Forensic attribution is the process of discovering what actually happened in a deal between two explicitly documented touchpoints. Instead of solely relying on CRM entries, forensic attribution analyzes the digital breadcrumbs left behind in a deal. It parses things like email sentiment changes, sales call transcripts, outbound marketing email content, and even chatbot conversations. By accessing and analyzing these micro-interactions, the real story for why a deal moved forW. (or stalled) becomes apparent.
Why This Matters Today
- Go-to-market complexity is growing: Buyers operate across community, peer referrals, partner ecosystems, and AI-driven sequences, most of which aren’t captured in structured Salesforce or HubSpot fields.
- Buying groups have multiplied: Enterprise deals involve on average 7+ stakeholders, often spanning functions, roles, and geographies.
- Budgets are under scrutiny: In an era of efficiency, boards and CFOs expect attribution models that can stand up to executive review and audit and won’t allocate budgets without clear ROI.
- AI content saturation demands evidence-based measurement: Marketing output has increased tenfold with more content, emails and engagement than ever before making it even harder to understand what actually works. A forensic model can distinguish noise from real influence.
What You Can Do With Forensic Attribution
“What should we double down on?”
Channel and campaign ROI linked to closed-won revenue—not just MQLs.
Each weighted model will give different results, the best channels might not get the right credit depending on how weights are assigned.
“Why is this deal stalling?”
Timeline flags missing champions, executive drop-off, or a cold gap in activity.
No way of showing deal level attribution or engagement.
“Where’s the budget waste?”
Cost-per-influence and momentum analysis highlight non-performing spend.
Impossible to tell, every model shows a different result.
“Can we trust the data?”
Yes. Every insight is backed by system-level evidence, traceable to original interactions.
Most insights are a black box.