Last week we caught something mid-campaign that would have normally gone unnoticed until the post-mortem. One of our partner creators suddenly had a spike in followers that didn’t correlate with campaign traffic. The timing was too perfect, the follower source was concentrated in a few countries we weren’t even targeting, and the engagement on their new content dropped by 60% compared to their baseline.
Usually, we’d spot this after the campaign ended. But we set up automated monitoring that flags anomalies—sudden follower spikes, engagement shifts, audience demographic changes—and alerts us daily. When AI flagged this pattern, we had a conversation with the creator, found out their account was compromised (someone was running follow-buying schemes), and we paused the campaign before real damage happened.
The thing is, the AI couldn’t have made that decision alone. It caught the anomaly, but a human had to interpret it, talk to the creator, and understand context. That’s where the real value is.
I’m wondering: how many of you are actually monitoring campaigns in real-time, and when something looks off, how do you decide whether to escalate or keep running?
We moved to real-time dashboards about 18 months ago, and it changed everything. You’re right that AI catches the anomalies, but human judgment is essential. Here’s our workflow: algorithms flag anything outside 2-3 standard deviations from baseline, but we have humans review before any action. Too many false positives otherwise. For live campaigns, we check alerts every 4 hours minimum. The cost of missing a brand safety issue outweighs the overhead of monitoring.
One critical piece: you need historical data for each creator. If you don’t have 3-6 months of baseline behavior, your anomaly detection is useless. We learned that the hard way.
We built this out systematically. Real-time monitoring requires: (1) automated data collection from social platforms, (2) statistical models that learn each creator’s baseline behavior, (3) alert rules that flag genuine anomalies vs. normal variance, and (4) human verification workflows.
The accuracy depends heavily on your training data. We used 6 months of historical data for each creator, built separately for each platform, and we’re still debugging false positives. But the positive cases we’ve caught—account compromises, fake engagement schemes, sudden audience shifts—have been worth it. One case saved a €50K campaign.
From my perspective, real-time monitoring also changed how I manage relationships. If a creator’s account is compromised or something looks sketchy, I can reach out immediately and help them fix it rather than discovering problems post-campaign. It’s actually strengthened partnerships because creators see we’re watching out for them too, not just policing them.
We’re running smaller campaigns, so we don’t have the infrastructure for continuous monitoring yet. But I’m curious—what’s the actual tooling you’re using? Are you building custom systems or using off-the-shelf platforms? Because if there’s a way to do this without a data engineering team, I’d rather not build it internally.
The way I see it, real-time monitoring is table stakes now if you’re running high-value campaigns. But here’s what we learned: you can’t do it manually. You need systems. And those systems need human oversight. We check alerts three times daily, and we have a protocol for what to do if something flags—pause, wait, investigate, then decide.
Brand safety isn’t just about catching fraudsters. It’s about protecting the creator too. If their account is compromised and we don’t notice, they lose trust with their audience. So real-time monitoring benefits everyone.
Honestly, I love this. A brand caught that my account was getting weird login attempts and alerted me before anything bad happened. That kind of real-time partnership—where they’re actually watching and helping—builds so much trust. It’s not surveillance to me; it’s partnership.