Next Level Customer Pathing Analysis in Google Analytics 360

So, how does Path Analysis differ from what there is in Analytics today?

  1. The analysis is based on 100% of data for a specified date range. It is analyzing all user paths, as opposed to the Flow Visualization reports which are only based on 100K of sessions, and thus, provides a more accurate view of user activity.
  2. The technique enables to explore multiple user paths at once instead of analyzing a pre-defined, rule-based path in the Funnel visualization. You’d have to define each step in a funnel set up, yet still will not give you an idea of where else users navigating.
  3. Start analyzing user paths from a specific page or event. The feature allows taking a detailed look at the most frequent user paths, from a specified starting point. You can select any page, screen, or event from the site/app and see how users navigated starting from this step.In the Flow Visualization reports this was only available for a landing page/starting page which limited analyzing the paths that occurred later in a session. Now you can easily explore how users navigated between pages A, B, and C irrespective of the position of page A in a user flow.
  4. User-based analysis. Path Analysis includes interactions that occurred in different sessions of the same user. This eliminates limiting the exploration to uniquely in-session behavior.
  5. The ability to apply segments and filters to user paths to focus on activity of a certain user segment. Using filters in the report will help isolate all the data flows that are meaningful for the analysis, e.g. showing only paths with a minimum number of users or events at each data flow within each step.
  6. Easily switch between events and pages as path dimensions. This allows user paths to be more thoroughly analyzed by adding a level of detail that could potentially include every user action and is not limited to pageviews/screen views.
  7. Customize the analysis to dive into greater detail. You can define which data points within each step you want to further explore using step filters. This allows to go to a particular dimension value and see how this user segment behaved.
  8. Flexibility. The technique allows you to expand some nodes, collapse others, add and delete new steps for different dimension values. This helps improve visibility for analyzing required paths which makes Path Analysis a considerably flexible tool.

Pathing in Analytics is taking the customer journey analysis to the next level. What was previously available only via the integration with BigQuery is now becoming a powerful feature in the Analytics UI. The new app + web functionality is still in beta but this is something that you may consider using today!

Anton Dolgiy
anton.dolgiy@delvepartners.com


Next Level Customer Pathing Analysis in Google Analytics 360

So, how does Path Analysis differ from what there is in Analytics today? The analysis…

Next Level Customer Pathing Analysis in Google Analytics 360

So, how does Path Analysis differ from what there is in Analytics today?

  1. The analysis is based on 100% of data for a specified date range. It is analyzing all user paths, as opposed to the Flow Visualization reports which are only based on 100K of sessions, and thus, provides a more accurate view of user activity.
  2. The technique enables to explore multiple user paths at once instead of analyzing a pre-defined, rule-based path in the Funnel visualization. You’d have to define each step in a funnel set up, yet still will not give you an idea of where else users navigating.
  3. Start analyzing user paths from a specific page or event. The feature allows taking a detailed look at the most frequent user paths, from a specified starting point. You can select any page, screen, or event from the site/app and see how users navigated starting from this step.In the Flow Visualization reports this was only available for a landing page/starting page which limited analyzing the paths that occurred later in a session. Now you can easily explore how users navigated between pages A, B, and C irrespective of the position of page A in a user flow.
  4. User-based analysis. Path Analysis includes interactions that occurred in different sessions of the same user. This eliminates limiting the exploration to uniquely in-session behavior.
  5. The ability to apply segments and filters to user paths to focus on activity of a certain user segment. Using filters in the report will help isolate all the data flows that are meaningful for the analysis, e.g. showing only paths with a minimum number of users or events at each data flow within each step.
  6. Easily switch between events and pages as path dimensions. This allows user paths to be more thoroughly analyzed by adding a level of detail that could potentially include every user action and is not limited to pageviews/screen views.
  7. Customize the analysis to dive into greater detail. You can define which data points within each step you want to further explore using step filters. This allows to go to a particular dimension value and see how this user segment behaved.
  8. Flexibility. The technique allows you to expand some nodes, collapse others, add and delete new steps for different dimension values. This helps improve visibility for analyzing required paths which makes Path Analysis a considerably flexible tool.

Pathing in Analytics is taking the customer journey analysis to the next level. What was previously available only via the integration with BigQuery is now becoming a powerful feature in the Analytics UI. The new app + web functionality is still in beta but this is something that you may consider using today!

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