Traffic-source analysis explains where measured visits or outcomes originate. Attribution is the method used to assign credit for a conversion across eligible marketing interactions. These are related but different questions: acquisition describes entry sources, while attribution describes credit under a defined model and scope.
Source, medium and channel #
- Source: the specific origin, such as a search engine, publisher or email platform.
- Medium: the traffic classification, such as organic, cpc, email or referral.
- Campaign: the named marketing initiative.
- Default channel group: a rule-based grouping of sources and mediums into categories such as Organic Search, Paid Search, Email or Referral.
Attribution models #
Common approaches include last-click, first-click, linear, position-based, time-decay and data-driven attribution. Availability and terminology differ across analytics and advertising platforms. A model allocates credit; it does not prove that a channel caused the outcome.
Why reports differ #
Platforms may use different attribution windows, identity rules, eligible touchpoints, time zones, conversion definitions and consent signals. Click-through and view-through conversions may also be handled differently. As a result, GA4, an ad platform and a CRM can legitimately show different totals.
Analysis workflow #
- Agree on the conversion definition and reporting window.
- Separate user acquisition, session acquisition and conversion attribution questions.
- Use consistent campaign tagging and naming.
- Compare trends and quality—not just credited volume—across channels.
- Validate leads or sales in the downstream system where possible.
Example #
A person may discover a brand through organic search, click a paid ad later, and return directly to buy. A first-touch model credits discovery, a last-touch model may credit direct or paid depending on its rules, and a data-driven model may distribute credit differently. Interpret the report in light of the model used.
Related: UTM Parameters, Campaign Performance Analysis and Attribution Models.
