[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-multi-touch-attribution::en":3,"gloss-cluster-multi-touch-attribution::en":23,"gloss-next-multi-touch-attribution::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"multi-touch-attribution","analytics","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\u002FW-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.","Multi-touch attribution splits conversion credit across every touchpoint instead of giving 100% to the last click — linear, time-decay, U-shaped, or data-driven.",null,[11,14,17,20],{"slug":12,"name":13},"customer-data-platform","Customer Data Platform (CDP)",{"slug":15,"name":16},"funnel","Funnel",{"slug":18,"name":19},"north-star-metric","North Star Metric",{"slug":21,"name":22},"product-analytics","Product Analytics",[24,28,31,35,38,39,42,45,48,51,54,57],{"slug":25,"category":5,"name":26,"updated_at":27},"ab-testing","A\u002FB Testing","2026-08-24T02:46:38+00:00",{"slug":29,"category":5,"name":30,"updated_at":27},"active-user","Active User (DAU, WAU, MAU)",{"slug":32,"category":5,"name":33,"updated_at":34},"autocapture","Autocapture","2026-08-24T02:46:37+00:00",{"slug":36,"category":5,"name":37,"updated_at":27},"cost-per-resolution","Cost per Resolution",{"slug":12,"category":5,"name":13,"updated_at":34},{"slug":40,"category":5,"name":41,"updated_at":27},"deflection-rate","Deflection Rate",{"slug":43,"category":5,"name":44,"updated_at":27},"guardrail-metric","Guardrail Metric",{"slug":46,"category":5,"name":47,"updated_at":34},"identity-resolution","Identity Resolution",{"slug":49,"category":5,"name":50,"updated_at":27},"novelty-effect","Novelty Effect",{"slug":52,"category":5,"name":53,"updated_at":34},"retention-curve","Retention Curve",{"slug":55,"category":5,"name":56,"updated_at":27},"sample-ratio-mismatch","Sample Ratio Mismatch (SRM)",{"slug":58,"category":5,"name":59,"updated_at":27},"seat-utilization","Seat Utilization"]