I’ve been wrestling with this for months now. We run campaigns across US and Russian markets, and the engagement metrics look great on the surface—thousands of likes, comments, shares—but something felt off. The conversions just weren’t matching what the numbers suggested.
Turns out, we were looking at the wrong signals. When I started layering in behavioral data—comment velocity, follower-to-engagement ratios, audience overlap with known bot networks—patterns emerged that simple engagement counts never showed. It’s not rocket science, but it requires connecting dots that most dashboards don’t put side by side.
What really helped was getting input from both sides of the market. Russian market experts flagged engagement patterns that looked normal to US-based colleagues, and vice versa. Cross-market context changed everything about how we interpreted the data.
I’m curious: when you’re vetting influencers, are you building your own signal stack, or relying on platform scores? And if you’ve caught fake engagement, what actually tipped you off—was it a single metric or a combination?
This is exactly why I built a custom tracking system. Platform scores are too generalized. What we do now: we pull raw engagement data (not aggregated), analyze comment sentiment with NLP, and cross-reference influencer followers against known bot networks. In our last audit, we found that 40% of ‘high-performing’ creators we were considering had engagement that was 60%+ from fake accounts. The ROI differential was huge—we saved €200K on a failed campaign by catching it early.
The bilingual context matters too. Russian creators sometimes have engagement patterns that look suspicious to Western metrics but are actually normal behavior in that market—like group engagement tactics that are common there. You need domain experts interpreting the data, not just algorithms.
One more thing: I’d recommend tracking engagement decay over time. Real audiences engage with content in predictable waves. Fake engagement is usually front-loaded and then drops off. We now flag any creator whose engagement doesn’t show this natural decay pattern.
You’re touching on something really important here. I see this from the relationship side too—when introductions happen between brands and creators, the ones with authentic engagement tend to communicate differently. They ask real questions about the brand, have opinions, suggest adjustments. The ones with padded metrics? They’re transactional from day one. That’s been my unscientific but reliable red flag.
I’d love to see more collaboration between data teams and partnership managers on this. We catch different patterns.
We ran into this exact problem scaling across European markets. What worked for us: we partner with local experts who know the ‘normal’ engagement patterns for their market. Then we layer algorithmic checks on top of that local knowledge. Without the local context, we were flagging legitimate creators or missing obvious fakes.
The real issue is that most brands don’t have an escalation process. They see engagement, they book the creator, campaign runs, ROI disappoints, and then they blame the influencer or the platform. What we’ve built: a pre-campaign vetting gate where we spend 2-3 hours per creator doing deep dives. It added overhead, but it’s saved us from disasters. And honestly, it’s become a competitive advantage because we can tell clients we’ve actually vetted their partners.
This connects to a bigger strategic question: are we measuring the right thing? Engagement is a vanity metric. What actually matters for DTC is: does this creator’s audience convert? Does it repeat purchase? Does it have lifetime value? We’ve shifted entirely to outcome-based vetting. Who cares if engagement is real if the sales don’t materialize? That’s the real fraud—performance that doesn’t deliver business results. Start there, then work backwards to understand what signals predict actual outcomes.