like Claude with MCP integration to help audit your HubSpot instance—AI can spot patterns and gaps that humans miss, but you still need human intelligence to interpret the business implications.
What I’d Do Differently
If I could start over, I wouldn’t have waited to begin the audit. I would have questioned the “working” system from day one.
The key lesson: Your attribution model should be sophisticated enough to support market changes without breaking. If you can’t adapt your attribution when regulations change, buyer behavior shifts, or new competitors enter the market, you’ve built a house of cards.
The Path Forward for HubSpot Users
Immediate Actions:
- Audit your nurture gaps – Where are rejected MQLs going?
- Analyze content performance by buyer stage – Educational vs. solution content
- Map your actual customer journey – Not what you think it is, but what the data shows
- Implement closed-loop reporting between HubSpot and your CRM
- Test attribution models using HubSpot’s comparison features
Advanced Implementation:
- Build custom attribution models that weight different content types appropriately
- Integrate account intelligence (6sense, ZoomInfo, etc.) into your attribution
- Create “highway” nurture tracks for different buyer personas and stages
- Implement time-decay weighting that reflects your actual sales cycle length
The Bottom Line
*If I spend $1 more on this activity, will I get $2+ back in pipeline? If you can't answer this confidently, your attribution is broken. The only attribution question that matters for budget decisions and marketing ROI.
Traditional attribution models aren’t just incomplete—they’re actively misleading teams about what’s working. In an era where every marketing dollar needs to be justified, “good enough” attribution is a luxury B2B teams can’t afford.
The solution isn’t another vendor or tool—it’s building attribution that reflects how your buyers actually behave, not how you wish they behaved.
Your attribution model should answer one critical question: “If I spend $1 more on this activity, will I get $2+ back in pipeline?” If you can’t answer that confidently, it’s time for an audit.