Path analysis
Path analysis (path exploration) visualises the real routes users take through a site as a branching tree of steps, rather than the single idealised funnel. Read forward from a starting point it shows where people actually go; read backward from a conversion or drop-off it shows what preceded it. It surfaces loops, detours, and unexpected entries a fixed funnel cannot.
What this means
Path analysis takes the ordered events each session produced and aggregates them into a tree. Forward exploration starts from a node — a landing page, a key event — and branches out to show the next steps users took and in what proportion. Backward exploration starts from an endpoint, such as a purchase or an exit, and shows the steps that led into it. GA4 ships this as the Path exploration technique in its Explore module.
What it reveals beyond a funnel
A funnel assumes a known, linear order of steps and measures drop-off between them. Real behaviour is messier: people loop back, skip steps, arrive from unexpected pages, and wander. Path analysis embraces that branching, so you can spot a popular detour, a page that keeps sending users in circles, or an entry point you never designed for.
It complements rather than replaces the funnel: the funnel quantifies a hypothesis you already have; path analysis helps you find the hypotheses you did not.
- Forward from a start, or backward from a goal
- Shows loops, detours, and unexpected entries
- Complements the fixed, linear funnel view
How it appears in analytics and logs
A path tree showing heavy branching or loops before a goal means users are not following the route you assumed. Backward paths from an exit reveal the steps that commonly precede abandonment.
Diagnostic use case
Use path analysis to discover the unscripted journeys — backtracking, side trips, surprising entry points — that a predefined linear funnel cannot represent.
What WebmasterID can help detect
WebmasterID records first-party page and event sequences, which are the raw material path analysis aggregates into forward and backward flows.
Common mistakes
- Expecting one dominant path when behaviour is branchy.
- Reading aggregate flows as individual user journeys.
- Using path analysis where a defined funnel is more precise.
Privacy and accuracy notes
Path exploration aggregates sequences of events across many users; it should be read as flows, not individual journeys. This page is educational.
Related pages
- Funnel analysis: finding the leak
Funnel analysis follows visitors through an ordered set of steps (view → add to cart → checkout → purchase) and shows where they fall out. It turns a single conversion rate into a map of where the loss happens. The pitfalls are step definition, small-sample noise, and assuming a strict order where users actually skip around.
- Drop-off analysis
Drop-off analysis measures, step by step, how many users fail to advance to the next stage of a funnel and where the largest losses occur. By isolating the single biggest leak it directs limited optimisation effort to the step with the most upside, instead of guessing or polishing stages that already convert well.
- Micro and macro conversions
A macro conversion is a primary business goal — a purchase, a signup. A micro conversion is a smaller, intermediate action that signals progress toward it, like viewing a product or starting a form. Tracking both gives a richer picture of the funnel, but only the macro conversion should be treated as the headline success metric.
- Event Explorer
Trace the event sequences paths are built from.
Sources and verification notes
Last reviewed 2026-06-24. Facts are checked against primary/official sources where available; uncertain specifics are marked “Data not yet verified” rather than guessed.