Google Analytics 4 (GA4) and Looker Studio are incredible tools for tracking standard metrics. In fact, you can - and absolutely should - experiment with building basic versions of some of the charts below (like Treemaps and static Sankeys) using Looker Studio's community visualisations.
However, as your analysis grows more complex, you will inevitably hit the ceiling of what those applications can handle. Native dashboards are often restricted by API limits, lack dynamic state-switching, and struggle to render massive, high-density datasets. To find the underlying drivers of engagement and revenue, you eventually need to export your raw BigQuery data. Here is how custom, physics-driven visualisations can expose the insights that standard dashboards miss.
Advanced Behavioural Visualisations
How do users navigate through a multi-step process?
The Visualisation: The Sankey Diagram
The standard funnel exploration reports in GA4 force you to define a rigid, linear path. But users rarely move in a straight line; they loop back, they compare, they abandon, and they return.
A Sankey diagram replaces the rigid funnel with a more accurate flow mapping. In the interactive diagram below, the width of the bands represents the volume of users flowing through a complex matrix. Instead of just seeing a simple drop-off percentage, you can visually trace exactly where the massive rivers of traffic are flowing, and more importantly, where they are leaking out of the system before reaching the checkout.
How do seasonal trends affect cross-category purchasing?
The Visualisation: The Dynamic Chord Diagram
GA4's ecommerce reports can show cross-sell revenue in a flat table, but they struggle to communicate the complex, interconnected web of relationships between product categories - especially as those relationships shift over time.
A Chord Diagram arranges product categories in a circle, using thick ribbons to show what is being bought together. By adding a month-by-month timeline and playback UI, you can literally watch the cross-sell matrix morph. Hit the play button below: in the Australian spring months (September through November), massive thick ribbons bind "Seeds," "Seedling Trays," and "Potting Mix" together. As Autumn approaches in March and April, those ribbons gracefully shrink, instantly replaced by a tight, high-volume cluster connecting "Pruning Saws," "Compost," and "Bare-Root Trees" that peaks in July. It makes seasonal bundling strategies immediately obvious.
Where are the underperforming segments in a massive product catalogue?
The Visualisation: The Treemap
Scrolling through thousands of rows in a GA4 item performance report is an incredibly inefficient way to spot anomalies. When you have a massive catalogue with dozens of nested categories, you need a high-density solution.
The Treemap uses nested rectangles to display hierarchical data. By setting the area of each rectangle to represent total page views (traffic), and the colour to represent the conversion rate, you create a perfect diagnostic tool. A massive red box instantly alerts you to a highly-trafficked product line that is failing to convert (a leaking bucket), while a tiny bright green box reveals a high-converting product that desperately needs more marketing spend.
What is the true central hub of user navigation?
The Visualisation: The Force-Directed Network Graph
GA4 path exploration can show you the next click, but it can't map the entire ecosystem of your website. Buyers rarely move in straight lines - they might read a blog post, leave, return days later to check pricing, read a case study, and then convert.
To map this, every page on your site becomes a "node" and the user journeys become the connecting lines. Interact with the physics-driven network below. Because the simulation applies tension based on traffic volume, the sheer gravitational pull of the traffic forces the "Case Study" node right into the dead center of the cluster. It proves visually that while the homepage gets the initial traffic, the case study is the true engine driving users toward pricing and demos.
When do users finally convert after their first visit?
The Visualisation: The Cohort Heatmap
Tracking when conversions happen is just as important as tracking where they happen. Standard attribution models in GA4 offer some insight, but visualising the exact velocity of your pipeline requires a different approach.
A cohort heatmap maps the velocity of your conversions. If your heatmap is heavily concentrated on "Day 1", your business relies on impulse buys. But if a large portion of your pipeline value reliably glows hot between days 30 and 45, you have visually identified the exact window where your retargeting campaigns need to be the most aggressive.
What specific sequence of micro-interactions leads to a dead-end?
The Visualisation: The Branching Tree Graph
While GA4 offers a basic path exploration tool for pageviews, exporting the raw event data to build a custom branching tree graph allows for far deeper analysis of specific, granular micro-interactions.
By starting at a central root node (like a complex form submission or a specific checkout step) and branching outward, you can trace the exact sequence of clicks and errors, instantly highlighting the dead-end paths that are trapping your most valuable users.
Which specific pages and folders are consuming user attention?
The Visualisation: The Radial Sunburst
Standard content drill-down reports in GA4 require you to click through folders one by one. You cannot see the macro structure of your website and the micro-level page views simultaneously.
The Sunburst diagram solves this by pushing hierarchical site structure outward. The inner ring represents your root domain, expanding into major sub-folders, and finally shattering into individual pages on the outermost edge. By sizing the segments according to active users, you can instantly see which specific deep-level pages are driving disproportionate value compared to their parent categories.
Stop Relying on Flat Dashboards
The raw data required to build these visualisations already exists in your GA4 BigQuery exports. The problem is that most teams are trying to understand complex, multi-dimensional user behaviour using basic dashboards designed for simple accounting.
By exporting your data and building custom visualisations like Network Graphs, Sankeys, and Dynamic Chords, you stop looking at what happened, and start seeing exactly how it happened - and more importantly, how to improve it.
Written by Johari Lanng
Johari is a Principal Analyst and Data Engineer who loves turning chaotic marketing data into clear business strategies. When he isn't architecting BigQuery pipelines or building machine learning models, he's usually experimenting with WebGL and generative coding.