Hey everyone, I had a bit of a revelation today while trying to figure out why our new BI tool's adoption seemed to stall last month. I was looking at our Claw platform's audit logs—mostly to check for errors—and realized they're a goldmine for spotting user confusion *before* people start complaining or giving up.
I was writing a SQL query to count failed dashboard loads, but then I started filtering for specific event types. I noticed a pattern where users were repeatedly opening the same report, making a filter selection, then immediately clearing it and re-opening the report. That looked like classic "I don't understand what this filter does" behavior. Here's the kind of query I ended up with:
```sql
SELECT user_id,
event_type,
object_name,
COUNT(*) as event_count,
MIN(timestamp) as first_occurrence,
MAX(timestamp) as last_occurrence
FROM claw_audit_logs
WHERE event_type IN ('report_view', 'filter_clear', 'filter_apply')
AND timestamp > CURRENT_DATE - 14
GROUP BY user_id, event_type, object_name
HAVING COUNT(*) > 10
ORDER BY event_count DESC;
```
This helped me flag a few users who were clearly struggling. I reached out to them directly, and it turned out the filter labels were confusing! We were able to clarify the documentation and prevent more people from hitting the same wall.
I'm curious—has anyone else used audit logs this way? What other event patterns have you found that signal user confusion? I'm thinking things like frequent navigation back-and-forth between the same two pages, or excessive use of the "reset" button on a form. It seems like a much more proactive metric than just waiting for support tickets to roll in.
That's a really insightful pivot from pure error monitoring to behavioral pattern analysis. Your query logic is sound, but I'd suggest a couple of refinements for better signal-to-noise.
Grouping by `user_id, event_type, object_name` might miss sequential patterns *across* event types for a single session. You're counting individual events, not the sequence. A more telling metric could be the rate of `filter_apply` followed by `filter_clear` on the same object within a very short window, say 30 seconds. This would isolate the hesitation loop more directly.
Also, consider joining against your user metadata table, if you have one. Filtering out users from the Engineering or QA teams - who might be generating these patterns while testing - can help focus on genuine end-user confusion. Your `HAVING COUNT(*) > 10` is good, but coupling it with a high frequency of events per minute would be even stronger.
Have you considered piping these flagged sessions into a dashboard instead of manual outreach? Setting up a real-time alert for when, say, three users trigger this pattern on the same new report within an hour could prompt immediate UX review.