I’m quite fascinated by the potential of using page_ping events to calculate time on page and scroll depth, but most of the posts I found about this topic are either dated or is for RedShift.
this thread @alex mentioned that page ping events are essentially children of page view events.
Is there a way (in redshift) to join the page ping events back to the “parent” page view event? If for instance we wanted to do things like
look up attributes of the original page view event or
calculate a standard deviation of number of page pings per view (or scroll depth per view)
determine what percent of pageviews scroll to a certain depth on the page
same question for link_click events, i…
page_pings heading in the event model, I am fascinated by the x and y offset events, showing where a user has scrolled since the last page ping.
But I struggle a bit with the best way to use this data analytically. On a user-by-user basis, I can step through and reconstruct where they are scrolling, perhaps, but what have others done toward, a more aggregate measure here?
For instance, is “total % of website scrolled to”, or “scrolling per seconds” things that others have computed? …
We use BigQuery as our DW, so will be interested in hearing from someone have related experience doing this. Thanks!
The data is exactly the same regardless of which warehouse you use, the only difference is the table structure - in BigQuery your context data will be stored in an array column of the events table rather than a separate table of its own.
So, the exact same logic can be applied to BigQuery - you can use the page view context to identify instances of a page view, and aggregate per page view to calculate page time and scroll depth.