SQL Tricks for Streamlining User Path Analysis

Quick tips for transforming user path data for cleaner insights using SQL

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Ever needed to trace a user’s journey through an app or website? Building SQL queries to reconstruct these paths can be both fascinating and incredibly useful. Whether it’s mapping clickstreams, analyzing conversion funnels, or understanding user behavior, path analysis is a powerful tool.

But here’s the rub: these pathing queries often spiral into a tangled mess of results. The sheer volume of possible paths can obscure valuable insights, leaving you swimming in data without a clear direction.

This “quick tip” post hopes to be your guide to cleaning up those messy paths. We’ll explore two helpful techniques while showcasing some advanced SQL along the way.

The setup

We’ll be using a freely available dataset housed within Google BigQuery’s public data project. This dataset contains Google Analytics 4 (GA4) data. If you’re not familiar, GA4 is a platform used to monitor user activity on websites. Think of page views, button clicks, product views, scrolling, “add to cart” actions — anything a user does on a site. In our case, the dataset tracks activity on the Google Store, so we can imagine it as a typical retail website keeping tabs on…