Multi-Touch Attribution
An attribution model that splits conversion credit across multiple observed touchpoints of the same identified visitor, instead of giving all credit to the first or last interaction. It requires per-user tracking to link touchpoints across sessions.
What are the common multi-touch attribution models?
Multi-touch attribution comes in several variants: linear (equal credit to all touchpoints), time-decay (more credit to recent touchpoints), position-based (40% first, 40% last, 20% middle), and data-driven (ML-determined weights). Each has trade-offs, but all share a fundamental requirement: visibility into every touchpoint of the same identified visitor — which means a persistent per-user identifier must exist.
Why does multi-touch attribution break with incomplete data?
Multi-touch attribution is only as accurate as the touchpoint data feeding it. When cookie-based analytics miss 87% of visitor interactions due to consent rejection, ad blockers, and browser restrictions, the model distributes credit across a fragment of the observed data.
This systematically undervalues top-of-funnel channels (organic, social, display) because first touches are most likely to be lost when cookies are not yet active.
Why does SealMetrics not implement multi-touch attribution?
Multi-touch attribution requires the analytics system to identify the same visitor across multiple sessions so the touchpoints can be linked. That identification requires a persistent per-user identifier — a cookie, a device fingerprint or another tracking mechanism that makes the analytics subject to GDPR consent rules.
SealMetrics is designed as anonymous, aggregate event measurement. No per-user identifier is ever created, so there is no basis for linking touchpoints of the same person across sessions. Attribution is last-click on the observed conversion event: whichever source was recorded on the pageview where the conversion fired gets credit. Channel totals roll up from those events.
The trade-off is deliberate: you give up modelled credit-splitting across touchpoints, and in exchange you get aggregate channel totals on 100% of traffic with no consent dependency.
Related concepts
- Attribution ModelA rule or algorithm that determines how credit for conversions is distributed across marketing touchpoints. Common models include first-touch, last-touch, linear, time-decay, and data-driven attribution.
- Revenue AttributionConnecting revenue events (purchases, subscriptions) to the marketing channels that drove them. SealMetrics uses last-click on 100% of observed events — no per-user journey tracking, no multi-touch models.
- Event TrackingThe method of recording specific user interactions on a website beyond pageviews — clicks, form submissions, video plays, downloads, and eCommerce actions. GA4 uses an event-based data model where every interaction is an event.
- Data Loss in AnalyticsThe gap between actual website traffic and what analytics tools report. Caused by consent rejection, ad blockers, browser restrictions, and data sampling. Typically 70-87% in the EU.