Influencer Marketing Isn't an Investment – It's a Gamble

Scroll through any marketing dashboard today and you’ll see the same old stats: average views, follower counts, and audience demographics. Those numbers make influencer marketing look scientific — measurable, predictable, safe. But here’s the uncomfortable truth: they’re lying to you.

In the world of short-form content, those averages mean almost nothing.

Take TikTok, for example. Around 92% of a creator’s views come from the For You Page, not from followers. That means almost every view comes from people who have no prior connection to the creator — and crucially, the algorithm decides who sees what based on the content, not the creator.

I’ve seen this firsthand across 500+ influencer campaigns. One creator’s “average” video might get 50,000 views. But when she posted a trending dance challenge, it hit 1.5 million — and the audience skewed 80% male. A week later, her skincare tutorial reached 400,000 women aged 25-34. Same creator, same account, completely different audience.

So why are brands still planning campaigns using profile-level averages?

Because that’s how influencer marketing evolved — from the world of YouTube sponsorships and Instagram posts, where follower demographics actually mattered. But short-form algorithms like TikTok’s flipped that model on its head.

When the platform itself decides who sees what, the creator’s content style — not their follower makeup — drives performance. A “beauty creator” isn’t a single data point anymore; she’s a portfolio of content types, each with its own audience and virality potential.

The Real Problem: We’re Measuring the Wrong Thing

Most marketers still treat influencers like mini media publishers. They look at reach, impressions, CPM — as if creators were TV channels with fixed audiences. But in reality, every TikTok or Reel is its own experiment.

Think of it this way: if you invest in a stock, you expect predictable returns based on past performance. But influencer marketing is more like betting on a hand of poker — unless you understand the dynamics of each round (in this case, each piece of content), you’re gambling blind.

The irony is that we already have the data to fix this. Every platform publicly exposes signals — views, engagement, comments — on individual videos. By analyzing content-level trends, you can spot which video formats actually perform.

For example:

• Storytime videos might consistently outperform lip-syncs for one creator.

• POV skits might drive double the comments compared to tutorials.

• Collaborations using trending audio might spike reach by 300%.

When you classify and analyze this content by type, patterns start to emerge — not just for one creator, but across entire categories.

What the Research Shows

Recent studies mirror what I’ve seen in practice. In 2023, a Nielsen InfluenceScope report found that “content relevance” explained up to 67% of engagement variance, while follower demographics explained less than 15%. TikTok’s own Creator Solutions team has publicly stated that campaign success now depends more on “creative resonance” than follower affinity.

In other words: the algorithm doesn’t care who you are — it cares what you post.

To Be Sure

Does this mean intuition and historical metrics are worthless? Not at all. They can still help filter obvious mismatches (you probably shouldn’t hire a car mechanic to promote lipstick). But intuition and averages should be a starting point, not the strategy.

The real challenge is that content-level analysis isn’t scalable if done manually. No one has time to watch hundreds of videos per creator, categorize formats, and cross-reference engagement patterns. That’s why most teams default back to easy metrics like follower count — they’re simple, even if they’re wrong.

The Way Forward

If we want influencer marketing to become an investment, not a gamble, we need to measure what actually drives performance: content type.

Before your next campaign:

• Stop asking “Who has the right audience?”

• Start asking “What kind of content reaches my target audience?”

That shift changes everything — from how you select creators to how you brief them.

And the good news? Tools are emerging that make this possible. AI systems (like the one we’re building at Surge) can analyze thousands of videos across creators, classify content formats, and predict which types are trending — and which creators are best suited to execute them.

Because when you match the right creator with the right content type, you’re not gambling anymore. You’re investing — with data that actually reflects how the algorithm works today.