Why we built hourly correlation detection into Monitr

The alert fog problem

A user reports that the payment button doesn't work. Two hours later, someone on Reddit mentions the same thing. Three mentions hit Twitter around midnight. Then Google News picks up a comment thread. Without context linking them together, your team is playing detective instead of responding to a crisis.

The real problem isn't spotting signals. It's understanding the narrative they're telling.

What happens when you stop treating signals as individual events

We spent the better part of six weeks thinking through this. The engineering team at MRVL pushed back hard on the complexity of it. Grouping related signals in real time isn't trivial. You're running correlation logic across five different sources, each with different signal types and metadata. You can't wait 24 hours to do it, because by then the moment has passed. You need to do it hourly.

The breakthrough came when we stopped thinking about "grouping" and started thinking about "storytelling." Each hour, Monitr now looks across all your incoming signals and asks a simple question: which of these are talking about the same thing? A bug report in the App Store, a Reddit thread, a Twitter mention, and a Google News article about the same feature or problem get bundled into a single narrative. Your team sees one story, not four separate alerts.

What changed for that customer wasn't the number of mentions. It was the clarity of what they meant.

The fifteen-minute rule lives alongside it

We didn't kill individual alerts. We live-balanced them. If something looks like a crisis - multiple signals coming through that our classifier marks as pr_crisis - you still get a notification every 15 minutes. You need to know immediately if your reputation is on fire. But the context behind those alerts is now grouped into narratives, so when you open Slack or check your Linear board, you're not drowning in duplicates.

The hourly correlation window sits somewhere between "immediate panic" and "let's think about this at standup." It's frequent enough to catch emerging patterns before they explode, but lazy enough that you can batch your thinking into meaningful units.

How it lands for different teams

For app studios managing five or six products, hourly correlation means someone can actually stay on top of what players are saying without watching Slack like a hawk. For SaaS founders, it's the difference between seeing 40 individual feature requests and understanding that 12 of them are variations on the same idea. Marketing teams get early warning when a narrative is forming - not when it's already viral.

The decision to keep it simple

We could have added machine learning bells and whistles. Sentiment scoring. Predictive escalation. Semantic clustering across platforms. We chose not to. Hourly correlation detection does one thing: it looks at the signals you're getting, traces the common threads, and groups them into narratives you can actually understand. It's not trying to predict the future or read between the lines. It's trying to reduce noise without losing signal.

The result is that whether you're on the free tier watching a single app or running the Portfolio plan across 40 products, the feature works the same way. You get clarity. Faster.