Latest / Influencer Marketing with Fexingo: Creators, Sponsorships, and Modern Endorsement Deals / The Real Cost of Influencer Marketing Attribution
Transcript
- Lucas: If you’ve been watching the influencer marketing space over the last six months, there’s one metric that keeps getting buried under all the hype: actual incremental sales. Brands are still writing checks based on view counts and engagement rates, which feels increasingly like paying for theater tickets when you’re trying to buy groceries. Luna: It’s funny because creators themselves are starting to complain about it. They’re posting content that gets millions of views, but the brand says, 'Where’s the conversion?' It’s a disconnect that’s costing everyone money. Lucas: Right. And the worst part is that neither side really knows who’s at fault. The creator thinks the product was bad, the brand thinks the creator is lazy, and in reality, the measurement framework is just blind. Let’s look at a specific example from a mid-sized outdoor apparel brand I tracked recently. They had a campaign with a creator who had two million followers. Luna: Okay, so what happened? Did the sales drop? Lucas: Not exactly. The sales stayed flat. But here’s the kicker: when they ran an A-B test, they found that the people who saw the post were already in the market for new gear. The creator didn’t bring them new customers; they just amplified the message to people who would have bought anyway through organic search or email newsletters. Luna: So the creator was essentially getting paid to shout into a room full of people who were already holding their wallets. That sounds like wasted budget. Lucas: Exactly. It’s called false attribution, and it’s rampant. When you rely solely on unique promo codes or last-click affiliate links, you’re ignoring the upper funnel entirely. You’re missing the people who saw the video, thought about it for three days, and then came back to buy using a different channel. That’s the invisible gap in our current system. Luna: But isn’t that hard to track now that Apple and Google are tightening up cookie policies? I feel like we lost a lot of visibility last year. Lucas: We did lose some visibility, but not all of it. The shift isn’t about tracking individuals anymore; it’s about tracking audiences in aggregate. This is where incremental lift testing comes in. Instead of asking, 'Did this person click?', you ask, 'Did this group buy more than that group?' Luna: How do you even set that up without giving away your entire user database to a third-party vendor? Lucas: You don’t give away the data. You use geo-holdouts or time-sliced tests. For example, the outdoor brand showed the influencer’s content only to users in the Pacific Northwest for two weeks. Then they compared those sales to the same demographic in the Northeast, where the ad never ran. If the Northwest group bought significantly more, that delta is the true value of the creator. Luna: That’s actually pretty clever. It isolates the variable without needing to know who clicked the link. Lucas: It’s cleaner than last-click attribution because it accounts for brand lift. People aren’t always ready to buy immediately after seeing a video. Sometimes they just need to hear the name once more. If you’re only measuring direct response, you’re undervaluing the awareness play. Luna: So brands are essentially throwing money at influencers who are good at being loud, rather than good at driving new demand. That seems like a structural flaw in how agencies pitch these deals. Lucas: Agencies love it because it’s easy to sell. 'Look, five million impressions!' It’s a nice number to put in a slide deck. But for the CFO, it’s noise. The problem is that most CMOs haven’t pushed back yet because they’re afraid of looking like they don’t support creativity. So they pay for vanity and call it strategy. Luna: I can see why they’d be hesitant. If you start cutting budgets based on lift, you might accidentally fire the creator who builds your community, even if they don’t drive immediate sales. Lucas: That’s a valid concern. There’s a difference between a performance driver and a brand builder. But right now, we’re treating every dollar the same. We’re paying premium rates for influencers who have low lift, just because they have high engagement. Engagement is a leading indicator, not a lagging one. It doesn’t predict revenue until you validate it with a control group. Luna: What about smaller creators? Can they do this kind of testing too, or is it just for big brands with huge budgets? Lucas: Actually, smaller creators are easier to test. Because their audience is tighter, the signal to noise ratio is higher. A micro-influencer with fifty thousand followers might have eighty percent overlap with a brand’s existing customer base. If you run a small test with them, you can see very quickly if they’re reaching anyone new. It’s less expensive to prove negative results on a small scale. Luna: So the lesson is: stop trusting the follower count and start trusting the control group. It’s about proving you’re not just talking to the choir. Lucas: Precisely. And that changes the contract structure. Instead of a flat fee per post, you see more brands moving toward hybrid models. Base fee plus bonus for incremental lift. It aligns incentives. The creator has to bring something new to the table, not just repeat what the brand is already saying. Luna: That puts a lot of pressure on the creator though. What if their style is so niche that it naturally repels broad appeal? They might get penalized for being authentic. Lucas: Good point. Authenticity does matter. But here’s the thing: if you’re truly authentic to a specific niche, you should be able to drive high conversion within that niche. If you’re failing to convert your own audience, maybe the product-market fit is off, not the creator. It forces a harder conversation about whether the brand is actually solving a problem for that person. Luna: Or whether the creator is just selling out to brands that don’t fit their vibe. It filters out the mismatched partnerships early. Lucas: Exactly. It cleans up the ecosystem. The brands that survive this shift will be the ones willing to invest in rigorous testing, even if it means admitting that their favorite influencer isn’t pulling their weight. It’s uncomfortable, but it’s necessary for long-term growth. Luna: I think listeners who manage social media accounts would appreciate this. It gives them a concrete way to talk to their bosses about budget allocation without just guessing. Lucas: Absolutely. Data is the only language that survives budget cuts. If you can show that Creator A drives ten percent incremental lift while Creator B drives zero, you have a defensible argument. It’s not personal; it’s mathematical. Luna: Let’s take a quick break here to talk about how we keep this show running. If these marketing conversations have sparked something you've actually used, consider supporting us directly. Lucas: Listener support is what keeps us independent and ad-free. If you find value in our deep dives, you can visit buy me a coffee dot com slash fexingo. It helps us cover production costs and lets us keep digging into the details you care about. Luna: Thanks for helping us stay on the air. Now, let’s get back to the numbers. Lucas: One more layer to this: the rise of platform-native analytics. TikTok and Instagram are building better tools for creators to see their own audience demographics. In theory, this should help brands vet partners better before signing contracts. Luna: Do brands actually look at those dashboards, or do they just trust the creator’s screenshot? Lucas: Most still trust the screenshot. That’s a risk. Third-party verification firms are growing fast because brands want independent validation. But honestly, the best move is to integrate the creator’s audience data into your own CRM. If you can match their followers to your existing customer list, you’ll see the overlap instantly. Luna: That sounds like a privacy minefield. Matching email lists across platforms is tricky with all the new regulations. Lucas: It is tricky, but it’s possible if you use hashed data and consent-based matching. You’re not looking at individual names; you’re looking at aggregate patterns. If you can identify that twenty percent of a creator’s audience is already yours, you know you’re paying for redundancy. If only five percent is overlap, you’re paying for expansion. Luna: So the goal is to minimize the overlap and maximize the new reach. That simplifies the decision matrix significantly. Lucas: It does. And it shifts the power dynamic. Creators who can demonstrate access to non-overlapping audiences command higher fees. Those who just recycle your existing customers get squeezed. It’s a market correction that’s overdue. Luna: I wonder if this will lead to more long-term partnerships. If a brand finds a creator who consistently brings in new people, they might lock them in for a year instead of buying single posts. Lucas: Definitely. Performance-based deals tend to evolve into retainers. Once you prove the lift, you want to scale it. You don’t want to lose that asset. So we’ll likely see more multi-year contracts tied to clear KPIs, not just deliverables. Luna: That makes sense. It turns the creator into a true partner rather than a vendor. Less transactional, more strategic. Lucas: Exactly. And that’s where the real money is saved. Not by negotiating down the fee, but by ensuring every dollar spent actually moves the needle. It’s boring, unsexy work, but it’s the only way to build a sustainable marketing machine in 2026. Luna: So next time you see a viral post, ask yourself: did this actually change behavior, or did it just entertain the crowd? Lucas: That’s the question. And the answer usually tells you everything you need to know about the ROI of that partnership. Luna: Interesting. I’m curious to see if more brands start publishing their lift reports openly. Transparency might become the new standard for credibility. Lucas: Maybe. Or maybe they’ll just keep the data private and use it to quietly cut the underperformers. Either way, the era of free money for big faces is ending. The math is finally catching up. Luna: Time will tell. But for now, at least we have a better way to measure it. Lucas: We do. And that’s worth knowing. Thanks for listening.