Classic analytics tools show you which pages have a lot of views. What they don't show you: the way there and afterwards. A page with 10,000 hits sounds good, but if 90% of visitors jump off and never see the product page, that's not a success, it's a problem.
Without navigation paths, you optimize in the dark. You know that something is wrong, but you don't know where.
Path analysis, often referred to as user journey analysis, shows you the actual navigation sequences of your visitors. In other words, not “which pages were visited,” but “in which order, from where to where, and how many users jumped off.”
The result is a Sankey diagram: a flow chart that shows how visitors flow through your website. Each column is a step in the session, each connecting line shows the transition from page to page. The wider the line, the more users take this route.
This is how you can see at a glance:
In bchic Analytics, you can find path analysis under Paths. The diagram is automatically built from session data. No manual configuration, no pre-defined funnels.
Each box in the diagram shows three things:
Above each column, you can also see the percentage loss compared to the previous step. A loss of 52% between step 1 and step 2 means that more than half of your visitors have already left after the first click.
bchic offers three different display modes for different questions.
overview shows all paths in the standard view. Good for a first overview: Which paths do most users take?
losses Highlight pages with high bounce rates in orange If you want to know specifically where you're losing traffic, this is the fastest way to get an answer.
Loops shows pages that users return to multiple times in the same session. A loop on the start page or in the menu is a warning sign: Users can't find what they're looking for. A loop on a content page can mean the opposite, namely that the content is so relevant that users actively navigate back.
Probably the most powerful function is the path configuration. Instead of looking at the entire website, you can narrow down the analysis to a specific starting point or landing page.
Define starting point: Put for example /pricing as a starting point. Now you only see paths from users who started on the pricing page. Where are they going? For signup? Back to the homepage? Straight away?
Define landing page: Setze /checkout/success as a landing page. The graph is inverted and shows you backwards how users actually converted. This is the most honest form of conversion path analysis, because it is based on real behavior, not on assumed behavior.
The analysis depth can be adjusted between 3 and 7 steps. For most websites, 5 steps are a good starting point. More steps show longer sessions, but quickly become confusing.
Why do users leave the checkout?
Setze /checkout as a starting point. What percentage continues to /checkout/success? And where are the others going? Are they jumping back to the product page because they're still uncertain? Or are they leaving the website completely? This shows you whether the problem lies in the checkout process itself or earlier in the purchase decision.
Which pages really lead to conversion?
Set your thank you page or signup confirmation as your landing page. The diagram shows you the actual paths that converting users have taken. You'll be surprised how often the presumed “most important pages” don't even appear in the conversion paths.
Understanding loop behavior on navigation pages
If the start page or main menu appears marked as a loop, many users have visited these pages multiple times in the same session. This is almost always a sign that the navigation isn't working, or that users can't find a specific page. Combined with the filter, this can be quickly narrowed down to specific user groups or sources of entry.
Path analysis becomes even more meaningful when you combine it with user groups. In bchic, you can classify users by intent, i.e. separate transactional users from informative users, for example.
Then take a look at the conversion paths for transactional users only. You'll see that these users take completely different paths than the average. And it is precisely these paths that you should prioritize and optimize.
Other tools show you navigation paths, but only based on the data they collect. Anyone who clicks away a cookie banner or uses an ad blocker is missing from the analysis. Depending on the website, this can be up to 40, 50 or 60% of all visitors.
bchic collects cookies without consent banners, fully GDPR-compliant. This means that the path analysis is based on 100% of visitors, not on a distorted subset. This is particularly important for conversion path analyses, because privacy-conscious users often show different behavior than the rest.
Anyone who connects bchic Analytics with Claude or another AI assistant via MCP integration can query navigation data directly in the chat. “Which pages do users visit most often before checkout?” or “Where am I losing the most users on the way to the pricing page?” Go as direct questions, without a dashboard click.
This makes path analyses accessible even to non-analysts, developers, content teams or managing directors who need quick answers without first working through charts.
