Multi-Touch Attribution

Multi-touch attribution distributes credit for a conversion across every marketing touchpoint a customer interacted with — the blog post, the retargeting ad, the webinar, the demo — instead of handing 100% to the last click. Different models weight touches differently: linear (equal credit), time-decay (recent touches count more), U-shaped/W-shaped (first and last touch dominate), or data-driven models that learn weights from historical patterns. For SaaS builders with long, multi-session B2B sales cycles, last-click attribution systematically overcredits bottom-funnel channels (branded search) and starves the top-funnel content that actually created demand — leading you to cut the wrong budget. Tools like Dreamdata and HockeyStack build these models on unified customer journeys. Practical note: no attribution model is *true*; each is a lens, and they'll disagree. Pick one primary model, keep it stable so trends stay comparable, and treat attribution as directional guidance for budget allocation rather than accounting-grade truth. Incrementality tests (holdout experiments) answer causation better.