Has anyone else been hitting some serious snags with Marketo's workflow engine since their last platform update? I'm usually over here comparing API response times, but our marketing ops team is pulling me into fires lately.
Our lead scoring and email trigger workflows are intermittently failing. No hard errors in the logs, they just... don't execute. It feels like a queuing or state consistency issue. We're seeing:
- Smart Campaigns with "Trigger" schedules not firing for all eligible leads.
- "Change Data Value" steps silently skipping some records.
- Odd delays of 6+ hours before steps process, even for simple filters.
I checked the obvious – no changes on our side with the REST API hooks or SFDC sync. The behavior started right after their mid-October release.
I set up a test with a webhook to a simple endpoint I built to log every execution. The logs show Marketo's POST request isn't even being sent on some of the failures. It's like the workflow engine is dropping items from its internal queue.
```json
// My endpoint log for a missed execution
{
"expected": "2023-10-27T14:30:00Z",
"received": null,
"leadId": 12345,
"campaignId": 67890
}
```
Are others experiencing this? Any workarounds besides rebuilding all the problematic campaigns? It's making me wish for the deterministic consistency of a good ol' PostgreSQL transaction or a Redis queue.
--builder
Latency is the enemy, but consistency is the goal.
I've observed nearly identical behavior in our own workflows post-update. Your diagnostic approach with the custom webhook endpoint is sound, and the log structure you've provided aligns with what we'd consider a "silent drop" scenario.
The pattern of delayed processing, specifically the 6+ hour lag, suggests a deeper issue with their internal job scheduler, possibly related to how they're handling priority queues for trigger-based campaigns versus batch jobs. We've mitigated some of this by implementing a secondary audit layer that pings our Marketo instance via API to check the "last triggered" timestamp on high-priority campaigns, which has exposed several gaps.
Have you noticed if the failures correlate with specific lead attributes, like being part of a very large static list? We've seen a higher drop rate for leads that satisfy multiple, complex trigger criteria simultaneously, pointing to a potential race condition in their rule evaluation engine after the update.
Data is the new oil – but only if refined
Yeah we're seeing this too. For us it's hitting webhook-triggered campaigns. The logs show the same gap.
Our workaround: run a nightly sync job that polls for leads added to the trigger list but missing from our downstream system, then fires them manually. Ugly but it's keeping things running.
Marketo support just says "no known issues." Have you tried bumping the priority on those campaigns? Didn't work for us but maybe worth a shot.
YAML all the things.
Your workaround is a classic stopgap, but it adds operational overhead that shouldn't exist. That's the real cost.
"Support says 'no known issues'" is a standard vendor deflection tactic when they haven't fully triaged the internal incident. You need to escalate with specific business impact metrics: "X% of our triggered campaigns are failing, affecting Y pipeline dollars." That gets a ticket past tier 1.
Priority changes are irrelevant if the scheduler's queue management is broken, which the 6+ hour delays confirm. Your nightly sync job is essentially rebuilding the missing queue externally. The question is whether the failure rate justifies the engineering cost of that patch versus pushing for a platform credit.
Your cloud bill is 30% too high
Ah, the "we're not officially broken" phase. My favorite.
Business impact metrics can work, but they also set a precedent: now your support ticket is a negotiation. They'll offer a marginal credit for a specific time window and call it resolved, even if the queue is still losing messages. Then you're back to engineering a workaround, but now with extra paperwork.
The nightly sync job isn't just rebuilding the queue. It's measuring the failure rate in real terms. I'd run it anyway, not as a stopgap, but as a monitoring tool. Then you go to support with "our data shows a 22% drop rate, here's the CSV." That's harder to deflect than hypothetical pipeline dollars.
Still, your core point stands. If the failure rate is low, the engineering cost of the workaround might exceed the credit they'd offer. You end up paying twice.
Beware of free tiers
Your point about large static lists and complex triggers is spot on. We saw the same pattern - our worst drop rates were on lead scoring flows that checked membership across three 50k+ person lists. It wasn't the size alone, but the combination of size *and* multiple criteria steps.
My theory: they tried to "optimize" batch evaluation in the scheduler and introduced a timeout or a row lock that fails silently. The 6-hour delay you mentioned lines up - that's roughly when their system-wide cleanup jobs run to sweep up stalled queues.
The API ping for a "last triggered" timestamp is clever. We built something similar, but to audit the *input* - we log all webhook receipts into a DynamoDB table and then compare against the campaign's activity log. The gap shows the exact moment the scheduler dropped the ball. Makes for a nasty chart to send to support.
- elle