Run history
Run history is the main operational view for a live Journey.

What run history shows
Each row in the run history table represents one execution of the Journey. The table shows:
Started
Timestamp when the run began
Status
Success or Fail
Run type
Whether the run was scheduled or triggered manually
What run history helps you answer
Did the run succeed or fail?
When did it happen?
Was it manual (Run now or Full run) or part of the normal schedule?
Is there a recent failure that needs attention?
Did a failure coincide with a recent configuration change?
When to check run history
After publishing a Journey
Confirm the first run started and completed successfully
After running manually
Confirm the manual run produced the expected result
Before resuming a paused Journey
Check whether there were failures before the pause
When business results look unexpected
Correlate Journey runs with the period where results diverged
After a configuration change
Confirm the change did not introduce a failure
How to read a run
A successful run means the Journey completed its execution cycle without errors. It does not guarantee that contacts entered or that destinations were activated β it means the orchestration ran as expected.
A failed run means an error occurred during execution. Check the run details to understand the failure before retrying or resuming.
A failed run does not automatically retry. If you see a failed run, investigate the cause before manually triggering another run. Retrying without understanding the failure may reproduce the same error.
Good operating habits
Check immediately after launch. The first run after publishing is the most important one to review. Confirm it started, completed, and that entry volume looks reasonable.
Review failures before editing. If the most recent run is a failure, understand why before making changes to the Journey. The failure may be caused by a configuration issue that your edit will not address.
Capture the failure context before retrying. Note the timestamp and any error details before taking action. This helps you explain what happened if the issue recurs.
Track entry volume over time. Significant changes in the number of contacts entering per run can indicate a problem with the entry rule or the underlying audience. An unexpectedly large entry volume may mean more contacts are being activated than intended.
Related pages
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