How to use User Journey Analysis
User journey analysis maps the actual paths shoppers take through your store — detours, backtracks, and all. Here's how to read the data and act on it.
User Journey Analysis
Mapping the real, multi-step paths visitors take through a site — including detours and backtracks — to find where intent breaks down.
User journey analysis examines the sequences of pages, events, and sessions that lead a visitor from arrival to outcome (purchase, signup, exit). Unlike a funnel report, which collapses behaviour into a fixed series of stages, journey analysis preserves the actual order, branching, and loops — the messy reality.
The technique is usually rendered as a Sankey diagram, a path tree, or a flow graph. The goal is to surface the high-frequency journeys that drive most revenue, the unexpected detours that signal confusion, and the dead-end loops where shoppers bounce between two pages before leaving.
Most analytics dashboards lie to you by simplifying. They show a five-step funnel where 100% of users enter at the top and a percentage trickles out the bottom. That picture is clean, executive-friendly, and misleading — because real shoppers don't move in straight lines.
They land on a product page, jump to a category, come back via search, open a size guide in another tab, leave for two days, return through a retargeting ad, and finally check out. Journey analysis is what you reach for when the funnel report tells you the conversion rate is 2.1% but doesn't tell you why.
How journey analysis differs from funnel analytics
Funnel analytics — the parent discipline — defines the stages up front: view product, add to cart, start checkout, purchase. It then counts who reaches each one. The strength is clarity; the weakness is that any behaviour outside the predefined path is invisible.
Journey analysis inverts the question. Instead of asking "how many people completed step 3?", it asks "what are the ten most common sequences of pages that ended in a purchase, and how do they differ from the ten most common sequences that ended in an exit?" The output is descriptive, not prescriptive.
In practice you use both. The funnel tells you where the drop-off is; the journey tells you what shoppers did instead. A 40% drop between cart and checkout is a funnel finding. Discovering that two-thirds of those shoppers opened your shipping page and never came back is a journey finding — and it's actionable.
Rule of thumb
If your funnel has more than four stages or your product allows multiple entry points (search, category, paid, email), the funnel view alone is hiding more than it reveals. Layer journey analysis on top before you brief any test.
Reading a Sankey diagram without getting lost
A Sankey is a flow chart where the width of each band represents the number of users moving between two states. Source pages are on the left, destinations on the right; the bands in between show every transition. On a busy storefront the diagram can have hundreds of bands, which is why most teams give up after one look.
The trick is to filter aggressively. Start with one entry point (say, paid social landing pages) and trace forward three steps. Then segment by outcome — keep only sessions that ended in checkout. Suddenly the same diagram shows you three or four dominant paths instead of three hundred faint ones.
Top 6 next-page transitions from a Shopify product page
The chart above is the kind of view journey analysis gives you in one glance. The add-to-cart band looks small (14%) but the second-PDP band is bigger (22%) — meaning most shoppers are comparing products, not deciding. That tells you a comparison feature or a clearer differentiator on the PDP would move the needle more than another checkout tweak.
Benchmarks: what "normal" looks like
There's no universal benchmark for a journey because the journey depends on your catalogue, traffic mix, and price point. But there are useful ranges. A high-consideration category (furniture, electronics) typically has longer sessions, more PDP-to-PDP jumps, and more multi-session journeys before purchase than a low-consideration one (consumables, basics).
The table below shows typical patterns we see across stores in the €1M-€15M revenue range. Use it to sanity-check whether your shoppers behave the way the category does, or whether something specific to your site is forcing more steps.
Typical journey patterns by vertical (Shopify / WooCommerce stores)
| Vertical | Avg pages per session | PDPs viewed before ATC | Sessions before purchase | Path-to-purchase length |
|---|---|---|---|---|
| Apparel | 6-9 | 3-5 | 1.8 | Medium |
| Beauty & skincare | 5-7 | 2-3 | 1.4 | Short |
| Home & furniture | 9-14 | 4-7 | 3.2 | Long |
| Electronics | 8-12 | 3-6 | 2.6 | Long |
| Food & supplements | 4-6 | 1-2 | 1.2 | Short |
| Jewellery | 7-10 | 3-5 | 2.1 | Medium |
If your apparel store shows shoppers viewing eight PDPs before adding to cart, you're well above the typical 3-5 range — which usually points to weak filtering, missing size information, or pricing that doesn't anchor. Journey analysis identifies the pattern; the benchmark tells you whether it's a problem worth fixing.
Turning journey findings into tests
The output of journey analysis is a shortlist of friction points, each tied to an observed behaviour. "Shoppers open the shipping page from checkout and 64% don't return" is a finding. The test that follows might surface shipping cost on the cart page instead, removing the need to leave checkout in the first place.
Good journey-led hypotheses share three traits: they reference a specific observed path, they predict a direction of change, and they target a step where the drop is large enough to detect within your traffic. If a journey carries 200 sessions a week, you'll wait months for significance — pick the high-volume paths first.
Working pattern
Spend an hour a week in journey view. Pick one high-frequency path that ends in exit, one that ends in purchase, and ask what's different between them. Most months that comparison alone generates two or three test hypotheses worth shipping.
Frequently asked questions
Funnel analytics measures movement through a fixed, predefined set of stages. Journey analysis examines the actual sequences shoppers follow, including detours, loops, and re-entries. Funnels tell you where drop-off happens; journeys tell you what shoppers did instead.
Most modern analytics platforms — GA4, product analytics tools, and dedicated CRO platforms — include path or Sankey reports. The differentiator is usually how easily you can segment journeys by traffic source, device, or outcome without writing custom queries.
As a rough floor, around 10,000 sessions per month gives you enough volume to see the dominant 5-10 paths clearly. Below that, the top paths exist but the long tail is too noisy to draw conclusions about specific transitions.
Both, but start at the page-type level (product, category, cart, content). URL-level views explode in complexity on stores with thousands of SKUs. Drop into URL-level only when you've identified a page type that warrants closer inspection.
Cross-device stitching requires a logged-in user ID or a deterministic identifier like an email captured early. Without that, a mobile-research-then-desktop-purchase journey looks like two separate sessions and inflates your apparent abandonment.
Yes, and it should. First-time visitors typically take longer, more exploratory paths; returning visitors often skip discovery and go straight to PDPs or cart. Mixing them hides both patterns. Segment by visitor type before drawing conclusions.
A Sankey shows flow volumes between states as bands of varying width. It's great for two or three transitions; beyond five steps the diagram becomes unreadable. For longer journeys, switch to a path tree or a tabular top-paths report.
They answer different questions. Attribution credits revenue to marketing touchpoints; journey analysis describes on-site behaviour after the click. A complete picture uses attribution to value the channel and journey analysis to optimise what happens once the visitor lands.
No — they're complementary. A heatmap shows what shoppers clicked on a single page; journey analysis shows how pages connect across a session. Use heatmaps to diagnose individual pages identified as friction points by the journey report.
Quarterly for strategic reviews, weekly for active CRO work. Journeys shift when you change navigation, launch a campaign, or update PDPs. After any structural change, give traffic two weeks to settle before re-reading the paths.
Get an AI expert review of your site
Paste your URL — Metricuno's AI runs the same heuristic checks a senior CRO consultant would, scoring your page and prioritising the fixes that'll move conversion fastest.