Cosmetic Brands, It’s Not The Metrics - You Aren’t Vetting Influencers Properly.

Every few months, a cosmetic brand chooses a problematic influencer to promote its product(s), and the backlash blows up across socials.

Invariably, the influencer gets called out because:

• They overstate or flat-out lie about their experience with a product to get the brand’s attention and, hopefully, secure a sponsorship (TikTok).

• They repeat a brand’s marketing copy directly from a press release (instead of using their own words), sounding exactly like a dozen other lazy influencers - causing people to distrust the brand, not the influencer.

• They use filters or digital editing to make their results look more impressive, which is deceptive, dishonest, and ILLEGAL (FTC Truth in Advertising).
  Looking directly at you, Mikayla

• They don’t disclose their affiliation or sponsorship clearly - if at all.
  Still looking at you, Mikayla

Then we all get to watch the fallout unfold across multiple social platforms:
The post gets ratioed and stitched, the brand goes quiet and hopes it blows over, the influencer plays the victim and uploads an insincere apology video (which no one believes)… It’s an endless loop, and no one learns from their mistakes.

The bizarre thing is that cosmetic social media marketing keeps relying on very basic metrics to choose affiliates and “partners,” and keeps making deals with problematic influencers that damage consumer trust in their brand (not the influencer).
Yes, still looking at you, Mikayla

There’s no shortage of services telling brands how to fix this, and to be fair, they’re not wrong - looking only at follower counts, demographics, and engagement rates are not the numbers to build a decision on (we’ll do a deeper dive into this below).
Platforms like TRIBE, GRIN, Aspire, and CreatorIQ have built successful business models on selling brands a smarter way to pick influencers, using more in-depth data, better filters, and a sharper spreadsheet.

But smarter data is still just a bunch of numbers. Some of the worst influencer partnerships in recent years occurred even when every available metric looked perfect.

The Metrics Look Accurate, But Are They?

Follower count sounds like the safest number a brand can trust. It isn’t. YouTube says most Shorts views come from people who don’t even subscribe to the channel, and Instagram reports something similar for Reels: over half the views come from non-followers. Translation: a big chunk of who’s watching an influencer today isn’t the audience that influencer built. It’s whoever the algorithm decided to show their content.

Demographics have the same blind spot. A brand picks an influencer whose followers are mostly women 25 to 40 and assumes that’s who’s buying. But online purchase behavior isn’t that simple. Google discovered an interesting phenomenon years ago: for video game searches, you’d expect most buyers to be men 18 to 34. Only 31% actually were.

Beauty proved the same point loudly a few years ago. Anti-aging retinoid serums and acidic chemical exfoliators built for consumers in their 30s and 40s started selling out to pre-teens who’d seen them on TikTok.
The “Sephora kids” uproar prompted dermatologists to frantically warn about the dangers of kids buying and using highly active skincare meant for much older skin. These kids were nowhere near the target demographic age range, yet the algorithm flooded their feeds with brightly colored Drunk Elephant packaging anyway, and the sales followed. You can try all you want to target the “right” demographic, and the algorithm will still show the ad to whoever it wants.

Engagement rate poses another interesting problem: it treats every reaction like it means the same thing. A like, a comment, a save, and a share are all lumped into one number, but they’re not the same action, and they don’t always reflect REAL engagement. A nano or micro influencer’s engagement is often built on real conversations in the comment section, thoughtful reposts, shares, and saves. A macro or mega influencer account can rack up huge engagement numbers simply by having followers click “like” as they scroll past, without ever actually reading a word of their content.

Nano and micro accounts consistently post higher REAL engagement percentages than macro and mega ones for exactly this reason: it’s real interaction, not a reflex “like” click because you follow the account.

These platforms aren’t wrong that follower count, demographics, and engagement rate are shaky ground on which to base a decision. But swap in whatever “smarter” version they’re selling instead - growth instead of count, affinity instead of demographics, storytelling instead of engagement rate - and the problem doesn’t go away. We’re still relying on NUMBERS.

None of these metrics, old or new, measure whether an influencer is authentic or truthful.

The Numbers Reward Reach, Not Honesty

Here’s the part that keeps me up at night. The bigger the following, the more likely it is that a dishonest or problematic influencer will avoid getting canceled for bad behavior.

Mikayla Nogueira’s 2023 sponsored video for L’Oréal Telescopic Mascara sparked what the internet dubbed #Lashgate. Viewers accused her of slipping in false lashes (Ardell Wispies) to the mascara application “after” shot while she “acted” amazed by the mascara’s lash-lengthening ability. The backlash was massive: millions of views, tens of thousands of comments, weeks of coverage (heck, we’re still talking about it). If the accusations were proven true, that’s not just an ooooops, it’s a violation of FTC Truth in Advertising rules, the same regulation that lets the agency fine an influencer for deception.
But neither Mikayla nor L’Oréal ever admitted to the deception, so no one was fined. She kept her deal, and she’s still one of the biggest names in the social media beauty community, still landing major brand partnerships years after #Lashgate - because 17.4 million willfully ignorant TikTok followers provide a lot of cover for bad behavior.

Now picture the nano-influencer or micro-influencer with 5,000-10,000 truly engaged followers who actually use what they promote, disclose every sponsorship, and never apply a filter to make product results look better. By every real measure, they are the logical choice. Nano and Micro-influencers’ post engagement rates are up to three times higher than macro and mega accounts, and beauty brands see $4 to $6 back for every dollar spent on the right smaller influencer, sometimes as high as $18. BUT, even though those numbers are impressive, they aren’t “reaching” a large number of people. So many nano and micro-influencers get passed over for the macro or mega-influencer with a larger following (on paper). Reach is a top priority for most brands and sits at the top of a pitch deck, yet there is no metric that measures honesty.

It gets worse. Buying fifty thousand fake followers only costs a few hundred dollars, and it’s often enough to push an account past the 100K mark, where brand deal rates jump. One recent review of 100,000 social media influencer accounts found that more than a third showed signs of fake followers. Beauty influencers were the biggest offenders, with over half flagged for suspicious follower activity. So, a brand that looks only at an influencer’s follower count while turning a blind eye to potential dishonesty isn’t just rewarding them for bad behavior; it’s probably paying for followers that don’t even exist.

It’s Not Just About Faked Reviews

Dishonesty about a product isn’t the only way this backfires, and it was proven three times without a single false lash in sight.

• e.l.f. Cosmetics pulled a campaign within days after comedian Matt Rife’s history of misogynistic jokes, especially one about domestic violence, angered a HUGE chunk of the brand’s audience.

• Huda Beauty was already weeks into a partnership with Love Island USA’s Huda Mustafa, and had to drop her after she let a racial slur go unaddressed during a TikTok livestream.

• Urban Decay named OnlyFans influencer Ari Kytsya as a brand ambassador for their “Battle The Bland” marketing campaign, and immediately drew fire from parent groups claiming they were “glamorizing the pornography industry” to teenage followers.

None of these social media influencers lied about a product. They had the reach, the engagement, often exactly the “metrics” a brand wanted. The mistake was that no one took into account the influencer’s behavioral history and how it could affect the affiliation before the deal was signed.

The Data Is Created By the Company Selling You DATA

The tools that are supposed to help brands avoid exactly this kind of mess have their own blind spot. TRIBE isn’t the only one doing this. Every service in this space backs up its “smarter metrics” pitch with its own success stories. TRIBE’s blog, for example, cites a campaign that reportedly outperformed its own audience size 8-to-1, a duo that pulled in 2.6 million views, and 7 influencers who supposedly outperformed a brand’s own ads by 38%.

That’s a service asking you to trust its own internal results, while it sells you the exact thing those results are supposed to prove works. I’ve made this argument about cosmetic brands citing the results of internal “studies” in their ad campaigns. When a company cites results from an INTERNAL study, it’s controlled marketing data to make them look good. It is NOT legitimate validation from peer-reviewed, independent, third-party testing.

What Brands SHOULD Be Doing

Guess what’s never part of a pitch by TRIBE, GRIN, Aspire…a background check. These services will hand a brand an influencer’s growth curve, affinity score, and storytelling rating. None of them will tell you if that influencer has a history of being problematic or dishonest and could get the brand in trouble, because that’s not their business model. They’re matchmaking tools, not vetting services, and brands need to stop treating them like influencer authorities.

Brands need to stop relying so much on metrics, and shouldn’t assume paying for a service like TRIBE, GRIN, or Aspire means the vetting is handled. Their entire business is about matching brands to influencers using smarter data, not running background checks. A brand needs someone to actually do a background check on the influencer BEFORE they’re contacted.

• Does the influencer’s reputation hold up to a basic Google search?

• Would this influencer still use and promote the brand without being paid?

• Has this influencer been caught overexaggerating or faking their product experience in a review?

• Has this influencer posted digitally filtered images or video to make their review results look more impressive?

I get it, doing a background check takes time, which is why it’s often skipped in favor of simply glancing at the influencer’s follower count or relying on an external service’s engagement metrics. If you want consistently successful influencer campaigns, stop being lazy and do the background work yourself to make sure you’re investing in the right influencer.

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#MyTwoCents

I’ve watched brands hand the biggest deals to the influencer with the largest following, scandals and all, while a smaller influencer who’s never lied or posted false results gets passed over because their numbers don’t look as impressive.

That’s backward, and it can be an expensive mistake. Every dollar invested in a dishonest mega-influencer’s reach is a dollar not spent on someone who actually earns trust for a living. The fake-follower numbers alone should be reason enough to stop defaulting to whoever has the biggest audience.

The metrics don’t guarantee honesty or trust. PERIOD.

What do you think? Let’s discuss this in the comments.

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Kevin James Bennett is the publisher of In My Kit®. He is an Emmy Award-winning makeup artist, cosmetic developer, educator, and consumer advocate. Learn more at www.kjbennett.com